Many practical control problems for the complex, uncertain or large-scale plants, need to simultaneously achieve a number of objectives, which may conflict or compete with each other. If the conventional optimization methods are applied to solve these control problems, the solution process may be time-consuming and the resulting solution would often lose its original meaning of optimality. Nevertheless, the human operators usually performs satisfactory results based on their qualitative and heuristic knowledge. In this study, we investigate the control strategies of the human operators, and propose a fuzzy model-based multi-objective satisfactory controller. We also apply it to the automatic train operation(ATO) system for the magnetically levitated vehicles(MAGLEV).
One of the human operator``s strategies is to predict the control result in order to find the meaningful solution. In this study, Takagi-Sugeno fuzzy model is used to simulate the prediction procedure. Another strategy is to evaluate the multiple objectives with respect to their own standards. To realize this strategy, we propose the concept of a satisfactory solution and a satisfactory control scheme.
The fuzzy model-based multi-objective satisfactory control scheme combines these two strategies and solves a specific type of nonlinear programming problem to find the satisfactory solution at every moment. Since the objective functions are, in general, piecewise linear or quadratic, we could find more efficient solution processes, such as linear programming, quadratic constrained linear programming or second-order cone programming problem. These programmings are solved much more easily than nonlinear programming problems. The simulation study proves the proposed control schemes are effective and useful.
The MAGLEV train is a typical example of the uncertain, complex and large-scale plants. Moreover, the ATO system has to satisfy multiple objectives, such as speed pattern tracking, stop gap accuracy,...