Feedforward-feedback hybrid control for magnetic shape memory alloy actuators based on the Krasnosel'skii-Pokrovskii model.
As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel'skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system.
College of Communication Engineering, Jilin University, Changchun, China.
Miaolei
M
Zhou
College of Communication Engineering, Jilin University, Changchun, China.
Qi
Q
Zhang
College of Communication Engineering, Jilin University, Changchun, China.
Jingyuan
J
Wang
D000465:Algorithms; D000497:Alloys; D003198:Computer Simulation; D004867:Equipment Design / Q000295:instrumentation* / Q000379:methods*; D019544:Equipment Failure Analysis / Q000379:methods; D005246:Feedback; D060328:Magnetic Phenomena; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D014159:Transducers
D000497:Alloys