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2010, Volume 5, Issue 4

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Frontiers of Mechanical Engineering >> 2010, Volume 5, Issue 4 doi: 10.1007/s11465-010-0117-7

PID neural network control of a membrane structure inflation system

1. School of Mechanical & Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China; 2. Beijing Key Laboratory (Measurement and Control of Mechanical and Electrical System), Beijing Information Science & Technology University, Beijing 100081, China

Available online: 2010-12-05
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Abstract

Because it is difficult for the traditional PID algorithm for nonlinear time-variant control objects to obtain satisfactory control results, this paper studies a neuron PID controller. The neuron PID controller makes use of neuron self-learning ability, complies with certain optimum indicators, and automatically adjusts the parameters of the PID controller and makes them adapt to changes in the controlled object and the input reference signals. The PID controller is used to control a nonlinear time-variant membrane structure inflation system. Results show that the neural network PID controller can adapt to the changes in system structure parameters and fast track the changes in the input signal with high control precision.

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