针对轨迹跟踪系统的改进型数据驱动高阶无模型自适应迭代学习控制,可实现快速收敛

Liangpei Huang, Hua Huang
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引用次数: 0

摘要

数据驱动的基于高阶伪偏导的无模型自适应迭代学习控制(HOPPD-MFAILC)总是收敛缓慢,难以获得优异的跟踪效果。针对这一问题,本文提出了一种收敛速度快的改进型基于伪部分导数的无模型自适应迭代学习控制(iHOPPD-MFAILC)。首先,为了减少伪部分导数(PPD)初始值对算法收敛速度的影响,通过引入高阶模型估计误差来修正初始 PPD。其次,为了降低系统噪声对控制性能的影响,通过引入时变迭代比例项和时变迭代积分项,改进了原始的 HOPPD-MFAILC 控制律。然后,通过理论分析证明了所提出的改进控制算法的收敛性。最后,滚珠丝杠运动系统的仿真和实验表明,所提出的 iHOPPD-MFAILC 能够更好地跟踪所需的轨迹。此外,iHOPPD-MFAILC 在噪声环境下具有更好的鲁棒性,并在不同的初始 PPD 条件下实现了更好的收敛性和轨迹跟踪性能。所提出的控制方案在精密运动控制中具有很好的应用前景。
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Improved data-driven high-order model-free adaptive iterative learning control with fast convergence for trajectory tracking systems
The data-driven high-order pseudo-partial derivative-based model-free adaptive iterative learning control (HOPPD-MFAILC) is always slow to converge and difficult to have excellent tracking results. To address the problem, an improved high-order pseudo-partial derivative-based model-free adaptive iterative learning control (iHOPPD-MFAILC) with fast convergence is proposed. First, to reduce the impact of the initial value of the pseudo-partial derivative (PPD) on the convergence speed of the algorithm, the initial PPD is corrected by introducing the high-order model estimation error. Second, to reduce the influence of system noise on the control performance, the original HOPPD-MFAILC control law is improved by introducing time-varying iterative proportional and time-varying iterative integral terms. Then, the convergence of the proposed improved control algorithm is demonstrated by theoretical analysis. Finally, simulations and experiments on the ball screw motion system show that the proposed iHOPPD-MFAILC can track the desired trajectory better. In addition, iHOPPD-MFAILC has better robustness in the noisy environment and achieves better convergence as well as trajectory tracking performance under different initial PPD conditions. The proposed control scheme has excellent application potential in precision motion control.
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