Double Inverted Pendulum Control Based on Three-loop PID and Improved BP Neural Network - Yingjun Sang; Yuanyuan Fan; Bin Liu (2011 Second International Conference on Digital Manufacturing and Automation, 2011)
To deal with the defects of BP neural networks used in balance control of inverted pendulum, such as longer train time and converging in partial minimum, this article realizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks (ANN), builds up a training model of test simulation and the BP network is 6-10-1 structure. Tansig function is used in hidden layer and Purelin function is used in output layer, LM is used in training algorithm. The training data is acquired by three-loop PID algorithm. The model is learned and trained with Matlab calculating software, and the simulink simulation experiment results prove that improved BP algorithm for inverted pendulum control has higher precision, better astringency and lower calculation. This algorithm has wide application on nonlinear control and robust control field in particular.