基于积分反演的自适应鲁棒终端滑模控制方法研究

H. Taghavifar, A. Mohammadzadeh
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摘要

在复杂环境中运行的自主地面车辆(agv)面临着在考虑不确定性、外部干扰和初始条件的同时准确遵循期望路径的挑战,需要鲁棒和自适应控制策略。本文通过考虑结构化和非结构化不确定性的多级速度agv控制框架,解决了agv的关键路径跟踪问题。该控制系统采用了一种非线性自适应方法,将积分反演与终端滑模控制(IBTSMC)相结合。通过整合积分动作,IBTSMC不断调整控制输入以减少跟踪误差,提高跟踪性能。终端滑模方法的杂交实现了有限时间收敛、鲁棒性和无颤振响应,同时降低了对初始条件的灵敏度。此外,设计了自适应控制补偿器,以保证对未知但有界的外部干扰的鲁棒性。利用Lyapunov稳定性定理保证了闭环系统的全局渐近稳定性和跟踪误差在有限时间内收敛到原点。为了验证所提控制方案的有效性,利用CarSim和MATLAB进行了高保真的联合仿真。与文献中报道的其他方法进行比较分析。结果表明,所提出的控制器在路径跟踪任务中表现出竞争性的有效性,并且在各种道路条件、参数不确定性和未知干扰下表现出很强的效率。
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Adaptive Robust Terminal Sliding Mode Control with Integral Backstepping Synthesized Method for Autonomous Ground Vehicle Control
Autonomous ground vehicles (AGVs) operating in complex environments face the challenge of accurately following desired paths while accounting for uncertainties, external disturbances, and initial conditions, necessitating robust and adaptive control strategies. This paper addresses the critical path-tracking task in AGVs through a novel control framework for multilevel speed AGVs, considering both structured and unstructured uncertainties. The control system introduced in this study utilizes a nonlinear adaptive approach by integrating integral backstepping with terminal sliding mode control (IBTSMC). By incorporating integral action, IBTSMC continuously adjusts the control input to minimize tracking errors, improving tracking performance. The hybridization of the terminal sliding mode method enables finite time convergence, robustness, and a chatter-free response with reduced sensitivity to initial conditions. Furthermore, adaptive control compensators are developed to ensure robustness against unknown but bounded external disturbances. The Lyapunov stability theorem is employed to guarantee the global asymptotic stability of the closed-loop system and the convergence of tracking errors to the origin within finite time. To validate the effectiveness of the proposed control scheme, high-fidelity cosimulations are conducted using CarSim and MATLAB. Comparative analysis is performed with other methods reported in the literature. The results confirm that the proposed controller demonstrates competitive effectiveness in path-tracking tasks and exhibits strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances.
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