模型不确定非线性系统的自适应模糊Backstepping预测控制

为解决一类模型不确定严格反馈非线性系统的跟踪控制问题，提出一种使闭环系统稳定且滚动时域性能指标在线最小化的自适应模糊反步预测控制策略。模糊系统用来逼近该设计过程中的未知非线性项，自适应参数直接用来估计最优逼近权值向量范数的平方，从而只有一个自适应参数需要在线调节；同时考虑模糊基函数的性质，所设计的控制律与自适应律均不含模糊基函数项，理论证明该方法设计的控制器保证闭环系统所有信号是半全局有界的，并且跟踪误差收敛于零的某一邻域。该方法所设计的控制器形式简单，计算量小，更易于实际应用，仿真算例验证提出算法的有效性。

Adaptive fuzzy backstepping predictive control for a class for nonlinear systems with model uncertainty

To overcome the tracking control problem for a class of strict-feedback nonlinear system with model uncertain, an adaptive fuzzy backstepping predication control algorithm which can make the closed-loop system stable and minimize the receding horizon guaranteed cost on-line is proposed. Fuzzy logic systems are employed to approximate the unknown term in the design process. As the adaptive parameter are directly used to estimate the norm of the optimal approximation weight vector, only one parameter need to be tuned on-line. Considering the property of the fuzzy basis function, the designed control laws and adaptive laws do not contain the fuzzy basis function term. Theoretically, it is proved that the using the constructed controller can guarantee that all signals in closed-loop are semi-globally uniformly ultimately bounded, and the tracking error convergence to a small neighborhood of the origin. As the form of the controller designed in this way is simplicity and the computation is small, this control strategy is easily realized in practice. Finally, the simulation results demonstrate the feasibility of the proposed scheme.