基于ESO的NLPID神经网络控制器的设计

针对传统PID控制自适应和抗扰能力欠佳的问题,提出了一种具有强抗扰动能力的NLPID神经网络控制方法。该方法通过扩张状态观测器对系统建模中不确定性因素以及系统的外部扰动实时观测进行前馈补偿,并与非线性PID神经网络控制相结合,实现对非线性、时变、不确定性、受未知外扰系统的最优PID自适应抗扰控制。通过Matlab仿真结果与传统PID控制对比分析,表明该方法具有优良的动态品质和静态性能,在非线性系统控制领域拥具有重要的应用价值。

Design of NLPID PID Neural Network Controller Based on ESO

A nonlinear PID neural network control method of strong anti-disturbance ability is proposed for better adaptability and immunity.Through the ESO,the method feeds the forward compensation for the uncertainties in modeling and the external disturbance of the system in real time,and combines with the nonlinear PID neural network to achieve the optimal control of PID control for the nonlinear,time-varying,uncertainty and unknown external disturbance immunity system,thus solving the problem of the large computation and poor immunity for PID control.Comparison between the Matlab simulation results and the traditional method of PID control and the classic ADRC method shows that the method has better dynamic and static performance and is of great application value in the field of nonlinear control system.