期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 24, 期 5, 页码 837-843出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2013.2238638
关键词
Filter; neural compensator; nonlinear system; self-tuning control
类别
资金
- Natural Science Foundation of China [61004091]
- Research Funds for the Central Universities [N100408002]
- 985 Project Technology Innovation Platform of Integrated Automation of Process Industry
- National Fundamental Research Program of China [2009CB320601]
- 111 Project [B08015]
Considering the mismatching of model-process order, in this brief, a self-tuning proportional-integral-derivative (PID)-like controller is proposed by combining a pole assignment self-tuning PID controller with a filter and a neural compensator. To design the PID controller, a reduced order model is introduced, whose linear parameters are identified by a normalized projection algorithm with a deadzone. The higher order nonlinearity is estimated by a high order neural network. The gains of the PID controller are obtained by pole assignment, which together with other parameters are tuned on-line. The bounded-input bounded-output stability condition and convergence condition of the closed-loop system are presented. Simulations are conducted on the continuous stirred tank reactors system. The results show the effectiveness of the proposed method.
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