Advanced Control of Solar Plants by Eduardo F. Camacho, Manuel Berenguel, Francisco R. Rubio

By Eduardo F. Camacho, Manuel Berenguel, Francisco R. Rubio (auth.)

There is a few measure of separation among the improvement of complex keep watch over algorithms in the learn group and their use in commercial perform. a number of options constructed from experimental learn into bettering the potency of sunlight thermal energy crops are right here tested within the context in their business application.

The suggestions defined and utilized are:

  • modeling and simulation;
  • adaptive control;
  • model-based predictive control;
  • frequency area keep watch over and strong optimum keep watch over; and
  • fuzzy common sense keep an eye on.

Their effectiveness during this regulate technique is classified and some of the thoughts' merits and downsides are analyzed and in comparison. the consequences bought may be without problems prolonged to different commercial methods; during this context, the sunlight keep an eye on procedure tested presents a terrific test-bed because it indicates the various difficulties present in different approaches, equivalent to nonlinearities, altering dynamics and powerful exterior disturbances.

This is a finished research of the sensible software of other regulate suggestions that would be of curiosity to regulate engineers operating in solar energy platforms and all through different approach industries, and to researchers, scientists and graduate scholars during this field.

Advances in commercial Control goals to document and inspire the move of know-how up to speed engineering. The swift improvement of regulate expertise has an impression on all parts of the regulate self-discipline. The sequence bargains a chance for researchers to give a longer exposition of latest paintings in all points of business control.

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Example text

Poor behaviour may be produced at critical points of the operation. In consequence, there are incentives to develop control strategies based on nonlinear process models, especially if said models can be obtained from physical principles of evolution of the characteristic process variables. This is a very active field of research, as is shown by recent publications [54], [64], etc. The most recent techniques tend to be solutions to a specific problem (as is the case shown in this book) or they can be based on an approximation with a gain scheduling approach, where the control signals vary according to changes in the process .

Gain scheduling controller auto-tuning ones. These types of controllers normally adapt their parameters under operator demand. They work in two phases: - An experimental phase when a test signal is injected into the process, usually in an open loop configuration, and - A parameter tuning phase. Process dynamics are inferred from the response to the test signal (usually a step or impulse) and the controller parameters (usually PIDs) are tuned by simple heuristic rules. 5 Model-based Predictive Control (MPC) Predictive Control is made up of a very wide field of control methodologies which are developed using common ideas.

One of them is that of calculating a linear quadratic gaussian controller LQG with transfer function recovery of 16 1. Introduction the linear quadratic regulator LQR, a scheme known as LQG/LTR. Another possible way is to minimize the worst possible case which is measured by a certain norm of a transfer function (usually Roo norm). There are other possibilities which combine the design taking into account the model uncertainties with adaptive control, as is analyzed in chapter 5. 1 The robust control problem Real processes are too complex to be described in an absolute and precise manner by a mathematical model.

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