Dynamic Model With Hysteresis Nonlinearity And Control Technique For Giant Magnetostrictive Actuator

Giant magnetostrictive actuators, characterized by large strain, high force, fast response and nanometer solution and so on, have a wide range of potential applications in super-precision positioning, robotics, and vibration control, etc. However, the relation between the input magnetic field and output displacement for magnetostrictive actuators exhibits the hysteresis nonlinearity, which presents a challenge in the applications of actuators. To control and use the actuators, the dynamic model with hysteresis nonlinearity and control technique for giant magnetostrictive actuators are selected as the subject of dissertation for Ph.D.Firstly, giant magnetostrictive materials and theirs applications are introduced, both the models and control techniques for giant magnetostrictive actuators are analyzed in depth, and the actuality and the perspective of applications for neural networks are summarized.Subsequently, the dynamic model with hysteresis nonlinearity for magnetostrictive actuators is founded according to the Jiles-Atherton magnetization model, quadratic moment domain rotation model, nonlinear piezomagnetic equation and actuator structural dynamics principle. The output strain and force for a magnetostrictive actuator has been calculated. It is found that the calculated results are in a good agreement with the experimental ones. This indicates the model's validity and practicability.Two kinds of hybrid genetic algorithms (HGAs), which are respectively called as the HGA1 and the HGA2, are proposed by combining trust-region algorithm (TRA) with a hybrid coded genetic algorithm. The key techniques and realization processes of the HGAs are thoroughly studied. The HGAs are then applied to identify parameters of dynamic model with hysteresis nonlinearity for magnetostrictive actuators. The simulation and experimental results show that the HGA2 obviously excels the HGA1. The HGA2 can obtain accurate parameters value with a rather convergence speed and a rather large probability, and has the definite ability to resist noise.In order to obtain nanometer resolution, a displacement closed loop control system for the actuator is presented based on TMS320C31 DSP. The control system hardware is studied, including the TMS320C31-based digital signal processor board, constant current source, displacement data collected channel. The control system software is designed and compiled, and the automatic measurement for the actuator system is realized.A kind of single parameter fuzzy self-tuning PID control system is designed, and the simulation study of the control system is carried out. On the base, the single parameter fuzzy self-tuning method is used to tune the PID parameters of the actuator system, the PID control algorithm is compiled, and the displacement automatic control of the actuator system is realized. The numerical simulation and experimental results show the fuzzy self-tuning method's efficiency and utility. At the same time, the experimental results and theory analysis indicate that the actuator closed-loop control system has good stability and high anti-noise ability, and can achieve 40nm resolution with the range of 40um.Due to the inherent hysteresis nonlinearity, the magnetostrictive actuator always causes position error in the open-loop system, and causes instability of the closed-loop system in complicated tracking problems. In order to remedy this problem, a dynamic recurrent neural network (DRNN) is designed, and a control strategy combining the DRNN feedforward and PID feedback controllers is applied. Numerical simulation results show the control strategy can on-line obtain inverse model of hysteresis nonlinearity for the actuator in very short time and eliminate the impact of hysteresis nonlinearity. Thus, the system output can better track reference input.