A nonlinear control method for induction motor drives has been proposed to guarantee robust stability in the large as an alternative of the vector control method. This paper examines the robust stability of the proposed nonlinear control system under the persistent torque load. A sufficient condition for the existence of locally stable equilibrium points is derived, which clarifies the relation between allowable torques and parameter variations. The local stability condition for vector control method is also derived. These two control methods are compared, from the robust stability view point, by means of a numerical example of 150 kW induction motor model.
This paper discusses the design of a type-1 deadbeat servo system by minimizing the deviation input energy for a multi-input, multi-output linear time-invariant, discrete-time system. The type-1 servo system is formulated by using the augmented system and its deviation system. By using the deadbeat principle, the deviation system is transformed into the new time-variant system with the time-variant structure of the new inputs. The performance index is transformed into the new one, and the new terminal state is free. The optimal deviation inputs are made up of three phases, and they are given by linear combinations of the constant gain state feedback and the variable gain state feedback. The variable gains are obtained by using the special nonstationary Riccati equation. The optimal gains are independent of the initial state of the controlled system, the step command signal and the step disturbance.
This paper presents a nonlinear compensator using neural networks for trajectory control of robotic manipulators. The nonlinear compensator has a new architecture using both the computed torque method with the model of the manipulator and the neural networks. The neural networks in the compensator are to compensate only the parameter deviations and unmodeled effects of the manipulator and do not have to compensate all the nonlinearities of the manipulator. The neural networks are efficient in learning.
In this paper, we deal with modeling consensus formation process in a road construction project between two conflicting groups such as regional inhabitant and road enterpriser. For this purpose, we tried to evaluate the effectiveness of various countermeasures for preventing the environmental impact of heavy road traffic. The disutility functions are constructed from the questionnaires to the environmental specialists to evaluate the effect of constructing a noise barrier taking into account the trade-off relation between the obstruction of landscape and the prevention of environmental impact. The disutility function of the road enterpriser is measured as the difficulties of realizing the countermeasures based on the costs. We construct the group disutility functions for two conflicting groups from the questionnaires to the environmental specialists. The group disutility theory based on convex dependence is applied to express the mutual concessions between two conflicting groups in the model. The group disutilities are evaluated for various countermeasures using the group disutility functions developed in this paper.