基于新型小波神经网络和灰预测的电动负载模拟器控制

针对某火炮随动系统电动负载模拟器自身复杂的非线性以及多余力矩对系统加载性能的影响，提出了一种基于新型小波神经网络和灰预测的控制策略。该策略主要由变结构的粒子群小波神经网络（VSPSO-WNN）控制器和灰预测补偿器（GPC）构成，前者利用粒子群优化（PSO）算法小波神经网络（WNN）的权值等参数，加快了系统的收敛速度，并利用自学习算法动态改变隐含神经元数目，降低了系统的计算复杂度，提高了系统的动静态响应性能；后者在Lyapunov意义下系统稳定的基础上构造出灰预测补偿器，利用灰理论来预测输入力矩偏差，进一步提高了系统的稳定性和准确性。半实物台架仿真实验结果表明：该复合控制策略具有较强的鲁棒性和较高的控制精度，保证了系统动态加载时的稳定性和抗干扰能力。

Electric Load Simulator Control Based on a Novel Wavelet Neural Network and Grey Prediction

A new type of wavelet neural network and grey prediction control strategy is proposed for the complex nonlinearity of some artillery servo system of electric load simulator and the influence of extra torque on the system. The control strategy is mainly composed of a variable structure wavelet neural network controller with particle swarm optimization and a grey prediction compensator(GPC). The variable structure wavelet neural network controller optimizes the parameters of wavelet neural network with particle swarm optimization(PSO) to speed up the convergence of the system, and changes the number of hiden neurons using the self-learning algorithm dynamically to reduce the calculation complexity and improve the dynamic and static performances of the system. The grey prediction compensator is constructed based on the stability of the system in the sense of Lyapunov, which predicts the input torque deviation and further improves the stability and accuracy of the system. The hardware-in-the-loop simulation results show that the hybrid control strategy has strong robustness and high control precision and ensures the stability and anti-interference ability of the system under dynamic load.