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

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

There is a few measure of separation among the advance of complicated regulate algorithms in the learn neighborhood and their use in business perform. a number of recommendations built from experimental examine into enhancing the potency of sunlight thermal energy crops are the following tested within the context in their business application.

The recommendations 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 good judgment keep watch over.

Their effectiveness during this keep watch over method is classified and many of the strategies' benefits and downsides are analyzed and in comparison. the consequences acquired will be without problems prolonged to different commercial tactics; during this context, the sun keep an eye on technique tested presents an incredible test-bed because it shows the various difficulties present in different strategies, equivalent to nonlinearities, altering dynamics and robust exterior disturbances.

This is a entire research of the sensible program of alternative keep watch over techniques that might be of curiosity to regulate engineers operating in solar energy platforms and all through different technique industries, and to researchers, scientists and graduate scholars during this field.

Advances in business Control goals to record and inspire the move of expertise up to speed engineering. The fast improvement of keep an eye on know-how has an effect on all components of the keep an eye on self-discipline. The sequence bargains a chance for researchers to offer a longer exposition of recent paintings in all elements of business control.

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Extra resources for Advanced Control of Solar Plants

Example text

O situations in which the model is nonlinear [15J. In this case a nonlinear programming problem must be solved in real time at every sampling period instead of the quadratic linear problem typical of standard MPC . The main difficulties of these methods are: 1. The theoretical analysis of properties of the closed loop such as stability and robustness is very complicated because of the appearance of nonlinear models in the formulation . Application or simulation examples are, therefore, usually used which do not guarantee the generality of the results or are not representative of them.

In practice, MPC has proved to be a reasonable strategy for industrial control, in spite of the original lack of theoretical results at some crucial points such as stability or robustness. 4: 1. The future outputs for a determined horizon N, called the prediction horizon, are predicted at each instant t using the process model. N depend on the known values up to instant t (past inputs and outputs) and on the future control signals u(t + kit), k = O .. N - I, which are those to be sent to the system and to be calculated.

The multivariable case can easily be dealt with. - It intrinsically has compensation for dead times. - It introduces feedforward control in a natural way to compensate for measurable disturbances. - The resulting controller is an easy to implement linear control law. - Its extension to the treatment of constraints is conceptually simple and these can be included systematically during the design process. - It is very useful when future references (robotics or batch processes) are known. - It is a totally open methodology based on certain basic principles which allow for future extensions.

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