基于BP神经网络PID的高速清扫车摆臂控制系统

针对传统清扫车摆臂开环控制系统的动态响应特性不足、抗干扰能力弱，不能适应高速工况下清扫作业，设计了高速清扫车摆臂执行机构和闭环控制系统。为解决BP神经网络(BPNN)存在局部极值、收敛速度慢等问题，提出一种改进BPNN PID算法，其核心是通过主动串联校正，抑制PID前一次输出值u (k-1)对此次输出值u (k)的影响。通过搭建Simulink-AMESim联合仿真模型，研究了高速清扫车摆臂闭环控制系统的阶跃响应、抗干扰能力以及位置跟踪能力。研究结果表明:所改进的BPNN PID控制器能够动态调整PID参数，提高了系统的适应性、准确性和稳定性;改进BPNN PID控制器的抗干扰能力更强，鲁棒性更好，且系统近乎没有超调，超调量为0.5%，仅为PID控制超调量的2.34%，稳定时间0.62 s，相比PID提前了66.31%。

High Speed Sweeper Swing Arm Control System Based on BP Neural Network PID

The traditional sweeper swing arm open-loop control system cannot adapt to high speed cleaning operation,because of its insufficient dynamic response characteristics and weak anti-interference ability.In order to solve problems of local extreme value and slow convergence of BP neural network(BPNN),an improved BPNN PID algorithm was proposed,the core of which was to suppress influence of previous PID output u ( k -1) on output u ( k ) through active series correction.A high-speed sweeper swing arm actuator was introduced,a closed-loop control system using BPNN PID controller was designed.By building Simulink-AMESim co-simulation model,the step response,anti-interference ability and position tracking ability of control system were studied.The improved BPNN PID controller can adjust PID parameters dynamically,and improve adaptability,accuracy and stability of system.The improve BPNN PID controller has stronger anti-interference ability and better robustness.In addition,the system has almost no overshoot,the overshoot is 0.5%,which is only 2.34% of PID controller,the stability time is 0.62 s,which is 66.31% ahead of PID.