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Neural Network Based Adaptive Control For Autonomous Flight Of Fixed Wing Unmanned Aerial Vehicles
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Book Synopsis Neural Network Based Adaptive Control for Autonomous Flight of Fixed Wing Unmanned Aerial Vehicles by : Vishwas Ramadas Puttige
Download or read book Neural Network Based Adaptive Control for Autonomous Flight of Fixed Wing Unmanned Aerial Vehicles written by Vishwas Ramadas Puttige and published by . This book was released on 2009 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents the development of small, inexpensive unmanned aerial vehicles (UAVs) to achieve autonomous fight. Fixed wing hobby model planes are modified and instrumented to form experimental platforms. Different sensors employed to collect the flight data are discussed along with their calibrations. The time constant and delay for the servo-actuators for the platform are estimated. Two different data collection and processing units based on micro-controller and PC104 architectures are developed and discussed. These units are also used to program the identification and control algorithms. Flight control of fixed wing UAVs is a challenging task due to the coupled, time-varying, nonlinear dynamic behaviour. One of the possible alternatives for the flight control system is to use the intelligent adaptive control techniques that provide online learning capability to cope with varying dynamics and disturbances. Neural network based indirect adaptive control strategy is applied for the current work. The two main components of the adaptive control technique are the identification block and the control block. Identification provides a mathematical model for the controller to adapt to varying dynamics. Neural network based identification provides a black-box identification technique wherein a suitable network provides prediction capability based upon the past inputs and outputs. Auto-regressive neural networks are employed for this to ensure good retention capabilities for the model that uses the past outputs and inputs along with the present inputs. Online and offline identification of UAV platforms are discussed based upon the flight data. Suitable modifications to the Levenberg-Marquardt training algorithm for online training are proposed. The effect of varying the different network parameters on the performance of the network are numerically tested out. A new performance index is proposed that is shown to improve the accuracy of prediction and also reduces the training time for these networks. The identification algorithms are validated both numerically and flight tested. A hardware-in-loop simulation system has been developed to test the identification and control algorithms before flight testing to identify the problems in real time implementation on the UAVs. This is developed to keep the validation process simple and a graphical user interface is provided to visualise the UAV flight during simulations. A dual neural network controller is proposed as the adaptive controller based upon the identification models. This has two neural networks collated together. One of the neural networks is trained online to adapt to changes in the dynamics. Two feedback loops are provided as part of the overall structure that is seen to improve the accuracy. Proofs for stability analysis in the form of convergence of the identifier and controller networks based on Lyapunov's technique are presented. In this analysis suitable bounds on the rate of learning for the networks are imposed. Numerical results are presented to validate the adaptive controller for single-input single-output as well as multi-input multi-output subsystems of the UAV. Real time validation results and various flight test results confirm the feasibility of the proposed adaptive technique as a reliable tool to achieve autonomous flight. The comparison of the proposed technique with a baseline gain scheduled controller both in numerical simulations as well as test flights bring out the salient adaptive feature of the proposed technique to the time-varying, nonlinear dynamics of the UAV platforms under different flying conditions.
Book Synopsis Neural Network Based Adaptive Control for Nonlinear Dynamic Regimes by : Yoonghyun Shin
Download or read book Neural Network Based Adaptive Control for Nonlinear Dynamic Regimes written by Yoonghyun Shin and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named composite model reference adaptive control is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of pseudo-control hedging techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.
Book Synopsis Robust Discrete-Time Flight Control of UAV with External Disturbances by : Shuyi Shao
Download or read book Robust Discrete-Time Flight Control of UAV with External Disturbances written by Shuyi Shao and published by Springer Nature. This book was released on 2020-09-26 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book studies selected discrete-time flight control schemes for fixed-wing unmanned aerial vehicle (UAV) systems in the presence of system uncertainties, external disturbances and input saturation. The main contributions of this book for UAV systems are as follows: (i) the proposed integer-order discrete-time control schemes are based on the designed discrete-time disturbance observers (DTDOs) and the neural network (NN); and (ii) the fractional-order discrete-time control schemes are developed by using the fractional-order calculus theory, the NN and the DTDOs. The book offers readers a good understanding of how to establish discrete-time tracking control schemes for fixed-wing UAV systems subject to system uncertainties, external wind disturbances and input saturation. It represents a valuable reference guide for academic research on uncertain UAV systems, and can also support advanced / Ph.D. studies on control theory and engineering.
Book Synopsis Nonlinear Control of Fixed-Wing UAVs with Time-Varying and Unstructured Uncertainties by : Michail G. Michailidis
Download or read book Nonlinear Control of Fixed-Wing UAVs with Time-Varying and Unstructured Uncertainties written by Michail G. Michailidis and published by Springer Nature. This book was released on 2020-02-21 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a comprehensive and mathematically rigorous controller design for families of nonlinear systems with time-varying parameters and unstructured uncertainties. Although the presented methodology is general, the specific family of systems considered is the latest, NextGen, unconventional fixed-wing unmanned aircraft with circulation control or morphing wings, or a combination of both. The approach considers various sources of model and parameter uncertainty, while the controller design depends not on a nominal plant model, but instead on a family of admissible plants. In contrast to existing controller designs that consider multiple models and multiple controllers, the proposed approach is based on the ‘one controller fits all models’ within the unstructured uncertainty interval. The book presents a modeling-based analysis and synthesis approach with additive uncertainty weighting functions for accurate realization of the candidate systems. This differs significantly from existing designs in that it is capable of handling time-varying characteristics. This research monograph is suitable for scientists, engineers, researchers and graduate students with a background in control system theory who are interested in complex engineering nonlinear systems.
Book Synopsis Neural Network Based Identification and Control of an Unmanned Helicopter by : Mahendra Kumar Samal
Download or read book Neural Network Based Identification and Control of an Unmanned Helicopter written by Mahendra Kumar Samal and published by . This book was released on 2009 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research work provides the development of an Adaptive Flight Control System (AFCS) for autonomous hover of a Rotary-wing Unmanned Aerial Vehicle (RUAV). Due to the complex, nonlinear and time-varying dynamics of the RUAV, indirect adaptive control using the Model Predictive Control (MPC) is utilised. The performance of the MPC mainly depends on the model of the RUAV used for predicting the future behaviour. Due to the complexities associated with the RUAV dynamics, a neural network based black box identification technique is used for modelling the behaviour of the RUAV. Auto-regressive neural network architecture is developed for offline and online modelling purposes. A hybrid modelling technique that exploits the advantages of both the offline and the online models is proposed. In the hybrid modelling technique, the predictions from the offline trained model are corrected by using the error predictions from the online model at every sample time. To reduce the computational time for training the neural networks, a principal component analysis based algorithm that reduces the dimension of the input training data is also proposed. This approach is shown to reduce the computational time significantly. These identification techniques are validated in numerical simulations before flight testing in the Eagle and RMAX helicopter platforms. Using the successfully validated models of the RUAVs, Neural Network based Model Predictive Controller (NN-MPC) is developed taking into account the non-linearity of the RUAVs and constraints into consideration. The parameters of the MPC are chosen to satisfy the performance requirements imposed on the flight controller. The optimisation problem is solved numerically using nonlinear optimisation techniques. The performance of the controller is extensively validated using numerical simulation models before flight testing. The effects of actuator and sensor delays and noises along with the wind gusts are taken into account during these numerical simulations. In addition, the robustness of the controller is validated numerically for possible parameter variations. The numerical simulation results are compared with a base-line PID controller. Finally, the NN-MPCs are flight tested for height control and autonomous hover. For these, SISO as well as multiple SISO controllers are used. The flight tests are conducted in varying weather conditions to validate the utility of the control technique. The NN-MPC in conjunction with the proposed hybrid modelling technique is shown to handle additional disturbances successfully. Extensive flight test results provide justification for the use of the NN-MPC technique as a reliable technique for control of non-linear complex dynamic systems such as RUAVs.
Book Synopsis Neural Network Control of a Parallel Hybrid-electric Propulsion System for a Small Unmanned Aerial Vehicle by : Frederick G. Harmon
Download or read book Neural Network Control of a Parallel Hybrid-electric Propulsion System for a Small Unmanned Aerial Vehicle written by Frederick G. Harmon and published by . This book was released on 2005 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles (UAVs) used for military, homeland security, and disaster monitoring missions involving intelligence, surveillance, or reconnaissance (ISR). The benefits include increased time-on-station and range than electric-powered UAVs and stealth modes not available with gasoline-powered UAVs. A conceptual design of a small UAV with a parallel hybrid-electric propulsion system, an optimization routine for the energy use, the application of a neural network to approximate the optimization results, and simulation results are provided. The two-point conceptual design includes an internal combustion engine sized for cruise and an electric motor and lithium-ion battery pack sized for endurance speed. The flexible optimization routine allows relative importance to be assigned between the use of gasoline, electricity, and recharging. The Cerebellar Model Arithmetic Computer (CMAC) neural network approximates the optimization results and is applied to the control of the parallel hybrid-electric propulsion system. The CMAC controller saves on the required memory compared to a large look-up table by two orders of magnitude. The energy use for the hybrid-electric UAV with the CMAC controller during a one-hour and a three-hour ISR mission is 58% and 27% less, respectively, than for a gasoline-powered UAV.
Book Synopsis Neural Network Based Adaptive Control and Its Applications to Aerial Vehicles by : Seungjae Lee
Download or read book Neural Network Based Adaptive Control and Its Applications to Aerial Vehicles written by Seungjae Lee and published by . This book was released on 2001 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adaptive Estimation and Control with Application to Vision-based Autonomous Formation Flight by : Ramachandra Jayant Sattigeri
Download or read book Adaptive Estimation and Control with Application to Vision-based Autonomous Formation Flight written by Ramachandra Jayant Sattigeri and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The role of vision as an additional sensing mechanism has received a lot of attention in recent years in the context of autonomous flight applications. Modern Unmanned Aerial Vehicles (UAVs) are equipped with vision sensors because of their light-weight, low-cost characteristics and also their ability to provide a rich variety of information of the environment in which the UAVs are navigating in. The problem of vision based autonomous flight is very difficult and challenging since it requires bringing together concepts from image processing and computer vision, target tracking and state estimation, and flight guidance and control. This thesis focuses on the adaptive state estimation, guidance and control problems involved in vision-based formation flight. Specifically, the thesis presents a composite adaptation approach to the partial state estimation of a class of nonlinear systems with unmodeled dynamics. In this approach, a linear time-varying Kalman filter is the nominal state estimator which is augmented by the output of an adaptive neural network (NN) that is trained with two error signals. The benefit of the proposed approach is in its faster and more accurate adaptation to the modeling errors over a conventional approach. The thesis also presents two approaches to the design of adaptive guidance and control (G & C) laws for line-of-sight formation flight. In the first approach, the guidance and autopilot systems are designed separately and then combined together by assuming time-scale separation. The second approach is based on integrating the guidance and autopilot design process. The developed G & C laws using both approaches are adaptive to unmodeled leader aircraft acceleration and to own aircraft aerodynamic uncertainties. The thesis also presents theoretical justification based on Lyapunov-like stability analysis for integrating the adaptive state estimation and adaptive G & C designs. All the developed designs are validated in nonlinear, 6DOF fixed-wing aircraft simulations. Finally, the thesis presents a decentralized coordination strategy for vision-based multiple-aircraft formation control. In this approach, each aircraft in formation regulates range from up to two nearest neighboring aircraft while simultaneously tracking nominal desired trajectories common to all aircraft and avoiding static obstacles.
Book Synopsis Fully Tuned Radial Basis Function Neural Networks for Flight Control by : N. Sundararajan
Download or read book Fully Tuned Radial Basis Function Neural Networks for Flight Control written by N. Sundararajan and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fully Tuned Radial Basis Function Neural Networks for Flight Control presents the use of the Radial Basis Function (RBF) neural networks for adaptive control of nonlinear systems with emphasis on flight control applications. A Lyapunov synthesis approach is used to derive the tuning rules for the RBF controller parameters in order to guarantee the stability of the closed loop system. Unlike previous methods that tune only the weights of the RBF network, this book presents the derivation of the tuning law for tuning the centers, widths, and weights of the RBF network, and compares the results with existing algorithms. It also includes a detailed review of system identification, including indirect and direct adaptive control of nonlinear systems using neural networks. Fully Tuned Radial Basis Function Neural Networks for Flight Control is an excellent resource for professionals using neural adaptive controllers for flight control applications.
Book Synopsis Neural Network Based Adaptive Control of Uncertain and Unknown Nonlinear Systems by : Anthony Calise
Download or read book Neural Network Based Adaptive Control of Uncertain and Unknown Nonlinear Systems written by Anthony Calise and published by . This book was released on 2001 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Autonomous Control of Unmanned Aerial Vehicles by : Victor Becerra
Download or read book Autonomous Control of Unmanned Aerial Vehicles written by Victor Becerra and published by MDPI. This book was released on 2019-06-24 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unmanned aerial vehicles (UAVs) are being increasingly used in different applications in both military and civilian domains. These applications include surveillance, reconnaissance, remote sensing, target acquisition, border patrol, infrastructure monitoring, aerial imaging, industrial inspection, and emergency medical aid. Vehicles that can be considered autonomous must be able to make decisions and react to events without direct intervention by humans. Although some UAVs are able to perform increasingly complex autonomous manoeuvres, most UAVs are not fully autonomous; instead, they are mostly operated remotely by humans. To make UAVs fully autonomous, many technological and algorithmic developments are still required. For instance, UAVs will need to improve their sensing of obstacles and subsequent avoidance. This becomes particularly important as autonomous UAVs start to operate in civilian airspaces that are occupied by other aircraft. The aim of this volume is to bring together the work of leading researchers and practitioners in the field of unmanned aerial vehicles with a common interest in their autonomy. The contributions that are part of this volume present key challenges associated with the autonomous control of unmanned aerial vehicles, and propose solution methodologies to address such challenges, analyse the proposed methodologies, and evaluate their performance.
Book Synopsis Control of Autonomous Aerial Vehicles by : Andrea L'Afflitto
Download or read book Control of Autonomous Aerial Vehicles written by Andrea L'Afflitto and published by Springer Nature. This book was released on 2023-11-20 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control of Autonomous Aerial Vehicles is an edited book that provides a single-volume snapshot on the state of the art in the field of control theory applied to the design of autonomous unmanned aerial vehicles (UAVs), aka “drones”, employed in a variety of applications. The homogeneous structure allows the reader to transition seamlessly through results in guidance, navigation, and control of UAVs, according to the canonical classification of the main components of a UAV’s autopilot. Each chapter has been written to assist graduate students and practitioners in the fields of aerospace engineering and control theory. The contributing authors duly present detailed literature reviews, conveying their arguments in a systematic way with the help of diagrams, plots, and algorithms. They showcase the applicability of their results by means of flight tests and numerical simulations, the results of which are discussed in detail. Control of Autonomous Aerial Vehicles will interest readers who are researchers, practitioners or graduate students in control theory, autonomous systems or robotics, or in aerospace, mechanical or electrical engineering.
Book Synopsis Research in Neural Network Based Adaptive Control by :
Download or read book Research in Neural Network Based Adaptive Control written by and published by . This book was released on 2000 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most significant theoretical accomplishment has been the development of a new approach for dealing with control limits and nonlinearities in adaptive systems. This approach both prevents the Maptire system from doing harm to an otherwise stable system, and also allows adaptation to continue while the control is saturated. We regard this as a major step towards flight certification of adaptive controllers. The approach is more general in that it permits a broad class of input nonlinearities, including such effects as discrete and bang/bang control. In the area of output feedback, we continue to refine our curlier work, and have begun to take steps in the direction of decentralized adaptive systems in a state feedback setting. Our most significant interactions have been with NASA Marshall and NASA Ames. In particular, we arc fully exploiting our research in limited authority adaptive control in the areas of autopilot design for launch vehicles, and propulsion control for commercial aircraft subject to partial or total loss of conventional flight control.
Book Synopsis Autopilot Design for the Takeoff and Landing of a Tailwheel Airplane by : Gracelyne Allred
Download or read book Autopilot Design for the Takeoff and Landing of a Tailwheel Airplane written by Gracelyne Allred and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This work seeks to design and verify a flight control solution, using an adaptive control architecture, for the takeoff and landing of an autonomous fixed-wing vehicle with conventional gear. The International Aerial Robotics Competition (IARC) challenges teams to develop autonomous platforms to accomplish missions with problems not previously solved. The Pennsylvania UAV Research Lab (PURL) is competing in IARC's Mission 9, which partially requires high-speed flight with a large payload. The SIG Rascal 168, a radio-controlled aircraft with conventional gear, is the chosen platform and has motivated the model development and controller design presented in this work. The typical control challenges of this tailwheel aircraft, intensified by the added payload, make this vehicle a useful flight-test platform. This thesis presents the integration of the aircraft model into a previously developed flight simulation environment that uses a neural network-based adaptive flight controller to refine flight characteristics during specific mission phases, including take-off and landing. The resulting autopilot design is evaluated in simulation and prepared to be incorporated with the necessary hardware onboard Rascal 168.
Book Synopsis Fault-Tolerant Cooperative Control of Unmanned Aerial Vehicles by : Ziquan Yu
Download or read book Fault-Tolerant Cooperative Control of Unmanned Aerial Vehicles written by Ziquan Yu and published by Springer Nature. This book was released on 2023-12-06 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the fault-tolerant cooperative control (FTCC) of multiple unmanned aerial vehicles (multi-UAVs). It provides systematic and comprehensive descriptions of FTCC issues in multi-UAVs concerning faults, external disturbances, strongly unknown nonlinearities, and input saturation. Further, it addresses FTCC design from longitudinal motions to attitude motions, and outer-loop position motions of multi-UAVs. The book’s detailed control schemes can be used to enhance the flight safety of multi-UAVs. As such, the book offers readers an in-depth understanding of UAV safety in cooperative/formation flight and corresponding design methods. The FTCC methods presented here can also provide guidelines for engineers to improve the safety of aerospace engineering systems. The book offers a valuable asset for scientists and researchers, aerospace engineers, control engineers, lecturers and teachers, and graduates and undergraduates in the system and control community, especially those working in the field of UAV cooperation and multi-agent systems.
Book Synopsis Intelligent Adaptive Control for Nonlinear Applications by : Shaaban Ali Salman Ali
Download or read book Intelligent Adaptive Control for Nonlinear Applications written by Shaaban Ali Salman Ali and published by . This book was released on 2008 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: The thesis deals with the design and implementation of an Adaptive Flight Control technique for Unmanned Aerial Vehicles (UAVs). The application of UAVs has been increasing exponentially in the last decade both in Military and Civilian fronts. These UAVs fly at very low speeds and Reynolds numbers, have nonlinear coupling, and tend to exhibit time varying characteristics. In addition, due to the variety of missions, they fly in uncertain environments exposing themselves to unpredictable external disturbances. The successful completion of the UAV missions is largely dependent on the accuracy of the control provided by the flight controllers. Thus there is a necessity for accurate and robust flight controllers. These controllers should be able to adapt to the changes in the dynamics due to internal and external changes. From the available literature, it is known that, one of the better suited adaptive controllers is the model based controller. The design and implementation of model based adaptive controller is discussed in the thesis. A critical issue in the design and application of model based control is the online identification of the UAV dynamics from the available sensors using the onboard processing capability. For this, proper instrumentation in terms of sensors and avionics for two platforms developed at UNSW@ADFA is discussed. Using the flight data from the remotely flown platforms, state space identification and fuzzy identification are developed to mimic the UAV dynamics. Real time validations using Hardware in Loop (HIL) simulations show that both the methods are feasible for control. A finer comparison showed that the accuracy of identification using fuzzy systems is better than the state space technique. The flight tests with real time online identification confirmed the feasibility of fuzzy identification for intelligent control. Hence two adaptive controllers based on the fuzzy identification are developed. The first adaptive controller is a hybrid indirect adaptive controller that utilises the model sensitivity in addition to output error for adaptation. The feedback of the model sensitivity function to adapt the parameters of the controller is shown to have beneficial effects, both in terms of convergence and accuracy. HIL simulations applied to the control of roll stabilised pitch autopilot for a typical UAV demonstrate the improvements compared to the direct adaptive controller. Next a novel fuzzy model based inversion controller is presented. The analytical approximate inversion proposed in this thesis does not increase the computational effort. The comparisons of this controller with other controller for a benchmark problem are presented using numerical simulations. The results bring out the superiority of this technique over other techniques. The extension of the analytical inversion based controller for multiple input multiple output problem is presented for the design of roll stabilised pitch autopilot for a UAV. The results of the HIL simulations are discussed for a typical UAV. Finally, flight test results for angle of attack control of one of the UAV platforms at UNSW@ADFA are presented. The flight test results show that the adaptive controller is capable of controlling the UAV suitably in a real environment, demonstrating its robustness characteristics.
Book Synopsis Adaptive Mode Transition Control Architecture with an Application to Unmanned Aerial Vehicles by : Luis Benigno Gutiérrez Zea
Download or read book Adaptive Mode Transition Control Architecture with an Application to Unmanned Aerial Vehicles written by Luis Benigno Gutiérrez Zea and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, an architecture for the adaptive mode transition control of unmanned aerial vehicles (UAV) is presented. The proposed architecture consists of three levels: the highest level is occupied by mission planning routines where information about way points the vehicle must follow is processed. The middle level uses a trajectory generation component to coordinate the task execution and provides set points for low-level stabilizing controllers. The adaptive mode transitioning control algorithm resides at the lowest level of the hierarchy consisting of a mode transitioning controller and the accompanying adaptation mechanism. The mode transition controller is composed of a mode transition manager, a set of local controllers, a set of active control models, a set point filter, a state filter, an automatic trimming mechanism and a dynamic compensation filter. Local controllers operate in local modes and active control models operate in transitions between two local modes. The mode transition manager determines the actual mode of operation of the vehicle based on a set of mode membership functions and activates a local controller or an active control model accordingly. The adaptation mechanism uses an indirect adaptive control methodology to adapt the active control models. For this purpose, a set of plant models based on fuzzy neural networks is trained based on input/output information from the vehicle and used to compute sensitivity matrices providing the linearized models required by the adaptation algorithms. The effectiveness of the approach is verified through software-in-the-loop simulations, hardware-in-the-loop simulations and flight testing.
