Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

1. Introduction. 2. Linear Algebra. 3. Linear Systems. 4. H2 and Ha Spaces. 5. Internal Stability. 6. Performance Specifications and Limitations. 7. Balanced Model Reduction. 8. Uncertainty and Robustness. 9. Linear Fractional Transformation. 10. m and m- Synthesis. 11. Controller Parameterization. 12. Algebraic Riccati Equations. 13. H2 Optimal Control. 14. Ha Control. 15. Controller Reduction. 16. Ha Loop Shaping. 17. Gap Metric and ...u- Gap Metric. 18. Miscellaneous Topics. Bibliography. Index.

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Essentials of Robust Control

This augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems. It explores linear optimal control theory from an engineering viewpoint, with step-by-step explanations that show clearly how to make practical use of the material. The three-part treatment begins with the basic theory of the linear regulator/tracker for time-invariant and time-varying systems. The Hamilton-Jacobi equation is introduced using the Principle of Optimality, and the infinite-time problem is considered. The second part outlines the engineering properties of the regulator. Topics include degree of stability, phase and gain margin, tolerance of time delay, effect of nonlinearities, asymptotic properties, and various sensitivity problems. The third section explores state estimation and robust controller design using state-estimate feedback. Numerous examples emphasize the issues related to consistent and accurate system design. Key topics include loop-recovery techniques, frequency shaping, and controller reduction, for both scalar and multivariable systems. Self-contained appendixes cover matrix theory, linear systems, the Pontryagin minimum principle, Lyapunov stability, and the Riccati equation. Newly added to this Dover edition is a complete solutions manual for the problems appearing at the conclusion of each section.

Optimal Control: Linear Quadratic Methods

This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

A survey of iterative learning control

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.

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An eigensystem realization algorithm for modal parameter identification and model reduction

From the Publisher:
An extensive revision of the author's highly successful text, this third edition of Linear System Theory and Design has been made more accessible to students from all related backgrounds. After introducing the fundamental properties of linear systems, the text discusses design using state equations and transfer functions. The two main objectives of the text are to: use simple and efficient methods to develop results and design procedures; enable students to employ the results to carry out design. Striking a balance between theory and applications, Linear System Theory and Design, 3/e, is ideal for use in advanced undergraduate/first-year graduate courses in linear systems and multivariable system design in electrical, mechanical, chemical, and aeronautical engineering departments. It assumes a working knowledge of linear algebra and the Laplace transform and an elementary knowledge of differential equations.

Linear System Theory and Design

Introduction to linear system theory

All chapters except Chapter 2 begin with an Introduction. All chapters except Chapters 1 and 14 end with Problems 1. Introduction 2. Mathematical Preliminary 3. Development of Block Diagrams for Control Systems 4. Quantitative and Qualitative Analyses of Control Systems 5. Computer Simulation and Realizations 6. Design Criteria, Constraints, and Feedback 7. The Root-Locus Method 8. Frequency-Domain Techniques 9. The Inward Approach: Choice of Overall Transfer Functions 10. Implementation: Linear Algebraic Method 11. State Space Design 12. Discrete-Time System Analysis 13. Discrete-Time System Design 14. PID controllers Appendices A. The Laplace Transform B. Linear Algebraic Equations

Analog and Digital Control System Design: Transfer-Function, State-Space, and Algebraic Methods

Each chapter contains an Introduction, Summary and Problems. Chapters 3-8 and 10 also contain sections on using MATLAB 1. Signals 2. Systems 3. Convolutions, Difference and Differential Equations 4. The Laplace Transform and its Application to Continuous-Time System Analysis 5. The z-Transform and its Application to Discrete-Time System Analysis 6. Continuous-Time Signal Analysis 7. Discrete-Time Signal analysis and Computation of Spectra 8. BIBO Stability and Frequency Response 9. Op-Amp Circuits, Model Reduction, and Asymptotic Stability 10. State-Variable Equations and Computer Simulations Appendix: Linear Algebraic Equations References

System and Signal Analysis

Now welcome, the most inspiring book today from a very professional writer in the world, analysis and synthesis of linear control systems . This is the book that many people in the world waiting for to publish. After the announced of this book, the book lovers are really curious to see how this book is actually. Are you one of them? That's very proper. You may not be regret now to seek for this book to read.

Chi-Tsong Chen

Papers

Linear System Theory and Design

Introduction to linear system theory

Analog and Digital Control System Design: Transfer-Function, State-Space, and Algebraic Methods

System and Signal Analysis

Analysis and Synthesis of Linear Control Systems