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Abstract

In this chapter, we will introduce the basic ideas and terms about model predictive control. In Section 1.2, a single-input and single-output state-space model with an embedded integrator is introduced, which is used in the design of discrete-time predictive controllers with integral action in this book. In Section 1.3, we examine the design of predictive control within one optimization window. This is demonstrated by simple analytical examples.With the results obtained from the optimization, in Section 1.4, we discuss the ideas of receding horizon control, and state feedback gain matrices, and the closed-loop configuration of the predictive control system. The results are extended to multi-input and multi-output systems in Section 1.5. In a general framework of state-space design, an observer is needed in the implementation, and this is discussed in Section 1.6. With a combination of estimated state variables and the predictive controller, in Section 1.7, we present state estimate predictive control.

Keywords

Model Predictive Control Prediction Horizon Minimal Realization Optimization Window Observer Gain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer London 2009

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