On Motion Planning And Control For Truck And Trailer Systems

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On motion planning and control for truck and trailer systems

Author : Oskar Ljungqvist
Publisher : Linköping University Electronic Press
ISBN 13 : 9176851303
Total Pages : 78 pages
Book Rating : 4.1/5 (768 download)

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Book Synopsis On motion planning and control for truck and trailer systems by : Oskar Ljungqvist

Download or read book On motion planning and control for truck and trailer systems written by Oskar Ljungqvist and published by Linköping University Electronic Press. This book was released on 2019-01-22 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, improved sensor and hardware technologies as well as new methods and algorithms have made self-driving vehicles a realistic possibility in the near future. Thanks to this technology enhancement, many leading automotive and technology companies have turned their attention towards developing advanced driver assistance systems (ADAS) and self-driving vehicles. Autonomous vehicles are expected to have their first big impact in closed areas, such as mines, harbors and loading/offloading sites. In such areas, the legal requirements are less restrictive and the surrounding environment is more controlled and predictable compared to urban areas. Expected positive outcomes include increased productivity and safety, reduced emissions and the possibility to relieve the human from performing complex or dangerous tasks. Within these sites, different truck and trailer systems are used to transport materials. These systems are composed of several interconnected modules, and are thus large and highly unstable while reversing. This thesis addresses the problem of designing efficient motion planning and feedback control frameworks for such systems. First, a cascade controller for a reversing truck with a dolly-steered trailer is presented. The unstable modes of the system is stabilized around circular equilibrium configurations using a gain-scheduled linear quadratic (LQ) controller together with a higher-level pure pursuit controller to enable path following of piecewise linear reference paths. The cascade controller is then used within a rapidly-exploring random tree (RRT) framework and the complete motion planning and control framework is demonstrated on a small-scale test vehicle. Second, a path following controller for a reversing truck with a dolly-steered trailer is proposed for the case when the obtained motion plan is kinematically feasible. The control errors of the system are modeled in terms of their deviation from the nominal path and a stabilizing LQ controller with feedforward action is designed based on the linearization of the control error model. Stability of the closed-loop system is proven by combining global optimization, theory from linear differential inclusions and linear matrix inequality techniques. Third, a systematic framework is presented for analyzing stability of the closed-loop system consisting of a controlled vehicle and a feedback controller, executing a motion plan computed by a lattice planner. When this motion planner is considered, it is shown that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, a novel method is presented for analyzing the behavior of the tracking error, how to design the feedback controller and how to potentially impose constraints on the motion planner in order to guarantee that the tracking error is bounded and decays towards zero. Fourth, a complete motion planning and control solution for a truck with a dolly-steered trailer is presented. A lattice-based motion planner is proposed, where a novel parametrization of the vehicle’s state-space is proposed to improve online planning time. A time-symmetry result is established that enhance the numerical stability of the numerical optimal control solver used for generating the motion primitives. Moreover, a nonlinear observer for state estimation is developed which only utilizes information from sensors that are mounted on the truck, making the system independent of additional trailer sensors. The proposed framework is implemented on a full-scale truck with a dolly-steered trailer and results from a series of field experiments are presented.

Motion planning and feedback control techniques with applications to long tractor-trailer vehicles

Author : Oskar Ljungqvist
Publisher : Linköping University Electronic Press
ISBN 13 : 9179298583
Total Pages : 119 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Motion planning and feedback control techniques with applications to long tractor-trailer vehicles by : Oskar Ljungqvist

Download or read book Motion planning and feedback control techniques with applications to long tractor-trailer vehicles written by Oskar Ljungqvist and published by Linköping University Electronic Press. This book was released on 2020-04-20 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, improved sensor and hardware technologies as well as new methods and algorithms have made self-driving vehicles a realistic possibility in the near future. At the same time, there has been a growing demand within the transportation sector to increase efficiency and to reduce the environmental impact related to transportation of people and goods. Therefore, many leading automotive and technology companies have turned their attention towards developing advanced driver assistance systems and self-driving vehicles. Autonomous vehicles are expected to have their first big impact in closed environments, such as mines, harbors, loading and offloading sites. In such areas, the legal requirements are less restrictive and the surrounding environment is more controlled and predictable compared to urban areas. Expected positive outcomes include increased productivity and safety, reduced emissions and the possibility to relieve the human from performing complex or dangerous tasks. Within these sites, tractor-trailer vehicles are frequently used for transportation. These vehicles are composed of several interconnected vehicle segments, and are therefore large, complex and unstable while reversing. This thesis addresses the problem of designing efficient motion planning and feedback control techniques for such systems. The contributions of this thesis are within the area of motion planning and feedback control for long tractor-trailer combinations operating at low-speeds in closed and unstructured environments. It includes development of motion planning and feedback control frameworks, structured design tools for guaranteeing closed-loop stability and experimental validation of the proposed solutions through simulations, lab and field experiments. Even though the primary application in this work is tractor-trailer vehicles, many of the proposed approaches can with some adjustments also be used for other systems, such as drones and ships. The developed sampling-based motion planning algorithms are based upon the probabilistic closed-loop rapidly exploring random tree (CL-RRT) algorithm and the deterministic lattice-based motion planning algorithm. It is also proposed to use numerical optimal control offline for precomputing libraries of optimized maneuvers as well as during online planning in the form of a warm-started optimization step. To follow the motion plan, several predictive path-following control approaches are proposed with different computational complexity and performance. Common for these approaches are that they use a path-following error model of the vehicle for future predictions and are tailored to operate in series with a motion planner that computes feasible paths. The design strategies for the path-following approaches include linear quadratic (LQ) control and several advanced model predictive control (MPC) techniques to account for physical and sensing limitations. To strengthen the practical value of the developed techniques, several of the proposed approaches have been implemented and successfully demonstrated in field experiments on a full-scale test platform. To estimate the vehicle states needed for control, a novel nonlinear observer is evaluated on the full-scale test vehicle. It is designed to only utilize information from sensors that are mounted on the tractor, making the system independent of any sensor mounted on the trailer. Under de senaste årtiondena har utvecklingen av sensor- och hårdvaruteknik gått i en snabb takt, samtidigt som nya metoder och algoritmer har introducerats. Samtidigt ställs det stora krav på transportsektorn att öka effektiviteten och minska miljöpåverkan vid transporter av både människor och varor. Som en följd av detta har många ledande fordonstillverkare och teknikföretag börjat satsat på att utveckla avancerade förarstödsystem och självkörande fordon. Även forskningen inom autonoma fordon har under de senaste årtiondena kraftig ökat då en rad tekniska problem återstår att lösas. Förarlösa fordon förväntas få sitt första stora genombrott i slutna miljöer, såsom gruvor, hamnar, lastnings- och lossningsplatser. I sådana områden är lagstiftningen mindre hård jämfört med stadsområden och omgivningen är mer kontrollerad och förutsägbar. Några av de förväntade positiva effekterna är ökad produktivitet och säkerhet, minskade utsläpp och möjligheten att avlasta människor från att utföra svåra eller farliga uppgifter. Inom dessa platser används ofta lastbilar med olika släpvagnskombinationer för att transportera material. En sådan fordonskombination är uppbyggd av flera ihopkopplade moduler och är således utmanande att backa då systemet är instabilt. Detta gör det svårt att utforma ramverk för att styra sådana system vid exempelvis autonom backning. Självkörande fordon är mycket komplexa system som består av en rad olika komponenter vilka är designade för att lösa separata delproblem. Två viktiga komponenter i ett självkörande fordon är dels rörelseplaneraren som har i uppgift att planera hur fordonet ska röra sig för att på ett säkert sätt nå ett överordnat mål, och dels den banföljande regulatorn vars uppgift är att se till att den planerade manövern faktiskt utförs i praktiken trots störningar och modellfel. I denna avhandling presenteras flera olika algoritmer för att planera och utföra komplexa manövrar för lastbilar med olika typer av släpvagnskombinationer. De presenterade algoritmerna är avsedda att användas som avancerade förarstödsystem eller som komponenter i ett helt autonomt system. Även om den primära applikationen i denna avhandling är lastbilar med släp, kan många av de förslagna algoritmerna även användas för en rad andra system, så som drönare och båtar. Experimentell validering är viktigt för att motivera att en föreslagen algoritm är användbar i praktiken. I denna avhandling har flera av de föreslagna planerings- och reglerstrategierna implementerats på en småskalig testplattform och utvärderats i en kontrollerad labbmiljö. Utöver detta har även flera av de föreslagna ramverken implementerats och utvärderats i fältexperiment på en fullskalig test-plattform som har utvecklats i samarbete med Scania CV. Här utvärderas även en ny metod för att skatta släpvagnens beteende genom att endast utnyttja information från sensorer monterade på lastbilen, vilket gör det föreslagna ramverket oberoende av sensorer monterade på släpvagnen.

On Motion Planning Using Numerical Optimal Control

Author : Kristoffer Bergman
Publisher : Linköping University Electronic Press
ISBN 13 : 9176850579
Total Pages : 91 pages
Book Rating : 4.1/5 (768 download)

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Book Synopsis On Motion Planning Using Numerical Optimal Control by : Kristoffer Bergman

Download or read book On Motion Planning Using Numerical Optimal Control written by Kristoffer Bergman and published by Linköping University Electronic Press. This book was released on 2019-05-28 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, motion planning for autonomous systems has become an important area of research. The high interest is not the least due to the development of systems such as self-driving cars, unmanned aerial vehicles and robotic manipulators. In this thesis, the objective is not only to find feasible solutions to a motion planning problem, but solutions that also optimize some kind of performance measure. From a control perspective, the resulting problem is an instance of an optimal control problem. In this thesis, the focus is to further develop optimal control algorithms such that they be can used to obtain improved solutions to motion planning problems. This is achieved by combining ideas from automatic control, numerical optimization and robotics. First, a systematic approach for computing local solutions to motion planning problems in challenging environments is presented. The solutions are computed by combining homotopy methods and numerical optimal control techniques. The general principle is to define a homotopy that transforms, or preferably relaxes, the original problem to an easily solved problem. The approach is demonstrated in motion planning problems in 2D and 3D environments, where the presented method outperforms both a state-of-the-art numerical optimal control method based on standard initialization strategies and a state-of-the-art optimizing sampling-based planner based on random sampling. Second, a framework for automatically generating motion primitives for lattice-based motion planners is proposed. Given a family of systems, the user only needs to specify which principle types of motions that are relevant for the considered system family. Based on the selected principle motions and a selected system instance, the algorithm not only automatically optimizes the motions connecting pre-defined boundary conditions, but also simultaneously optimizes the terminal state constraints as well. In addition to handling static a priori known system parameters such as platform dimensions, the framework also allows for fast automatic re-optimization of motion primitives if the system parameters change while the system is in use. Furthermore, the proposed framework is extended to also allow for an optimization of discretization parameters, that are are used by the lattice-based motion planner to define a state-space discretization. This enables an optimized selection of these parameters for a specific system instance. Finally, a unified optimization-based path planning approach to efficiently compute locally optimal solutions to advanced path planning problems is presented. The main idea is to combine the strengths of sampling-based path planners and numerical optimal control. The lattice-based path planner is applied to the problem in a first step using a discretized search space, where system dynamics and objective function are chosen to coincide with those used in a second numerical optimal control step. This novel tight combination of a sampling-based path planner and numerical optimal control makes, in a structured way, benefit of the former method’s ability to solve combinatorial parts of the problem and the latter method’s ability to obtain locally optimal solutions not constrained to a discretized search space. The proposed approach is shown in several practically relevant path planning problems to provide improvements in terms of computation time, numerical reliability, and objective function value.

Exploiting Direct Optimal Control for Motion Planning in Unstructured Environments

Author : Kristoffer Bergman
Publisher : Linköping University Electronic Press
ISBN 13 : 9179296777
Total Pages : 60 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Exploiting Direct Optimal Control for Motion Planning in Unstructured Environments by : Kristoffer Bergman

Download or read book Exploiting Direct Optimal Control for Motion Planning in Unstructured Environments written by Kristoffer Bergman and published by Linköping University Electronic Press. This book was released on 2021-03-16 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, motion planning for autonomous systems has become an important area of research. The high interest is not the least due to the development of systems such as self-driving cars, unmanned aerial vehicles and robotic manipulators. The objective in optimal motion planning problems is to find feasible motion plans that also optimize a performance measure. From a control perspective, the problem is an instance of an optimal control problem. This thesis addresses optimal motion planning problems for complex dynamical systems that operate in unstructured environments, where no prior reference such as road-lane information is available. Some example scenarios are autonomous docking of vessels in harbors and autonomous parking of self-driving tractor-trailer vehicles at loading sites. The focus is to develop optimal motion planning algorithms that can reliably be applied to these types of problems. This is achieved by combining recent ideas from automatic control, numerical optimization and robotics. The first contribution is a systematic approach for computing local solutions to motion planning problems in challenging unstructured environments. The solutions are computed by combining homotopy methods and direct optimal control techniques. The general principle is to define a homotopy that transforms, or preferably relaxes, the original problem to an easily solved problem. The approach is demonstrated in motion planning problems in 2D and 3D environments, where the presented method outperforms a state-of-the-art asymptotically optimal motion planner based on random sampling. The second contribution is an optimization-based framework for automatic generation of motion primitives for lattice-based motion planners. Given a family of systems, the user only needs to specify which principle types of motions that are relevant for the considered system family. Based on the selected principle motions and a selected system instance, the framework computes a library of motion primitives by simultaneously optimizing the motions and the terminal states. The final contribution of this thesis is a motion planning framework that combines the strengths of sampling-based planners with direct optimal control in a novel way. The sampling-based planner is applied to the problem in a first step using a discretized search space, where the system dynamics and objective function are chosen to coincide with those used in a second step based on optimal control. This combination ensures that the sampling-based motion planner provides a feasible motion plan which is highly suitable as warm-start to the optimal control step. Furthermore, the second step is modified such that it also can be applied in a receding-horizon fashion, where the proposed combination of methods is used to provide theoretical guarantees in terms of recursive feasibility, worst-case objective function value and convergence to the terminal state. The proposed motion planning framework is successfully applied to several problems in challenging unstructured environments for tractor-trailer vehicles. The framework is also applied and tailored for maritime navigation for vessels in archipelagos and harbors, where it is able to compute energy-efficient trajectories which complies with the international regulations for preventing collisions at sea.

On Informative Path Planning for Tracking and Surveillance

Author : Per Boström-Rost
Publisher : Linköping University Electronic Press
ISBN 13 : 9176850757
Total Pages : 86 pages
Book Rating : 4.1/5 (768 download)

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Book Synopsis On Informative Path Planning for Tracking and Surveillance by : Per Boström-Rost

Download or read book On Informative Path Planning for Tracking and Surveillance written by Per Boström-Rost and published by Linköping University Electronic Press. This book was released on 2019-05-23 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis studies a class of sensor management problems called informative path planning (IPP). Sensor management refers to the problem of optimizing control inputs for sensor systems in dynamic environments in order to achieve operational objectives. The problems are commonly formulated as stochastic optimal control problems, where to objective is to maximize the information gained from future measurements. In IPP, the control inputs affect the movement of the sensor platforms, and the goal is to compute trajectories from where the sensors can obtain measurements that maximize the estimation performance. The core challenge lies in making decisions based on the predicted utility of future measurements. In linear Gaussian settings, the estimation performance is independent of the actual measurements. This means that IPP becomes a deterministic optimal control problem, for which standard numerical optimization techniques can be applied. This is exploited in the first part of this thesis. A surveillance application is considered, where a mobile sensor is gathering information about features of interest while avoiding being tracked by an adversarial observer. The problem is formulated as an optimization problem that allows for a trade-off between informativeness and stealth. We formulate a theorem that makes it possible to reformulate a class of nonconvex optimization problems with matrix-valued variables as convex optimization problems. This theorem is then used to prove that the seemingly intractable IPP problem can be solved to global optimality using off-the-shelf optimization tools. The second part of this thesis considers tracking of a maneuvering target using a mobile sensor with limited field of view. The problem is formulated as an IPP problem, where the goal is to generate a sensor trajectory that maximizes the expected tracking performance, captured by a measure of the covariance matrix of the target state estimate. When the measurements are nonlinear functions of the target state, the tracking performance depends on the actual measurements, which depend on the target’s trajectory. Since these are unavailable in the planning stage, the problem becomes a stochastic optimal control problem. An approximation of the problem based on deterministic sampling of the distribution of the predicted target trajectory is proposed. It is demonstrated in a simulation study that the proposed method significantly increases the tracking performance compared to a conventional approach that neglects the uncertainty in the future target trajectory.

Enhanced Trustworthiness and End User Acceptance of Conditionally Automated Vehicles in the Transition Period

Author : Daniel Watzenig
Publisher : Springer Nature
ISBN 13 : 3030608611
Total Pages : 133 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Enhanced Trustworthiness and End User Acceptance of Conditionally Automated Vehicles in the Transition Period by : Daniel Watzenig

Download or read book Enhanced Trustworthiness and End User Acceptance of Conditionally Automated Vehicles in the Transition Period written by Daniel Watzenig and published by Springer Nature. This book was released on 2020-12-08 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: A key factor for the introduction of (conditionally) automated vehicles is a high level of trust in and acceptance of these vehicles by the end-user. To bring such so-called TrustVehicles on the road, the end-users and their expectations have to be strongly taken into consideration by, for instance, developing driver interfaces as well as reliable and robust automated driving controllers. The main topics of the book are ranging from the question of how these TrustVehicles should behave and interact with users, the development of reliable sense-plan-act approaches, the whole verification procedures starting with simulation to studies on the driving simulator and the verification on a test track. All these steps together provide an overall picture and pave the way to trustworthy and reliable automated vehicles – so-called TrustVehicles.

Control, Models and Industrial Manipulators

Author : Erik Hedberg
Publisher : Linköping University Electronic Press
ISBN 13 : 9179297404
Total Pages : 64 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Control, Models and Industrial Manipulators by : Erik Hedberg

Download or read book Control, Models and Industrial Manipulators written by Erik Hedberg and published by Linköping University Electronic Press. This book was released on 2020-11-23 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two topics at the heart of this thesis are how to improve control of industrial manipulators and how to reason about the role of models in automatic control. On industrial manipulators, two case studies are presented. The first investigates estimation with inertial sensors, and the second compares control by feedback linearization to control based on gain-scheduling. The contributions on the second topic illustrate the close connection between control and estimation in different ways. A conceptual model of control is introduced, which can be used to emphasize the role of models as well as the human aspect of control engineering. Some observations are made regarding block-diagram reformulations that illustrate the relation between models, control and inversion. Finally, a suggestion for how the internal model principle, internal model control, disturbance observers and Youla-Kucera parametrization can be introduced in a unified way is presented.

Uncertainties in Neural Networks

Author : Magnus Malmström
Publisher : Linköping University Electronic Press
ISBN 13 : 9179296807
Total Pages : 103 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Uncertainties in Neural Networks by : Magnus Malmström

Download or read book Uncertainties in Neural Networks written by Magnus Malmström and published by Linköping University Electronic Press. This book was released on 2021-04-06 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: In science, technology, and engineering, creating models of the environment to predict future events has always been a key component. The models could be everything from how the friction of a tire depends on the wheels slip to how a pathogen is spread throughout society. As more data becomes available, the use of data-driven black-box models becomes more attractive. In many areas they have shown promising results, but for them to be used widespread in safety-critical applications such as autonomous driving some notion of uncertainty in the prediction is required. An example of such a black-box model is neural networks (NNs). This thesis aims to increase the usefulness of NNs by presenting an method where uncertainty in the prediction is obtained by linearization of the model. In system identification and sensor fusion, under the condition that the model structure is identifiable, this is a commonly used approach to get uncertainty in the prediction from a nonlinear model. If the model structure is not identifiable, such as for NNs, the ambiguities that cause this have to be taken care of in order to make the approach applicable. This is handled in the first part of the thesis where NNs are analyzed from a system identification perspective, and sources of uncertainty are discussed. Another problem with data-driven black-box models is that it is difficult to know how flexible the model needs to be in order to correctly model the true system. One solution to this problem is to use a model that is more flexible than necessary to make sure that the model is flexible enough. But how would that extra flexibility affect the uncertainty in the prediction? This is handled in the later part of the thesis where it is shown that the uncertainty in the prediction is bounded from below by the uncertainty in the prediction of the model with lowest flexibility required for representing true system accurately. In the literature, many other approaches to handle the uncertainty in predictions by NNs have been suggested, of which some are summarized in this work. Furthermore, a simulation and an experimental studies inspired by autonomous driving are conducted. In the simulation study, different sources of uncertainty are investigated, as well as how large the uncertainty in the predictions by NNs are in areas without training data. In the experimental study, the uncertainty in predictions done by different models are investigated. The results show that, compared to existing methods, the linearization method produces similar results for the uncertainty in predictions by NNs. An introduction video is available at https://youtu.be/O4ZcUTGXFN0 Inom forskning och utveckling har det har alltid varit centralt att skapa modeller av verkligheten. Dessa modeller har bland annat använts till att förutspå framtida händelser eller för att styra ett system till att bete sig som man önskar. Modellerna kan beskriva allt från hur friktionen hos ett bildäck påverkas av hur mycket hjulen glider till hur ett virus kan sprida sig i ett samhälle. I takt med att mer och mer data blir tillgänglig ökar potentialen för datadrivna black-box modeller. Dessa modeller är universella approximationer vilka ska kunna representera vilken godtycklig funktion som helst. Användningen av dessa modeller har haft stor framgång inom många områden men för att verkligen kunna etablera sig inom säkerhetskritiska områden såsom självkörande farkoster behövs en förståelse för osäkerhet i prediktionen från modellen. Neuronnät är ett exempel på en sådan black-box modell. I denna avhandling kommer olika sätt att tillförskaffa sig kunskap om osäkerhet i prediktionen av neuronnät undersökas. En metod som bygger på linjärisering av modellen för att tillförskaffa sig osäkerhet i prediktionen av neuronnätet kommer att presenteras. Denna metod är välbeprövad inom systemidentifiering och sensorfusion under antagandet att modellen är identifierbar. För modeller såsom neuronnät, vilka inte är identifierbara behövs det att det tas hänsyn till tvetydigheterna i modellen. En annan utmaning med datadrivna black-box modeller, är att veta om den valda modellmängden är tillräckligt generell för att kunna modellera det sanna systemet. En lösning på detta problem är att använda modeller som har mer flexibilitet än vad som behövs, det vill säga en överparameteriserad modell. Men hur påverkas osäkerheten i prediktionen av detta? Detta är något som undersöks i denna avhandling, vilken visar att osäkerheten i den överparameteriserad modellen kommer att vara begränsad underifrån av modellen med minst flexibilitet som ändå är tillräckligt generell för att modellera det sanna systemet. Som avslutning kommer dessa resultat att demonstreras i både en simuleringsstudie och en experimentstudie inspirerad av självkörande farkoster. Fokuset i simuleringsstudien är hur osäkerheten hos modellen är i områden med och utan tillgång till träningsdata medan experimentstudien fokuserar på jämförelsen mellan osäkerheten i olika typer av modeller.Resultaten från dessa studier visar att metoden som bygger på linjärisering ger liknande resultat för skattningen av osäkerheten i prediktionen av neuronnät, jämfört med existerande metoder.

Decentralized Estimation Using Conservative Information Extraction

Author : Robin Forsling
Publisher : Linköping University Electronic Press
ISBN 13 : 9179297242
Total Pages : 110 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Decentralized Estimation Using Conservative Information Extraction by : Robin Forsling

Download or read book Decentralized Estimation Using Conservative Information Extraction written by Robin Forsling and published by Linköping University Electronic Press. This book was released on 2020-12-17 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensor networks consist of sensors (e.g., radar and cameras) and processing units (e.g., estimators), where in the former information extraction occurs and in the latter estimates are formed. In decentralized estimation information extracted by sensors has been pre-processed at an intermediate processing unit prior to arriving at an estimator. Pre-processing of information allows for the complexity of large systems and systems-of-systems to be significantly reduced, and also makes the sensor network robust and flexible. One of the main disadvantages of pre-processing information is that information becomes correlated. These correlations, if not handled carefully, potentially lead to underestimated uncertainties about the calculated estimates. In conservative estimation the unknown correlations are handled by ensuring that the uncertainty about an estimate is not underestimated. If this is ensured the estimate is said to be conservative. Neglecting correlations means information is double counted which in worst case implies diverging estimates with fatal consequences. While ensuring conservative estimates is the main goal, it is desirable for a conservative estimator, as for any estimator, to provide an error covariance which is as small as possible. Application areas where conservative estimation is relevant are setups where multiple agents cooperate to accomplish a common objective, e.g., target tracking, surveillance and air policing. The first part of this thesis deals with theoretical matters where the conservative linear unbiased estimation problem is formalized. This part proposes an extension of classical linear estimation theory to the conservative estimation problem. The conservative linear unbiased estimator (CLUE) is suggested as a robust and practical alternative for estimation problems where the correlations are unknown. Optimality criteria for the CLUE are provided and further investigated. It is shown that finding an optimal CLUE is more complicated than finding an optimal linear unbiased estimator in the classical version of the problem. To simplify the problem, a CLUE that is optimal under certain restrictions will also be investigated. The latter is named restricted best CLUE. An important result is a theorem that gives a closed form solution to a restricted best CLUE. Furthermore, several conservative estimation methods are described followed by an analysis of their properties. The methods are shown to be conservative and optimal under different assumptions about the underlying correlations. The second part of the thesis focuses on practical aspects of the conservative approach to decentralized estimation in configurations where the communication channel is constrained. The diagonal covariance approximation is proposed as a data reduction technique that complies with the communication constraints and if handled correctly can be shown to preserve conservative estimates. Several information selection methods are derived that can reduce the amount of data being transmitted in the communication channel. Using the information selection methods it is possible to decide what information other actors of the sensor network find useful.

On Complexity Certification of Active-Set QP Methods with Applications to Linear MPC

Author : Daniel Arnström
Publisher : Linköping University Electronic Press
ISBN 13 : 9179296920
Total Pages : 45 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis On Complexity Certification of Active-Set QP Methods with Applications to Linear MPC by : Daniel Arnström

Download or read book On Complexity Certification of Active-Set QP Methods with Applications to Linear MPC written by Daniel Arnström and published by Linköping University Electronic Press. This book was released on 2021-03-03 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: In model predictive control (MPC) an optimization problem has to be solved at each time step, which in real-time applications makes it important to solve these efficiently and to have good upper bounds on worst-case solution time. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depends on parameters such as system states and reference signals. A popular class of methods for solving such QPs is active-set methods, where a sequence of linear systems of equations is solved. The primary contribution of this thesis is a method which determines which sequence of subproblems a popular class of such active-set algorithms need to solve, for every possible QP instance that might arise from a given linear MPC problem (i.e, for every possible state and reference signal). By knowing these sequences, worst-case bounds on how many iterations, floating-point operations and, ultimately, the maximum solution time, these active-set algorithms require to compute a solution can be determined, which is of importance when, e.g, linear MPC is used in safety-critical applications. After establishing this complexity certification method, its applicability is extended by showing how it can be used indirectly to certify the complexity of another, efficient, type of active-set QP algorithm which reformulates the QP as a nonnegative least-squares method. Finally, the proposed complexity certification method is extended further to situations when enhancements to the active-set algorithms are used, namely, when they are terminated early (to save computations) and when outer proximal-point iterations are performed (to improve numerical stability).

Estimation of Nonlinear Greybox Models for Marine Applications

Author : Fredrik Ljungberg
Publisher : Linköping University Electronic Press
ISBN 13 : 9179298400
Total Pages : 124 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Estimation of Nonlinear Greybox Models for Marine Applications by : Fredrik Ljungberg

Download or read book Estimation of Nonlinear Greybox Models for Marine Applications written by Fredrik Ljungberg and published by Linköping University Electronic Press. This book was released on 2020-05-27 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: As marine vessels are becoming increasingly autonomous, having accurate simulation models available is turning into an absolute necessity. This holds both for facilitation of development and for achieving satisfactory model-based control. When accurate ship models are sought, it is necessary to account for nonlinear hydrodynamic effects and to deal with environmental disturbances in a correct way. In this thesis, parameter estimators for nonlinear regression models where the regressors are second-order modulus functions are analyzed. This model class is referred to as second-order modulus models and is often used for greybox identification of marine vessels. The primary focus in the thesis is to find consistent estimators and for this an instrumental variable (IV) method is used. First, it is demonstrated that the accuracy of an IV estimator can be improved by conducting experiments where the input signal has a static offset of sufficient amplitude and the instruments are forced to have zero mean. This two-step procedure is shown to give consistent estimators for second-order modulus models in cases where an off-the-shelf applied IV method does not, in particular when measurement uncertainty is taken into account. Moreover, it is shown that the possibility of obtaining consistent parameter estimators for models of this type depends on how process disturbances enter the system and on the amount of prior knowledge about the disturbances’ probability distributions that is available. In cases where the first-order moments are known, the aforementioned approach gives consistent estimators even when disturbances enter the system before the nonlinearity. In order to obtain consistent estimators in cases where the first-order moments are unknown, a framework for estimating the first and second-order moments alongside the model parameters is suggested. The idea is to describe the environmental disturbances as stationary stochastic processes in an inertial frame and to utilize the fact that their effect on a vessel depends on the vessel’s attitude. It is consequently possible to infer information about the environmental disturbances by over time measuring the orientation of a vessel they are affecting. Furthermore, in cases where the process disturbances are of more general character it is shown that supplementary disturbance measurements can be used for achieving consistency. Different scenarios where consistency can be achieved for instrumental variable estimators of second-order modulus models are demonstrated, both in theory and by simulation examples. Finally, estimation results obtained using data from a full-scale marine vessel are presented. I takt med att marina farkoster blir mer autonoma ökar behovet av noggranna matematiska farkostmodeller. Modellerna behövs både för att förenkla utvecklingen av nya farkoster och för att kunna styra farkosterna autonomt med önskad precision. För att erhålla allmängiltiga modeller behöver olinjära hydrodynamiska effekter samt systemstörningar, främst orsakade av vind- och vattenströmmar, tas i beaktning. I det här arbetet undersöks metoder för att skatta okända storheter i modeller för marina farkoster givet observerad data. Undersökningen gäller en speciell typ av olinjära modeller som ofta används för att beskriva marina farkoster. Huvudfokus i arbetet är att erhålla konsistens, vilket betyder att de skattade storheterna ska anta rätt värden när mängden observerad data ökar. För det används en redan etablerad statistisk metod som baseras på instrumentvariabler. Det visas först att noggrannheten i modellskattningsmetoden kan förbättras om datainsamlingsexperimenten utförs på ett sätt så att farkosten har signifikant nollskild hastighet och instrumentvariablernas medelvärde dras bort. Den här tvåstegslösningen påvisas vara fördelaktig vid skattning av parametrar i den ovan nämnda modelltypen, framför allt då mätosäkerhet tas i beaktning. Vidare så visas det att möjligheten att erhålla konsistenta skattningsmetoder beror på hur mycket kännedom om systemstörningarna som finns tillgänglig på förhand. I fallet då de huvudsakliga hastigheterna på vind- och vattenströmmar är kända, räcker den tidigare nämnda tvåstegsmetoden bra. För att även kunna hantera det mer generella fallet föreslås en metod för att skatta de huvudsakliga hastigheterna och de okända modellparametrarna parallellt. Denna idé baserar sig på att beskriva störningarna som stationära i ett globalt koordinatsystem och att anta att deras effekt på en farkost beror på hur farkosten är orienterad. Genom att över tid mäta och samla in data som beskriver en farkosts kurs, kan man således dra slutsatser om de störningar som farkosten påverkas av. Utöver detta visas det att utnyttjande av vindmätningar kan ge konsistens i fallet med störningar av mer generell karaktär. Olika scenarion där konsistens kan uppnås visas både i teori och med simuleringsexempel. Slutligen visas också modellskattningsresultat som erhållits med data insamlad från ett fullskaligt fartyg.

Timing-Based Localization using Multipath Information

Author : Andreas Bergström
Publisher : Linköping University Electronic Press
ISBN 13 : 9179299172
Total Pages : 119 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Timing-Based Localization using Multipath Information by : Andreas Bergström

Download or read book Timing-Based Localization using Multipath Information written by Andreas Bergström and published by Linköping University Electronic Press. This book was released on 2020-01-09 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: The measurements of radio signals are commonly used for localization purposes where the goal is to determine the spatial position of one or multiple objects. In realistic scenarios, any transmitted radio signal will be affected by the environment through reflections, diffraction at edges and corners etc. This causes a phenomenon known as multipath propagation, by which multiple instances of the transmitted signal having traversed different paths are heard by the receiver. These are known as Multi-Path Components (MPCs). The direct path (DP) between transmitter and receiver may also be occluded, causing what is referred to as non-Line-of-Sight (non-LOS) conditions. As a consequence of these effects, the estimated position of the object(s) may often be erroneous. This thesis focuses on how to achieve better localization accuracy by accounting for the above-mentioned multipath propagation and non-LOS effects. It is proposed how to mitigate these in the context of positioning based on estimation of the DP between transmitter and receiver. It is also proposed how to constructively utilize the additional information about the environment which they implicitly provide. This is all done in a framework wherein a given signal model and a map of the surroundings are used to build a mathematical model of the radio environment, from which the resulting MPCs are estimated. First, methods to mitigate the adverse effects of multipath propagation and non-LOS conditions for positioning based on estimation of the DP between transmitter and receiver are presented. This is initially done by using robust statistical measurement error models based on aggregated error statistics, where significant improvements are obtained without the need to provide detailed received signal information. The gains are seen to be even larger with up-to-date real-time information based on the estimated MPCs. Second, the association of the estimated MPCs with the signal paths predicted by the environmental model is addressed. This leads to a combinatorial problem which is approached with tools from multi-target tracking theory. A rich radio environment in terms of many MPCs gives better localization accuracy but causes the problem size to grow large—something which can be remedied by excluding less probable paths. Simulations indicate that in such environments, the single best association hypothesis may be a reasonable approximation which avoids the calculation of a vast number of possible hypotheses. Accounting for erroneous measurements is crucial but may have drawbacks if no such are occurring. Finally, theoretical localization performance bounds when utilizing all or a subset of the available MPCs are derived. A rich radio environment allows for good positioning accuracy using only a few transmitters/receivers, assuming that these are used in the localization process. In contrast, in a less rich environment where basically only the DP/LOS components are measurable, more transmitters/receivers and/or the combination of downlink and uplink measurements are required to achieve the same accuracy. The receiver’s capability of distinguishing between multiple MPCs arriving approximately at the same time also affects the localization accuracy.

Hybrid Systems: Computation and Control

Author : Claire J. Tomlin
Publisher : Springer
ISBN 13 : 3540458735
Total Pages : 480 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Hybrid Systems: Computation and Control by : Claire J. Tomlin

Download or read book Hybrid Systems: Computation and Control written by Claire J. Tomlin and published by Springer. This book was released on 2003-07-31 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Workshop on Hybrid Systems: Computation and Control, HSCC 2002, held in Stanford, California, USA, in March 2002. The 33 revised full papers presented were carefully reviewed and selected from 73 submissions. All current issues in hybrid systems are addressed including formal models and methods and computational representations, algorithms and heuristics, computational tools, and innovative applications.

Advances in Soft Computing - AFSS 2002

Author : Nikhil R. Pal
Publisher : Springer Science & Business Media
ISBN 13 : 9783540431503
Total Pages : 1046 pages
Book Rating : 4.4/5 (315 download)

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Book Synopsis Advances in Soft Computing - AFSS 2002 by : Nikhil R. Pal

Download or read book Advances in Soft Computing - AFSS 2002 written by Nikhil R. Pal and published by Springer Science & Business Media. This book was released on 2002-01-23 with total page 1046 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is our great pleasure to welcome you all to the 2002 AFSS International Conference on Fuzzy Systems (AFSS 2002) to be held in Calcutta, the great City of Joy. AFSS 2002 is the ?fth conference in the series initiated by the Asian Fuzzy Systems Society (AFSS). AFSS 2002 is jointly being organized by theIndianStatisticalInstitute(ISI)andJadavpurUniversity(JU). Likeprevious conferencesinthisseries,wearesure,AFSS2002willprovideaforumforfruitful interaction and exchange of ideas between the participants from all over the globe. The present conference covers all major facets of soft computing such as fuzzy logic, neural networks, genetic algorithms including both theories and applications. Wehopethismeetingwillbeenjoyableacademicallyandotherwise. We are thankful to the members of the International Program Committee and the Area Chairs for extending their support in various forms to make a strong technical program. Each submitted paper was reviewed by at least three referees, and in some cases the revised versions were again checked by the ref- ees. As a result of this tough screening process we could select only about 50% of the submitted papers. We again express our sincere thanks to all referees for doing a great job. We are happy to note that 19 di?erent countries from all over the globe are represented by the authors, thereby making it a truly inter- tional conference. We are proud to have a list of distinguished speakers including Profs. Z. Pawlak, J. Bezdek, D. Dubois, and T. Yamakawa.

Control Problems in Robotics

Author : Antonio Bicchi
Publisher : Springer Science & Business Media
ISBN 13 : 354036224X
Total Pages : 283 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Control Problems in Robotics by : Antonio Bicchi

Download or read book Control Problems in Robotics written by Antonio Bicchi and published by Springer Science & Business Media. This book was released on 2007-07-12 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ?eld of robotics continues to ?ourish and develop. In common with general scienti?c investigation, new ideas and implementations emerge quite spontaneously and these are discussed, used, discarded or subsumed at c- ferences, in the reference journals, as well as through the Internet. After a little more maturity has been acquired by the new concepts, then archival publication as a scienti?c or engineering monograph may occur. The goal of the Springer Tracts in Advanced Robotics is to publish new developments and advances in the ?elds of robotics research – rapidly and informally but with a high quality. It is hoped that prospective authors will welcome the opportunity to publish a structured presentation of some of the emerging robotics methodologies and technologies. The edited volume by Antonio Bicchi, Henrik Christensen and Domenico Prattichizzo is the outcome of the second edition of a workshop jointly sponsored by the IEEE Control Systems Society and the IEEE Robotics and Automation Society. Noticeably, the previous volume was published in the Springer Lecture Notes on Control and Information Sciences. The authors are recognised as leading scholars internationally. A n- ber of challenging control problems on the forefront of today’s research in robotics and automation are covered, with special emphasis on vision, sensory-feedback control, human-centered robotics, manipulation, planning, ?exible and cooperative robots, assembly systems.

Computational Intelligence: Research Frontiers

Author : Gary G. Yen
Publisher : Springer Science & Business Media
ISBN 13 : 3540688587
Total Pages : 402 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Computational Intelligence: Research Frontiers by : Gary G. Yen

Download or read book Computational Intelligence: Research Frontiers written by Gary G. Yen and published by Springer Science & Business Media. This book was released on 2008-05-13 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This state-of-the-art survey offers a renewed and refreshing focus on the progress in nature-inspired and linguistically motivated computation. The book presents the expertise and experiences of leading researchers spanning a diverse spectrum of computational intelligence in the areas of neurocomputing, fuzzy systems, evolutionary computation, and adjacent areas. The result is a balanced contribution to the field of computational intelligence that should serve the community not only as a survey and a reference, but also as an inspiration for the future advancement of the state of the art of the field. The 18 selected chapters originate from lectures and presentations given at the 5th IEEE World Congress on Computational Intelligence, WCCI 2008, held in Hong Kong, China, in June 2008. After an introduction to the field and an overview of the volume, the chapters are divided into four topical sections on machine learning and brain computer interface, fuzzy modeling and control, computational evolution, and applications.

Robot Motion and Control 2011

Author : Krzysztof Kozłowski
Publisher : Springer
ISBN 13 : 1447123433
Total Pages : 420 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Robot Motion and Control 2011 by : Krzysztof Kozłowski

Download or read book Robot Motion and Control 2011 written by Krzysztof Kozłowski and published by Springer. This book was released on 2012-01-13 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot Motion Control 2011 presents very recent results in robot motion and control. Forty short papers have been chosen from those presented at the sixth International Workshop on Robot Motion and Control held in Poland in June 2011. The authors of these papers have been carefully selected and represent leading institutions in this field. The following recent developments are discussed: Design of trajectory planning schemes for holonomic and nonholonomic systems with optimization of energy, torque limitations and other factors. New control algorithms for industrial robots, nonholonomic systems and legged robots. Different applications of robotic systems in industry and everyday life, like medicine, education, entertainment and others. Multiagent systems consisting of mobile and flying robots with their applications The book is suitable for graduate students of automation and robotics, informatics and management, mechatronics, electronics and production engineering systems as well as scientists and researchers working in these fields.