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Many practical control problems are dominated by characteristics such as state, input and operational constraints, alternations between different operating regimes, and the interaction of continuous-time and discrete event systems. At present no methodology is available to design controllers in a systematic manner for such systems. This book introduces a new design theory for controllers for such constrained and switching dynamical systems and leads to algorithms that systematically solve control synthesis problems. The first part is a self-contained introduction to multiparametric programming, which is the main technique used to study and compute state feedback optimal control laws. The book's main objective is to derive properties of the state feedback solution, as well as to obtain algorithms to compute it efficiently. The focus is on constrained linear systems and constrained linear hybrid systems. The applicability of the theory is demonstrated through two experimental case studies: a mechanical laboratory process and a traction control system developed jointly with the Ford Motor Company in Michigan.
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.
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.
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