基于改进克隆选择算法的自抗扰控制参数整定

Parameter Tuning of Active Disturbance Rejection Control Based on Improved Clonal Selection Algorithm

针对自抗扰控制器参数多、整定困难的问题,提出了基于改进克隆选择算法的自抗扰参数优化整定方法。该算法通过在进化过程中采用了三层不同的变异进化策略,形成多策略混合协同进化机制,有效平衡了算法的全局探索与局部开发,较好克服了基本克隆选择算法容易陷入局部最优解以及算法后期收敛速度慢的不足。用经典的标准测试函数来检验所提出算法的可行性与有效性,实验结果表明该算法具有全局搜索能力强、稳定性好、收敛速度快、收敛精度高等优点。以时滞系统的自抗扰控制器参数优化整定进行仿真验证,结果表明经文中提出算法优化后的控制器具有更小的超调量、更快的调节时间及更高的控制精度。

In order to solve the problem that the parameters of active disturbances rejection controller(ADRC) are difficult to be set, a parameters optimization method based on the improved clonal selection algorithm is proposed. By adopting three different mutation evolution strategies in the evolution process, a multi-strategy hybrid co-evolutionary mechanism is formed, which effectively balances the global exploration and local exploitation of the algorithm. Thus, the problems that the basic clonal selection algorithm is easy to fall into local optimum and has slow convergence speed are well solved. The simulation experiments are carried out with benchmark functions, the experimental results show that the proposed algorithm has the advantages of stronger global search ability, better stability, faster convergence speed and higher convergence accuracy. The parameters tuning of ADRC for time-delay system is simulated, the results indicate that the controller optimized by the algorithm proposed in this paper has less overshoot, faster adjusting time and higher control accuracy.