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Robust intelligent sliding model control using recurrent cerebellar model articulation controller for uncertain nonlinear chaotic systems

机译:基于递归小脑模型关节控制器的不确定非线性混沌系统鲁棒智能滑模控制

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摘要

In this paper, a robust intelligent sliding model control (RISMC) scheme using an adaptive recurrent cerebellar model articulation controller (RCMAC) is developed for a class of uncertain nonlinear chaotic systems. This RISMC system offers a design approach to drive the state trajectory to track a desired trajectory, and it is comprised of an adaptive RCMAC and a robust controller. The adaptive RCMAC is used to mimic an ideal sliding mode control (SMC) due to unknown system dynamics, and a robust controller is designed to recover the residual approximation error for guaranteeing the stable characteristic. Moreover, the Taylor linearization technique is employed to derive the linearized model of the RCMAC. The all adaptation laws of the RISMC system are derived based on the Lyapunov stability analysis and projection algorithm, so that the stability of the system can be guaranteed. Finally, the proposed RISMC system is applied to control a Van der Pol oscillator, a Genesio chaotic system and a Chuas chaotic circuit. The effectiveness of the proposed control scheme is verified by some simulation results with unknown system dynamics and existence of external disturbance. In addition, the advantages of the proposed RISMC are indicated in comparison with a SMC system.
机译:本文针对一类不确定的非线性混沌系统,提出了一种基于自适应递归小脑模型关节控制器(RCMAC)的鲁棒智能滑模控制(RISMC)方案。该RISMC系统提供了一种设计方法来驱动状态轨迹以跟踪所需轨迹,它由自适应RCMAC和鲁棒控制器组成。自适应RCMAC用于模拟由于未知的系统动力学而产生的理想滑模控制(SMC),并且设计了鲁棒的控制器来恢复残留的近似误差,以保证稳定的特性。此外,采用泰勒线性化技术来推导RCMAC的线性化模型。基于Lyapunov稳定性分析和投影算法推导了RISMC系统的所有自适应律,从而可以保证系统的稳定性。最后,将所提出的RISMC系统应用于控制范德波尔振荡器,Genesio混沌系统和Chuas混沌电路。仿真结果验证了所提控制方案的有效性。仿真结果具有未知的系统动力学特性,并且存在外部干扰。此外,与SMC系统相比,所建议的RISMC的优势也得到了体现。

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