A new T-S fuzzy model predictive control for nonlinear processes

Ilyes Boulkaibet, Khaled Belarbi, Sofiane Bououden, Tshilidzi Marwala, Mohammed Chadli

Research output: Contribution to journalArticlepeer-review

76 Citations (Scopus)

Abstract

In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems.

Original languageEnglish
Pages (from-to)132-151
Number of pages20
JournalExpert Systems with Applications
Volume88
DOIs
Publication statusPublished - 1 Dec 2017

Keywords

  • Clustering algorithm
  • Generalized Predictive Control
  • Kernel ridge regression
  • Particle Swarm Optimization
  • Sliding-window Kernel Recursive Least squares
  • Takagi-Sugeno fuzzy system
  • Takagi-Sugeno system based Kernel ridge regression

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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