2022 年 30 巻 2 号 p. 185-190
The spherical synchronous actuator can be driven in multiple-degrees-of-freedom by a single unit. This simplifies the mechanism and is expected to be applied to industrial robots. The position of the actuator is controlled using a torque map which is a torque data at each posture. However, there is a problem that the positional accuracy is degraded by measurement errors in the torque map and friction. To solve this problem, we propose a novel attitude control system for the spherical actuator using a deep reinforcement learning algorithm. The simulation results showed that the control system was successfully implemented without a torque map. The system was able to follow the step response within 0.1 seconds, and the steady-state deviation was less than 0.1 degrees.
