基于改进遗传算法的单神经元自适应PID控制

Single Neuron Adaptive PID Control Based On Improved Genetic Algorithm

单神经元自适应PID控制器不但结构简单,而且能够适应环境变化,有较强的鲁棒性。本文基于生物进化机制提出一种改进遗传算法,并给出算法的详细步骤。该算法在交叉、变异操作以前对亲和度较低的个体进行替换操作,改善了算法的收敛性能。在函数寻优的实验中验证了算法的有效性。最后将算法应用于神经网络的突触权值寻优,通过突触权值的调整来自适应地实现PID的比例、积分和微分系数的可调节控制,仿真对比结果表明了该算法的优越性。

Single neuron adaptive PID controller not only has simple structure, but also is able to adapt to environmental changes with stronger robustness. An improved genetic algorithm based on evolutionary mechanism is proposed in the paper and the detail process is given. In order to improve the convergence performance of the algorithm, the replacement operation is done to individuals which have lower affinities before cross and mutation operation. Function optimization experiments verify the effectiveness of the algorithm. Finally, the algorithm is applied to the synaptic weights of the neural network optimization. Through the adjustment of synaptic weights, the proportional, integral and differential coefficients control of the PID can be achieved adaptively. The simulation comparison results show the superiority of the algorithm.