基于互联网无刷直流电机传动系统的控制策略

为了方便处理网络控制系统中的时变延时问题,运用时间戳BP神经网络对每一采样周期的延时数据进行在线、实时预测,建立无刷直流电机网络控制系统的数学模型,导出系统离散状态方程,并基于时间乘误差绝对值积分最小(ITAE)优化控制策略提出初次优化设计方法；为了对无刷直流电机传动系统的量测噪声、突加负载扰动及模型随机干扰进行有效补偿和抑制,采用卡尔曼滤波进行状态估计,同时引入李雅普诺夫稳定性理论求取该系统最优状态反馈矩阵,实现网络控制系统的再次优化.仿真结果表明,该方法能够有效提高传动系统的静动态性能和抗干扰水平．

Internet based control strategy for brushless DC motor drive systems

In order to conveniently process the time-varying delay of networked control system,time delay real-time online prediction by time-stamped BP neural network was applied in each sampling period. Mathematic model of the brushless DC motor drive system using networked control was obtained and discrete state equations of the system were derived. Based on integration of the production of time and absolute error (ITAE) rule an initial optimization design method was given；For the sake of compensation and suppression for the effects of measurement noise, load disturbance, and model perturbation,the state variables estimation by Kalman filter theory was applied and an optimal feedback control matrix based on Lyapunov stability theory was deduced for this system. Eventually, an additional optimization of networked control system was realized. Simulation demonstrates that the static performance,dynamic responses,and capacity of resisting disturbance of system can be obviously improved．