Self-Tuning Closed-Loop Fuzzy Logic Control Algorithm for Adaptive Prestressed Structures

Abstract

Constantly increasing traffic loads, unpredictable influences on and unplanned conditions of bridges represent new challenges in the field of engineering. Innovative strategies and construction techniques are required to meet these and future challenges. Adaptive prestressing offers great potential to increase a structure’s load-carrying capacity and to optimize its load-induced stresses. In this manner, cost efficiency and sustainability of constructions can be enhanced. Therefore, a control algorithm with high reliability and redundancy is required to meet the new challenges. In this paper, an adaptive fuzzy logic-based closed-loop control system for realization of adaptive prestressed structures is presented. Based on the new concept, two different specialized control algorithms are developed. In order to decrease deflections, the prestressing force of an aluminium truss is controlled. The algorithm includes a fuzzy system with model-based knowledge base. The learning process is established on direct synchronization of the calculated deflections of the model with the measured reactions of the structure. Another fuzzy system for adaptive prestressing of a reinforced concrete T-beam uses a model-free knowledge base. Optimization of load-induced stresses is accomplished utilizing expert knowledge. An incremental learning process is performed in accordance with the algorithm’s basic rules. Experiments on the two prototypes are conducted to investigate the functionality of the developed algorithms. In addition to efficiency, high reliability and redundancy are shown by the control system. In this article, the potential of fuzzy-controlled adaptive prestressing is illustrated.