| Summary: | 碩士 === 中原大學 === 化學工程研究所 === 91 === Abstract
Controlled processes in nearly all-chemical industries frequently encounter with inherently more than one variable to be controlled. They are known as multivariable or multi-input multi-output (MIMO) processes. The control of multivariable systems is not always an easy task due to its complex and interactive nature. The goal of this paper is to identify and control MIMO processes by means of the dynamic partial least squares (PLS) model, which consists of a memoryless PLS model connected in series with linear dynamic models. Unlike traditional decoupling MIMO processes, the dynamic PLS model can decompose the MIMO process into a multiloop control system in a reduced subspace. Without the decoupler design, the optimal tuning multiloop PID controller based on the concept of general minimum variance and the constrained criteria can be directly and separately applied to each control loop under the proposed PLS modeling structure. As for the nonlinear MIMO processes, the dynamic PLS (DynPLS) model, which are obtained by the instantaneous linearized neural network model at each sampling time, can still be used to decompose the MIMO process into a multiloop control system in a reduced subspace. The proposed algorithm has the following advantages: (i) It is easy to identify DynPLS since it is not necessary to identify the MIMO system by a sequence of relay identification. (ii) The coupling effect in the MIMO system is now overcome effectively. The PLS structure can be decomposed into each pair of input and output and it selects the number of control loops based on the variation captured by each pair. (iii) Unlike the sequential tuning of the multiple control loop for the iterative design in each control loop, the adaptive tuning PID controller strategy in the SISO system can be directly and simultaneously applied to each loop of the multiloop control design in the MIMO system under the decomposed structure of PLS. This simplicity and feasibility of the scheme can easily be extended to any multiloop control strategies. Simulation case studies are provided to demonstrate the effectiveness of the control design procedures of the MIMO process.
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