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The model reduction algorithm is experimentally validated.
However, owing to the high complexity of the theoretical controller, a practical controller design procedure is developed with the help of the proposed model reduction algorithm.
In the second stage, a RCM with the desired structure is estimated from the Volterra series model through a model reduction algorithm that takes into account closed-loop performance requirements.
A model reduction algorithm is implemented to simplify the controller, and a prefilter design reduces the control-loop overshoot, thereby improving the robust stability and tracking performance during application.
The main advantage of our results is that we use the information already available in the model reduction algorithm in order to compute the H2-norm instead of computing a new Gramian of the corresponding error system, which is computationally expensive.
We provided a model reduction algorithm in KiMoSys whose major features is the ability to reduce the model a priori, i.e. before the parameter estimation task.
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As a consequence these systems cannot be treated efficiently without model reduction algorithms.
We also contribute with an empirical comparison among: the state-of-the-art model reduction algorithms and MDP solvers (that do not perform model reduction).
The section "Results and discussion" empirically compares our technique combined with traditional planners against traditional model reduction algorithms and planners that do not employ model reduction.
The results show that our approach dominates the current model reduction algorithms in all problems, specially in sparse domain problems where reachability analysis prunes a significant part of the state space.
The results show that our approach dominates the current model reduction algorithms and outperforms general MDP solvers in dense problems, i.e., problems in which actions have many probabilistic outcomes.
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