Aiming at the problem of nonlinear power steering in the automobile electric power steering system, an advanced control algorithm is required for the practical system. This paper introduces back propagation neural network arbitrary nonlinear approximations to discretize the vehicle’s power assistance characteristic. Steering power is also realized in the whole range of speed, which overcomes the steering blind zone and lays a foundation for the design of subsequent controllers. In addition, considering the nonlinear frictional resistance problem of the electric power steering system, the traditional proportional–integral–derivative remote control algorithm will result in poor dynamic performance or system instability. Therefore, this paper proposes a control algorithm based on back propagation neural network proportional–integral–derivative parameter self-tuning. Using the error between the expected current and the actual motor current, the back propagation neural network algorithm is used to learn and realize the adaptive adjustment of proportional–integral–derivative parameters. Simulation results show that the proposed control system effectively realizes the nonlinear steering assistance in the whole vehicle range speed, eliminates the influence of nonlinear friction in the electric power steering system, and improves the robustness of the control system.

中文翻译：

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基于反向传播神经网络比例-积分-微分参数自整定的电动助力转向非线性问题

针对汽车电动助力转向系统中的非线性动力转向问题，需要一种适用于实际系统的先进控制算法。本文引入反向传播神经网络任意非线性近似来离散化车辆的助力特性。转向力也在全速度范围内实现，克服了转向盲区，为后续控制器的设计奠定了基础。此外，考虑到电动助力转向系统的非线性摩擦阻力问题，传统的比例-积分-微分遥控算法会导致动态性能差或系统不稳定。所以，本文提出了一种基于反向传播神经网络比例-积分-微分参数自整定的控制算法。利用期望电流与实际电机电流之间的误差，采用反向传播神经网络算法学习并实现比例-积分-微分参数的自适应调整。仿真结果表明，所提出的控制系统有效地实现了全车速范围内的非线性转向助力，消除了电动助力转向系统中非线性摩擦的影响，提高了控制系统的鲁棒性。采用反向传播神经网络算法学习并实现比例-积分-微分参数的自适应调整。仿真结果表明，所提出的控制系统有效地实现了全车速范围内的非线性转向助力，消除了电动助力转向系统中非线性摩擦的影响，提高了控制系统的鲁棒性。采用反向传播神经网络算法学习并实现比例-积分-微分参数的自适应调整。仿真结果表明，所提出的控制系统有效地实现了全车速范围内的非线性转向助力，消除了电动助力转向系统中非线性摩擦的影响，提高了控制系统的鲁棒性。