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This paper deals with the design of robust DSMC (Discrete-Time Sliding Mode Control) scheme in order to overcome system uncertainty in steering system with mechanically joined structure. The proposed control scheme is one of robust control schemes based on system dynamics. Therefore, system dynamics required is not obtained from physical law but SCM (Signal Compression Method) through experiment in order to avoid complicate mathematical development and save time. However, SCM has a shortcoming that is the limitation of with order linear model which does not include the dynamic of high-frequency band. Thus, considering system uncertainty, DSMC is designed. In addition, to reduce the chattering problem of DSMC, DSMC is derived from the reaching law and the Lyapunov stability condition. It is found that the proposed control scheme has robustness in spite of the perturbation of system uncertainty through computer simulation.

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.

To improve position tracking performance of servo systems, a position tracking control using adaptive back-stepping control(ABSC) scheme and recurrent fuzzy neural networks(RFNN) is proposed. An adaptive rule of the ABSC based on system dynamics and dynamic friction model is also suggested to compensate nonlinear dynamic friction characteristics. However, it is difficult to reduce the position tracking error of servo systems by using only the ABSC scheme because of the system uncertainties which cannot be exactly identified during the modeling of servo systems. Therefore, in order to overcome system uncertainties and then to improve position tracking performance of servo systems, the RFNN technique is additionally applied to the servo system. The feasibility of the proposed control scheme for a servo system is validated through experiments. Experimental results show that the servo system with ABS controller based on the dual friction observer and RFNN including the reconstruction error estimator can achieve desired tracking performance and robustness.

This paper deals with a tracking control problem of a mechanical servo system with nonlinear dynamic friction which contains a directly immeasurable friction state variable and an uncertainty caused by incomplete parameter modeling and its variations. In order to provide an efficient solution to these control problems, we propose a composite control scheme, which consists of a friction state observer, a RFNN approximator and an approximation error compensator with sliding mode control. In first, a sliding mode controller and friction state observer are designed to estimate the unknown internal state of the LuGre friction model. Next, a RFNN is developed to approximate an unknown lumped friction uncertainty. Finally, an adaptive error compensator is designed to compensate an approximation error of RFNN. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are executed. Their results give a satisfactory performance of the proposed control scheme.

Friction is an important factor for precise position tracking control of servo systems. Servo systems with highly nonlinear friction are sensitive to the variation of operating condition. To overcome this problem, we use the LuGre friction model which can consider dynamic characteristics of friction. The LuGre friction model is used as a feed-forward compensator to improve tracking performance of servo systems. The parameters of the LuGre friction model are identified through experiments. The experimental result shows that the tracking performance of servo systems with higherly nonlinear friction can be improved by using feed-forward friction compensation.

A new mathematical model of a web transport system (WTS) in a continuous annealing process (CAP) is proposed. In general, temperature and gravity effects influence the web of a vertical WTS in a CAP. However, these effects are not systematically considered in the conventional mathematical model of a WTS. Disregard for these effects causes a low quality of webs in the CAP. Therefore, in order to improve the quality of webs, a precise web tension control is required based on a mathematical model with thermal and gravity effects. Thus, the mathematical model with thermal and gravity effects in the CAP was established in this paper. In addition, the feed-forward velocity compensator for the web tension control system was suggested, which was based on the proposed mathematical model with thermal and gravity effects. In order to evaluate the validity of the proposed mathematical model for a vertical WTS and the proposed web tension control system in a CAP, computer simulations that considered thermal and gravity effects were executed and an experiment was implemented.

Conventional hydraulic actuator (CHA) systems have been widely used as power units because they can generate very large power compared to their size. In general, a CHA system consists of an electric motor, a pump, a reservoir, various valves, hoses, which are used to transfer the working fluid and an actuator. CHA systems, however, have some problems such as environmental pollution caused by the leakage of the working fluid, maintenance load, heavy weight and limited installation space. These shortcomings can be overcome by compactly integrating the components of CHA systems and by applying a suitable control scheme for the electric motor. To overcome these shortcomings of CHA systems, electro-hydraulic actuator (EHA) systems have been developed, having merits such as smaller size, higher energy efficiency and faster response than existing CHA systems (Kokotovic, 1999). However, for the robust position control of EHA systems, system uncertainties such as the friction between the piston and cylinder and the pump leakage coefficient have to be considered. To solve these system uncertainty problems of EHA systems and to achieve the robustness of EHA systems with system disturbance and bounded parameter uncertainties, Wang et. al. presented a sliding mode control and a variable structure filter based on the variable structure system (Wang, 2005). Perron et. al proposed a sliding mode control scheme for the robust position control of EHA systems showing volumetric capacity perturbation of the pump (Perron, 2005). However, these control methods have some chattering problem due to the variable structure control scheme. The chattering vibrates the system and may reduce the life cycle of the system. Jun et. al. presented a fuzzy logic self-tuning PID controller for regulating the BLDC motor of EHA systems which has nonlinear characteristics such as the saturation of the motor power and dead-zone due to the static friction (Jun, 2004). Chinniah et. al. used a robust extended Kalman filter, which can estimate the viscous friction and effective bulk modulus, to detect faults in EHA systems (Chinniah, 2006). Kaddissi et. al. applied a robust indirect adaptive back-stepping control (ABSC) scheme to EHA systems having perturbations of the viscous friction coefficient and the effective bulk modulus due to temperature variations (Kaddissi, 2006). However, in spite of the variation of the effective bulk modulus due to the temperature and pressure variations, Chinniah et. al. considered

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.

This paper deals with robust position control of electro-hydrostatic actuator (EHA) systems with system uncertainties. EHA systems compared with conventional hydraulic actuator (CHA) systems are controlled by electric motor directly connected to bidirectional pump and its size is much smaller due to the component integration such as a pump, a reservoir, various valves and horses. In addition, EHA systems have fast response property because of high stiffness. Therefore, EHA systems can replace CHA systems as well as satisfy system performance. In spite of the merit of EHA systems, EHA systems in construction field are exposed to many system uncertainties due to the distinct characteristics of construction field. However, it is difficult to measure or estimate system uncertainties. Thus, sliding mode controller (SMC) and radial basis function neural networks (RBFNNs) algorithm are designed to compensate system uncertainties and to achieve robust control performance with EHA systems.

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.