一种基于隶属度函数在线学习优化策略的T-S模糊系统的L_(2)-L_(∞)/H_(∞)混合控制

An L_(2)-L_(∞)/H_(∞) mixed control for T-S fuzzy systems with membership functions online learning

基于Takagi-Sugeno(T-S)模糊模型,研究了在非并行分布式补偿框架下的非线性系统L_(2)-L_(∞)/H_(∞)混合性能指标优化问题.首先,给出了使得模糊系统渐近稳定并满足混合性能的模糊静态控制器设计的充分条件.然后,在保证系统性能的前提下,根据每一个模糊控制器的隶属度函数的可行域,提出了一种新颖的隶属度函数在线学习优化策略来实时地优化控制器隶属度函数进而得到优越的L_(2)-L_(∞)/H_(∞)混合性能指标.相比已有的传统非并行分布式补偿模糊控制方法,该优化算法能够有效地降低实际的抗干扰衰减性能指标.根据李雅普诺夫稳定性理论,得到了成本函数误差收敛的充分条件.最后,通过仿真实例验证了所提出的在线学习优化算法的有效性.

Based on Takagi-Sugeno fuzzy model,the L_(2)-L_(∞)/H_(∞)mixed performance index optimization problem for nonlinear systems under the non-parallel distributed compensation framework is studied.First,sufficient conditions to design fuzzy static controller to guarantee asymptotic stability as well as mixed performance for fuzzy systems are given.Then,in the context of guaranteeing systems performance,based on the feasible region of each controller membership functions,a novel membership functions online learning strategy is first proposed to optimize controller membership functions in real time to achieve a superior L_(2)-L_(∞)/H_(∞)performance.Compared with conventional existing fuzzy control scheme,the actual response of interference attenuation performance can be decreased efficaciously.In the light of Lyapunov stability theory,sufficient condition is derived to ensure the errorconvergence of cost function.At last,the effectiveness of the proposed online learning optimization algorithm is verified by a simulation.