基于RBF神经网络的智能负载控制策略研究

本文在电力弹簧的数学模型和控制电路的基础上,提出了一种基于RBF神经网络的智能负载控制方法,利用RBF神经网络算法可以有效弥补传统PI控制器参数固定,无法更改的缺点。通过对控制器参数实时在线调整,可以有效地减少智能负载失稳情况,从而确保系统母线电压稳定。最后,在MATLAB/Simulink的仿真环境中进行仿真验证,结果表明：与传统PI控制下的智能负载相比,本文所提的控制方法具有更强的调节性能。

Intelligent load control strategy based on RBF neural network

Aiming at the problem that the traditional PI controller used for the control of electric springs has poor adjustment performance and the control method does not take into account the sudden changes of non-critical loads, an smart load control method is proposed based on RBF neural network of the network on the basis of the mathematical model and control circuit of electric spring.The RBF neural network algorithm is used to make up for the shortcomings of the traditional PI controller that the parameters are fixed and cannot be changed.The real-time online adjustment of the controller parameters reduces the intelligent load instability and ensures the stability of the system bus voltage.Simulation verification in the simulation environment of Matlab/Simulink shows that, compared with traditional PI control, the intelligent load under the proposed control strategy has better performance in regulating the system.Therefore, the smart load under the new PI control strategy based on RBF neural network has better robustness and system control capability.