Single neuron adaptive control and parameter optimization of lower limb rehabilitation exoskeleton

For the bottom position control of the lower limb exoskeleton robot driven by DC motor, a single neuron adaptive control method with parameters selected by the improved whale optimization algorithm is proposed. Since the parameter tuning process existing in traditional control is cumbersome and it is difficult to achieve optimal control accuracy, we present a single neuron adaptive controller to improve the control accuracy. In addition, in order to complete the optimal selection of controller parameters, the whale optimization algorithm is used to select parameters, this algorithm is improved, and the optimization simulation results are displayed. Finally, an adaptive underlying controller is designed, and the simulation results are compared with the traditional PID control. The results show that, compared with the traditional method, the single neuron adaptive control with the optimized parameters of the improved whale optimization algorithm has better control