ABSTRACT

This paper presents an iterative learning control (ILC) scheme augmented with the feedback control for solving the nonlinear stabilisation and tracking control problem of ball on plate system, which is a class of under-actuated visual servo system. To enhance the trajectory tracking performance and deal with the real-time challenges of ball on plate system including the nonlinearity, inter-axis coupling and uncertain dynamics, we present a feed-forward learning control scheme, which iteratively updates the control input from one trial to the next, integrated with the cascade control. The ILC update law is synthesised based on the current iteration tracking error (CITE), and the uniform convergence of the input control sequence is presented using the contraction mapping technique. From image processing standpoint, for detecting the foreground objects from a video stream, a background subtraction algorithm using frame difference technique is employed. The efficacy of the proposed scheme is tested on a laboratory scale ball on plate system using hardware-in-the-loop (HIL) testing. Experimental results substantiate that augmenting the learning control with the feedback control not only reduces the tracking error significantly but also enhances the robustness of the closed loop system against the poor lighting conditions.

摘要

提出了一种基于反馈控制的迭代学习控制（ILC）方案，用于解决板载球系统的非线性稳定和跟踪控制问题，该系统是一类欠驱动的视觉伺服系统。为了提高轨迹跟踪性能并应对板球系统的实时挑战，包括非线性，轴间耦合和不确定的动力学，我们提出了一种前馈学习控制方案，该方案迭代地更新了一次试验的控制输入到下一个，与级联控件集成。基于当前迭代跟踪误差（CITE）合成了ILC更新定律，并使用收缩映射技术给出了输入控制序列的一致收敛。从图像处理的角度来看，为了从视频流中检测前景物体，采用了采用帧差技术的背景减法算法。使用硬件在环（HIL）测试，在实验室规模的板球系统上测试了该方案的有效性。实验结果证实，通过反馈控制来增强学习控制，不仅可以显着降低跟踪误差，而且还可以增强闭环系统在恶劣照明条件下的鲁棒性。