Control of Stewart Simulator Platform Based on Iterative Feedforward and Multi-agent Consensus Algorithm

The Stewart platform is often used as a motion simulator. The stability of the motion and the accuracy of the trajectory help it improve work efficiency and safety during work. Aimed at the problem of the stability of the platform movement caused by the disturbance of Stewart under heavy load. This paper proposes a trajectory tracking control strategy for Stewart platform based on multi-agent consistent iterative learning. This paper combines the idea of multi-agent consistency control to coordinate the motors controlled by six servo drives. During the control process, the six actuators can communicate with each other to achieve the coordinated control of the position can improve the stability of its motion process; at the same time, in the problem of trajectory tracking accuracy, iterative learning control is a commonly used control method in trajectory tracking. By comparing with PID control, the multi-agent consensus algorithm can make the six actuators reach the same level faster when the platform is disturbed, and make the platform more stable. Combined with iterative learning control algorithm, Stewart platform conducts trajectory tracking experiment, the platform has high accuracy while being stable.