This paper addresses the use of deep learning algorithm to design the controller; to explore the feasibility of applying deep learning into control problems. The proposed deep learning controller is designed by learning PID controller which is most commonly used in industry. | Journal of Automation and Control Engineering Vol. 3, No. 6, December 2015 On Replacing PID Controller with Deep Learning Controller for DC Motor System Kangbeom Cheon, Jaehoon Kim, Moussa Hamadache, and Dongik Lee School of Electronics Engineering, Kyungpook National University, Daegu, Korea Email: ckb0120@, destiny-be@, assuom25@, dilee@ Abstract—Many techniques are implemented in the industry to control the operation of different actuators on field. Within these actuators, the DC motor is a popular tool. The output of the DC motor, the speed can be controlled to drive several industrial parts. There are different type of controllers for such application including linear and nonlinear controllers, adaptive controllers, and artificial neural network controllers. This paper addresses the use of deep learning algorithm to design the controller; to explore the feasibility of applying deep learning into control problems. The proposed deep learning controller is designed by learning PID controller which is most commonly used in industry. The input/output of the PID controller are used as the learning data set for the deep learning network. ADBN (Deep Belief Network) algorithm is used to design the deep learning controller. The simulation is performed using Matlab/Simulink and the detailed results of a comparison study between the proposed deep learning controller and a PIDcontroller was conducted to demonstrate the performance and effectiveness of the proposed algorithm. Index Terms—deep learning, deep learning controller, conventional neural networks, DBN, PID controller I. INTRODUCTION The machine learning algorithms can lead to significant advances in automatic control. The biggest single advance occurred nearly four decades ago with the introduction of the Expectation-Maximization (EM) algorithm for training Hidden Markov Models (HMMs) [1]. With the EM algorithm, it became possible to develop control systems for real world .
