The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. The book demonstrates that a powerful technique does not always require complex control algorithms. Many new exercises and examples have also been added throughout.
This thesis addresses two neural network based control systems. The ﬁrst is a neural network based predictive controller. System identiﬁcation and controller design are discussed. The second is a direct neural network controller. Parameter choice and training methods are discussed. Both controllers are tested on two diﬀerent plants. Problems regarding implementations are discussed. First the neural network based predictive controller is introduced as an extension to the generalised predictive controller (GPC) to allow control of non-linear plant.
Since the earliest algorithm of Model Predictive Control was proposed by French
engineer Richalet and his colleagues in 1978, the explicit background of industrial
application has made MPC develop rapidly. Different from most other control
algorithms, the research trajectory of MPC is originated from engineering application
and then expanded to theoretical field, while ordinary control algorithms often have
applications after sufficient theoretical work.
The new edition of this comprehensive digital controls book integrates MATLAB throughout the book. The book has also increased inflexibility and reader friendliness through the streamlining of coverage in Chapters 6 & 7 (controllability, pole placement and observability, and optimal control). The previous edition ISBN is: 0-13-216102-8.
Since Model Predictive Heuristic Control (MPHC), the earliest algorithm of Model Predictive
Control (MPC), was proposed by French engineer Richalet and his colleagues in 1978, the
explicit background of industrial application has made MPC develop rapidly to satisfy the
increasing request from modern industry. Different from many other control algorithms, the
research history of MPC is originated from application and then expanded to theoretical field,
while ordinary control algorithms often has applications after sufficient theoretical research....