Design and Analysis of Bayesian Model Predictive Controller

Yijian Liu, Weixing Qian, Liming Di

Abstract


In this article, a novel predictive controller based on a Bayesian inferring nonlinear model (BMPC) is presented and analyzed. In the construction of the BMPC, the Bayesian inferring model is selected as the predictive model with the characteristics of on-line tracing ability to the actual controlled object. The nonlinear programming method called the steepest gradient is set as the receding horizon optimization algorithm of the BMPC. The on-line controller output is obtained using this method. The convergence analysis of the proposed BMPC is given and the examples (nonminimum phase and nonlinear objects) are selected to validate the performance of the BMPC. The simulation results show that with the help of the presented BMPC algorithm, the closed loop control system demonstrates the abilities of anti-disturbance and robustness.

Full Text: PDF DOI: 10.5539/cis.v7n3p58

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This work is licensed under a Creative Commons Attribution 3.0 License.

Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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