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Adaptive Prediction and Predictive Control
Partha Pratim Kanjilal
The Institution of Engineering and Technology, 1995
Control often follows predictions: predictive control has been highly successful in producing robust and practical solutions in many real-life, real-time applications. Adaptive prediction covers a variety of ways of adding 'intelligence' to predictive control techniques. Many different groups, with widely varying disciplinary backgrounds and approaches, are tackling the same problem from different angles; these groups are sometimes unaware of alternative approaches from other disciplines.
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front cover of Non-linear Predictive Control
Non-linear Predictive Control
Theory and practice
Basil Kouvaritakis
The Institution of Engineering and Technology, 2001
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.
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front cover of Robust and Adaptive Model Predictive Control of Nonlinear Systems
Robust and Adaptive Model Predictive Control of Nonlinear Systems
Martin Guay
The Institution of Engineering and Technology, 2016
Most physical systems possess parametric uncertainties or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control (MPC), mechanisms to update the unknown or uncertain parameters are desirable in application. One possibility is to apply adaptive extensions of MPC in which parameter estimation and control are performed online. This book proposes such an approach, with a design methodology for adaptive robust nonlinear MPC (NMPC) systems in the presence of disturbances and parametric uncertainties. One of the key concepts pursued is the concept of set-based adaptive parameter estimation, which provides a mechanism to estimate the unknown parameters as well as an estimate of the parameter uncertainty set. The knowledge of non-conservative uncertain set estimates is exploited in the design of robust adaptive NMPC algorithms that guarantee robustness of the NMPC system to parameter uncertainty.
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