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针对非线性系统提出了一种基于时变ARX模型的非线性自适应预测函数控制算法。该算法通过T-S模糊将非线性系统转换为时变ARX模型,其模型的参数利用遗忘因子法在线辨识获得。然后与预测函数控制方法相结合,采用状态空间方法来重新构造时变ARX模型,从而减少了计算量,并且利用实际增益与模型增益相等来递推预测控制量的最优解。以磁悬浮系统为研究对象,在模型匹配和失配的情况下,将所提的控制算法与直接自适应预测控制算法进行对比研究。仿真结果表明,所提方法具有更好的跟踪性、鲁棒性和抑制干扰的能力。
A nonlinear adaptive predictive function control algorithm based on time-varying ARX model is proposed for nonlinear systems. The algorithm transforms the nonlinear system into a time-varying ARX model by using T-S fuzzy, and the parameters of the model are obtained by on-line identification using the forgetting factor method. Then, combined with the predictive function control method, the state space method is used to reconstruct the time-varying ARX model, thus reducing the computational complexity. The optimal gain of the predictive control volume is recursed by using the actual gain equal to the model gain. Taking the magnetic levitation system as the research object, the proposed control algorithm is compared with the direct adaptive predictive control algorithm under the condition of model matching and mismatch. The simulation results show that the proposed method has better tracking ability, robustness and interference suppression.