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This paper deals with adaptive sliding mode control of servo systems with system uncertainty such as nonlinear friction. In the case of servo systems, friction phenomenon causes the reduction of tracking performance because friction is directly related with various internal and external environment conditions such as the level of lubricant, contamination, and temperature. Therefore, to overcome the problems with friction in servo systems, robust control scheme is required. In addition, to actively compensate system uncertainties, adaptive control scheme can be required. This paper proposes adaptive sliding mode control scheme integrating sliding mode control scheme with robustness property and adaptive control scheme with compensating system uncertainty by on-line estimating unknown parameters. The proposed control scheme is verified through computer simulation.

The scope of working motion is expressed in the angle of handling tool motion to move in required conditions with carrying a variety of tools such as hammer, grinder and roller. As this scope becomes larger and it is possible to do the handling work in complex shape and narrow block space. The driving axis of working motion is the number of axis of link type to construct the main axis of working assistant system. Generally, the working motion becomes much smoother as the number of this axis becomes larger. The kind of handling tools is generally classified into several categories to do the handling work in different handling method by using different handling tools such as hammer, grinder and roller. The number of driving axis of related motion is defined in relation with arm and wrist motion of worker when the operator in working suit is doing the handling work. The working velocity is defined as the velocity of handling tools to move in working path and it is closely related with the productivity of handling work. The working load is defined as the total force to be pulled and pushed at end of the working assistant system or to be hanged on it.

This paper deals with adaptive back-stepping position control system with FNNs(fuzzy neural networks) algorithm for servo system with system uncertainty. The proposed control scheme is induced from the result with the definition of continuative LCF(Lyapunov control functions). In addition, to guarantee the stability problem of the proposed control scheme, the connection weight vector of the FNNs is updated by adaptive rule. The effectiveness of the adaptive back-stepping control system with the FNNs was compared with that of the standard back-stepping control system through computer simulation.

This paper presents the power assistance system of working habiliment type for painting worker in ship-building. The painting worker carrying several tools such as spray gun, brush and roller should be stayed to do painting job in closed working space or inside internal wall of ship large block and in long time. Specially, the power habiliment system is realized by developing the mechanism and control technology to reduce the working load to be transferred to the muscular system of worker. And also it is developed to realize the human friendly optimal working motion. This paper shows the design of multi-axis link mechanism to do painting job of a variety of motion. It shows the control technology of painting work system to reduce the effect of the weight of painting tool and wearable painting system and the working load of painting process such as the reaction force of spray gun.

This paper deals with a sliding mode impedance control (SMIC) of end-effector of robot manipulator using a tracking control scheme and a real-time artificial intelligence algorithm based on a radial basis function neural networks(RBFNNs). To real-time estimate the design parameters of desired impedance model such as desired inertia, damping, and stiffness desired impedance, SMIC(sliding mode impedance control) with RBFNNs algorithm is proposed.

This paper deals with the robust position control of electro hydrostatic actuator(EHA). In general, the position control of EHA systems based on the model of itself is difficult because of system uncertainties such as parameter perturbation, friction, and external disturbance. To solve the problems due to these system uncertainties, an adaptive back-stepping control (ABSC) scheme with radial basis function neural networks (RBFNN) is proposed. The adaptive back-stepping controller consists of back-stepping controller and adaptive rule for reconstruction error. Moreover, to estimate the bounded uncertainties of the reconstruction error, the RBFNN with online update law is designed. The effectiveness of the adaptive back-stepping control system with RBFNN was compared with that of the standard back-stepping control system through computer simulation.