Stochastic Optimal Control Laws for Cellular Artificial Muscles

This paper presents a control architecture for artificial muscle materials such as shape memory alloys and polymer actuators. The active material is broken up into many small independent cells that can be regulated in a binary fashion into ON and OFF states, so that the actuator displacement is determined by the number of ON cells. In order to control the number of cells that contract, a novel closed loop feedback control method is employed. Each cell is given a small stochastic finite state machine that governs its transition between ON and OFF states. A central controller globally varies the probabilistic rate with which all of the cells make state transitions. Using this architecture, actuator displacement can be controlled in a stable, robust fashion. Different feedback laws are compared using the fixed policy value iteration algorithm to calculate expected settling time in response to a step reference. A simple law based on calculating expected future behavior is found to be very close to the optimal law computed using the value iteration algorithm. The performance of the control laws is verified on a 50 cell shape memory alloy cellular actuator.