基于神经网络的自适应PID控制器在矿井输送机中的应用

Application of Self-tuning PID Controller Based on Neural Network in Mine Conveyor

矿井输送机运行过程中被控对象具有时变、非线性等特性,传统自整定PID控制器难以满足控制要求。采用基于BP算法的神经网络对PID控制参数进行优化,引入FPGA来实现PID控制器的硬件设计,采用DSP Builder 7.0构建闭环测试系统,并基于ModelSim SE 6.2版本进行调试,完成了功能仿真,且对波形进行了比较及分析,证实了FPGA实现自整定PID控制器在矿井输送机中得到了很好的效果。

In mine conveyor operation process, the controlled object has characteristics of time-varying and nonlinear. Traditional self-tuning PID controller can not meet these control requirements. Using neural network based on BP algorithm can optimize the PID controller parameters commendably, and FPGA is introduced to implement the hardware design of PID controller. A close-loop test system is constructed by DSP Builder 7.0, and then debug based on ModelSim SE 6.2, and completed functional simulation. Last, the wave shape is compared and analyzed, and it confirms that the self-tuning PID controller based on FPGA has a good effect in mine conveyor.