关键词: 机器人, 多关节手臂, 故障观测器, RBF神经网络, 动态规划, 最优容错控制

Abstract: Aimming at the issue of control accuracy affected by malfunctions such as jamming, saturation and damage that are prone to occur in robot multi-joint manipulator, an optimal fault-tolerant control law was proposed using the designed fault observer. Firstly, a fault model of the robot multi-joint manipulator was established based on the relationship between the spatial position of the end of the robot multi-joint manipulator and the rotation angle of each joint. Then, the tracking errors of the multi-joint manipulator rotation angle and contact force were defined, and the fault observer was designed. The model parameters were estimated by the introduced RBF neural network. Finally, performance index matrix of robot multi-joint manipulator with the tracking error of position/contact force and faults was established according to the idea of dynamic programming, and the fault-tolerant control law including Hamiltonian equation was designed. The RBF neural network was used to solve the optimal performance index, so as to obtain the optimal fault-tolerant control law. The simulation results show that the designed optimal fault-tolerant control law can overcome the influence of various faults, the maximum error of fault estimation is only 0.09 N·m, the maximum error of position tracking is only 0.14 cm, and the maximum error of contact force control is only 0.18 N, which verifies the feasibility of the designed method. The measured results of the six degrees of freedom multi-joint manipulator show that the maximum error of positioning on 8 spatial coordinates is only 0.17 cm, and the maximum error of the contact force is only 0.22 N, which verifies that the designed optimal fault-tolerant control law has superior engineering practicability.

Key words: robot, multi-joint manipulator, fault observer, RBF neural network, dynamic programming, optimal fault-tolerant control