To deal with the coordination problem for multi-manipulator trajectory tracking systems with parametric uncertainties, this paper proposes a two-layer control scheme incorporating a model predictive strategy and an extended state observer. In the kinematic layer, the visual information is implemented and a visual servoing error model is derived by the image-based visual servoing strategy. A recurrent neural network model predictive control approach is proposed to obtain velocities which are regarded as the reference signals for the dynamic layer. For dynamics, a linear time-varying dynamic model of the multi-manipulator system coupled with the object is established, where the parametric uncertainty is recognized as an added disturbance. An extended state observer is sequentially designed to estimate the disturbance by using pole placement method. The input-to-state practical stability of the system is further analyzed with a bounded disturbance. Finally, simulations and comparison are given to verify the effectiveness and robustness of the proposed algorithm.

为解决具有参数不确定性的多机械手轨迹跟踪系统的协调问题，提出了一种结合模型预测策略和扩展状态观测器的两层控制方案。在运动学层中，实现了视觉信息，并通过基于图像的视觉伺服策略导出了视觉伺服误差模型。提出了一种递归神经网络模型预测控制方法来获得速度，该速度被视为动态层的参考信号。对于动力学，建立了与对象耦合的多机械手系统的线性时变动力学模型，其中参数不确定性被认为是增加的干扰。通过使用极点放置方法，依次设计了扩展状态观察器以估计干扰。利用有界扰动进一步分析了系统的输入状态实用稳定性。最后，通过仿真和比较验证了所提算法的有效性和鲁棒性。