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This paper considers the robust stabilization of singularly perturbed systems with time-varying unknown-but-bounded uncertainties. The implicit function theorem is used to prove the sufficient condition for stability of the closed-loop system. The construction of the stabilizing controller involves solving slow and fast algebraic Riccati equations. It is shown that if the reduced-order slow and fast algebraic Riccati equations have positive definite stabilizing solution then the obtained uncertain closed-loop system with the proposed ϵ-independent controller is quadratically stable. The main contribution of this paper is that the sufficient condition for stability derived here is independent of the parameter ϵ. Furthermore, our new results apply to the both standard and non-standard singularly perturbed systems.

Generally speaking, a level-fluctuation of road-traffic noise is brought on by various causes. It is obvious that the ultimate causes are due to a variety of noise sources, i.e., uncertain behavior of individual cars, and to some effect of the noise-propagation characteristics affected by reflections and/or absorptions owing to surrounding buildings and their topographical locations. By dividing the road into a suitable number of blocks, and paying close attention to the mean value of noise intensity in each block, the mean value can be proportional to the number of cars in the block. In this paper, on the basis of the additive property of sound energy, the unified method has been proposed to estimate the inherent characteristics of noise-propagation in each block in the form of a synthetical evaluation with the number of cars for each car-type, also considering the entire back-ground noise. We have confirmed the validity of our theoretical results, not only by means of digital simulation, but also by road-traffic noise-data experimentally observed near Hiroshima City. The experimental results clearly show a good agreement with the values recently reported by other official groups1).

The paper considers a problem of the modelling and the parameter estimation for a linear dynamical system, where the density of an observation noise is not available. The system parameters are estimated by a least squares method for candidate models, for the probability density and statistical values are not available here. The system structure is estimated by using a neural networks. The learning rules are used batchwise for candidate models with typical types of distributions. The connection coefficients of the neural network are optimized by the groups consisted of known structures. In the real case, since the system structure is unknown, the cases that the true system structure is not contained in the group for learning data are considerd.

This paper presents a method for solving a dynamic economic load dispatch problem of electric power systems. Since the expected system load varies over a given time horizon, the sum of generator outputs must follows the system load subject to power-rate limits on generators. In order to solve the dynamic ELD problem, the continuation method is employed in this paper. It is shown that each generator output is expressed as a piecewise-linear function of a continuation parameter, and this fact enables us to obtain the optimal solution efficiently. Numerical tests for a model system indicate the validity and effectiveness of the proposed method.

Syntheses of optimal actuators which minimize an energy are considered for oscillatory and non-oscillatory systems with a scalar input from the viewpoint of terminal control with an arbitrary terminal time. Under a certain assumption for the latter systems, hut no assumptions for the former systems, an optimal actuator matrix is shown to be given by the vector directed to the centre of gravity of the convex polyhedron which is constructed by the reciprocal vectors of the eigenvectors of a transition matrix. In the case where the above assumption is not satisfied for the non-oscillatory systems, a sequential approximation method, whose starting point is the vector directed to the centroid of the convex polyhedron, is proposed to obtain a suboptimal solution.

A suboptimal selection of observation times is considered which minimizes the quadratic control performance in the control and state error under a restricted number of observations. Two convenient observation algorithms are proposed for multiple available observations based upon the analytical results obtained for a continuous system, i.e. a periodic observation policy for an unstable system, and its modified versions, taking into account the transient state, for a -stable system.