Proceedings Article10.1109/ISIC.1999.796692
Fuzzy model predictive control: techniques, stability issues, and examples
Hazem Nounou,Kevin M. Passino +1 more
- 01 Jan 1999
- pp 423-428
27
TL;DR: Fuzzy model predictive control (FMPC) algorithms presented here are model-based control schemes in which the models used for prediction are Takagi-Sugeno fuzzy systems (TSFS).
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Abstract: Fuzzy model predictive control (FMPC) algorithms presented here are model-based control schemes in which the models used for prediction are Takagi-Sugeno fuzzy systems (TSFS). Three approaches to FMPC design are discussed. The fuzzy model in the first approach can be represented as a time-varying affine model that is used for control. In the second approach, the fuzzy system is a convex combination of multiple affine models, where the control is a convex combination of multiple controllers. Lastly, the control of the third algorithm is obtained when only the model with the highest certainty is used in the design. Also, we extend the idea to have an adaptive controller for the first algorithm, where the parameters of the fuzzy model are updated online.
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Kevin M. Passino,Stephen Yurkovich +1 more
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TL;DR: Drawing on their extensive experience working with industry on implementations, Kevin Passino and Stephen Yurkovich have written an excellent hands-on introduction for professionals and educators interested in learning or teaching fuzzy control.
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Robust receding horizon control of constrained nonlinear systems
Hannah Michalska,David Q. Mayne +1 more
TL;DR: This paper presents a method for the construction of a robust dual-mode, receding horizon controller which can be employed for a wide class of nonlinear systems with state and control constraints and model error, and requires considerably less online computation than existingReceding horizon controllers for nonlinear, constrained systems.
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