Model-based predictive control (MPC) has proved to be a fertile area of research. It has gained enormous success within industry, especially in the context of process control. Nonlinear model-based predictive control (NMPC) is of particular interest as this best represents the dynamics of most real plant. This book collects together the important results which have emerged in this field, illustrating examples by means of simulations on industrial models. In particular there are contributions on feedback linearisation, differential flatness, control Lyapunov functions, output feedback, and neural networks. The international contributors to the book are all respected leaders within the field, which makes for essential reading for advanced students, researchers and industrialists in the field of control of complex systems.

• Part I
• Chapter 1: Review of nonlinear model predictive control applications
• Chapter 2: Nonlinear model predictive control: issues and applications
• Part II
• Chapter 3: Model predictive control: output feedback and tracking of nonlinear systems
• Chapter 4: Model predictive control of nonlinear parameter varying systems via receding horizon control Lyapunov functions
• Chapter 5: Nonlinear model-algorithmic control for multivariable nonminimum-phase processes
• Part III
• Chapter 6: Open-loop and closed-loop optimality in interpolation MPC
• Chapter 7: Closed-loop predictions in model based predictive control linear and nonlinear systems
• Chapter 8: Computationally efficient non linear predictive control algorithm for control of constrained nonlinear systems
• Part IV
• Chapter 9: Long-prediction-horizon nonlinear model predictive control
• Chapter 10: Nonlinear control of industrial processes
• Chapter 11: Nonlinear model based predictive control using multiple local models
• Chapter 12: Neural network control of a gasoline engine with rapid sampling