基于PSO优化的形状记忆合金驱动器自适应滑模控制研究

Research on adaptive sliding mode control of shape memory alloy driverbased on PSO optimization

针对形状记忆合金(SMA)材料在控制过程中的非线性迟滞、精度差等问题,该文提出了一种基于粒子群优化的自适应滑模控制算法。首先,搭建形状记忆合金驱动器装置,并建立了驱动器的机理模型,在此基础上设计了自适应滑模控制器,其中针对滑模控制过程中存在的抖振及收敛速度慢等问题,引入了饱和函数趋近律,最后结合粒子群算法(PSO)优化滑模控制器参数。仿真结果表明,相较于传统PID和滑模控制器,基于PSO优化的自适应滑模控制算法对形状记忆合金驱动器的系统控制具有更高的响应速度、稳定性和鲁棒性。

This paper proposes an adaptive sliding mode control algorithm based on particle swarm optimization to address the issues of nonlinear hysteresis and poor accuracy in the control process of shape memory alloy(SMA)materials.Firstly,a shape memory alloy actuator device was constructed,and a mechanism model of the actuator was established.Based on this,an adaptive sliding mode controller was designed.In order to address the problems of chattering and slow convergence in the sliding mode control process,a saturation function approach law was introduced,and particle swarm optimization(PSO)was used to optimize the sliding mode controller parameters.The simulation results show that compared to traditional PID and sliding mode controllers,the adaptive sliding mode control algorithm based on PSO optimization has higher response speed,stability,and robustness for the system control of shape memory alloy actuators.