The Mecanum automated guided vehicle (AGV), which can move in any direction by using a special wheel structure with a LIM-wheel and a diagonally positioned roller, holds considerable promise for the field of industrial electronics. A conventional method for Mecanum AGV localization has certain limitations, such as slip phenomena, because there are variations in the surface of the road and ground friction. Therefore, precise localization is a very important issue for the inevitable slip phenomenon situation. So a sensor fusion technique is developed to cope with this drawback by using the Kalman filter. ENCODER and StarGazer were used for sensor fusion. StarGazer is a position sensor for an image recognition device and always generates some errors due to the limitations of the image recognition device. ENCODER has also errors accumulating over time. On the other hand, there are no moving errors. In this study, we developed a Mecanum AGV prototype system and showed by simulation that we can eliminate the disadvantages of each sensor. We obtained the precise localization of the Mecanum AGV in a slip phenomenon situation via sensor fusion using a Kalman filter.

In general, the position control of electro hydrostatic actuator(EHA) systems is difficult because of the large variation of the effective bulk modulus of the working fluid, which is due to the absence of a heat exchanger like a reservoir tank, the friction between the cylinder and piston, and the external disturbance force. Moreover, it is difficult to identify the values of the effective bulk modulus and friction. In this paper, the variation of the effective bulk modulus, friction, and external disturbance are considered as uncertainties of EHA systems. To solve the problems due to these system uncertainties, an adaptive back-stepping control scheme with fuzzy neural networks(FNNs) is proposed. The effectiveness of the adaptive back-stepping control(ABSC) system with FNNs was compared with those of the standard back-stepping control(BSC) system and the ABSC system through computer simulation.

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We propose a reference slip ratio generation algorithm that accounts for a large adhesion force to improve the braking performance of railway rolling stocks even if the rail conditions change. Our algorithm is based on fuzzy logic, the efficiency of which was evaluated by comparing the braking distances of rolling stocks using the proposed algorithm and using constant reference slip ratios under various rail conditions. Our proposed slip ratio generation algorithm was used as the basis of an adaptive sliding mode controller for a rolling stocks quarter model. In this design, an adaptive rule was developed using the Lyapunov stability theorem, and the performance of the proposed control system was evaluated by computer simulation.

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