In this paper, we propose a new model set identification method which determines both nominal models and uncertainty bounds in l1 norm. This method, first, obtains estimates of the plant impulse response and their error bounds based on the correlation analysis using given experimental data and a low-correlated noise model. Since we consider the low-correlated input and noise, the error bounds of the impulse response estimates tend to zero as the number of data is increased. And then, a low order model and the nonparametric error bound in l1 norm are obtained by using the obtained impulse response estimates and the error bounds. Therefore, this method gives less conservative model sets when we have more experimental data, which is one of the distinguished features compared with most of the existing model set identification methods. Numerical examples show the effectiveness of the proposed method.
Multi-story mechanical parking facilities are becoming popular in crowded cities, as they are spatially efficient. When many cars are waiting to enter or to leave, it is important to schedule the movement of two rail-mounted automated carriers on each floor so that the makespan is minimized. We consider the problem of finding an optimal schedule when an ordered set of car placing and car removing is given. We propose an exact algorithm based on dynamic programming, and approximate algorithms using metaheuristics.
This paper considers a continuous algebraic Riccati equation with an additional norm-type term. This equation is called a norm-type algebraic Riccati equation and arises in guaranteed cost control on the basis of a norm-type upper bound. It is shown that there exists a solution under the stabilizability condition and the assumption that the additional term is not too large. Also, the well-known Kleinman's algorithm is extended to this case.
In the synthesis of tracking control systems, the compensation signal, which is applied in the finite-horizon time, is effective for improving the performance of controlled system. In this paper, a design method of finite-horizon compensation signal and optimal internal state of controller is discussed for stabilized systems. By characterizing the singular-value problem for correspondingly defined Hankel operator, it is shown that the internal state and the compensation signal, which attains favorable transient, is constructively given based on the combination of singular vectors. The strength and the limitation of applying the compensation signal are illustrated with numerical examples.
Performance of preview control is investigated in terms of H∞-criterion. In the output feedback setting, a preview H∞ problem is discussed and the analytic solution is characterized with finite-dimensional operations. The strength and limitation of preview H∞ performance are illustrated with numerical examples.