Download or read book Discrete Time Adaptive Iterative Learning Control written by Ronghu Chi and published by Springer Nature. This book was released on 2022-03-21 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
Download or read book Data Driven Iterative Learning Control for Discrete Time Systems written by Ronghu Chi and published by Springer Nature. This book was released on 2022-11-15 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system’s output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
Download or read book Iterative Learning Control and Adaptive Control for Systems with Unstable Discrete Time Inverse written by Bowen Wang and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Another way to address the often unstable nature of the inverse of non-minimum phase systems is to use the in-house developed stable inverse theory Longman JiLLL, which can also be incorporated into other control algorithms including One-Step Ahead Control and Indirect Adaptive Control in addition to Iterative Learning Control. Using this stable inverse theory, One-Step Ahead Control has been generalized to apply to systems whose discrete-time inverses are unstable. The generalized one-step ahead control can be viewed as a Model Predictive Control that achieves zero tracking error with a control input bounded by the actuator constraints. In situations where one feels not confident about the system model, adaptive control can be applied to update the model parameters while achieving zero tracking error.
Download or read book Iterative Learning Control written by David H. Owens and published by Springer. This book was released on 2015-10-31 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops a coherent and quite general theoretical approach to algorithm design for iterative learning control based on the use of operator representations and quadratic optimization concepts including the related ideas of inverse model control and gradient-based design. Using detailed examples taken from linear, discrete and continuous-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately as their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates the underlying robustness of the paradigm and also includes new control laws that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference and auxiliary signals and also to support new properties such as spectral annihilation. Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.
Download or read book Intelligent Computing in Smart Grid and Electrical Vehicles written by Kang Li and published by Springer. This book was released on 2014-10-01 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the third part of the refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2014, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014, held in Shanghai, China, in September 2014. The 159 revised full papers presented in the three volumes of CCIS 461-463 were carefully reviewed and selected from 572 submissions. The papers of this volume are organized in topical sections on computational intelligence in utilization of clean and renewable energy resources, including fuel cell, hydrogen, solar and winder power, marine and biomass; intelligent modeling, control and supervision for energy saving and pollution reduction; intelligent methods in developing electric vehicles, engines and equipment; intelligent computing and control in distributed power generation systems; intelligent modeling, simulation and control of power electronics and power networks; intelligent road management and electricity marketing strategies; intelligent water treatment and waste management technologies; integration of electric vehicles with smart grid.
Download or read book Iterative Learning Control written by Hyo-Sung Ahn and published by Springer Science & Business Media. This book was released on 2007-06-28 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph studies the design of robust, monotonically-convergent iterative learning controllers for discrete-time systems. It presents a unified analysis and design framework that enables designers to consider both robustness and monotonic convergence for typical uncertainty models, including parametric interval uncertainties, iteration-domain frequency uncertainty, and iteration-domain stochastic uncertainty. The book shows how to use robust iterative learning control in the face of model uncertainty.
Download or read book Iterative Learning Control written by Zeungnam Bien and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Iterative Learning Control (ILC) differs from most existing control methods in the sense that, it exploits every possibility to incorporate past control informa tion, such as tracking errors and control input signals, into the construction of the present control action. There are two phases in Iterative Learning Control: first the long term memory components are used to store past control infor mation, then the stored control information is fused in a certain manner so as to ensure that the system meets control specifications such as convergence, robustness, etc. It is worth pointing out that, those control specifications may not be easily satisfied by other control methods as they require more prior knowledge of the process in the stage of the controller design. ILC requires much less information of the system variations to yield the desired dynamic be haviors. Due to its simplicity and effectiveness, ILC has received considerable attention and applications in many areas for the past one and half decades. Most contributions have been focused on developing new ILC algorithms with property analysis. Since 1992, the research in ILC has progressed by leaps and bounds. On one hand, substantial work has been conducted and reported in the core area of developing and analyzing new ILC algorithms. On the other hand, researchers have realized that integration of ILC with other control techniques may give rise to better controllers that exhibit desired performance which is impossible by any individual approach.
Download or read book Model Free Adaptive Control written by Zhongsheng Hou and published by CRC Press. This book was released on 2013-09-24 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Free Adaptive Control: Theory and Applications summarizes theory and applications of model-free adaptive control (MFAC). MFAC is a novel adaptive control method for the unknown discrete-time nonlinear systems with time-varying parameters and time-varying structure, and the design and analysis of MFAC merely depend on the measured input and output data of the controlled plant, which makes it more applicable for many practical plants. This book covers new concepts, including pseudo partial derivative, pseudo gradient, pseudo Jacobian matrix, and generalized Lipschitz conditions, etc.; dynamic linearization approaches for nonlinear systems, such as compact-form dynamic linearization, partial-form dynamic linearization, and full-form dynamic linearization; a series of control system design methods, including MFAC prototype, model-free adaptive predictive control, model-free adaptive iterative learning control, and the corresponding stability analysis and typical applications in practice. In addition, some other important issues related to MFAC are also discussed. They are the MFAC for complex connected systems, the modularized controller designs between MFAC and other control methods, the robustness of MFAC, and the symmetric similarity for adaptive control system design. The book is written for researchers who are interested in control theory and control engineering, senior undergraduates and graduated students in engineering and applied sciences, as well as professional engineers in process control.
Download or read book Adaptive Dynamic Programming Single and Multiple Controllers written by Ruizhuo Song and published by Springer. This book was released on 2018-12-28 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a class of novel optimal control methods and games schemes based on adaptive dynamic programming techniques. For systems with one control input, the ADP-based optimal control is designed for different objectives, while for systems with multi-players, the optimal control inputs are proposed based on games. In order to verify the effectiveness of the proposed methods, the book analyzes the properties of the adaptive dynamic programming methods, including convergence of the iterative value functions and the stability of the system under the iterative control laws. Further, to substantiate the mathematical analysis, it presents various application examples, which provide reference to real-world practices.
Download or read book Iterative Learning Control for Nonlinear Time Delay System written by Jianming Wei and published by Springer Nature. This book was released on 2023-01-01 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on adaptive iterative learning control problem for nonlinear time-delay systems.A universal adaptive learning control scheme is provided for a wide classes of nonlinear systems with time-varying delay and input nonlinearity. Proceeding from easy to difficult, this book deals with the adaptive iterative learning control problems for parameterized nonlinear time-delay systems, non-parameterized nonlinear time-delay systems, nonlinear time-delay systems with unknown control direction and nonlinear time-delay systems with un-measurable states. The proposed control schemes can be extended to the adaptive learning control problem for wider classes of nonlinear systems revelent to abovementioned nonlinear systems.The topics presented in this book are research hot spots of iterative learning control. This book will be a valuable reference for researchers and students working or studying in this area.
Download or read book Iterative Learning Control for Network Systems Under Constrained Information Communication written by Wenjun Xiong and published by Springer Nature. This book was released on with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book From Model Based to Data Driven Discrete Time Iterative Learning Control written by Bing Song and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The improvement here makes use of Carleman bilinearized models to capture more nonlinear dynamics, with the potential for faster convergence when compared to existing methods based on linearized models. The last work presented here finally uses model-free reinforcement learning (RL) to eliminate the need for an a priori model. It is analogous to direct adaptive control using data to directly produce the gains in the ILC law without use of a model. An off-policy RL method is first developed by extending a model-free model predictive control method and then applied in the trial domain for ILC. Adjustments of the ILC learning law and the RL recursion equation for state-value function updates allow the collection of enough data while improving the tracking accuracy without much safety concerns. This algorithm can be seen as the first step to bridge ILC and RL aiming to address nonlinear systems.
Download or read book Advanced Discrete Time Control written by Khalid Abidi and published by Springer. This book was released on 2015-03-25 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers a wide spectrum of systems such as linear and nonlinear multivariable systems as well as control problems such as disturbance, uncertainty and time-delays. The purpose of this book is to provide researchers and practitioners a manual for the design and application of advanced discrete-time controllers. The book presents six different control approaches depending on the type of system and control problem. The first and second approaches are based on Sliding Mode control (SMC) theory and are intended for linear systems with exogenous disturbances. The third and fourth approaches are based on adaptive control theory and are aimed at linear/nonlinear systems with periodically varying parametric uncertainty or systems with input delay. The fifth approach is based on Iterative learning control (ILC) theory and is aimed at uncertain linear/nonlinear systems with repeatable tasks and the final approach is based on fuzzy logic control (FLC) and is intended for highly uncertain systems with heuristic control knowledge. Detailed numerical examples are provided in each chapter to illustrate the design procedure for each control method. A number of practical control applications are also presented to show the problem solving process and effectiveness with the advanced discrete-time control approaches introduced in this book.
Download or read book Real time Iterative Learning Control written by Jian-Xin Xu and published by Springer Science & Business Media. This book was released on 2008-12-12 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time Iterative Learning Control demonstrates how the latest advances in iterative learning control (ILC) can be applied to a number of plants widely encountered in practice. The book gives a systematic introduction to real-time ILC design and source of illustrative case studies for ILC problem solving; the fundamental concepts, schematics, configurations and generic guidelines for ILC design and implementation are enhanced by a well-selected group of representative, simple and easy-to-learn example applications. Key issues in ILC design and implementation in linear and nonlinear plants pervading mechatronics and batch processes are addressed, in particular: ILC design in the continuous- and discrete-time domains; design in the frequency and time domains; design with problem-specific performance objectives including robustness and optimality; design in a modular approach by integration with other control techniques; and design by means of classical tools based on Bode plots and state space.
Download or read book Iterative Learning Control written by Yangquan Chen and published by Springer. This book was released on 2007-10-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a comprehensive coverage of iterative learning control. The book can be used as a text or reference for a course at graduate level and is also suitable for self-study and for industry-oriented courses of continuing education. Ranging from aerodynamic curve identification robotics to functional neuromuscular stimulation, Iterative Learning Control (ILC), started in the early 80s, is found to have wide applications in practice. Generally, a system under control may have uncertainties in its dynamic model and its environment. One attractive point in ILC lies in the utilisation of the system repetitiveness to reduce such uncertainties and in turn to improve the control performance by operating the system repeatedly. This monograph emphasises both theoretical and practical aspects of ILC. It provides some recent developments in ILC convergence and robustness analysis. The book also considers issues in ILC design. Several practical applications are presented to illustrate the effectiveness of ILC. The applied examples provided in this monograph are particularly beneficial to readers who wish to capitalise the system repetitiveness to improve system control performance.
Download or read book New Stable Inverses of Linear Discrete Time Systems and Application to Iterative Learning Control written by Xiaoqiang Ji and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Before it can be useful, it must be phased in to honor actuator saturation limits, and being a true inverse it requires that the system have a stable inverse. One could generalize this to p-step ahead control, updating the control action every p steps instead of every one step. It determines how small p can be to give a stable implementation using skip step, and it can be quite small. So it only requires knowledge of future desired control for a few steps. (3) Note that the statement in (2) can be reformulated as Linear Model Predictive Control that updates every p steps instead of every step. This offers the ability to converge to zero tracking error at every time step of the skip step inverse, instead of the usual aim to converge to a quadratic cost solution. (4) Indirect discrete time adaptive control combines one step ahead control with the projection algorithm to perform real time identification updates. It has limited applications, because it requires a stable inverse.
Download or read book Iterative Learning Control for Systems with Iteration Varying Trial Lengths written by Dong Shen and published by Springer. This book was released on 2019-01-29 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive and detailed study on iterative learning control (ILC) for systems with iteration-varying trial lengths. Instead of traditional ILC, which requires systems to repeat on a fixed time interval, this book focuses on a more practical case where the trial length might randomly vary from iteration to iteration. The iteration-varying trial lengths may be different from the desired trial length, which can cause redundancy or dropouts of control information in ILC, making ILC design a challenging problem. The book focuses on the synthesis and analysis of ILC for both linear and nonlinear systems with iteration-varying trial lengths, and proposes various novel techniques to deal with the precise tracking problem under non-repeatable trial lengths, such as moving window, switching system, and searching-based moving average operator. It not only discusses recent advances in ILC for systems with iteration-varying trial lengths, but also includes numerous intuitive figures to allow readers to develop an in-depth understanding of the intrinsic relationship between the incomplete information environment and the essential tracking performance. This book is intended for academic scholars and engineers who are interested in learning about control, data-driven control, networked control systems, and related fields. It is also a useful resource for graduate students in the above field.
