基于遗传算法优化的神经网络PID控制器

提出了一种新的改进遗传算法优化的神经网络PID控制器。该方法设计了基于性能指标的适应度函数、自适应的交叉概率、变异概率,引入移民的遗传算法,从而有效地抑制了早熟和维持种群多样性,保证了得到的优化参数为最优参数。该方法为非线性被控对象的控制提供了一种最优参数控制器设计途径。仿真结果证明:利用改进遗传算法设计的控制紧闭环性能优越,鲁棒性强。

Neural network PID controller optimized by GA

A neural network PID controller optimized by an improved genetic algorithm(GA) was presented. In this new algorithm, a special fitness function on the basis of the controller performance and adapted crossover and mutation probability were designed. And an improved immigration method was introduced so that premature convergence can be avoided and population diversity can be maintained. The improved GA ensures that the obtained parameters are optimal. And the proposed method provides a design approach for controller parameter optimization to nonlinear plants. Simulation results show that the improved algorithm possesses satisfied performance and strong robustness.