Toward human-in-the-loop PID control based on CACLA reinforcement learning

A self-tuning PID control strategy using a reinforcement learning method, called CACLA (Continuous Actor-critic Learning Automata) is proposed in this paper with the example application of humanin-the-loop physical assistive control. An advantage of using reinforcement learning is that it can be done in an online manner. Moreover, since human is a time-variant system. The demonstration also shows that the reinforcement learning framework would be beneficial to give semi-supervision signal to reinforce the positive learning performance in any time-step.