This paper deals with an estimation method for sideslip angle by using an unknown disturbance observation technique in 4WS passenger car systems. Firstly, a 4WS vehicle model with 3DOF is derived under the constant velocity and same tyres properties. The vehicle dynamics is transformed into the linear state space model with considering the external disturbances. Secondly, and unknown disturbance observer is introduced and its property which estimating the states of system without any disturbance information is shown. Lastly, the estimated sideslip angle of the 4WS vehicle system is verified through numerical simulation.

This paper deals with a fault detection algorithm for linear descriptor systems via unknown input PI observer. An unknown input PI observer is presented and its realization conditions is proposed by using the rank condition of system matrices. From the characteristics of unknown input PI observer, the states of system with unknown inputs are estimated and the occurrences of fault are detected, and its magnitudes are estimated easily by using integrated output estimation error under the step faults. Finally, a numerical example is given to verify the effectiveness of the proposed fault detection algorithm.

The robust linear H(sub)$\infty$ FIR filter, which guarantees a prescribed H(sub)$\infty$ performance, is designed for continuous time-varying systems with unknown cone-bounded nonlinearity. The infinite horizon filtering for time-varying systems is systems is investigated in therms of two Riccati equations by the finite moving horizon.

This paper presents an approach to leak detection and location of gas pipelines based on a strong tracking filter(STF). The STF has strong robustness against model uncertainties, which will deteriorate the performance of the extended Kalman filter. Hence, much faster and more accurate leak detection and location has been obtained. Computer simulation results demonstrate the effective-ness of the proposed approach.

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control(AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model(SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied for the control of an uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control(CMFC).

The external force disturbance is the one of the main causes that deteriorate the performance of the magnetic suspension. Thus, this paper develops a fuzzy estimator for gain scheduling control of magnetic suspension system suffering from the unknown disturbance. The propose fuzzy estimator computes the disturbance injected to the plant the gain scheduled controller generates the corresponding stabilizing control input associated with estimated disturbance. In the simulation results we confirm the novelty of the proposed control scheme comparing with the other method using a feedback linearization.

A neural network compensator for stick-slip friction phenomena in meashartonics servo systems is practically proposed to supplement the traditionally available position and velocity control loops for precise motion control. The neural network compensa-tor plays the role of canceling the effect of nonlinear slipping friction force. It works robustly and effectively in a real control system. This enables the mechatronics servo systems to provide more precise control in the digital computer. It was confirmed that the con-trol accuracy is improved near zero velocity and points of changing the moving direction through numerical simulation. However, asymptotic property on the steady state error of the normal operation points is guaranteed by the integral term of traditional velocity loop controller.

To improve control performance of a non-linear system, many other reserches have used the sliding model control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However, this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network. The perturbation estimator based on the fuzzy adaptive network generates the control input of compensating unmodeled dynamics terms and disturbance. And the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluation control performance of the proposed approach, tracking control simulation is carried is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.

Autonomous of welding process in shipyard is ultimately necessary., since welding site is spatially enclosed by floors and girders, and therefore welding operators are exposed to hostile working conditions. To solve this problem, a welding robot that can navigate autonomously within the enclosure needs to be developed. To achieve the welding ra나, the robotic welding systems needs a sensor system for the recognition of the working environments and the weld seam tracking, and a specially designed environment recognition strategy. In this paper, a three-dimensional laser vision system is developed based on the optical triangulation technology in order to provide robots with work environmental map. At the same time a strategy for environment recognition for welding mobile robot is proposed in order to recognize the work environment efficiently. The design of the sensor system, the algorithm for sensing the structured environment, and the recognition strategy and tactics for sensing the work environment are described and dis-cussed in detail.

This paper describes a system that can used to recognize an unknown material regardless of the change of ambient tem-perature using temperature response curve fitting and fuzzy neural network(FNN). There are some problems to realize the recogni-tion system using temperature response. It requires too many memories to store the vast temperature response data and it has to be filtered to remove noise which occurs in experiment. And the temperature response is influenced by the change of ambient tempera-ture. So, this paper proposes a practical method using curve fitting the remove above problems of memories and nose. And FNN is propose to overcome the problem caused by the change of ambient temperature. Using the FNN which is learned by temperature responses on fixed ambient temperature and known thermal conductivity, the thermal conductivity of the material can be inferred on various ambient temperature. So the material can be recognized by the thermal conductivity.