A Novel PID-like Neural Network Controller
| Authors: | Cong Shuang, University of Science and Technology of China, China
Li Guodong, University of Science and Technology of China, China
Ji Beichen, University of Science and Technology of China, China |
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| Topic: | 1.2 Adaptive and Learning Systems |
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| Session: | Applications of Adaptive and Learning Control |
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| Keywords: | multivariable control system, neural networks, neural control system design, PID controllers, adaptive algorithms |
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Abstract
A novel PID-like neural network controller (PIDNNC) is created. It is composed of a neural network with no more than 3 neural nodes in hidden layer, and there are an activation feedback and (or) an output feedback in hidden layer, respectively. This special structure makes the network be able to be a P, PI, PD, or PID controller as needed. The proposed controller weights can be updated on-line according to errors caused by non-linear and uncertain factors of system, based on some adaptation mechanism. The resilient back-propagation algorithm with sign instead of the gradient is used to derive the rule of updating network weights. The basic ideas, techniques and analysis are presented. Finally, we give the simulation experiment of a double inverted-pendulum system and the comparison of the effects between the proposed control strategy and the conventional one.
