ILC-RBNNF-Based Vibration Control of a Rotatable Manipulator With Time-Varying Output Constraints
针对参数不确定、输入饱和、时变输出约束和周期边界扰动的柔性可旋转机械臂，提出一种结合径向基神经网络和迭代学习控制的鲁棒自适应边界控制方案，实现振动抑制和姿态跟踪。
This article focuses on the problem of vibration suppression and attitude tracking of a flexible rotatable manipulator. For the manipulator system suffering from parameter uncertainties, input saturations, time-varying output constraints, and periodic boundary disturbances, a new type of robust adaptive boundary control scheme is proposed. To cope with system parameter uncertainties and input saturations, radial basis neural network functions (RBNNFs) are introduced. To compensate for the periodic disturbance errors, the iterative learning control (ILC) is designed. In order to obtain a controller to guarantee the system stability, the backstepping technique is employed. Then, a modified Lyapunov function is constructed and system stability and uniform boundedness of output variables are proved. By conducting simulation experiment, the robustness and prescribed performance of the adaptive ILC-RBNNF-based controllers are testified.