Closed-loop trajectory generation for robust time-optimal path tracking

In previous work the authors (1996) developed a method for planning robust time-optimal reference trajectories for robots under computed-torque control. That scheme was based on the idea of holding enough torque on reserve during trajectory planning to ensure that actuators would not saturate due to disturbances. This, together with the correct controller gain settings, ensured that tracking was accurate to a specified tolerance. This work extends that approach to a less conservative “closed-loop” architecture for “online trajectory generation” which is similar to Dahl and Neilsen´s scheme (1990) for “online trajectory time-scaling”. We propose a feedback control law for setting the reference path acceleration such that path tracking is nearly time-optimal, robustly controllable, and accurate to a prescribed tolerance. We show how this law can be determined offline using shooting methods and implemented with little online memory. Experimental results demonstrate that the tracking times are reduced compared to our previous approach, while tracking accuracy and robustness remain approximately the same