This paper is concerned with H∞ output feedback control design for a general class of fuzzy systems. When the premise variable is the measurement output, the control design is easy but a fuzzy system representation is limited. On the other hand, when it is the state variable, a fuzzy system describes a general class of nonlinear systems but the control design becomes difficult. This is because a conventional parallel distributed compensator (PDC) is not feasible any more. In this paper, we introduce an output feedback controller whose premise variable is independent of the premise variable of an original fuzzy system. Then, we formulate an H∞ control problem for a general class of fuzzy system where the premise variable is not always available. Our control design method is based on a set of strict LMI conditions. No scaling parameter is necessary a priori to solve LMI conditions for designing an H∞ controller. Our method includes matrices that can tune control gain matrices in an H∞ controller and hence it can reflect the control performance of the resulting closed-loop system. A numerical example is given to illustrate our H∞ output feedback control design.
District heating and cooling (DHC) systems have been actively introduced as energy supply systems in urban areas. Since there exist a number of large-size freezers, heat exchangers and boilers in a DHC plant to generate and supply cold water, hot water and steam to a DHC system, the control under an operation plan for these instruments on the basis of the demand of cold water, hot water and steam, called heat load, is important for stable and economical management of DHC systems. In this paper, we formulate an operation planning problem of an actual DHC plant as a nonlinear integer programming problem in consideration of various transaction forms. Furthermore, in order to reflect actual decision making situations for DHC plants more appropriately, we formulate a multiobjective operation planning problem to minimize not only the running cost but the amount of primary energy consumption from the viewpoint of saving energy. Then, we propose an interactive fuzzy satisficing method through tabu search with strategic oscillation for multiobjective operation planning problems to derive a satisficing solution for the decision maker.
In this paper, we focus on two-level nonlinear programming problems with no coordination between the decision maker at the upper level (the leader) and the decision maker at the lower level (the follower), and propose a computational method using particle swarm optimization (PSO) for obtaining Stackelberg solutions to two-level nonlinear programming problems. We can realize global search and reduce computational time by obtaining a rational response in every certain generation. Moreover, we carry out numerical experiments in order to demonstrate the feasibility and effectiveness of the proposed method by comparing with existing methods.
In this paper, we focus on multiobjective stochastic linear programming problems, and propose a fuzzy approach to obtain a satisfactory solution based on the fuzzy decision. In the proposed method, it is assumed that the decision maker has fuzzy goals for not only permissible objective levels of a probability maximization model but also permissible stochastic levels of a fractile criterion optimization model, and such fuzzy goals are quantified by eliciting the corresponding membership functions. According to the fuzzy decision, the satisfactory solution of the decision maker is obtained on the basis of linear programming technique.