用于二维源搜索的梯度自适应极值搜索算法

摘要

Sinusoidal-perturbation-based two-dimensional source extremum searching methods have the drawbacks of poor adaptability and mutual restriction between rapidity and accuracy. Aiming at this problem,a gradient-estimation-based parameter adaptive extremum seeking algorithm is proposed. On the basis of traditional extremum seeking algorithm,this algorithm estimates gradient of the local area with three historical sample points and adjusts feedback gain parameter adaptively according to the gradient value. Moreover,the average value theory is used to theoretically analyze and prove the convergence of the proposed algorithm. Simulation comparison in different environments show that the proposed algorithm increase source search efficiency and adaptability to complicated gradient environments.%基于正弦扰动的二维源极值搜索算法存在着适应性差和快速性与准确性相互制约的缺点。针对这一问题，提出一种基于梯度估计的参数自适应极值搜索算法，该算法在传统极值搜索算法基础上，通过三个历史采样点估计当前区域的梯度，并依据当前区域梯度值自适应调整反馈增益参数。此外，利用平均值理论对所提算法进行了理论分析和收敛性证明。不同环境下的仿真对比表明本方法提高了源搜索效率和对复杂梯度环境的适应性。