Model-based control performance

The Smith predictor is a model-based control strategy that involves a more complicated block diagram than that for a conventional feedback controller, although a PID controller is still central to the control strategy (see Fig. 8-37). The key concept is based on better coordination of the timing of manipulated variable action. The loop configuration takes into account the facd that the current controlled variable measurement is not a result of the current manipulated variable action, but the value taken 0 time units earlier. Time-delay compensation can yield excellent performance however, if the process model parameters change (especially the time delay), the Smith predictor performance will deteriorate and is not recommended unless other precautions are taken. 

The design procedures depend heavily on the dynamic model of the process to be controlled. In more advanced model-based control systems, the action taken by the controller actually depends on the model. Under circumstances where we do not have a precise model, we perform our analysis with approximate models. This is the basis of a field called "system identification and parameter estimation." Physical insight that we may acquire in the act of model building is invaluable in problem solving. 

Recall Eq. (10-46) that Hj and H2 are defined entirely by the four plant functions Gy. This is another example of model-based control. With the definitions of Hj and H2 given in (10-46), the calculations are best performed with a computer. 

One final comment should be made about model-based control before we leave the subject. These model-based controllers depend quite strongly on the validity of the model. If we have a poor model or if the plant parameters change, the performance of a model-based controller is usually seriously affected. Model-based controllers are less robust than the more conventional PI controllers. This lack of robustness can be a problem in the single-input-single-output (SISO) loops that we have been examining. It is an even more serious problem in multi-variable systems, as we will find out in Chaps. 16 and 17. 

Aguiar P., Adjiman C.S., Brandon N.P. (2005) Anode-supported intermediate-temperature direct internal reforming solid oxide fuel cell II Model-based dynamic performance and control. Journal of Power Sources 147, 136-147. 

Therefore, the chapter is mainly focused on the design of model-based control approaches. Namely, a controller-observer control strategy is considered, where an observer is designed to estimate the heat released by the reaction, together with a cascade temperature control scheme. The performance of this control strategy are further improved by introducing an adaptive estimation of the heat transfer coefficient. Finally, the application of the proposed methods to the phenol-formaldehyde reaction studied in the previous chapters is presented. 

After performing the kinetic analysis of the reacting system, the researchers possess suitable kinetic models of different complexity to be used to design and control the entire process. The more complex model should be used to design the reactor this subject is outside the purpose of this book and is only briefly considered in Sect. 7.4. On the contrary, in Chaps. 5 and 6 the kinetic model is used to design adaptive model-based control and fault diagnosis schemes for a class of reactions taking place in batch reactors. 

In the case study, the adaptive model-based approach is designed on the basis of a reduced model of the phenol-formaldehyde reaction introduced in the Chap. 2. Noticeably, the results show that the model-based control scheme achieves very good performance even when a strongly simplified mathematical model of the reactive system is adopted for the design. 

RS Patwardhan and SL Shah. Issues in performance diagnostics of model-based controllers. J. Process Control, 12(3) 413-427, 2002. 

Active combustion control in a swirl-stabilized combustor is being investigated to reduce combustion instabilities and to control mixing in order to enhance certain key performance metrics (pattern factor, emissions, and volumetric release). For instability control, it has been demonstrated that experimental model-based controllers can provide substantially greater reductions in pressure oscillations relative to time-delay controllers. Phase-locked CH measurements are presented to provide improved understanding of the heat-release dynamics. It has also been shown that active modulation of dilution air jets can be utilized to control the heat-release and temperature distributions. 

The work was performed under ONR contract N00014-97-1-0957. The work on model-based control was done in collaboration with Profs. Annaswamy and Ghoneim of MIT. Their help is sincerely appreciated. 

There are several approaches that can be used to tune PID controllers, including model-based correlations, response specifications, and frequency response (Smith and Corripio 1985 Stephanopoulos 1984). An approach that has received much attention recently is model-based controller design. Model-based control requires a dynamic model of the process the dynamic model can be empirical, such as the popular first-order plus time delay model, or it can be a physical model. The selection of the controller parameters Kc, ti, to) is based on optimizing the dynamic performance of the system while maintaining closed-loop stability. 

Naturally, the type of controller plays an important role. In this chapter we limit the analysis to classical PID controllers. These form over 90% of the control loops in industry. As mentioned, from a plantwide control viewpoint multi-SISO controllers are the most adapted. Naturally, we do not exclude more sophisticated MIMO control systems, as DMC or Model Based Control systems, but these are typically applied to stand-alone complex units, as FCC reactors, complex distillation units in refining, etc. Hence, the controllability analysis presented here aims more to get a conceptual insight in the dynamics of a process related to its design than to offer a high-performance control solution. 

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