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首页> 外文期刊>International Journal of Innovative Computing Information and Control >A NOVEL ISS-MODULAR ADAPTIVE NEURAL CONTROL OF PURE-FEEDBACK NONLINEAR SYSTEMS
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A NOVEL ISS-MODULAR ADAPTIVE NEURAL CONTROL OF PURE-FEEDBACK NONLINEAR SYSTEMS

机译：纯反馈非线性系统的新型ISS模块化自适应神经控制

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摘要

In this paper, an ISS-modular adaptive neural tracking control approach is presented for a class of completely non-affine pure-feedback systems combining back-stepping with input-to-state stability (ISS) and small gain theorem. From the second step of backstepping, correlative interconnection terms are defined and introduced in implicit functions. Since the introduction of the correlative interconnection terms does not add any variable, radial basis function (RBF) neural networks are still valid to approximate the implicit functions as the existing results. Subsequently, the construction of the quadratic-type ISS-Lyapunov function makes the correlative interconnection terms completely eliminate the interconnected terms, so that ISS neural controller design is simplified by the combination of the small gain theorem. The proposed approach not only overcomes the difficulty in controlling non-affine pure-feedback systems, but also simplifies the stability analysis of the closed-loop system. Simulation studies are performed to demonstrate the effectiveness of the proposed scheme.
机译：针对一类完全非仿射的纯反馈系统，提出了一种ISS模块自适应神经跟踪控制方法，该方法结合了反步，输入状态稳定性（ISS）和小增益定理。从第二步开始，定义了相关的互连术语并将其引入隐式函数中。由于引入相关的互连项不会增加任何变量，因此径向基函数（RBF）神经网络仍然可以有效地将隐式函数近似为现有结果。随后，二次型ISS-Lyapunov函数的构造使相关的互连项完全消除了互连项，从而通过小增益定理的组合简化了ISS神经控制器的设计。所提出的方法不仅克服了控制非仿射纯反馈系统的困难，而且简化了闭环系统的稳定性分析。进行仿真研究以证明所提出方案的有效性。

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