Interval model predictive control

6TH INTERNATIONAL WORKSHOP ON ALGORITHMS AND ARCHITECTURES FOR REAL TIME CONTROL (6) (6.2000.PALMA DE MALLORCA. ESPAÑA)Model Predictive Control is one of the most popular control strategy in the process industry. One of the reason for this success can be attributed to the fact that constraints and uncertainties can be handled. There are many techniques based on interval mathematics that are used in a wide range of applications. These interval techniques can mean an important contribution to Model Predictive Control giving algorithms to achieve global optimization and constraint satisfaction

A new sequential covering strategy for inducing classification rules with ant colony algorithms

Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with the problem of rule interaction, i.e., the outcome of a rule affects the rules that can be discovered subsequently since the search space is modified due to the removal of examples covered by previous rules. This paper proposes a new sequential covering strategy for ACO classification algorithms to mitigate the problem of rule interaction, where the order of the rules is implicitly encoded as pheromone values and the search is guided by the quality of a candidate list of rules. Our experiments using 18 publicly available data sets show that the predictive accuracy obtained by a new ACO classification algorithm implementing the proposed sequential covering strategy is statistically significantly higher than the predictive accuracy of state-of-the-art rule induction classification algorithms

Exploiting Chordality in Optimization Algorithms for Model Predictive Control

In this chapter we show that chordal structure can be used to devise efficient optimization methods for many common model predictive control problems. The chordal structure is used both for computing search directions efficiently as well as for distributing all the other computations in an interior-point method for solving the problem. The chordal structure can stem both from the sequential nature of the problem as well as from distributed formulations of the problem related to scenario trees or other formulations. The framework enables efficient parallel computations.Comment: arXiv admin note: text overlap with arXiv:1502.0638