A neural-network-based model reference PID-like controller for process control

Lee, T.H.; Yue, Pik Kong; Tan, K. K.

doi:10.1016/0952-1976(94)90070-1

Cited by 9 publications

(1 citation statement)

References 11 publications

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“…This problem causes a barrier to apply the neural-net based controllers to the real systems. In addition, as far aswe know, there are few studies of neural-net based PID control schemes for nonlinear multivariable systems [9], although lots of works with respect to SISO neural-net based PID controller [10,11] design have been reported.…”

Section: Introductionmentioning

confidence: 99%

A design of multiloop PID controllers with neural-net based decoupler

Tokuda

Yamamoto

Monden

Proceedings of the IEEE Internatinal Symposium on Intelligent Control

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In process industries, PID control schemes have been widely used due to their simple structures and easiness of comprehending the physical meanings of control parameters. However, the good control performance cannot be obtained by simply using PID controlschemes, since most processes are considered as nonlinear multivariable systems with mutual interactions. In this paper, a design method of multiloop PID controllers neuralnet based decoupler is proposed for nonlinear multivariable systems with mutual interactions. The proposed method consists of a decoupler given by the sum of a static decoupler and a neural-net based decoupler, and multiloop PID controllers. Finally, the effectiveness of the proposed control scheme is evaluated on the simulation examples.

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Section: Introductionmentioning

confidence: 99%