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Adaptive Neural Prescribed Performance DSC for Non-affine SISO Nonlinear Systems with External Disturbances

机译：具有外部扰动的非仿射SISO非线性系统的自适应神经规定性能DSC

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摘要

Explosion of complexity and undesirable transient response of systems, are two major problems that conventional backstepping methods suffer from it. Furthermore, lack of information about the system and undesirable external disturbances are other problems that have been addressed in this paper. Therefore, an adaptive neural controller is designed to consider the proposed problems in this paper. The presented controller is constructed for the class of single-input, single-output (SISO) non-affine strict feedback systems with unknown gain signs and a neural network is employed to approximate unknown functions. By applying dynamic surface control (DSC) and prescribed performance functions, two major problems of an explosion in terms and the transient response of the system will be solved, respectively. Nussbaum functions are also utilized to address the problem of unknown gain signs. The proposed controller guarantees that all the closed-loop signals are semi-globally, uniformly ultimately bounded (SGUUB). Finally, in order to show the feasibility of this approach, a simulation example is provided.
机译：复杂性的爆炸和系统不良的瞬态响应是传统的后推方法遭受的两个主要问题。此外，缺乏有关系统的信息和不希望的外部干扰是本文要解决的其他问题。因此，设计了一种自适应神经控制器来考虑本文提出的问题。提出的控制器针对具有未知增益符号的单输入单输出（SISO）非仿射严格反馈系统而构造，并且采用神经网络来近似未知函数。通过应用动态表面控制（DSC）和规定的性能函数，将分别解决爆炸和系统瞬态响应这两个主要问题。 Nussbaum函数也用于解决增益符号未知的问题。所提出的控制器保证了所有闭环信号都是半全局统一一致的最终有界（SGUUB）。最后，为了说明该方法的可行性，提供了一个仿真实例。

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