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Model Predictive Control Strategies For Constrained Unmanned Vehicles
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Book Synopsis Model Predictive Control Strategies For Constrained Unmanned Vehicles by : Shima Savehshemshaki
Download or read book Model Predictive Control Strategies For Constrained Unmanned Vehicles written by Shima Savehshemshaki and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis deals with two control problems for unmanned vehicles, namely battery shortage prevention for electric unmanned vehicles and collision avoidance strategy for constrained multi unmanned vehicle systems. In the battery shortage prevention problem, we design a novel control architecture, equipped with a battery manager module, capable of avoiding energy shortage by appropriately imposing time-varying upper bounds on the vehicle's maximum acceleration. Here, the dual-mode control paradigm known as set-theoretic model predictive control is applied to couple the reference tracking and the battery shortage problems. First, we offline design a conservative maximum acceleration profile capable of assuring that the electric unmanned vehicle will reach the desired target without incurring into a battery shortage along the given path. Then, online, by following a receding horizon approach, we show that the battery manager can enhance the performance by using the current battery's state-of-charge. Moreover, a simulation example is presented to clarify and show the proposed control framework's potential and features. In the collision avoidance problem, we deal with vehicles moving in a shared environment where each UV follows a trajectory given by a local planner. We assume that the planners are uncoordinated and each vehicle is subject to different constraints and disturbances. In this context, we design a new centralized traffic manager that, in conjunction with ad-hoc designed local model predictive controller, can ensure the absence of collisions while minimizing the total vehicle's stops occurrences. In particular, in a receding horizon fashion, the traffic manager exploits available previews on the successive vehicle's waypoints to speed-up or speed-down the vehicles and minimize the chance of collisions. Moreover, by exploiting basic set-theoretic arguments, traffic manager can impose a vehicle to stop and safely prevent collisions whenever necessary. Finally, two different simulation examples are presented to better illustrate the capability of the proposed solution.
Book Synopsis Advanced Model Predictive Control for Autonomous Marine Vehicles by : Yang Shi
Download or read book Advanced Model Predictive Control for Autonomous Marine Vehicles written by Yang Shi and published by Springer Nature. This book was released on 2023-02-13 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of marine control system design related to underwater robotics applications. In particular, it presents novel optimization-based model predictive control strategies to solve control problems appearing in autonomous underwater vehicle applications. These novel approaches bring unique features, such as constraint handling, prioritization between multiple design objectives, optimal control performance, and robustness against disturbances and uncertainties, into the control system design. They therefore form a more general framework to design marine control systems and can be widely applied. Advanced Model Predictive Control for Autonomous Marine Vehicles balances theoretical rigor – providing thorough analysis and developing provably-correct design conditions – and application perspectives – addressing practical system constraints and implementation issues. Starting with a fixed-point positioning problem for a single vehicle and progressing to the trajectory-tracking and path-following problem of the vehicle, and then to the coordination control of a large-scale multi-robot team, this book addresses the motion control problems, increasing their level of challenge step-by-step. At each step, related subproblems such as path planning, thrust allocation, collision avoidance, and time constraints for real-time implementation are also discussed with solutions. In each chapter of this book, compact and illustrative examples are provided to demonstrate the design and implementation procedures. As a result, this book is useful for both theoretical study and practical engineering design, and the tools provided in the book are readily applicable for real-world implementation.
Book Synopsis Nonlinear Model Predictive Control by : Frank Allgöwer
Download or read book Nonlinear Model Predictive Control written by Frank Allgöwer and published by Birkhäuser. This book was released on 2012-12-06 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central importance for NMPC are covered, namely receding horizon control theory, modeling for NMPC, computational aspects of on-line optimization and application issues. The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control – Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland. The book is geared towards researchers and practitioners in the area of control engineering and control theory. It is also suited for postgraduate students as the book contains several overview articles that give a tutorial introduction into the various aspects of nonlinear model predictive control, including systems theory, computations, modeling and applications.
Book Synopsis Robot Operating System (ROS) by : Anis Koubaa
Download or read book Robot Operating System (ROS) written by Anis Koubaa and published by Springer. This book was released on 2017-05-25 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second volume is a continuation of the successful first volume of this Springer book, and as well as addressing broader topics it puts a particular focus on unmanned aerial vehicles (UAVs) with Robot Operating System (ROS). Consisting of three types of chapters: tutorials, cases studies, and research papers, it provides comprehensive additional material on ROS and the aspects of developing robotics systems, algorithms, frameworks, and applications with ROS. ROS is being increasingly integrated in almost all kinds of robots and is becoming the de-facto standard for developing applications and systems for robotics. Although the research community is actively developing applications with ROS and extending its features, amount of literature references is not representative of the huge amount of work being done. The book includes 19 chapters organized into six parts: Part 1 presents the control of UAVs with ROS, while in Part 2, three chapters deal with control of mobile robots. Part 3 provides recent work toward integrating ROS with Internet, cloud and distributed systems. Part 4 offers five case studies of service robots and field experiments. Part 5 presents signal-processing tools for perception and sensing, and lastly, Part 6 introduces advanced simulation frameworks. The diversity of topics in the book makes it a unique and valuable reference resource for ROS users, researchers, learners and developers.
Book Synopsis Autonomous Ground Vehicles by : Ümit Özgüner
Download or read book Autonomous Ground Vehicles written by Ümit Özgüner and published by Artech House. This book was released on 2011 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the near future, we will witness vehicles with the ability to provide drivers with several advanced safety and performance assistance features. Autonomous technology in ground vehicles will afford us capabilities like intersection collision warning, lane change warning, backup parking, parallel parking aids, and bus precision parking. Providing you with a practical understanding of this technology area, this innovative resource focuses on basic autonomous control and feedback for stopping and steering ground vehicles.Covering sensors, estimation, and sensor fusion to percept the vehicle motion and surrounding objects, this unique book explains the key aspects that makes autonomous vehicle behavior possible. Moreover, you find detailed examples of fusion and Kalman filtering. From maps, path planning, and obstacle avoidance scenarios...to cooperative mobility among autonomous vehicles, vehicle-to-vehicle communication, and vehicle-to-infrastructure communication, this forward-looking book presents the most critical topics in the field today.
Book Synopsis Model Predictive Control in the Process Industry by : Eduardo F. Camacho
Download or read book Model Predictive Control in the Process Industry written by Eduardo F. Camacho and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.
Book Synopsis Constrained Nonlinear Model Predictive Control for Vehicle Regulation by : Yongjie Zhu
Download or read book Constrained Nonlinear Model Predictive Control for Vehicle Regulation written by Yongjie Zhu and published by . This book was released on 2008 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: As a successful control method in both engineering and academia, model predictive control (MPC), especially nonlinear one (NMPC) has been extensively researched not only for improving its performance but also for extending its application fields. Originally proposed for complex interacting industrial process, MPC is well suited to deal with nonlinearities and constraints in a much more straightforward way than other methods. Autonomous vehicle related research, with the nonholonomic constraint and mechanical saturations, becomes such a new and promising field for exploring MPC. This dissertation concentrates on the design of a model predictive control architecture based on a discrete time nonlinear car model to solve regulation ("parking") problem. Discrete time MPC is proposed here not only to overcome the difficulties encountered by smooth feedback stabilization for nonholonomic systems but also to integrate the input and state constraints into the controller design process. An important consideration for finite horizon NMPC, stability is achieved by considering terminal state constraints combined with a terminal state penalty in the cost function, as well as the terminal controller design. The generated trajectory satisfies minimum curvature requirements and obstacle avoidance is also realized by considering distance constraints in the open-loop optimization process. It is well known that a primary concern for NMPC strategies is the evaluation of their control performance, especially robustness. Many researchers show the existence of robustness as a byproduct of stability which is achieved by monotonicity of the cost function. However the design of a control architecture within the MPC frame and the analysis of its robustness to additive uncertainties are far from well solved together as a complete topic. A robustness analysis is provided for the designed MPC control architecture so that the bound for additive uncertainties could be found under which the closed-loop system is input-to-state stable. The results are fit for general cases where more than one control values solved from the optimal problem are applied to real systems. As a further example of the possibility of applying MPC in vehicle industry, energy efficient cruise control is proposed to realize optimal energy management for vehicles. This concept is realized based on MPC strategy with an adaptive prediction horizon.
Book Synopsis Handbook of Model Predictive Control by : Saša V. Raković
Download or read book Handbook of Model Predictive Control written by Saša V. Raković and published by Springer. This book was released on 2018-09-01 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today. The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using “computationally intensive controls,” so the second part of this book addresses the solution of optimization problems in “real” time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance. The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading.
Book Synopsis Model-Based Predictive Control by : J.A. Rossiter
Download or read book Model-Based Predictive Control written by J.A. Rossiter and published by CRC Press. This book was released on 2017-07-12 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Predictive Control (MPC) has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Until now, no book has addressed in detail all key issues in the field including apriori stability and robust stability results. Engineers and MPC researchers now have a volume that provides a complete overview of the theory and practice of MPC as it relates to process and control engineering. Model-Based Predictive Control, A Practical Approach, analyzes predictive control from its base mathematical foundation, but delivers the subject matter in a readable, intuitive style. The author writes in layman's terms, avoiding jargon and using a style that relies upon personal insight into practical applications. This detailed introduction to predictive control introduces basic MPC concepts and demonstrates how they are applied in the design and control of systems, experiments, and industrial processes. The text outlines how to model, provide robustness, handle constraints, ensure feasibility, and guarantee stability. It also details options in regard to algorithms, models, and complexity vs. performance issues.
Book Synopsis Motion Control of Autonomous Underwater Vehicles Using Advanced Model Predictive Control Strategy by : Chao Shen
Download or read book Motion Control of Autonomous Underwater Vehicles Using Advanced Model Predictive Control Strategy written by Chao Shen and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing reliance on oceans, rivers and waterways in a spectrum of human activities have demonstrated the large demand for advanced marine technologies that facilitate multifarious in-water services and tasks. The autonomous underwater vehicle (AUV) is a representative marine technology which has been contributing continuously to many ocean-related fields. An elaborate control system is essential to AUVs. However, AUVs present difficult control system design problems due to their nonlinear dynamics, the unpredictable environment and the poor knowledge about the hydrodynamic coupling of the vehicle degrees of freedom. When designing the motion controller, the practical constraints on the AUV system such as limited perceiving, computing and actuating capabilities should also be respected. The model predictive control (MPC) is an advanced control technology that leverages optimization to calculate the control command. Thanks to the optimization nature, MPC can conveniently handle the complex nonlinearity in system dynamics as well as the state and control constraints. MPC takes the receding horizon control paradigm which gains satisfactory robustness against model uncertainties and external disturbances. Therefore, MPC is an ideal candidate for solving the AUV motion control problems. On the other hand, since the optimization is solved by iterative numerical algorithms, the obtained control signal is an implicit function of the system state, which complicates the characterization of the closed-loop properties. Moreover, the nonlinear system dynamics makes the online optimization nonlinear programming (NLP) problems. The high computational complexity may cause an issue on the real-time control for embedded platforms with limited computing resources. In order to push the advanced MPC technology towards real-world AUV applications, this PhD dissertation is concerned with fundamental AUV motion control problems and attempts to address the aforementioned challenges and provide novel solutions. This dissertation proceeds with Chapter 1 by providing state-of-the-art introductions to related research areas. The mathematical model used for the AUV motion control is elaborated in Chapter 2. In Chapter 3, we consider the AUV navigation and control problem in constrained workspace. A unified receding horizon optimization framework consisting of the dynamic path planning and the nonlinear model predictive control (NMPC) tracking control is developed. Although the NMPC tracking controller well accommodates the practical constraints on the AUV system, it presents a brand new design philosophy compared with the existing control systems that are implemented on real AUVs. Since the existing AUV control systems are reliable controllers, AUV practitioners tend not to fully replace them but to improve the control performance by adding features. By considering this, in Chapter 4, we develop the Lyapunov-based model predictive control (LMPC) scheme which builds on the existing AUV control system and invoke online optimization to improve the control performance. Chapter 5 focuses on the path following (PF) problem. Unlike the trajectory tracking control which equally emphasizes the spatial and temporal control objectives, the PF control often prioritizes the path convergence over the speed assignment. To incorporate this objective prioritization into the controller design, a novel multi-objective model predictive control (MOMPC) scheme is developed. While the MPC technique provides several salient features (e.g., optimality, constraints handling, objective prioritization, robustness, etc.), those features come at a price: a computational bottleneck is formed by the heavy burden of solving online optimizations in real time. To explicitly address this issue, in Chapter 6, the computational complexity of the MPC algorithms is particularly emphasized. Two novel strategies which potentially alleviate the computational burden of the MPC-based AUV tracking control are proposed. In Chapter 7, some conclusive remarks are provided and a few avenues for future research are identified.
Book Synopsis Computationally Efficient Robust Model Predictive Control Strategies for Linear Constrained Systems by : Maryam Bagherzadeh
Download or read book Computationally Efficient Robust Model Predictive Control Strategies for Linear Constrained Systems written by Maryam Bagherzadeh and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis deals with control problem of designing low computationally demanding robust model predictive controllers (MPC) for constrained systems subject to states/input limitations and bounded disturbances. In particular, the proposed solutions are based on a dual-mode control paradigm known as Set-Theoretic MPC (ST-MPC). This control schemes are particularly appealing for their capability of reducing the typical computation burden of robust MPC controllers. The latter is obtained by moving most of the required computations into an off-line phase, while leaving a simple and real-time affordable computational algorithm in the on-line phase. In this work, such a paradigm has been properly extended to deal with regulation and tracking problems appearing in two different control applications, namely transient stability regulation in smart grid and reference tracking in multi autonomous vehicles. In the transient stability control problem, we consider an operative scenario where a physical fault or a cyber-attack produces an impulsive state perturbation, and a controller must be designed to robustly recover, in a finite-time, transient stability despite initial perturbation and uncertainties. In such scenario, first we have used the standard feedback linearizion technicalities to linearize the smart grid model, then, we have applied a set-theoretic MPC scheme to robustly regulate the state trajectory towards the transient stability region. Moreover, to validate the proposed theory, a simulation campaign has been performed to contrast the proposed solution with a state-of-the-art competitor. Simulation results has shown that the proposed strategy outperforms the competitor scheme both in terms of settling time and robustness. In the multi-vehicle control problem, we exploit set-theoretic arguments to solve the reference tracking problem when the vehicles have different dynamics and/or constraints and/or disturbance, and each vehicle must follow uncoordinated reference trajectories. More in specific, we propose a novel control architecture where robust collision-free reference tracking is ensured by jointly using the set-theoretic control scheme and graph theory. To better clarify the potential and effectiveness of the proposed architecture, a simulation example involving 5 heterogeneous vehicles has been conducted.
Book Synopsis Constrained Model Predictive Control, State Estimation and Coordination by :
Download or read book Constrained Model Predictive Control, State Estimation and Coordination written by and published by . This book was released on 2006 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we study the interaction between the control performance and the quality of the state estimation in a constrained Model Predictive Control (MPC) framework for systems with stochastic disturbances. This consists of three parts: (i) the development of a constrained MPC formulation that adapts to the quality of the state estimation via constraints; (ii) the application of such a control law in a multi-vehicle formation coordinated control problem in which each vehicle operates subject to a no-collision constraint posed by others' imperfect prediction computed from finite bit-rate, communicated data; (iii) the design of the predictors and the communication resource assignment problem that satisfy the performance requirement from Part (ii). Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. However, if the state constraints were handled in the same certainty-equivalence fashion, the resulting control law could drive the real state to violate the constraints frequently. Part (i) focuses on exploring the inclusion of state estimates into the constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. Using a gaussian assumption, the original problem is approximated by a standard deterministic constrained MPC problem or the conditional mean process of the state (the prediction). The state estimates' conditional covariances appear in tightening the constraints as measuring the necessary standoff from the bound on the real state. `Closed-loop covariance' is introduced to reduce the infeasibility and the conservativeness caused by using long-horizon, open-loop prediction covariances. The resulting control law is applied to a telecommunications network traffic control problem as an example. The idea of posing and transforming a probabilistic MPC problem works well, but not limited to, linear systems. In Part (ii), we consider applying constrained MPC as a local control law in a coordinated control problem of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. First, we inspect the application of constrained MPC to a completely deterministic case. Formation stability theorems are derived for the subsystems and conditions on the local constraint set are derived in order to guarantee local stability or convergence to a target state. If these conditions are met for all subsystems, then this stability is inherited by the overall system. For the case when each subsystem suffers from disturbances in the dynamics, own self-measurement noises, and quantization errors on neighbors' information due to the finite-bit-rate channels, the constrained MPC strategy developed in Part (i) is appropriate to apply. Disturbance attenuation, or ``string stability", is studied in this framework and it is shown that inactivity of the MPC constraints implies stability. This then provides a connection between control objective, communications resource assignment and performance. A one-dimensional vehicle example is computed to crystallize ideas. The application of this part is not restricted to linear systems. In Part (iii), we discuss the local predictor design and bandwidth assignment problem in a coordinated vehicle formation context. The MPC controller used in Part (ii) relates the formation control performance and the information quality in the way that large standoff implies conservative performance. If the communication channels used to exchange local information are noiseless, but have only finite bit-rate, the bits assigned to each variable in the information package will change the prediction error covariance, and hence the control performance, via the quantization errors which can be regarded as measurement noises. In this part, we aim at deriving the minimal communication resource and the corresponding bit-rate assignment strategy the corresponding stable state predictors that is used to formulate the MPC constraints. We first develop an LMI (Linear Matrix Inequality) formulation for cross-estimator design in a simple two-vehicle scenario with non-standard information: one vehicle does not have access to the other's exact control value applied at each sampling time, but to its known, pre-computed, coupling linear feedback control law. Then a similar LMI problem is formulated for the bandwidth assignment problem that minimizes the total number of bits by adjusting the prediction gain matrices and the number of bits assigned to each variable. This LMI formulation takes care of the constraint on steady state prediction error covariance imposed by the formation performance requirement, the constraint on the limited total bandwidth, and the constraint on the predictors being stable. Some linear approximation is used to include the bandwidth assignment variables in the LMI formulation. The solution of the resulting LMIs guarantees the feasibility of the bandwidth assignment scheme and stable predictors, but not optimality. An example of a three-vehicle formation is also provided. The LMI formulation here is restricted to linear systems.
Book Synopsis Model Predictive Control by : Ridong Zhang
Download or read book Model Predictive Control written by Ridong Zhang and published by Springer. This book was released on 2018-08-14 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering.
Book Synopsis Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning by : Adnan Tahirovic
Download or read book Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning written by Adnan Tahirovic and published by Springer Science & Business Media. This book was released on 2013-04-18 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include: • how to use an MPC optimization framework for the mobile vehicle navigation approach; • how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and • what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal. The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimization step is continuously repeated to take into account new local sensor measurements. These ongoing changes make the path generated rather robust in comparison with techniques that fix the entire path prior to task execution. In addition to researchers working in MPC, engineers interested in vehicle path planning for a number of purposes: rescued mission in hazardous environments; humanitarian demining; agriculture; and even planetary exploration, will find this SpringerBrief to be instructive and helpful.
Book Synopsis Model Predictive Control System Design and Implementation Using MATLAB® by : Liuping Wang
Download or read book Model Predictive Control System Design and Implementation Using MATLAB® written by Liuping Wang and published by Springer Science & Business Media. This book was released on 2009-02-14 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Predictive Control System Design and Implementation Using MATLAB® proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: - continuous- and discrete-time MPC problems solved in similar design frameworks; - a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and - a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters. After the theoretical presentation, coverage is given to three industrial applications. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained. The technical contents of this book is mainly based on advances in MPC using state-space models and basis functions. This volume includes numerous analytical examples and problems and MATLAB® programs and exercises.
Book Synopsis Robust Constrained Model Predictive Control by : Arthur George Richards
Download or read book Robust Constrained Model Predictive Control written by Arthur George Richards and published by . This book was released on 2005 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: (Cont.) multiple Uninhabited Aerial Vehicles (UAVs) demonstrate that the new DMPC algorithm offers significant computational improvement compared to its centralized counterpart. The controllers developed in this thesis are demonstrated throughout in simulated examples related to vehicle control. Also, some of the controllers have been implemented on vehicle testbeds to verify their operation. The tools developed in this thesis improve the applicability of MPC to problems involving uncertainty and high complexity, for example, the control of a team of cooperating UAVs.
Book Synopsis Applications of Model Predictive Control to Vehicle Dynamics for Active Safety and Stability by : Craig Earl Beal
Download or read book Applications of Model Predictive Control to Vehicle Dynamics for Active Safety and Stability written by Craig Earl Beal and published by Stanford University. This book was released on 2011 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Each year in the United States, thousands of lives are lost as a result of loss of control crashes. Production driver assistance systems such as electronic stability control (ESC) have been shown to be highly effective in preventing many of these automotive crashes, yet these systems rely on a sensor suite that yields limited information about the road conditions and vehicle motion. Furthermore, ESC systems rely on gains and thresholds that are tuned to yield good performance without feeling overly restrictive to the driver. This dissertation presents an alternative approach to providing stabilization assistance to the driver which leverages additional information about the vehicle and road that may be obtained with advanced estimation techniques. This new approach is based on well-known and robust vehicle models and utilizes phase plane analysis techniques to describe the limits of stable vehicle handling, alleviating the need for hand tuning of gains and thresholds. The resulting state space within the computed handling boundaries is referred to as a safe handling envelope. In addition to the boundaries being straightforward to calculate, this approach has the benefit of offering a way for the designer of the system to directly adjust the controller to accomodate the preferences of different drivers. A model predictive control structure capable of keeping the vehicle within the safe handling boundaries is the final component of the envelope control system. This dissertation presents the design of a controller that is capable of smoothly and progressively augmenting the driver steering input to enforce the boundaries of the envelope. The model predictive control formulation provides a method for making trade-offs between enforcing the boundaries of the envelope, minimizing disruptive interventions, and tracking the driver's intended trajectory. Experiments with a steer-by-wire test vehicle demonstrate that the model predictive envelope control system is capable of operating in conjunction with a human driver to prevent loss of control of the vehicle while yielding a predictable vehicle trajectory. These experiments considered both the ideal case of state information from a GPS/INS system and an a priori friction estimate as well as a real-world implementation estimating the vehicle states and friction coefficient from steering effort and inertial sensors. Results from the experiments demonstrated a controller that is tolerant of vehicle and tire parameterization errors and works well over a wide range of conditions. When real time sensing of the states and friction properties is enabled, the results show that coupling of the controller and estimator is possible and the model predictive control structure provides a mechanism for minimizing undesirable coupled dynamics through tuning of intuitive controller parameters. The model predictive control structure presented in this dissertation may also be considered as a general framework for vehicle control in conjunction with a human driver. The structure utilized for envelope control may also be used to restrict other vehicle states for safety and stability. Results are presented in this dissertation to show that a model predictive controller can coordinate a secondary actuator to alter the planar states and reduce the energy transferred into the roll modes of the vehicle. The systematic approach to vehicle stabilization presented in this dissertation has the potential to improve the design methodology for future systems and form the basis for the inclusion of more advanced functions as sensing and computing capabilities improve. The envelope control system presented here offers the opportunity to advance the state of the art in stabilization assistance and provides a way to help drivers of all skill levels maintain control of their vehicle.
