基于改进鲨鱼优化算法的自抗扰控制参数整定

非线性自抗扰控制器耦合参数多,常规经验整定法难以获得最优参数,以至于影响控制器的控制精度.单一机制的优化算法整定出的自抗扰参数均可能是局部最优解,不能有效提高自抗扰控制器的控制精度.针对此问题, 提出一种基于改进鲨鱼优化算法的自抗扰控制器参数优化设计方法.为解决基本鲨鱼优化算法易陷入局部最优解、算法后期收敛速度慢的问题,提出混合交叉变异策略与双种群协同机制,以ITAE指标为自抗扰控制器参数选择的优化目标,并以二自由度机械臂为例进行仿真验证.结果表明,优化后的自抗扰控制器具有更小的超调量和更高的控制精度,在加入外界干扰后,控制器可以很快抑制干扰,具有很好的抗干扰能力,改进后的鲨鱼优化算法可以用于复杂非线性系统自抗扰控制器的参数优化.

Parameter tuning of active disturbance rejection control based on ameliorated shark smell optimization algorithm

There are many coupling parameters in a nonlinear active disturbances rejection controller(ADRC), but the optimal parameters are difficult to be obtained by the method of conventional empirical turning, which affects the control accuracy of the controller. A single-mechanism optimization algorithm is used to set the ADRC parameters that may be the local optimal solution, which can not effectively improve the control accuracy of the ADRC. For this problem, a parameter optimization design method based on the improved shark optimization algorithm is proposed. In order to solve the problem that the basic optimization algorithm is easy to fall into local optimum and converges slow, a hybrid cross mutation strategy and a double population co-evolution mechanism are proposed, which take the ITAE index as the optimization target of ADRC parameters selection and simulation with a two degrees of freedom manipulator as an example. The result shows that the optimized ADRC has less overshoot and higher control accuracy. After adding external interference, the controller can quickly suppress interference, so it has good anti-interference ability which can be used to optimize the parameters of the ADRC in complex nonlinear systems.