Na Jing, Huang Yingbo, Wu Xing, Su Shun-Feng, Li Guang
IEEE Trans Cybern. 2020 Jun;50(6):2639-2650. doi: 10.1109/TCYB.2019.2894724. Epub 2019 Feb 20.
This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with e -modification or σ -modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov-Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions.
本文提出了一种针对主动悬架系统的新型自适应模糊控制方案，该系统存在控制输入时延和未知非线性动力学。首先，构建了一种基于预测器的补偿方案，以解决闭环系统中输入时延的影响。然后，采用模糊逻辑系统（FLS）作为函数逼近器来处理未知非线性。最后，为了增强悬架的瞬态响应，开发了一种基于新型参数估计误差的有限时间（FT）自适应算法，用于在线更新未知的FLS权重，这与传统估计方法不同，例如带ε-修正或σ-修正的梯度算法。在此框架下，悬架误差和估计误差均可在有限时间内实现收敛。构造了一个Lyapunov-Krasovskii泛函来证明闭环系统的稳定性。基于专业车辆仿真软件Carsim中构建的动态模拟器提供了对比仿真结果，以证明所提控制方法的有效性，并表明其在各种道路条件下安全可靠地运行主动悬架系统的有效性。
