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（IBVS）系统的干扰抑制性能。提出的控制方法是基于与深度无关的交互矩阵开发的，该矩阵可以同时处理未知的相机内部和外部参数，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个部分，分别是基于自适应MPC的反馈调节部分和基于改进的DOB的前馈补偿部分。与基于固定名义工厂模型的传统DOB不同，此处的修改DOB基于估计的工厂模型。自适应MPC控制器由迭代识别算法组成，它不仅可以提供控制器和修改后的DOB的模型参数，而且还可以用于控制工厂动态并最小化DOB的影响。进行了手眼和手眼相机配置的仿真，以说明所提出方法的有效性。