This paper presents the state feedback control design for discrete time nonlinear systems where there exists a time delay in input. It is shown that under some boundedness condition, the time delay nonlinear systems can be transformed into the time delay linear systems with time varying parameters. Sufficient conditions for existence of stabilizing state feedback controller are characterized by linear matrix inequalities. Finally, an illustrative example is given in order to show the effectiveness of our design method.

In this paper, we propose a generalized index independent stability condition for a descriptor systemwithout any transformations of system matrices. First, the generalized Lyapunov equation with a specific right-handed matrix form is considered. Furthermore, the existence theorem and the necessary and sufficient conditions for asymptotically stable descriptor systems are presented. Finally, some suitable examples are used to show the validity of the proposed method.

Electric brake booster systems replace conventional pneumatic brake boosters with electric motors and rotary-todisplacement mechanisms including ECU (Electronic Control Unit). Electric booster brake systems require precise target pressure tracking and control robustness because vehicle brake systems operate properly given the large range of loading and temperature, actuator saturation, load-dependent friction. Also for the implement of imbedded control system, the controller should be selected considering the limited memory size and the cycle time problem of real brake ECU. In this study, based on these requirements, a sliding mode controller has been chosen and applied considering both model uncertainty and external disturbance. A mathematical model for the electric booster is derived and simulated. The developed sliding mode controller considering chattering problem has been compared with a conventional cascade PID controller. The effectiveness of the controller is demonstrated in some braking cases.

This paper, we describe the vector control of surface mounted PMSM (Permanent Magnet Synchronous Motor) using the fuzzy controller which is suggested algorithm. In these days, when vehicle is operated or not, whether the road is covered or not, the sensitivity of the steering column is not stable. To make up for it, the PI gain of a steering column controller is adjusted by experience. It becomes the price because it need a lot of sensor. Also it is difficult to implement robust control because we need a lot of parameters for variable road conditions which are the off road, the on road, a low battery voltage, a high battery voltage, a vehicle speed. In this paper, we propose fuzzy controller using the suggested algorithm which suitable for steering system. We test the fuzzy controller with the various condition. We get the good performance of fuzzy controller even if it is nonlinear system. We check a robust the fuzzy controller using the suggested algorithm.

This paper proposes a balancing and driving control system for a bicycle robot. A reaction wheel pendulum control method is adopted to maintain the balance while the bicycle robot is driving. For the driving control, PID control algorithm with a variable gain adjustment has been developed in this paper, where the gains are heuristically adjusted during the experiments. To measure the angles of the wheels the encoders are used. For the balancing control, a roll controller is designed with a non-model based algorithm to make the shortest cycle. The tilt angle is measured by the fusion of the acceleration and gyroscope sensors, which is used to generate the control input of the roll controller to make the tilt angle zero. The performance of the designed control system has been verified through the real experiments with the developed bicycle robot.

In this paper, we propose a fault diagnosis method using artificial neural network and SVM (Support Vector Machine) to detect and isolate faults in the nonlinear systems. The proposed algorithm consists of two main parts: fault detection through threshold testing using a artificial neural network and fault isolation by SVM fault classifier. In the proposed method a fault is detected when the errors between the actual system output and the artificial neural network nominal system output cross a predetermined threshold. Once a fault in the nonlinear system is detected the SVM fault classifier isolates the fault. The computer simulation results demonstrate the effectiveness of the proposed SVM and artificial neural network based fault diagnosis method.

This paper suggests a design method of an improved phase control loop for tracking resonant frequency of solid type precision resonant gyroscope. In general, a low cost MEMS gyroscope adapts the automatic gain control loops by taking a velocity feedback configuration. This control technique for controlling the resonance amplitude shows a stable performance. But in terms of resonant frequency tracking, this technique shows an unreliable performance due to phase errors because the AGC method cannot provide an active phase control capability. For the resonance control loop design of a solid type precision resonant gyroscope, this paper presents a phase domain control loop based on linear PLL (Phase Locked Loop). In particular, phase control loop is exploited using a higher order PLL loop filter by extending the first order active PI (Proportion-Integral) filter. For the verification of the proposed loop design, a hemispherical resonant gyroscope is considered. Numerical simulation result demonstrates that the control loop shows a robust performance against initial resonant frequency gap between resonator and voltage control oscillator. Also it is verified that the designed loop achieves a stable oscillation even under the initial frequency gap condition of about 25 Hz, which amounts to about 1% of the natural frequency of a conventional resonant gyroscope.

This paper describes the design of a fuzzy adaptive sliding mode controller for tracking control of robotic manipulators. The proposed controller incorporates a modified traditional sliding mode controller to drive the system state to a sliding surface and then keep the system state on this surface, and a fuzzy logic controller to accelerate the reaching phase. The stability of the control system is ensured by using Lyapunov theory. To verify the effectiveness of the proposed controller, computer simulation is conducted for a five-bar planar robotic manipulator. The simulation results show that the proposed controller can improve the reaching time and eliminate chattering of the control system at the same time.

This paper proposes a robust object tracking algorithm using object features and on-line learning based particle filter for mobile robots. Mobile robots with a side-view camera have problems as camera jitter, illumination change, object shape variation and occlusion in variety environments. In order to overcome these problems, color histogram and HOG descriptor are fused for efficient representation of an object. Particle filter is used for robust object tracking with on-line learning method IPCA in non-linear environment. The validity of the proposed algorithm is revealed via experiments with DBs acquired in variety environment. The experiments show that the accuracy performance of particle filter using combined color and shape information associated with online learning (92.4 %) is more robust than that of particle filter using only color information (71.1 %) or particle filter using shape and color information without on-line learning (90.3 %).

In the paper, we proposed a ring type structured light image based embedded ranging sensor for a mobile robot. Since the proposed ranging sensor obtains omnidirectional object distance, it is useful for autonomous navigation of a mobile robot. By matching the local omnidirectional distance map with a given global object map, it is possible to get position and heading angle of mobile robot in the global coordinates. Experiments for matching and navigation were carried out to verify the performance of the proposed ranging sensor.

The purpose of this paper is to suggest IDE (Incremental Displacement Estimation) algorithm for the previously proposed visually servoed paired structured light system. The system is composed of two sides facing with each other, each with one or two lasers with a 2-DOF manipulator, a camera, and a screen. The 6-DOF displacement between two sides can be estimated by calculating the positions of the projected laser beams and rotation angles of the manipulators. In the previous study, Newton-Raphson or EKF (Extended Kalman Filter) has been used as an estimation algorithm. Although the various experimental tests have validated the performance of the system and estimation algorithms, the computation time is relatively long since aforementioned algorithms are iterative methods. Therefore, in this paper, a non-iterative incremental displacement estimation algorithm which updates the previously estimated displacement with a difference of the previous and the current observed data is introduced. To verify the performance of the algorithm, experimental tests have been performed. The results show that the proposed non-iterative algorithm estimates the displacement with the same level of accuracy compared to the EKF with multiple iterations with significantly less computation time.

This paper introduces an AVP (Automated Valet Parking) system which applies an autonomous driving concept into the current PAS (Parking Assistant System). The present commercial PAS technology is limited into vehicle. It means vehicle only senses and controls by and for itself to assist the parking. Therefore, the present PAS is restricted to simple parking events. But AVP includes wider parking events and planning because it uses infra-sensor network as well as vehicle sensor. For the realization of AVP, the commercial steering system of a compact vehicle was modified into steer-by-wire structure and various sensors like LRF (Long Range Finder) and camera were installed in a parking area. And local & global server decides where and when the vehicle can go and park in the testing area after recognized the status of environment and vehicle from those sensors. GPS solution was used to validate the AVP performance. More various parking situations, vehicles and obstacles will be considered in the next research stages based on these results. And we expect this AVP solution with more intelligent vehicles can be applied in a big parking lot like a market, an amusement park, etc.

Accurate localization is very important to stable navigation of a mobile robot. This paper deals with local localization of a mobile robot especially for outdoor environments. The GPS information is the easiest way to obtain the outdoor position information. However, the GPS accuracy can be severely affected by environmental conditions. To deal with this problem, the GPS and wheel odometry can be combined using an EKF (Extended Kalman Filter). However, this is not enough for safe navigation of a mobile robot in outdoor environments. This paper proposes a novel method using lane features from the road image. The pose data of a mobile robot can be corrected by analyzing the detected lane features. This can improve the accuracy of the localization process substantially.

The recognition of elevator door is needed for mobile service robots to moving between floors in the building. This paper proposed the sensor fusion approach using LRF (Laser Range Finder) and camera to solve the problem. Using the laser scans by the LRF, we extract line segments and detect candidates as the elevator door. Using the image by the camera, the door candidates are verified and selected as real door of the elevator. The outliers are filtered through the verification process. Then, the door state detection is performed by depth analysis within the door. The proposed method uses extrinsic calibration to fuse the LRF and the camera. It gives better results of elevator door recognition compared to the method using LRF only.

This paper focuses on attitude stabilization performance improvement of the quadrotor flying robot. First, the dynamic model of quadrotor flying robot was estimated through PEM (Prediction Error Method) using experimental input/output data. And attitude stabilization performance was improved by increasing the generation frequency of PWM signal from 50 Hz to 500 Hz. Also, the controller is implemented using a standard PID (Proportional-Integral-Derivative) controller augmented with feedback on angular acceleration, allowed the gains to be significantly increased, yielding higher bandwidth. Improved attitude stabilization performance is verified by experiment.