基于鲨鱼优化算法的液压伺服系统自抗扰控制

Auto-disturbance Rejection Control of Hydraulic Servo System Based on Shark Smell Optimization Algorithm

针对冷轧机组液压伺服位置系统存在不一致性而引起两侧位置不同步的问题,提出一种基于改进鲨鱼优化(ISSO)算法的自抗扰同步控制方法。首先,建立了液压伺服位置同步系统的数学模型,并针对自抗扰控制器参数难以整定以至于影响同步控制精度的问题,通过引入反向学习综合策略和自学习系数,针对鲨鱼优化算法易陷入局部最优的问题进行了改进,并将改进后的鲨鱼优化算法用于自抗扰控制器的参数整定。最后,通过仿真验证了所提方法有效地减小了同步控制误差,且具有良好的抗扰动能力。

Aiming at the inconsistency of the hydraulic servo position system of the cold-rolling mill,which causes the non-synchronization of both sides of the position,an improved auto-disturbance-rejection synchronous control method based on the improved shark smell optimization(ISSO)algorithm was proposed. Firstly,the mathematical model of the hydraulic servo position synchronization system was established,and the problem that the parameters of the ADRC controller is difficult to set so as to affect the synchronization control accuracy was solved. By introducing the inverse learning comprehensive strategy and the self-learning coefficient,the improvement had been made for the problem that the shark optimization algorithm is easily trapped in the local optimal,and the improved shark optimization algorithm was used for the parameter tuning of the ADRC. Finally,the simulation shows that the proposed method can effectively reduce the synchronization control error and has good anti-disturbance capability.