炮控系统电动负载模拟器神经网络滑模控制

为解决炮控系统电动负载模拟器存在多余力矩的问题，对其神经网络自适应滑模控制进行研究。结合滑模控制器的特点，构建电动负载模拟器系统模型，通过滑模控制器对复杂非线性系统建立控制器，采用 RBF 神经网络与滑模相结合的控制方法，利用 RBF 神经网络对系统摄动参数和未建模动态进行自适应逼近，可降低切换增益及有效地抑制抖振，并对其进行仿真分析与验证。仿真结果表明：该控制策略具有较高的控制精度，且鲁棒性好，满足系统的控制要求。

Neural Network Sliding Mode Control for Electric Load Simulator of Gun Control System

In order to solve the problem of redundant torque in the electric load simulator of the gun control system, the neural network adaptive sliding mode control is studied. Combined with the sliding mode controller features, establish electric load simulator system model, the controller of the complex nonlinear system is established by using sliding mode controller. Based on combined control method of RBF neural network and sliding mode, the RBF neural network can make adaptive approximation of system perturbation parameters and un-modeled dynamics, which can reduce the switching gain and effectively suppress chattering. Carry out simulation analysis and verification. The simulation results show that the control strategy has high control precision and good robustness, which meets the system control requirements.