Download or read book Improved Methods in Neural Network based Adaptive Output Feedback Control with Applications to Flight Control written by Nakwan Kim and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

Download or read book Royaume de Belgique Minist re de l instruction publique R pertoire des mus es de Belgique written by and published by . This book was released on 1958 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book High level Feedback Control With Neural Networks written by Young Ho Kim and published by World Scientific. This book was released on 1998-09-28 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively “add intelligence” to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty.This book bridges the gap between feedback control and AI. It provides design techniques for “high-level” neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including “dynamic output feedback”, “reinforcement learning” and “optimal design”, as well as a “fuzzy-logic reinforcement” controller. The control topologies are intuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.

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:

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.

Download or read book The Museum Contemporary Events in Russia written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Adaptive Output Feedback Control of Nonlinear Systems written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural Network Based Adaptive Control of Uncertain written by Anthony Calise and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural Network Based Adaptive Control of Uncertain and Unknown Nonlinear Systems written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our main accomplishment this past year has been to finalize and apply two approaches to output feedback adaptive control. The first is a direct adaptive approach, while the second uses a new error state observe. Both approaches overcome the limitation of earlier adaptive state observer based methods, which require that the order of the plant be known, and impose severe restrictions on the relative degree of regulated output variables. Within this context, we also have continued to exploit our approach for adaptive hedging' of actuator limits, which was the highlight of last year's report. We have also made some progress in the area of decentralized adaptive control. Our most significant interactions have been with NASA Marshall, NASA Ames, Wright Patterson AFB, Eglin AFB, Boeing and Lockheed.

Download or read book Neural Network Based Adaptive Control of Uncertain and Unknown Nonlinear Systems written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our main accomplishment this past year has been to finalize and apply two approaches to output feedback adaptive control. The first is a direct adaptive approach, while the second uses a new error state observe. Both approaches overcome the limitation of earlier adaptive state observer based methods, which require that the order of the plant be known, and impose severe restrictions on the relative degree of regulated output variables. Within this context, we also have continued to exploit our approach for adaptive.

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:

Download or read book Adaptive Output feedback Control and Applications to Very Flexible Aircraft written by Zheng Qu (Ph. D.) and published by . This book was released on 2016 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Very Flexible Aircraft (VFA) corresponds to an aerial platform whose flight dynamics critically depends on its flexible wing shape, and has been investigated as a potential solution to generate high-altitude low-endurance flights. The dominant presence of model uncertainties and potential actuator anomalies motivate an adaptive approach for control of VFA. Another particular control challenge for VFA is that its flexible modes cannot be measured accurately, which necessitates an output-feedback multi-input multi-output (MIMO) control approach. The focus of this thesis is on an adaptive output-feedback controller for a generic class of MIMO plant models with an emphasis on the control of a VFA so as to execute desired flight maneuvers. The proposed adaptive controller includes a baseline design based on observers and parameter adaptation based on a closed-loop reference model (CRM), and is applicable for a generic class of MIMO plants of arbitrary relative degree, and therefore the overall design is suitable for control in the presence of uncertainties in flexible effects, sensor dynamics, and actuator dynamics. In addition, the proposed controller can accommodate plant models whose number of outputs exceeds number of inputs. One major advantage of the proposed design is that the number of integrators required for implementation is significantly less than that of previous methods and therefore the controller can be implemented even for large-dimensional VFA models. Conditions are delineated under which asymptotic stability and command tracking can be guaranteed, and the overall design is verified using realistic simulations on a high-fidelity VFA model with unknown varying wing shape and actuator anomalies.

Download or read book Adaptive Control written by Kwanho You and published by IntechOpen. This book was released on 2009-01-01 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems.

Download or read book Robust Flight Control Design with Parameter Space Method Enhanced by Neural Network Adaptive Control written by Sun K. Kim and published by . This book was released on 2020 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: The linear robust control is enhanced by an adaptive control system that is designed by the online Feed-Forward Neural Network (FFNN). The FFNN adaptive control compensates for the aerodynamic uncertainty and imperfect modeling of aircraft dynamics, and it gradually replaces the linear controller as the network gains converge to a value that minimizes the linear control law. Although the FFNN adaptively adjusts the controller gains, an additional stability augmentation system is designed by Sigma-Pi Neural Network (SPNN) for compensating for the nonlinearity of the aircraft dynamics. The SPNN predicts the control input at a specific flight condition by memorizing the previous flight empirically. The SPNN adapts both the engine speed and elevator commands in the aircraft speed/altitude control. Training the SPNN is performed using a recursive least square estimator, and the control design is demonstrated on a six-degree-of-freedom (6DOF) digital simulation.

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.

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.