Model Predictive Control System Design and Implementation Using MATLAB® proposes methods for design and implementation of MPC systems using basis functionsthat 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 alsointroduced and explained. The technical contents of this book is mainly basedon advances in MPC using state-space models and basis functions. Novel basis-function approach simplifies solution of discrete- and continuous-time problems in a widely-used control design methodology Helps to provide more computationally efficient algorithms for better on-line control than previously attainable with model predictive control Problems and MATLAB® exercises in every chapter render the basis-function techniques easily accessible INDICE: Continuous-time Basis Functions.- Continuous-time Model PredictiveControl without Constraint.- Constrained Continuous-time Model Predictive Control.- Discrete-time Basis Functions.- Discrete Model Predictive Control without Constraints.- Constrained Discrete Model Predictive Control.- Model Predictive Control Design Using Non-minimal State-space Model.- Model Predictive Control Using Subspace Methods.- Applications.- Introduction to Quadratic Programming.
- ISBN: 978-1-84882-330-3
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 405
- Fecha Publicación: 01/03/2009
- Nº Volúmenes: 1
- Idioma: Inglés