控制增益未知的多变量极值有哪些信誉好的足球投注网站系统神经网络自适应协同控制.PDFVIP

30 4 Vol. 30 No. 4 2013 4 Control Theory Applications Apr. 2013 DOI: 10.7641/CTA.2013.20149 , (1. , 264001; 2. , 264001; 3. , 100029) : , . , ; , ; Nussbaum ; . . . : ; ; Nussbaum; ; : TP273.23 : A Neural network adaptive synergetic control for multivariable extremum seeking system with unknown control gain ZUO Bin , LI Jing (1. Department of Control Engineering, Naval Aeronautical and Astronautical University, Yantai Shandong 264001, China; 2. Department of Strategic Missile Engineering, Naval Aeronautical and Astronautical University, Yantai Shandong 264001, China; 3. Beijing Institute of Graphics, Beijing 100029, China) Abstract: In the proposed synergetic control, the synergetic convergence of states can be realized, and the invariance against the system parameter variation and external perturbation can also be achieved. By using the search variables from the extremum-seeking control as the inputs, multilayer neural networks (MNN) are applied to approximate the differential of the state extrema as well as unknown parameters and functions. The problem of the unknown control gain is well solved by using Nussbaum gain function. At the same time, an adaptive parameter is adopted to compensate for the influence of MNN approximation errors. The stability analysis shows that tracking errors of states, errors between the output and its extrema, tracking errors of search variables, and estimation errors of MNN parameters, all conv