基于T-S模型的自适应模糊预测函数控制

针对多变量非线性系统，提出一种基于Takagi-Sugeno(T-S)模型的自适应模糊预测函数控制方法.在T-S模糊模型结构已确定情况下，利用加权递推最小二乘法对T-S模糊模型后件参数进行在线辨识.对模糊模型在每一采样点进行线性化，将描述非线性系统的T-S模型转化为线性时变的状态空间模型，并假设输入基函数为阶跃函数，推导出预测控制律的解析式.仿真结果表明，该方法在求解控制律时，无需求解非线性优化问题，并且有效克服了模型失配对系统控制性能的影响，增强了系统的跟踪性能和鲁棒性.

Adaptive fuzzy predictive functional control based on T-S model

Aimed at multivariable nonlinear systems,an adaptive fuzzy predictive functional control approach based on T-S fuzzy model was proposed.The structure parameters of T-S fuzzy model were confirmed,and the model consequent parameters were identified online using the weighting recursive least square method. The fuzzy model was linearized at each sample point,and then T-S fuzzy model describing nonlinear systems was transformed into time-varying state space model. After the input basic function was assumed as the step function,the analytic solution of the predictive control law was established.Simulation results show that the proposed approach doesn't need to solve the nonlinear optimization problem in the optimal control law.The approach can effectively overcome the influence of model mismatch on the performance of the control system,and strengthen the tracing ability and robustness of the system.