Kalman滤波在单神经元PID控制中的应用

针对单神经元PID控制器包含输出噪声, 从而导致控制性能下降的问题, 提出一种基于Kalman滤波理论的改进单神经元自适应PID控制算法. 该算法通过引入状态空间的概念, 采用时域上的递推方法进行数据滤波, 控制对象的输出值经过Kalman滤波算法处理后再返回闭环控制系统. 实验结果表明, 改进算法能有效消减控制系统的输出噪声, 接近于无噪声的理想状态, 提高了控制性能.

Application of Kalman Filtering in Single Neuron PID Control

Aiming at the problem that single neuron PID controllercontained output noise, which resulted in degradation of the control performance, we proposed an improved single neuron adaptive PID control algorithm based onKalman filtering theory. By introducing the concept of state space, the algorithm used recurrence method in the time domain to filter data. The output value of the controlled object was processed by Kalman filtering algorithm and then returned to closed loop control system. The experimental results show thatthe improved algorithm can effectively reduce output noise of the control system, which is close to an ideal state without noise, and improve the control performance.