Application of RBF Neural Network in the Model-free Adaptive Control
To solve the impact of the unmodelled dynamics of the model process, model-free adaptive control based on RBF neural network is proposed. In this algorithm nonlinear system is linearized by linearization of tight format. Then the system parameters are identified by the RBF neural network algorithm. The parameters are used to directly recursively compute model-free adaptive control input. The controller is designed only by using I/O data of the controlled system, and no structural information or external testing signals are needed. Simulation result shows that the proposed algorithm is an effective strategy with excellent tracking ability and strong robustness.
RBF neural network non-parametric model adaptive control pseudo-partial derivative
Su Cheng-li Liu Bin Zhang Guang-hui Zhang Yong
the Information and Control Engineering Department, Liaoning Shihua University, Liaoning province, F Information and Control Engineering Department, Liaoning Shihua University, Liaoning province, Fushu the Instrument Management Workshop, Shenyang Oil and Gas Transmission Branch of PetroChina Company L
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
3331-3334
2011-05-23(万方平台首次上网日期,不代表论文的发表时间)