School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou, China.
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China.
ISA Trans. 2020 Nov;106:40-50. doi: 10.1016/j.isatra.2020.06.013. Epub 2020 Aug 11.
This paper presents an adaptive model predictive control (MPC) method based on disturbance observer (DOB) to improve the disturbance rejection performance of the image-based visual servoing (IBVS) system. The proposed control method is developed based on the depth-independent interaction matrix, which can simultaneously handle unknown camera intrinsic and extrinsic parameters, unknown depth parameters, system constraints, as well as external disturbances. The proposed control scheme includes two parts which are the feedback regulation part based on the adaptive MPC and the feedforward compensation part based on the modified DOB. Unlike the traditional DOB that is based on the fixed nominal plant model, the modified DOB here is based on the estimated plant model. The adaptive MPC controller consists of an iterative identification algorithm, which not only can provide the model parameters for both the controller and the modified DOB, but also can be used to control plant dynamics and to minimize the effects of DOB. Simulations for both the eye-in-hand and eye-to-hand camera configurations are conducted to illustrate the effectiveness of the proposed method.
本文提出了一种基于干扰观测器 (DOB) 的自适应模型预测控制 (MPC) 方法，以提高基于图像的视觉伺服系统 (IBVS) 的抗干扰性能。所提出的控制方法是基于深度独立交互矩阵开发的，它可以同时处理未知的相机内参和外参、未知的深度参数、系统约束以及外部干扰。所提出的控制方案包括两部分，即基于自适应 MPC 的反馈调节部分和基于改进 DOB 的前馈补偿部分。与基于固定标称植物模型的传统 DOB 不同，这里的改进 DOB 基于估计的植物模型。自适应 MPC 控制器由迭代识别算法组成，该算法不仅可以为控制器和改进的 DOB 提供模型参数，还可以用于控制植物动力学和最小化 DOB 的影响。对眼在手和眼在手相机配置进行了仿真，以验证所提出方法的有效性。
