一种基于最速下降法的无模型自适应控制

摘要

无模型自适应算法是一种利用数据驱动的方法,它无需系统的精确模型,且计算量小,易于实现.目前已有的无模型自适应控制算法在选取惩罚因子时大多采用试凑或固定常数,往往难以获得满意的控制性能.针对这个问题,提出一种对控制律与伪偏导数的惩罚因子在线寻优的方法.利用最速下降法的迭代优化思想,对惩罚因子进行寻优,收敛速度明显提升,并取得更好的系统性能指标参数,达到系统最优性能,在此基础上进行了闭环系统稳定性的严格证明.最后,通过Matlab仿真验证了该方法与现有无模型自适应控制方法相比具有更好的控制品质,且抗扰性更强.%The model-free adaptive algorithm is a data-driven method, which does not require an accurate model of the system, has small calculation cost and is easy to implement. The existing model-free adaptive control algorithm often adopts the cut-and-trial method or uses a fixed constant when selecting the penalty factor, which is difficult to achieve a satisfactory control performance. To solve this problem, this paper proposes a method to online optimize the penalty factor of the control law and the pseudo partial derivative. By using the idea of iterative optimization in the steepest descent method, the penalty factor is optimized, the convergence speed is increased significantly, and better system performance parameters are obtained. Hence, the optimal performance of the system can be achieved. On this basis, the stability of the closed-loop system is strictly proved. Finally, Matlab simulation results show that the proposed method has better control quality and stronger anti-disturbance abilities than the existing model-free adaptive control method.