A Direct Adaptive MNN Control Method for Stage Having Paired Reluctance Linear Actuator with Hysteresis

Yu-Ping Liu, Kang-Zhi Liu, Xiaofeng Yang
2014 Intelligent Control and Automation  
Reluctance linear actuator, which has a unique property of small volume, low current and can produce great force, is a very promising actuator for the fine stage of the next-generation lithographic scanner. But the strong nonlinearities including the hysteresis, between the current and output force limits the reluctance linear actuator applications in nanometer positioning. In this paper, a new nonlinear control method is proposed for the stage having paired reluctance linear actuator with
more » ... resis using the direct adaptive neural network, which is used as a learning machine of nonlinearity. The feature of this method lies in that the nonlinear compensator in conventional methods, which computed the current reference from that of the input and output force is not used. This naturally overcomes the robustness issue with respect to parameter uncertainty. Simulation results show that the proposed method is effective in overcoming the nonlinearity between the input current and output force and promising in precision stage control. Keywords Direct Adaptive MNN, Reluctance Linear Actuator, Hysteresis Y.-P. Liu et al. 214 stage is playing the key role in lithography. The more functionality is packed into each IC, the smaller feature size indicative of the smallest component that can be manufactured in one IC is required. Today, this minimum feature size is about [2] , and in a foreseeable future it will be smaller. And the chip manufacturers continually increase the productivity in such demanding nanoprecision conditions. The high speed, high acceleration and nanopositioning precision requirements make the lithographic scanner challenging from a position control perspective. Due to its low efficiency and high power dissipation, the voice coil actuator is no longer the best choice as the main driving actuator for the next-generation fine stage [3] . Since the reluctance linear actuator can provide a greater force with a small volume and low power gain than the voice coil actuators, it can provide a solution for driving the fine stage [4] . However, the reluctance linear actuator has a strong nonlinearity between the input current and output force, so we need to study the control method to obtain a predictable force for the requirements in nanopositioning accuracy. Conventionally, the positioning control design of a stage falls into two parts: 1) the actuator dynamics is omitted and the force is designed; 2) the current reference is computed from the designed force based on their static nonlinear relation [5] . But it does not consider the effect of hysteresis and parameter uncertainty [6] on the force accuracy. For the hysteresis compensation, using the inverse hysteresis model [7] is the most noticeable. Reference [8] proposed an inverse hysteresis model and obtained a good performance for the reluctance linear actuator. However, the above methods both need precise hysteresis model, which is generally complex and hardly to obtain. Owing to its online self-learning ability, the neural network provides a good solution for solving nonlinear problems. Especially, the multi-layer neural network (MNN) is effectively used in nonlinear discrete-time system identification and control [9] [10]. Although a lot of researches have been done on the neural network application to the hysteresis [11] [12] , there is few hysteresis compensation algorithms for the reluctance actuator from the available information. A hysteresis compensation configuration for the current-driven reluctance actuator with hysteresis using adaptive MNN has been proposed in paper [13] by the authors, but this method has to use the reluctance actuator model. In this paper, a new and direct adaptive MNN [10] controller is proposed for the current-driven reluctance linear actuator with hysteresis. The main advantage of the proposed adaptive MNN controller is that the reluctance linear actuator model and the inverse hysteresis model are not required. Then, a control configuration is proposed for stages having paired E/I core actuator with hysteresis using the proposed adaptive MNN controller. Simulations are conducted on a stage having paired reluctance actuator with hysteresis and the results show that the adaptive MNN controller is effective in overcoming the nonlinearity and parameter uncertainty of the reluctance linear actuator.
doi:10.4236/ica.2014.54023 fatcat:2lqbpfd6cna3bgjb7od57aj7rm