Adaptive control applied to unmodelled multivariable systems

by Michael Chang

Publisher: National Library of Canada in Ottawa

Written in English
Published: Downloads: 181
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Edition Notes

Thesis (M.A.Sc.)--University of Toronto, 1993.

SeriesCanadian theses = Thèses canadiennes
The Physical Object
FormatMicroform
Pagination2 microfiches : negative.
ID Numbers
Open LibraryOL15100900M
ISBN 100315836016
OCLC/WorldCa31514917

  Adaptive control theory is concerned with control under uncertainty in the system parameters or dynamics (Narendra & Annaswamy, ), (Ioannou & Sun, ), (Ioannou & Fidan, ).In other words.   We consider the problem of adaptive control of a continuous-time plant of arbitrary relative degree, in the presence of bounded disturbances as well as unmodeled dynamics. The adaptation law we. Get this from a library! Adaptive dual control: theory and applications. [N M Filatov; Heinz Unbehauen] -- This monograph demonstrates how the performance of various well-known adaptive controllers can be improved significantly using the dual effect. The modifications to incorporate dual control are. Joint Forward Operating Base Elements of Command and Control. NASA Astrophysics Data System (ADS) Summers, William C. Since the Goldwater-Nichols Act directed the Chairman of the Joint Chiefs of Staff to develop doctrine for the joint employment of the armed forces, tactics, techniques, and procedures have evolved at different rates depending on the competency.

Multivariable output feedback robust adaptive tracking control design for a class of delayed systems Article in International Journal of Systems Science 46(3) April with 6 Reads. decision to use adaptive control, for a real engineering application, must be based upon a quantitative assessment of costs and benefits. One of the main goals of this research project is to quantitatively evaluate the performance benefits of an adaptive control system vis-a-vis the best fixed-parameter nonadaptive compensator for a linear plant.   Adaptive control has been a remarkable field for industrial and academic research since s. 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. We show that an adaptive input/output feedback linearization control scheme for minimum phase non- linear systems is robust with respect to unstructured plant uncertainties such as unmodelled.

Adaptive Control for Linear Uncertain Systems with Unmodeled Dynamics Revisited via Optimal Control Modification. Nhan T. Nguyen; AIAA Guidance, On Set-Theoretic Model Reference Adaptive Control of Uncertain Dynamical Systems Subject to Actuator Dynamics. Adaptive Actuator Nonlinearity Compensation for Multivariable Systems. Multivariable Feedback Control: Analysis and Design, Second Edition presents a rigorous, yet easily readable, introduction to the analysis and design of robust multivariable control systems. Focusing on practical feedback control and not on system theory in general, this book provides the reader with insights into the opportunities and limitations of feedback s: Design of Digital Control Systems {W-term only} Design of digital control systems, from frequency domain methods through state-variable methods. Assumes a background in undergraduate control. For more information, see more. ME Adaptive Control Systems{W-Term} Introduction to control of systems with undetermined or time-varying parameters. This paper is to pursue a general investigation of cooperative robust output regulation for linear continuous-time multiple multivariable systems with unknown system parameters and unmodeled external show that, under standard minimum-phase and relative degree like assumptions, an internal model principle based output-feedback protocol can be constructed by .

Adaptive control applied to unmodelled multivariable systems by Michael Chang Download PDF EPUB FB2

Linearization via optimal control (O.C.) of a discrete nonlinear multivariable system is analysed and an adaptive linear control is presented. The performances of the adaptive control applied to a simulated fermentation process linearized by Taylor series expansion and via O.C.

are compared, showing that the second option gives a very robust. Purchase Adaptive Control Systems - 1st Edition. Print Book & E-Book. ISBNPrice: $ Adaptive Systems in Control and Signal Processing is a compendium of papers presented at the International Federation of Automatic Control in San Francisco on JuneOne paper addresses the results through comparative alternative algorithms in adaptive control of linear time invariant and time varying systems.

Suitable either as a reference for practicing engineers or as a text for a graduate course in adaptive control systems, this book is a self -contained compendium of readily implementable adaptive.

Integrated flight and propulsion control systems will require propulsion systems to reduce in time to accelerate engine and to improve transient performance for a full flight envelope operation. A multivariate model reference adaptive control (MRAC) scheme is proposed in this paper.

The application of adaptive control for a class of multivariable processes in heating, ventilating and air conditioning (HVAC) systems is studied.

As an example, a two zone fan-coil heating (FCH) system is considered. The thermal dynamics of the FCH system and environmental zones are simulated by a nonlinear model.

The book is a collection of lectures on system modeling and stability, adap- adaptive control formulation and design, stability and robustness analysis, and adaptive system illustration and comparison, aimed at reflecting the state of the art in adaptive control as.

Further chapters focus unpon learning control for deterministic nonlinear systems, and a time-varying learning controller is presented which can be applied to a class of nonlinear systems. Changyun Wen, in Control and Dynamic Systems, I INTRODUCTION.

Adaptive control is an effective way of controlling unknown dynamical systems. The several decades history of adaptive control ideas has been amply documented in various surveys and books including [1] - [5].A significant milestone in the stability analysis of adaptive control loops was reached in with the publication.

successfully applied to a twin-tank level control system, in addition to three test examples. Indeed, it can be seen that the proposed adaptive switching control can be applied to a class of multivariable nonlinear systems with unknown model structure and parameters.

ACKNOWLEDGMENT The authors would like to thank the editors and the. This monograph demonstrates how the performance of various well-known adaptive controllers can be improved significantly using the dual effect.

The modifications to incorporate dual control are realized separately and independently of the main adaptive controller without complicating the algorithms. A new bicriterial approach for dual control is developed and applied to various types. Adaptive Systems in Control and Signal Processing A volume in IFAC Workshop Series can have essentially the same robustness properties to unmodelled dynamics as does a fixed robust control law.

As a case study a plastics extruder is investigated and a multivariable self-tuning control is applied to its temperature control. Select A. Adaptive control of nonsmooth dynamical systems is theoretically challenging and practically important.

This book is the first book on adaptive control of such systems, addressing all these nonsmooth nonlinear characteristics: backlash, dead-zone, failure, friction, hysteresis, saturation and. Adaptive Control Tutorial is designed to serve the needs of three distinct groups of readers: engineers and students interested in learning how to design, simulate, and implement parameter estimators and adaptive control schemes; graduate students who also want to understand the analysis of simple schemes and get an idea of the steps involved Reviews: 3.

Industrial processes are naturally multivariable in nature, which also exhibit non-linear behavior and complex dynamic properties. The multivariable four-tank system has attracted recent attention, as it illustrates many concepts in multivariable control, particularly interaction, transmission zero, and non-minimum phase characteristics that emerge from a simple cascade of tanks.

Michael Chang, “Adaptive Control Applied to Unmodelled Multi- variable Systems”, Master’s thesis, University of Toronto, Department of Electrical and Computer Engineering, April Google Scholar.

In this paper, the problem of adaptive neural network (NN) dynamic surface control (DSC) is discussed for a class of strict-feedback nonlinear systems with full state constraints and unmodeled. Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain.

For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that adapts itself to. Abstract. Adaptive Control covers a set of techniques which provide a systematic approach for automatic adjustment of the controllers in real time, in order to achieve or to maintain a desired level of performance of the control system when the parameters of the plant dynamic model are unknown and/or change in time.

Adaptive control is a control methodology capable of dealing with uncertain systems to ensure desired control performance.

This paper provides an overview of some fundamental theoretical aspects and technical issues of multivariable adaptive control, and a thorough presentation of various adaptive control schemes for multi-input-multi-output systems, literature reviews on adaptive control.

Neural network-based adaptive output feedback approaches that augment a linear control design are described in this thesis, and emphasis is placed on their real-time implementation with flexible systems.

Two different control architectures that are robust to parametric uncertainties and unmodelled dynamics are presented. The unmodelled effects can consist of minimum phase internal dynamics of. Introduction. In the previous chapters we considered the design, analysis, and robustness of adaptive systems for LTI plants.

We have shown that adaptive systems can deal with any size of parametric uncertainty as well as with dynamic uncertainties due to neglected dynamics, provided the proper robust algorithms are used. In recent years, some advanced control methods such as adaptive control, fuzzy control and, and neural network control have been developed to satisfy the strict control requirements of IPMSM drive systems.

In Mohamed (), an adaptive self-tuning MTPA vector control is presented to enhance the performance of IPMSM drives. decomposition for singularly perturbed and discretized systems are presented.

An approach to decentralized adaptive control of nonlinear systems based on speed-gradient method is described and justified. Chapter 8 is devoted to applications related to stabilization of the desired spatial motion of complex mechanical systems.

Multivariable Control Systems focuses on control design with continual references to the practical aspects of implementation. While the concepts of multivariable control are justified, the book emphasises the need to maintain student interest and motivation over exhaustively rigorous mathematical proof.

Tools of analysis and representation are Reviews: 2. Multivariable Feedback Control: Analysis and Design, Second Edition presents a rigorous, yet easily readable, introduction to the analysis and design of robust multivariable control systems. Focusing on practical feedback control and not on system theory in general, this book provides the reader with insights into the opportunities and limitations of feedback control.5/5(1).

This paper presents a novel adaptive finite-time tracking control scheme for nonlinear systems. During the design process of control scheme, the unmodeled dynamics in nonlinear systems are taken into account.

The radial basis function neural networks (RBFNNs) are adopted to approximate the unknown nonlinear functions.

Meanwhile, based on RBFNNs, the assumptions with respect to unmodeled. Applied Mechanics Reviews In summary, this book can be strongly recommended not only as a basic text in multivariable control techniques for graduate and undergraduate students, but also as a valuable source of information for control engineers.

International Journal of Adaptive Control and Signal Processing Skip to main content. In this paper, stable indirect adaptive control with recurrent neural networks (RNN) is presented for square multivariable non-linear plants with unknown dynamics.

The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected “Real-Time Recurrent Learning” (RTRL) networks and an online parameters updating law.

3 Adaptive Control in Virtual Dynamics Space Virtual Model Control without adaptive mechanisms can control a walking robot successfully over both level and sloped terrain [16,17]. However, it is beneficial to consider the higher order unmodelled dynamics of the robot and dynamically adapt to changing dynamics or disturbances.

Model Reference Control for Nonlinear Systems Adaptive Control of Linearizable Minimum Phase Systems Single-Input Single-Output, Relative Degree One Case Extensions to Higher Relative Degree SISO Systems Adaptive Control of MIMO Systems Decouplable by Static State Feedback Conclusions I consider this is book covers very specific about adaptive control method called simple adaptive control (SAC).

Its explanation starts from the simple adaptive control for simple basic and ideal systems, then extends to systems with disturbances, and nonlinear s: 1.ISBN: OCLC Number: Description: 1 online resource (xviii, pages): illustrations: Contents: 1 Faces of Complexity Nonlinear Systems: Analysis and Design Tools Speed-Gradient Method and Partial Stabilization Nonlinear Control of Multivariable Systems Nonlinear Control of MIMO Systems Adaptive and Robust Control Design