Controlled Shape Memory Alloy Actuator Research Articles

Model reference adaptive control based on kp model for magnetically controlled shape memory alloy actuators.

Magnetically controlled shape memory alloy (MSMA) actuators take advantages of their large deformation and high controllability. However, the intricate hysteresis nonlinearity often results in low positioning accuracy and slow actuator response. In this paper, a modified Krasnosel'skii-Pokrovskii model was adopted to describe the complicated hysteresis phenomenon in the MSMA actuators. Adaptive recursive algorithm was employed to identify the density parameters of the adopted model. Subsequently, to further eliminate the hysteresis nonlinearity and improve the positioning accuracy, the model reference adaptive control method was proposed to optimize the model and inverse model compensation. The simulation experiments show that the model reference adaptive control adopted in the paper significantly improves the control precision of the actuators, with a maximum tracking error of 0.0072 mm. The results prove that the model reference adaptive control method is efficient to eliminate hysteresis nonlinearity and achieves a higher positioning accuracy of the MSMA actuators.

Hysteresis Curve Fitting Optimization of Magnetic Controlled Shape Memory Alloy Actuator

As a new actuating material, magnetic controlled shape memory alloys (MSMAs) have excellent characteristics such as a large output strain, fast response, and high energy density. These excellent characteristics are very attractive for precision positioning systems. However, the availability of MSMAs in practical precision positioning is poor, caused by weak repeatability under a certain stimulus. This problem results from the error of a large magnetic hysteresis in an external magnetic field. A suitable hysteresis modelling method can reduce the error and improve the accuracy of the MSMA actuator. After analyzing the original hysteresis modelling methods, three kinds of hysteresis modelling methods are proposed: least squares method, back propagation (BP) artificial neural network, and BP artificial neural network based on genetic algorithms. Comparing the accuracy and convergence rate of three kinds of hysteresis modelling methods, the results show that the convergence rate of least squares method is the fastest, and the convergence accuracy of BP artificial neural networks based on genetic algorithms is the highest.

Hybrid Control Method of Magnetically Controlled Shape Memory Alloy Actuator Based on Inverse Prandtl-Ishlinskii Model

The hysteresis nonlinearity of magnetically controlled shape memory alloy actuator is an obstacle in the achievement of high positioning accuracy. To eliminate the influence of hysteresis nonlinearity, a PID hybrid control method which uses the inverse Prandtl-Ishlinskii model as a feedforward controller is proposed in this paper. The PID parameters are searched by particle swarm optimization with cross and heredity function. The simulation results show that when the influence of external disturbance is not considered, the positioning accuracy of open loop control method is 0.67%, and the positioning accuracy of PID hybrid control based on particle swarm optimization is 0.38%; when the influence of external disturbance is considered, the positioning accuracy of open loop control method is 3.29%, and the positioning accuracy of PID hybrid control based on particle swarm optimization is 1.65%. This indicates that the proposed control method can effectively eliminate the impact of hysteresis nonlinearity and external disturbances on the positioning accuracy.

Hybrid control of magnetically controlled shape memory alloy actuator based on Krasnosel’skii-Pokrovskii model

The hysteresis nonlinearity of the magnetically controlled shape memory alloy (MSMA) actuator exerts a great influence on the positioning accuracy. To effectively alleviate the hysteresis nonlinearity of the MSMA actuator, an inverse Krasnosel’skii-Pokrovskii model is established in the paper. Subs equently, a hybrid control scheme which is composed of a hysteresis inverse model feedforward controller and a PID feedback controller is proposed to further improve the positioning accuracy of the MSMA actuator. A differential evolution algorithm is adopted to tune the parameters of PID controller. The experimental results show that the proposed control scheme is effective for improving the positioning accuracy of the MSMA actuator, and the maximum positioning error rate is 0.55%.

Active Length Control of Shape Memory Alloy Wires Using Reinforcement Learning

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.