：Improving the performance of a micro positioning worktable is one of major approaches used to improve the processing performance of high-end manufacturing equipment. Aiming at the problems such as low drive force, short stroke and low positioning accuracy existing in the drive system of existing micro positioning worktable, a drive system of micro positioning worktable taking the giant magnetostrictive material as the core component is developed. In order to improve modeling accuracy of output displacement of the drive system, an output displacement model of the drive system is set up based on the Jiles-Atherton hysteresis model. In addition, in order to improve the identification precision of model parameters, a hybrid optimization parameter identification algorithm which has high accuracy and combines the rapid local search function of particle swarm algorithm and the global convergence of artificial fish swarm algorithm is proposed. And in order to compensate the output displacement error of the drive system, a dynamic recurrent neural network feedforward-fuzzy PID feedback control strategy is introduced. And in order to improve the execution efficiency of the program, a high-speed DSP chip is used to develop the control system. The prototype is built and verified with the experimental platform established. The research results indicate that the maximum displacement of the developed drive system is 30.8 μm, the maximum output force is 292.3 N, the positioning accuracy is 0.75 μm and the maximum repeatability error is 0.4 μm. This result lays a theoretical foundation for development of high-performance precision positioning devices.

A precision positioning stage based on giant magnetostrictive actuator (PPS-GMA) shows nonlinear displacement when it is used in the field of precision positioning control. To improve the defect, the Jiles-Atherton hysteresis model and the dynamic recurrent neural network (DRNN) feed forward-fuzzy PID feedback control strategy were adopted. An accurate hysteresis nonlinearity model of PPS-GMA was established with the Jiles-Atherton model and its parameters were identified using the particle swarm optimization (PSO) algorithm. A dynamics inverse model of the PPS-GMA was established with the DRNN learning method to compensate the hysteresis nonlinearity characteristic. A fuzzy PID feedback control was used to compensate for the mapping error of DRNN. Using these control methods, the positioning accuracy of the precision positioning stage was improved. The simulation and experimental results show that the Jiles-Atherton hysteresis model can describe the hysteresis nonlinear characteristic of the precision positioning stage, the PSO algorithm has high precision for parameter identification, the DRNN feed forward-fuzzy PID feedback control strategy can effectively eliminate the nonlinear characteristics of the PPS-GMA, which has practical significance for improving the positioning accuracy of the PPS-GMA.

Based on the principle of the Villari effect, a force sensor with a giant magnetostrictive material (GMM) as the sensitive element, high linearity, and a large range was studied. A Hall element integrated into the structure was used to detect the magnetic flux density and measure the external force. First, the finite element method was used to verify the validity of the intended magnetization process. Second, an equation for GMM magnetization was derived based on the Jiles–Atherton (J–A) model and the magneto-mechanical coupling effect. The relationships between magnetization and the biased magnetic field, and magnetization and the external force were analyzed under the dynamic coupling model of positive and negative effects. Finally, the influences of various biased magnetic fields and external forces on the sensor output characteristics were determined experimentally. The sensitivity of the designed force sensor was 0.337 mV/N when the bias current was 1.2 A and the preload was 120 N. When a force of 1000 N was applied, the linearity was 0.82%. The experimental results are consistent with the theoretical design values. These research results aid in the development of a highly linear, large-range force sensor. This study provides the theoretical and technical foundations for a high-performance force sensor that can be used in industrial testing.

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