Direct adaptive model predictive control tuning based on the first‐order plus dead time models
| Autor: | Ali Khaki-Sedigh, Tahereh Gholaminejad, Peyman Bagheri |
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| Rok vydání: | 2017 |
| Předmět: |
Recursive least squares filter
0209 industrial biotechnology Control and Optimization Adaptive control Heuristic Computer science Multivariable calculus 02 engineering and technology Dead time Computer Science Applications Human-Computer Interaction Model predictive control 020901 industrial engineering & automation 020401 chemical engineering Control and Systems Engineering Control theory Convergence (routing) 0204 chemical engineering Electrical and Electronic Engineering |
| Zdroj: | IET Control Theory & Applications. 11:2858-2869 |
| ISSN: | 1751-8652 |
| DOI: | 10.1049/iet-cta.2016.1174 |
| Popis: | A direct adaptive tuning strategy is proposed for model predictive controllers. Parameter tuning is essential for a satisfactory control performance. Various tuning methods are proposed in the literature which can be categorised as heuristic, numerical and analytical methods. The proposed tuning methodology is based on an analytical model predictive control tuning approach for plants described by first-order plus dead time models. For a fixed tuning scheme, the tuning performance deteriorates in dealing with unknown or time varying plants. To overcome this problem, an adaptive tuning strategy is utilised. It is suggested to employ a discrete-time model reference adaptive control with recursive least squares estimations for controller tuning. The proposed method is also extended to multivariable systems. The stability and convergence of the proposed strategy is proved using the Lyapunov approach. Finally, simulation and experimental studies are used to show the effectiveness of the proposed methodology. |
| Databáze: | OpenAIRE |
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