Active Length Control of Shape Memory Alloy Wires Using Reinforcement Learning

Abstract

Actively controlled shape memory alloy actuators are useful for a variety of applications that require accurate shape control. For shape memory alloy wires, strain is modulated with temperature, usually by an applied voltage difference across the length. Numerical simulation using reinforcement learning has previously been used for determining the temperature–strain relationship of a shape memory alloy wire and for synthesizing a limited control policy that relates applied temperature to desired strain. However, learning the voltage–strain relationship is of more practical interest in synthesizing feedback control laws for shape memory alloy wires since the control input in practical applications will be an applied voltage that modulates temperature. This article implements a Sarsa-based algorithm for determining a feedback control law in voltage–strain space and validates it experimentally. Experimental results presented in this article demonstrate the ability to control a shape memory alloy specimen from arbitrary initial strains ranging from zero to maximum, including intermediate strains, to an arbitrary intermediate strain. The results also demonstrate theability to control the specimen from similar arbitrary initial values of strain to zero strain. The voltage–strain learning algorithm developed in this article is a promising candidate for synthesizing practical shape memory alloy actuator feedback control laws.