Journal of Systems Engineering and Electronics ›› 2012, Vol. 34 ›› Issue (10): 2110-2116.doi: 10.3969/j.issn.1001-506X.2012.10.23

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State-dependent RBF-ARX model based nonlinear predictive control and application

ZENG Xiao-yong1,2,3, PENG Hui1,3, WEI Ji-min1,3   

  1. 1. School of Information Science and Engineering, Central South University, Changsha 410083, China; 2. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410076, China;
    3. Hunan Engineering Laboratory for Advanced Control and Intelligent Automation, Changsha 410083, China
  • Online:2012-10-19 Published:2010-01-03

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Abstract:

For a class of smooth nonlinear multivariable systems whose working-points vary with time, a state-dependent auto-regressive with exogenous (SD-ARX) model and its functional coefficients are composed of the Gaussian radial basis function (RBF) networks with some state variables representing the system’s nonlinear dynamic characteristics. The model is called a state-dependent RBF-ARX model and estimated by a structured nonlinear parameter optimization method (SNPOM) offline. The nonlinear predictive strategy is designed based on the state-dependent RBF-ARX model that does not require online parameter estimation so as to improve the real-time performance of control systems greatly and has a preferably control performance. A case study on a simulator of a quadrotor illustrates the effectiveness of the nonlinear modeling and the feasibility of the control method.

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