Multimodel Nonlinear Predictive Control with Gaussian Process Model

  • Ming Hu
  • Zonghai Sun
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 238)


In industrial practice, parameters of plants are often not fixed in production process. The variation of parameters gives rise to the variation of system model. With model-based predictive control, the plants will be out of control if a fixed predictive model is applied when the parameters of plants change frequently. This paper proposed a multimodel nonlinear predictive control based on Gaussian process models which can be applied to the nonlinear system control with varying parameters. On the basis of the management ability of Gaussian process in fitting nonlinear model, a feasible switch strategy based on deviation of the predictive output from the actual output was applied to identify change of parameters. A two order dynamical system with four models switch was demonstrated to illustrate this algorithm. According to simulation results, this switch strategy can identify the change of system model accurately and quickly.


Support Vector Machine Model Predictive Control Reference Trajectory Markov Chain Monte Carlo Method Switch Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.College of Automation Science and EngineeringSouth China University of TechnologyGuangzhouChina

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