Inverse Optimal Adaptive Tracking Control of Robotic Manipulators Driven by Compliant Actuators

Compliant actuator has considerable merits for safe robot control. Although the control problem of robotic manipulators with compliant actuators has been extensively investigated in recent years, limited result is presented for optimal trajectory tracking control. The reason is that a complex and time-consuming learning procedure is needed for solving the Hamilton-Jacobi-Bellman (HJB) equation in real time and it is difficult to reproduce for practical engineering systems. This work proposes an inverse optimal adaptive neural control scheme to remove such limitation. A tuning functions-based adaptive learning mechanism, which aims for boosting the control efficiency and providing a simple control implementation, is proposed to update the inverse optimal controller. It is proved that optimal performance is achieved with respect to a meaningful cost functional and the tracking error ultimately converges to a tunable residual around zero. Both simulations and experiments are carried out to validate the established results. It is the first time that experimental results are provided for inverse optimal adaptive control.