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Network-Based Static Output Feedback Tracking Control For Fuzzy-Model-Based Nonlinear Systems

机译:基于网络的基于网络的基于网络的静态输出反馈跟踪控制,用于基于模糊模型的非线性系统

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This paper is concerned with network-based static output feedback tracking control for a class of nonlinear systems that can not be stabilized by a static output feedback controller without a time-delay, but can be stabilized by a delayed static output feedback controller. For such systems, network-induced delay is intentionally introduced in the feedback loop to produce a stable and satisfactory tracking control. The nonlinear network-based control system is represented by an asynchronous T-S fuzzy system with an interval time-varying sawtooth delay due to sample-and-hold behaviors and network-induced delays. A new discontinuous complete Lyapunov-Krasovskii functional, which makes use of the lower bound of network-induced delays, the sawtooth delay and its upper bound, is constructed to derive a delay-dependent criterion on H_∞ tracking performance analysis. Since routine relaxation methods in traditional T-S fuzzy systems can not be employed to reduce the conservatism of the stability criterion, a new relaxation method is proposed by using asynchronous constraints on fuzzy membership functions to introduce some free-weighting matrices. Based on the feasibility of the derived criterion, a particle swarm optimization algorithm is presented to search the minimum H_∞ tracking performance and static output feedback gains. An illustrative example is provided to show the effectiveness of the proposed method.
机译:本文涉及一种基于网络的静态输出反馈跟踪控制,用于一类不能通过静态输出反馈控制器稳定的一类非线性系统,而是可以通过延迟静态输出反馈控制器稳定。对于这种系统,有意地在反馈回路中引入网络感应的延迟,以产生稳定且令人满意的跟踪控制。基于非线性网络的控制系统由异步T-S模糊系统表示,由于采样和保持行为和网络引起的延迟,具有间隔时间变化的锯齿延迟。一种新的不连续完整的Lyapunov-Krasovskii功能,它利用网络诱导的延迟,锯齿延迟及其上限的下限,以导出H_6跟踪性能分析的延迟依赖性标准。由于在传统T-S模糊系统中的日常放松方法不能用于降低稳定性标准的保守,因此通过对模糊隶属函数的异步约束来引入一些自由加权矩阵来提出一种新的松弛方法。基于派生标准的可行性,提出了一种粒子群优化算法,以搜索最小H_∞跟踪性能和静态输出反馈增益。提供了说明性示例以显示所提出的方法的有效性。

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