In this paper, a new design method for robust I-PD controller by using Pareto-optimality is proposed. The proposed design method is formulated as a two-objective optimization problem in which optimization of the settling time and damping ratio is translated into a pole assignment problem. The uncertainties of the plant are represented as a polytope of polynomials, and the computational cost is reduced by using the edge theorem. The genetic algorithm is applied to optimize this problem because of its multiple search property. In order to demonstrate the effectiveness of the proposed design method, we applied the proposed design method to a magnetic levitation system and compared our method with the design method based on the generalized ISE (Integral of Squared Error) criterion.
Reactive scheduling being discussed in the framework of dynamic scheduling is considered as an effective production control strategy in manufacturing systems. We have proposed a decision making model for reactive scheduling based on the critical number of delayed tasks which can be a measure to determine a suitable timing of schedule revision. In this paper, we propose a generalized policy called cumulative delay based reactive scheduling policy based on our previous work, which can be applicable to scheduling decision under a dynamic environment such as random job arrivals, changes of due date, and so on. Through some computational experiments, we examine not only basic properties but the effectiveness of the proposed policy by applying it to single-machine dynamic scheduling problems with sequence-dependent set-up, random machine breakdowns and underestimation of job processing times, where we minimize total set-ups as well as scheduling frequency. We also demonstrate that the proposed policy with even low scheduling frequency can outperform a typical event-driven rescheduling policy.
This paper deals with a trajectory tracking problem of a class of bimodal piecewise affine systems. First, we introduce an error variable as a generalization of the tracking error. A discontinuous function is adopted as an error variable to overcome an inherent difficulty in tracking of piecewise affine systems. Next, the system which governs the error variable is guaranteed to be locally stable using a Lyapunov-like function, which yields trajectory tracking of the original system. Furthermore, we propose a procedure to extend the local result to a global one. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.
This paper describes the optimization of multiple power plant maintenance based on the quantitative risk evaluation. In the plant maintenance decision, an optimal maintenance strategy is decided so that the total maintenance cost and possible default cost through the lifetime of the plants are minimized. In this calculation, the maintenance strategy should satisfy the plant operation constraints such as acceptable maintenance budget and maximum probability of plant default in every year. This problem becomes a nonlinear optimization problem defined on a high dimensional space, to which metaheuristic search method can be applied effectively. In this paper, a memetic algorithm that combines Genetic algorithm and Nelder-Mead simplex method is proposed. Through the comparison with an ad hoc method that has been used conventionally, we show that the proposed metaheuristic algorithm has an effective solution power for real-world plant maintenance problems.
A new class of adaptive nonlinear H∞ control systems for nonlinear and time-varying processes which include nonlinear parametric models approximated by neural networks (NN), is proposed in this manuscript. Those control schemes are derived as solutions of particular nonlinear H∞control problems, where unknown system parameters, approximation and algorithmic errors in NN, and estimation errors of layer weights in NN, are regarded as exogenous disturbances to the processes, and thus, in the resulting control systems, the L2 gains from those uncertain elements to generalized outputs are made less than γ (> 0) (the prescribed positive constants). The resulting control systems are bounded for arbitrarily large but bounded variations of time-varying parameters and layer weights, and modeling and algorithmic errors in NN approximators.