Adaptive Control of Robotic Manipulators With Unified Motion Constraints
针对参数不确定和运动约束的机器人机械臂，提出一种自适应神经网络控制方法，将位置和速度约束统一为名义输入约束，通过Lyapunov分析保证信号有界，仿真和实验验证了有效性。
In this paper, we present an adaptive control of robotic manipulators with parametric uncertainties and motion constraints. Position and velocity constraints are considered and they are unified and converted into the constraint of the nominal input. An adaptive neural network control is developed to achieve trajectory tracking, while the problems of motion constraints are addressed by considering the saturation effect of the nominal input. The uniform boundedness of all closed-loop signals is verified through Lyapunov analysis. Simulation and experiment results on a 2-degree-of-freedom robotic manipulator demonstrate the effectiveness of the proposed method.