In this paper, multi-degrees-of-freedom (multi-DOF) system identification of semiconductor exposure apparatus supported by anti-vibration units is discussed. To meet nanometer order semiconductor lithography demands for alignment accuracy, vibration control is one of the most important technologies in the semiconductor exposure apparatus supported by anti-vibration units. A dynamical model of the plant is necessary in order to design the microvibration controller and to evaluate the performance of the control systems. The controlled object is essentially a multivariable system, because the plant has 6-DOF motional modes. All the DOF of the plant are simultaneously excited and the multi-DOF dynamical model is identified from the exciting signals and the vibration output signals. The model is practically constructed in a short time using subspace method in system identification theory. Identification results are evaluated through experimental data in comparison with a conventional frequency response method.
There are many kinds of nonlinear optimal control problems, such as constrained/unconstrained problems, free/fixed terminal-time problems, parameter dependent/independent problems, and so on. It is said that those optimal conrtol problems, especially free-terminal-time, parameter-dependent, and initial-state-constraint ones, are very difficult to slove numerically. In this paper, based on a new idea, we present a unified computational approach that is applicable to those optimal conrtol problems. We demonstrate the effectiveness of our approach through some numerical simulations, includng time-optimal control problems, and a singular control problem.
In this paper, we discuss motion control of legged robot Emu which is set on a slope whose slope angle is not known exactly. According to the stability analysis, we found that the control system proposed by us has a property of parametric stability. At the same time, we found that there is a certain problem. Therefore, to overcome the problem, we propose a new control law based on nonlinear observer.
In this paper, we discuss a fuzzy classifier with polyhedral regions. First, we generate an initial convex hull of the maximum dimension using the data, included in a class, in the general positions. Next, we modify the convex hull using one training datum at a time by the dynamic convex hull generation method. Finally, for each convex hull we define a membership function using the minimum operator. We demonstrate the effectiveness of our method using iris and thyroid data sets.
This paper discusses robust function approximation when the Takagi-Sugeno type model is used for the consequent part of fuzzy rules. With this model, the parameters of the linear equation that defines the output value of the fuzzy rule are determined by the least-squares method. Therefore, if the training data include outliers, the method fails to determine the parameter values correctly. To overcome this problem we use the least-median-of-squares method. Among the original training data set, we randomly select training data more than the number of parameters, and determine the parameter values using the least-squares method. We repeat this many times and determine the parameters with the smallest median of squared errors. We compare the proposed method with the least-squares method and the conventional least-median-of-squares method using the data generated by the Mackey-Glass differential equation.