交流稳压电源的改进神经网络PID控制

建立了交流稳压电源主电路数学模型并分析其闭环稳压控制原理。由于装置具有较强的非线性和变结构、变参数特性,采用经典PID控制器很难获得理想的控制效果。将人工神经网络与传统PID控制器相结合,构成一种不依赖于被控对象精确数学模型的神经网络PID控制器。为了提高神经网络的收敛速度,采用Levenberg-Marquardt算法计算连接权值更新量,并对当前解施加一个以一定概率保留的随机扰动,加快迭代过程跳出局部极小点。对装置主电路和改进神经网络PID控制器进行仿真,结果表明:系统动态响应快,鲁棒性强,调节平滑,具有较好的控制效果。最后,制造并测试了额定电压660 V、容量400 k VA的实验样机,对理论研究进行了实验验证。

Improved neural network PID controller for regulated power supply

The mathematical model of device′s main circuit is established and the closed-loop voltage stabilization control method is analyzed.With the strong non-linearity and variable structures and variable parameters, it is difficult to achieve ideal control effects using the classic PID controller.Artificial neural network was combined with conventional PID regulator to construct a neural network PID controller that did not rely on the precise mathematical model of controlled objects.To attain faster convergence speed of the neural network, the Levenberg-Marquardt algorithm was adopted to calculate the updating quantities of connection weights, to which random disturbances retained in certain probability were applied for speeding up the iterative process out of local minima.The device′s main circuit together with neural network PID controller was simulated and the results show that the system has quick responses, strong robustness and smooth adjustment.Testing and validation of such controller were also conducted experimentally using a prototype with voltage rating 660 V and volume rating 400 kVA.