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A novel adaptive fuzzy control for a class of discrete-time nonlinear systems in strict-feedback form

机译:一类严格反馈形式的一类离散非线性系统的新型自适应模糊控制

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In this paper, a backstepping based adaptive fuzzy control algorithem is presented for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. By introducing the "minimal learning parameter (MLP)" technique, the proposed scheme is able to circumvent the problem of "curse of dimension" for high-dimensional systems. Meanwhile, all the virtual control laws and actual control law in the system are updated by a novel actual adaptive update law, thus the number of parameters updated online for whole system is only by one. Takagi-Sugeno (T-S) fuzzy systems are used to approximate the unknown system functions. It is shown via Lyapunov theory that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Finally, a simulation example is employed to illustrate the effectiveness and advantages of the proposed scheme.
机译:本文介绍了基于反馈形式的一类不确定的非线性离散时间系统的基于反向的自适应模糊控制算法。 通过介绍"最小的学习参数(mlp)" 技术,所提出的方案能够规避&#x0022的问题;维度诅咒" 用于高维系统。 同时,系统中的所有虚拟控制法律和实际控制法由新颖的实际适应性更新法更新,因此整个系统在线更新的参数数量仅是一个。 Takagi-Sugeno(T-S)模糊系统用于近似未知系统功能。 它通过Lyapunov理论示出,即闭环系统中的所有信号是半全球均匀的最终限定(Sgub)。 最后,采用模拟示例来说明所提出的方案的有效性和优点。

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