| Summary: | 碩士 === 逢甲大學 === 化學工程研究所 === 85 === In this dissertation, a learning-type multi-loop control system
is developedfor interacting multi-input/multi-output industrial
process systems. The recently developed single neural
controllers are adopted as the decentralized controllers. With
a simple parameter tuning algorithm, the single neural
controller in each loop is able to learn to control a changing
process by merely observing the process output errors in the
same loop. To circumvent strong loop interactions, static
decouplers are incorporated in the presented scheme. The only a
priori knowledge of the controlled plant is the steady state
process gains, which can be easily obtained from open-loop test.
The presented learning-type multi-loop control system was tested
successfully with some typical multivariable processes.
Extensive comparisons with decentralized PI controllers were
also performed. Simulationresults show that the performances of
the proposed nonlinear control strategywere superior to those of
conventional PI controllers,mainly due to its learning ability.
Based on its simple structure, efficient algorithm, and
goodperformance, it is convinced that proposed learning-type
decentralized control system has high potential for controlling
interacting multivariableindustrial processes, alternative to
existing decentralized control strategies.
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