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This partial differential equation model is then approximated in finite dimension in order to be used by the predictive controller.
The results are promising and the strategy found by the predictive controller leads to a smoother operation compared to the currently used heuristic controller.
Adaptive modeling is demonstrated in the exercise of updating and refining the model that is being used by the predictive controller.
In this configuration the manipulated variables calculated by the Predictive Controller are used as command signals for the Distributed Control Systems, which provide references to the operator-tuned local PID controllers that act on the physical system.
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The switched structure of the system is taken into account and by using some mathematical tools, the predictive controller design problem is turned into a Linear Matrix Inequalities (LMIs) feasibility problem.
The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor.
An advantage of the method is that the optimization problem that needs to be solved for the predictive controller can still be solved by using quadratic programming.
To satisfy the thermal comfort required by the occupants, an adaptive model is considered explicitly in the economic objective function of the predictive controller.
The predictive controllers offer good tracking performance and robustness.
The optimal precooling strategy given by the model predictive controller reduces peak energy consumption from the air conditioning unit by an average of 70% and reduces operating costs by 60%.
The stability region characterization provided by the robust predictive controller is subsequently utilized in a safe-parking algorithm that appropriately selects 'safe-park' points from the safe-park candidates (equilibrium points subject to failed actuators) to preserve closed-loop stability upon fault-repair.
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