The authors proposed a method for order reduction of linear dynamic systems using the advantages of the Interpolation criterion and Routh method. The denominator polynomial of the reduced order model is determined by using the Routh method while the numerator coefficients are computed by minimizing the integral square error between the original and the reduced system using Interpolation method. The proposed method guarantees stability of the reduced model, if the original high order system is stable system. A PID controller is designed for the high order original systems through its low order model proposed. Some numerical examples were considered to explain the effectiveness of the method.

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Multistage flash (MSF) desalinization plants are a major means of desalting seawater for human use in several arid regions of the world in the present times. The MSF plants are physically large amd their control usually involves more than twenty control loops. According to the present practice, the controllers are of the PI or PID type and their tuning is largely based on experience rather than on systematic modeling of the plant. Plant modeling based on physical principles gives rise to a leirge and complex set of coupled nonlinear differential equations which has to be linearized about a chosen set of operating conditions. As the operating point changes, the resulting linearized model also changes. This requires retuning of the controllers in certain loops depending on the changing linear plant model in the related loops. The linearized model happens to be enormously large in size requiring reduction for controller design and pretctical implementation. There exist several model reduction methods and they have to be chosen to meet the objectives of adequate modeling. In a nonlinear plant, the linearized miodel parameters vary with the operating conditions. To make a controller to simultaneously meet the demands of model reduction aind vziriable operating conditions, the conventional approaches of control are either inadequate or too involved. In this chapter, a technique based on artificial neural networks (ANN) for model reduction under plant parameter perturbations is proposed. The complexity of zinalysis, reduction, computation and controller design in the variable conditions of operation is avoided by simply training an ANN to give the parameters of a reduced model for use in controller design. An automated decision support may be provided to choose the best ANN configuration for reduced order modeling of a large, complex and variable plant which provides the basis for a robust design of a simple controller. Certain well established model reduction methods are employed in the mainstream of the procedure and the related results are impressive. Based on these results, a scheme based on an added ANN, for direct controller 423 424 CHAPTER 15 implementation under the discussed conditions is proposed. The results of this modest attempt point out to the strong possibility of more intelligent control of liu-ge complex plants under uncerteiinty eind/or variable plant dynamics. The present discussion is centred on SISO loop designs, which does not rule out the possibility of simple extensions to MIMO designs.