【摘要】 针对电-气比例力控制系统的非线性和时变特性导致力控制精度低问题，设计一种基于GA-BP神经网络的电-气比例力控制系统。建立系统数学模型，提出基于GA-BP神经网络的系统控制结构和算法，利用BP神经网络建立的系统内模型和经遗传算法优化的BP神经网络建立的系统逆动力学模型实现力控制，通过AMESim/Simulink联合仿真和实验研究了系统在变负载容腔和变负载位移情况下的随机力跟踪控制精度。结果表明：随机力跟踪控制平均绝对误差比常规PID控制降低48.6%。该算法简单实用，鲁棒性强，可为气动力控制系统的设计提供指导。更多还原

【Abstract】 In order to address the problem of low force control precision caused by the nonlinear and time-varying characteristics of electro-pneumatic proportional force control systems, an electro-pneumatic proportional force control system based on genetic algorithm-back propagation(GA-BP) neural network was designed.The mathematical model of the system was established, and the system control structure and algorithm based on GA-BP neural network were proposed.The force control was achieved through the system internal model established by the BP neural network and the system inverse dynamics model established by the BP neural network optimized by the genetic algorithm.The random force tracking control precision of the system under varying load capacity and varying load displacement was studied through AMESim/Simulink joint simulation and experimental research.The results show that compared to conventional PID control, the mean absolute error of random force tracking of this algorithm can be reduced by 48.6%.This algorithm is simple and practical with strong robustness, and it can provide guidance for the design of pneumatic force control systems.更多还原