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为了提高井下机车的运行效率及稳定性,提出一种基于神经网络算法的运行轨迹优化方法。根据机车多轴控制特点,完成了控制系统硬件设计。通过空间轨迹状态的最优控制理论,建立了多目标动态评价函数,将机车在侧翻约束条件下的轨迹要求作为优化目标,与神经网络算法相结合,实现多目标优化。将优化算法应用于Matlab分析,对机车侧向速度、加速度以及横摆角速度进行数值模拟,结果表明,优化后的轨迹可缩短运行时间,并降低运行的波动性,提高控制精度。
In order to improve the operation efficiency and stability of downhole locomotives, a method of operation trajectory optimization based on neural network algorithm is proposed. According to locomotive multi-axis control features, completed the control system hardware design. Through the optimal trajectory state control theory, a multi-objective dynamic evaluation function is established. The trajectory requirements of the locomotive under rollover constraints are taken as the optimization objectives, and combined with the neural network algorithm to achieve multi-objective optimization. The optimization algorithm was applied to Matlab analysis, and the lateral velocity, acceleration and yaw rate of the locomotive were numerically simulated. The results show that the optimized trajectory can shorten the running time, reduce the fluctuation of operation and improve the control precision.