基于单级倒立摆的遗传神经网络控制研究

Research on Genetic Neural Network Control Based on Single-Stage Inverted Pendulum

由于倒立摆是一个高阶次、非线性和自然不稳定系统,为了改善单级倒立摆的稳定性问题,提出一种改进遗传算法的RBF(径向基函数)神经网络控制方法。该方法以传统遗传算法为基础,通过对适应度值的自适应缩放、交叉率和变异率自适应调整等策略实现对RBF神经网络基函数参数和权值的优化。通过单级倒立摆仿真实验分析表明,虽然改进遗传算法的RBF神经网络控制比LQR控制的摆杆稳定时间只减少了0.2 s,但具有更小的超调和稳态误差,证明了该方法的可行性和有效性,较好地改善了单级倒立摆的稳定性能。

Due to the inverted pendulum is a high-order,nonlinear and naturally unstable system,in order to improve the stability of the single-stage inverted pendulum,an improved genetic algorithm RBF(radial basis function)neural network control method is proposed.Based on the traditional genetic algorithm,the parameters and weights of the basis function of the RBF neural network are optimized through strategies such as adaptive scaling of the fitness value,adaptive adjustment of the crossover rate and the mutation rate.Through the simulation experiment analysis of the single-stage inverted pendulum,although the RBF neural network control of the improved genetic algorithm reduces the stabilization time of the pendulum by only 0.2s compared with the LQR control,it has smaller overshoot and steady-state error,which proves the feasibility of the method And effectiveness,which better improves the stability of the single-stage inverted pendulum.