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Predictive Control for Linear and Hybrid Systems [Mīkstie vāki]

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(University of California, Berkeley), ,
  • Formāts: Paperback / softback, 440 pages, height x width x depth: 246x190x20 mm, weight: 960 g, 11 Tables, black and white; 86 Halftones, black and white; 30 Line drawings, black and white
  • Izdošanas datums: 22-Jun-2017
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 1107652871
  • ISBN-13: 9781107652873
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Predictive Control for Linear and Hybrid SystemsPalielināt
  • Formāts: Paperback / softback, 440 pages, height x width x depth: 246x190x20 mm, weight: 960 g, 11 Tables, black and white; 86 Halftones, black and white; 30 Line drawings, black and white
  • Izdošanas datums: 22-Jun-2017
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 1107652871
  • ISBN-13: 9781107652873
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781107652873.html
Keywords:
Model Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers predictive control theory including the stability, feasibility, and robustness of MPC controllers. The theory of explicit MPC, where the nonlinear optimal feedback controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design predictive control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement predictive control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.

Papildus informācija

With a simple approach that includes real-time applications and algorithms, this book covers the theory of model predictive control (MPC).
Preface xi
Acknowledgments xvii
Symbols and Acronyms xix
I Basics of Optimization
1(92)
1 Main Concepts
3(16)
1.1 Optimization Problems
3(3)
1.2 Convexity
6(3)
1.3 Optimality Conditions
9(1)
1.4 Lagrange Duality Theory
10(3)
1.5 Complementary Slackness
13(1)
1.6 Karush-Kuhn-Tucker Conditions
14(5)
2 Linear and Quadratic Optimization
19(14)
2.1 Polyhedra and Polytopes
19(1)
2.2 Linear Programming
20(7)
2.3 Quadratic Programming
27(3)
2.4 Mixed-Integer Optimization
30(3)
3 Numerical Methods for Optimization
33(38)
3.1 Convergence
33(2)
3.2 Unconstrained Optimization
35(12)
3.3 Constrained Optimization
47(24)
4 Polyhedra and P-Collections
71(22)
4.1 General Set Definitions and Operations
71(2)
4.2 Polyhedra and Representations
73(3)
4.3 Polytopal Complexes
76(2)
4.4 Basic Operations on Polytopes
78(10)
4.5 Operations on P-Collections
88(5)
II Multiparametric Programming
93(52)
5 Multiparametric Nonlinear Programming
95(12)
5.1 Introduction to Multiparametric Programs
95(3)
5.2 General Results for Multiparametric Nonlinear Programs
98(9)
6 Multiparametric Programming: A Geometric Approach
107(38)
6.1 Multiparametric Programs with Linear Constraints
107(3)
6.2 Multiparametric Linear Programming
110(15)
6.3 Multiparametric Quadratic Programming
125(11)
6.4 Multiparametric Mixed-Integer Linear Programming
136(4)
6.5 Multiparametric Mixed-Integer Quadratic Programming
140(2)
6.6 Literature Review
142(3)
III Optimal Control
145(36)
7 General Formulation and Discussion
147(16)
7.1 Problem Formulation
147(2)
7.2 Solution via Batch Approach
149(1)
7.3 Solution via Recursive Approach
150(2)
7.4 Optimal Control Problem with Infinite Horizon
152(4)
7.5 Lyapunov Stability
156(7)
8 Linear Quadratic Optimal Control
163(8)
8.1 Problem Formulation
163(1)
8.2 Solution via Batch Approach
164(1)
8.3 Solution via Recursive Approach
165(1)
8.4 Comparison of the Two Approaches
166(2)
8.5 Infinite Horizon Problem
168(3)
9 Linear 1/∞ Norm Optimal Control
171(10)
9.1 Problem Formulation
171(1)
9.2 Solution via Batch Approach
172(3)
9.3 Solution via Recursive Approach
175(2)
9.4 Comparison of the two Approaches
177(1)
9.5 Infinite Horizon Problem
178(3)
IV Constrained Optimal Control of Linear Systems
181(166)
10 Controllability, Reachability and Invariance
183(28)
10.1 Controllable and Reachable Sets
183(7)
10.2 Invariant Sets
190(5)
10.3 Robust Controllable and Reachable Sets
195(9)
10.4 Robust Invariant Sets
204(7)
11 Constrained Optimal Control
211(32)
11.1 Problem Formulation
211(2)
11.2 Feasible Solutions
213(5)
11.3 2-Norm Case Solution
218(11)
11.4 1-Norm and ∞-Norm Case Solution
229(10)
11.5 State Feedback Solution, Minimum-Time Control
239(2)
11.6 Comparison of the Design Approaches and Controllers
241(2)
12 Receding Horizon Control
243(34)
12.1 RHC Idea
243(1)
12.2 RHC Implementation
244(7)
12.3 RHC Main Issues
251(6)
12.4 State Feedback Solution of RHC, 2-Norm Case
257(3)
12.5 State Feedback Solution of RHC, 1-Norm, ∞-Norm Case
260(2)
12.6 Tuning and Practical Use
262(4)
12.7 Offset-Free Reference Tracking
266(8)
12.8 Literature Review
274(3)
13 Approximate Receding Horizon Control
277(24)
13.1 Stability of Approximate Receding Horizon Control
278(2)
13.2 Barycentric Interpolation
280(5)
13.3 Partitioning and Interpolation Methods
285(16)
14 On-Line Control Computation
301(16)
14.1 Storage and On-Line Evaluation of the PWA Control Law
301(11)
14.2 Gradient Projection Methods Applied to MPC
312(3)
14.3 Interior Point Method Applied to MPC
315(2)
15 Constrained Robust Optimal Control
317(30)
15.1 Problem Formulation
317(7)
15.2 Feasible Solutions
324(6)
15.3 State Feedback Solution, Nominal Cost
330(1)
15.4 State Feedback Solution, Worst-Case Cost, 1-Norm and ∞-Norm Case
331(5)
15.5 Parametrizations of the Control Policies
336(6)
15.6 Example
342(1)
15.7 Robust Receding Horizon Control
343(2)
15.8 Literature Review
345(2)
V Constrained Optimal Control of Hybrid Systems
347(58)
16 Models of Hybrid Systems
349(26)
16.1 Models of Hybrid Systems
349(1)
16.2 Piecewise Affine Systems
350(6)
16.3 Discrete Hybrid Automata
356(5)
16.4 Logic and Mixed-Integer Inequalities
361(2)
16.5 Mixed Logical Dynamical Systems
363(2)
16.6 Model Equivalence
365(1)
16.7 The HYSDEL Modeling Language
365(3)
16.8 Literature Review
368(7)
17 Optimal Control of Hybrid Systems
375(30)
17.1 Problem Formulation
375(2)
17.2 Properties of the State Feedback Solution, 2-Norm Case
377(7)
17.3 Properties of the State Feedback Solution, 1-Norm, ∞-Norm Case
384(1)
17.4 Computation of the Optimal Control Input via Mixed Integer Programming
384(5)
17.5 State Feedback Solution via Batch Approach
389(1)
17.6 State Feedback Solution via Recursive Approach
390(9)
17.7 Discontinuous PWA Systems
399(1)
17.8 Receding Horizon Control
400(5)
References 405(16)
Index 421
Francesco Borrelli is a chaired Professor at the Department of Mechanical Engineering of the University of California, Berkeley. Since 2004 he has served as a consultant for major international corporations in the area of real-time predictive control. He was the founder and CTO of BrightBox Technologies Inc., and is the co-director of the Hyundai Center of Excellence in Integrated Vehicle Safety Systems and Control at the University of California, Berkeley. His research interests include constrained optimal control, model predictive control and its application to advanced automotive control, robotics and energy efficient building operation. Alberto Bemporad is a Professor and former Director of the IMT School for Advanced Studies, Lucca. He has published numerous papers on model predictive control and its application in multiple domains. He has been a consultant for major automotive companies and cofounder of ODYS S.r.l., a company specializing in advanced control and optimization software for industrial production. He is the author or coauthor of various MATLAB® toolboxes for model predictive control design, including the Model Predictive Control Toolbox and the Hybrid Toolbox. Manfred Morari was a Professor and Head of the Department of Information Technology and Electrical Engineering at the Swiss Federal Institute of Technology (ETH), Zurich. During the last three decades he shaped many of the developments and applications of model predictive control (MPC) through his academic research and interactions with companies from a wide range of sectors. The analysis techniques and software developed in his group are used throughout the world. He has received numerous awards and was elected to the US National Academy of Engineering and is a Fellow of the Royal Academy of Engineering.