Model Predictive Control System Design and Implementation Using MATLAB 1st Edition by Liuping Wang ISBN 1848823304 9781848823303

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ISBN 10: 1848823304
ISBN 13: 978-1848823303
Author: Liuping Wang
Model Predictive Control System Design and Implementation Using MATLAB 1st Edition: Model Predictive Control System Design and Implementation Using MATLAB® proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: – continuous- and discrete-time MPC problems solved in similar design frameworks; – a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and – a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters.

After the theoretical presentation, coverage is given to three industrial applications. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained.

The technical contents of this book is mainly based on advances in MPC using state-space models and basis functions. This volume includes numerous analytical examples and problems and MATLAB® programs and exercises.

 

Model Predictive Control System Design and Implementation Using MATLAB 1st Edition Table of contents:

1 Discrete-time MPC for Beginners 

1.1 Introduction
1.2 State-space Models with Embedded Integrator
1.3 Predictive Control within One Optimization Window 
1.4 Receding Horizon Control 

2 Discrete-time MPC with Constraints

2.1 Introduction
2.2 MotivationalExamples
2.3 FormulationofConstrained Control Problems

3 Discrete-time MPC Using Laguerre Functions

3.1 Introduction
3.2 Laguerre Functions and DMPC
3.3 Useof Laguerre Functionsin DMP CDesign
3.4 Extensi onto MIMO Systems
3.5 MATLABTutorialNotes

4 Discrete-time MPC with Prescribed Degree of Stability

4.1 Introduction
4.2 Finite Prediction Horizon: Re-visited
4.3 Use of Exponential Data Weighting
4.4 Asymptotic Closed-Loop Stability with Exponential Weighting

5. Continuous-Time Orthonormal Basis Functions

5.1 Introduction
5.2 Orthonormal Expansion
5.3 Laguerre Functions
5.4 Approximating Impulse Responses
5.5 Kautz Functions

6. Continuous-Time MPC

6.1 Introduction
6.2 Model Structures for CMPC Design
6.3 Model Predictive Control Using Finite Prediction Horizon

7. Continuous-Time MPC with Constraints

7.1 Introduction
7.2 Formulation of the Constraints
7.3 Numerical Solutions for the Constrained Control Problem
7.4 Real-Time Implementation of Continuous-Time MPC

8. Continuous-Time MPC with Prescribed Degree of Stability

8.1 Introduction
8.2 Motivating Example
8.3 CMPC Design Using Exponential Data Weighting
8.4 CMPC with Asymptotic Stability

9. Classical MPC Systems in State-Space Formulation

9.1 Introduction
9.2 Generalized Predictive Control in State-Space Formulation
9.3 Alternative Formulation to GPC

10. Implementation of Predictive Control Systems

10.1 Introduction
10.2 Predictive Control of DC Motor Using a Micro-controller
10.3 Implementation of Predictive Control Using xPC Target
10.4 Control of Magnetic Bearing Systems

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