- 1: Qin and Badgwell, A Review of Non-linear Model Predictive Control Applications
- 2: RS Parker et al, Non-linear model predictive control: issues and applications
- 3: L. Magni et al, Model predictive control: output feedback and tracking of non-linear systems
- 4: M Sznaier and J Cloutier, Model predictive control of non-linear parameter varying systems via receding horizon control Lyapunov functions
- 5: M Niemiec and C Kravaris, Non-linear model-algorithm control for multivariable nonminimum-phase processes--.4: A Zheng, A computationally efficient non-linear model predictive control algorithm for large-scale constrained non-linear systems
- 6: M Cannon and B Kouvaritakis, Interpolation techniques for efficient NMPC
- 7: B Kouvaritakis et al, Closed-loop predictions in model based predictive control of linear and non-linear systems
- 8: Zheng, A computationally efficient non-linear model predictive control algorithm for control of constrained non-linear systems
- 9: M Soroush, Long-prediction-horizon non-linear model predictive control
- 10: B. A. Ogunnaike, An industrial perspective of applicable non-linear model-based control
- 11: S Townsend and G Irwin, Non-linear model-based predictive control using multiple local models
- 12: B Lennox and GA Montague, Neural network based predictive control of non-linear model predictive control.
- (source: Nielsen Book Data)

Model based predictive control has proved to be a fertile area of research but above all has gained enormous success with industry, especially in the context of process control. Non-linear model based predictive control is of particular interest as this best represents the dynamics of most real plant, and this book collects together the important results which have emerged in this field which are illustrated 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.

(source: Nielsen Book Data)