遗传算法和神经网络融合的滑模控制系统及其在印刷机中的应用(英文)

GA-NN-integrated sliding-mode control system and its application in the printing press

针对一类非线性系统 ,提出了一种综合集成滑模控制器设计方法 .首先 ,采用遗传算法进行参数调节 ,从而构造出一个最佳切换函数 ,使得系统既具有良好的动态性能 ,又扩大了鲁棒区域 .然后 ,采用神经网络在线调整控制器参数 ,从而克服了由不确定性引起的系统轨迹偏离切换函数 ,并且通过变学习率学习算法来加快神经网络的收敛性 .最后 ,研究了所提出方法在凹版印刷机张力调节中的应用 .仿真结果表明该控制器具有较好的动态性能及鲁棒性 ,并有效减少了抖动 .

A sliding mode controller design method based on the synthetically integrated approach is proposed for a nonlinear system. The genetic algorithm is first applied to adjusting the parameters so as to construct an optimized switching function, which ensures the system with better dynamic behavior and enlarged robust range. Then, the method of adjusting the controller parameters online based on neural networks is proposed to overcome the system trajectory's deviating from the switching function due to the uncertainty, and by a learning algorithm with variable learning rate, the convergence of the neural networks is improved. In final, the proposed method is applied to the tension adjusting of the gravure press. The simulation results show the high performance dynamic characteristics and robustness of the proposed controller, as well as the efficiently reduced chattering phenomenon.