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Author Kouvaritakis, Basil, author

Title Model predictive control : classical, robust and stochastic / Basil Kouvaritakis, Mark Cannon
Published Cham New York Springer, [2016]

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Description xiii, 384 pages ; 24 cm
Series Advanced textbooks in control and signal processing
Advanced textbooks in control and signal processing.
Contents Machine generated contents note: 1.Introduction -- 1.1.Classical MPC -- 1.2.Robust MPC -- 1.3.Stochastic MPC -- 1.4.Concluding Remarks and Comments on the Intended Readership -- References -- pt. I Classical MPC -- 2.MPC with No Model Uncertainty -- 2.1.Problem Description -- 2.2.The Unconstrained Optimum -- 2.3.The Dual-Mode Prediction Paradigm -- 2.4.Invariant Sets -- 2.5.Controlled Invariant Sets and Recursive Feasibility -- 2.6.Stability and Convergence -- 2.7.Autonomous Prediction Dynamics -- 2.7.1.Polytopic and Ellipsoidal Constraint Sets -- 2.7.2.The Predicted Cost and MPC Algorithm -- 2.7.3.Offline Computation of Ellipsoidal Invariant Sets -- 2.8.Computational Issues -- 2.9.Optimized Prediction Dynamics -- 2.10.Early MPC Algorithms -- 2.11.Exercises -- References -- pt. II Robust MPC -- 3.Open-Loop Optimization Strategies for Additive Uncertainty -- 3.1.The Control Problem -- 3.2.State Decomposition and Constraint Handling --
Contents note continued: 3.2.1.Robustly Invariant Sets and Recursive Feasibility -- 3.2.2.Interpretation in Terms of Tubes -- 3.3.Nominal Predicted Cost: Stability and Convergence -- 3.4.A Game Theoretic Approach -- 3.5.Rigid and Homothetic Tubes -- 3.5.1.Rigid Tube MPC -- 3.5.2.Homothetic Tube MPC -- 3.6.Early Robust MPC for Additive Uncertainty -- 3.6.1.Constraint Tightening -- 3.6.2.Early Tube MPC -- 3.7.Exercises -- References -- 4.Closed-Loop Optimization Strategies for Additive Uncertainty -- 4.1.General Feedback Strategies -- 4.1.1.Active Set Dynamic Programming for Min-Max Receding Horizon Control -- 4.1.2.MPC with General Feedback Laws -- 4.2.Parameterized Feedback Strategies -- 4.2.1.Disturbance-Affine Robust MPC -- 4.2.2.Parameterized Tube MPC -- 4.2.3.Parameterized Tube MPC Extension with Striped Structure -- References -- 5.Robust MPC for Multiplicative and Mixed Uncertainty -- 5.1.Problem Formulation -- 5.2.Linear Matrix Inequalities in Robust MPC --
Contents note continued: 5.2.1.Dual Mode Predictions -- 5.3.Prediction Dynamics in Robust MPC -- 5.3.1.Prediction Dynamics Optimized to Maximize the Feasible Set -- 5.3.2.Prediction Dynamics Optimized to Improve Worst-Case Performance -- 5.4.Low-Complexity Poly topes in Robust MPC -- 5.4.1.Robust Invariant Low-Complexity Polytopic Sets -- 5.4.2.Recursive State Bounding and Low-Complexity Polytopic Tubes -- 5.5.Tubes with General Complexity Polytopic Cross Sections -- 5.6.Mixed Additive and Multiplicative Uncertainty -- 5.7.Exercises -- References -- pt. III Stochastic MPC -- 6.Introduction to Stochastic MPC -- 6.1.Problem Formulation -- 6.2.Predicted Cost and Unconstrained Optimal Control Law -- 6.3.Mean-Variance Predicted Cost -- 6.4.Early Stochastic MPC Algorithms -- 6.4.1.Auto-Regressive Moving Average Models -- 6.4.2.Moving Average Models -- 6.5.Application to a Sustainable Development Problem -- References -- 7.Feasibility, Stability, Convergence and Markov Chains --
Contents note continued: 7.1.Recursive Feasibility -- 7.2.Prototype SMPC Algorithm: Stability and Convergence -- 7.2.1.Expectation Cost -- 7.2.2.Mean-Variance Cost -- 7.2.3.Supermartingale Convergence Analysis -- 7.3.Probabilistically Invariant Ellipsoids -- 7.4.Markov Chain Models Based on Tubes with Polytopic Cross Sections -- References -- 8.Explicit Use of Probability Distributions in SMPC -- 8.1.Polytopic Tubes for Additive Disturbances -- 8.2.Striped Prediction Structure with Disturbance Compensation in Mode 2 -- 8.3.SMPC with Bounds on Average Numbers of Constraint Violations -- 8.4.Stochastic Quadratic Bounds for Additive Disturbances -- 8.5.Polytopic Tubes for Additive and Multiplicative Uncertainty -- References -- 9.Conclusions
Subject Predictive control.
Nonlinear control theory.
Author Cannon, Mark, 1971- author
ISBN 9783319248516
3319248510