多变量极值搜索系统神经网络自适应协同控制

针对一类仿射型多变量极值搜索系统的协同控制问题，提出了一种基于神经网络的自适应协同控制方法。该方法利用协同控制实现状态变量之间的协同收敛，并确保对系统内部参数扰动和外界干扰具有不变性；以系统的状态变量、输入量、搜寻变量以及已知模型参数作为输入量，分别设计两个3层神经网络来估计状态变量极值的动态变化过程及未知参数；并采用可调参数消除此神经网络的残余估计误差。详细的理论分析证明了闭环系统的所有误差信号均指数收敛至原点的有界可调邻域内。仿真结果也说明了理论分析方法的正确性和有效性。

Neural network adaptive synergetic control for multivariable extremum seeking system

A systematic procedure for synthesis of neural network adaptive synergetic control is proposed for a class of affine multivariable extremum seeking system. By employing the synergetic control, the synergetic convergence among the states can be realized, and the invariance against system parameter variation and external perturbation can also be achieved. By using the system’s states and intput, the search variables from the extremum seeking control, and the known model parameters as the inputs, two three-layer neural networks are designed to estimate the dynamic process of the states extrema and unknown parameters, respectively. At the same time, an adjustable parameter is used to minify the estimation errors of the three-layer neural networks. The detailed theoretical analysis proves that all errors of the closed-loop system exponentially converge to a small tunable neighborhood of the origin by appropriately choosing design constants. Simulation results show the effectiveness of the proposed control method.