This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant. A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages ofneural Wiener models are demonstrated.
| ISBN: | 9783030838171 |
| Publication date: | 23rd September 2022 |
| Author: | Maciej awryczuk |
| Publisher: | Springer Nature Switzerland AG |
| Format: | Paperback |
| Pagination: | 343 pages |
| Series: | Studies in Systems, Decision and Control |
| Genres: |
Automatic control engineering Maths for engineers Cybernetics and systems theory |
This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant. A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages ofneural Wiener models are demonstrated.
Nonlinear Predictive Control Using Wiener Models features in the following genres: Automatic control engineering, Maths for engineers, Cybernetics and systems theory
Nonlinear Predictive Control Using Wiener Models is available in Paperback
Nonlinear Predictive Control Using Wiener Models was written by Maciej awryczuk and published by Springer Nature Switzerland AG
Nonlinear Predictive Control Using Wiener Models has 343 pages
Yes it is part of Studies in Systems, Decision and Control series
£116.99