We propose an optimized adaptive PID control scheme. This paper is focused on the development of model reduction as well as a new adoptive control structure (viz. a recursive least square estimation (RLSE) method-based structure) that is constructed with smith-predictor structure and a real time estimator. The estimator adjust parameters of a reduced model in real time. It leads to robust and superb control performance for the noise or variation of parameters of process. Experimental study reveals that the proposed control structure exhibits more superb output performance in comparison to some previous methods.

A dual micro gas sensor array was fabricated using nano sized $SnO_2$ thin films which had good sensitivities to CO and combustible gases, or $H_2S$ gas for air quality sensors in automobile. The already existed air quality sensor detects oxidizing gases and reducing gases, the air quality sensor(AQS), located near the fresh air inlet detected the harmful gases, the fresh air inlet door/ventilation flap was closed to reduce the amount of pollution entering the vehicle cabin through HVAC(heating, ventilating, and air conditioning) system. In this study, to make $SnO_2$ thin film AQS sensor, thin tin metal layer between 1000 and $2000{\AA}$ thick was oxidized between 600 and $800^{\circ}C$ by thermal oxidation. The gas sensing layers such as $SnO_2$, $SnO_2$(pt) and $SnO_2$(+CuO) were patterned by metal shadow mask for simple fabrication process on the silicon substrate. The micro gas sensors with $SnO_2$(+Pt) and $SnO_2$(CuO) showed good selectivity to CO gas among reducing gases and good sensitivity to $H_2S$ that is main component of bad odor, separately.

The dynamic window approach(DWA) is a well known technique for reactive collision avoidance. It shows safe and efficient performance in real-world experiments. However, a robot can get stuck in local minima because no information about the connectivity of the free space is used to determine the motion. The global DWA can solve this problem of local minima by adding a navigation function. Even with the global DWA, it is still difficult for a robot to execute an abrupt change in its direction, for example, entering from the corridor to a doorway. This paper proposes a modified global DWA using the included angles of waypoints extracted from an optimal path. This scheme enables the robot to decelerate in advance before turning into the doorway. Therefore the robot can reach the goal position more safely and efficiently at high speeds.

An efficient obstacle avoidance algorithm is proposed in this paper to avoid dynamic obstacles using a collision vector while a tele-operated mobile robot is moving. For the verification of the algorithm, an operator watches through a monitor and controls the mobile robot with a force-reflection joystick. The force-reflection joystick transmits a virtual force to the operator through the Inter-net, which is generated by an adaptive impedance algorithm. To keep the mobile robot safe from collisions in an uncertain environment, the adaptive impedance algorithm generates the virtual force which changes the command of the operator by pushing the operator's hand to a direction to avoid the obstacle. In the conventional virtual force algorithm, the avoidance of moving obstacles was not solved since the operator cannot recognize the environment realistically by the limited communication bandwidth and the narrow view-angle of the camera. To achieve the dynamic obstacle avoidance, the adaptive virtual force algorithm is proposed based on the collision vector that is a normal vector from the obstacle to the mobile robot. To verify the effectiveness of the proposed algorithm, mobile robot navigation experiments with multiple moving obstacles have been performed, and the results are demonstrated.

This paper presents a sensor fusion method based on indirect Kalman filter(IKF) for error compensation of low-cost inertial sensors and its application to the determination of attitude and position of small flying robots. First, the analysis of the measurement error characteristics to zero input is performed, focusing on the bias due to the temperature variation, to derive a simple nonlinear bias model of low-cost inertial sensors. Moreover, from the experimental results that the coefficients of this bias model possess non-deterministic (stochastic) uncertainties, the bias of low-cost inertial sensors is characterized as consisting of both deterministic and stochastic bias terms. Then, IKF is derived to improve long term stability dominated by the stochastic bias error, fusing low-cost inertial sensor measurements compensated by the deterministic bias model with non-inertial sensor measurement. In addition, in case of using intermittent non-inertial sensor measurements due to the unreliable data link, the upper and lower bounds of the state estimation error covariance matrix of discrete-time IKF are analyzed by solving stochastic algebraic Riccati equation and it is shown that they are dependant on the throughput of the data link and sampling period. To evaluate the performance of proposed method, experimental results of IKF for the attitude determination of a small flying robot are presented in comparison with that of extended Kaman filter which compensates only deterministic bias error model.

In this paper, a time-delayed controller for position control of humanoid robot arms is designed and implemented on a field programmable gate array(FPGA) chip. The time-delayed control algorithm is simple to implement, and robust to reject disturbances. The time-delayed control method uses the one sample time-delayed previous information to cancel out uncertainties in the system. Since the sampling time is so fast with the current hardware technology, the time-delayed controller can be implemented. However, inertia values should be correctly estimated to have the better performance. The position tracking tasks of humanoid robot arms are tested to compare performances of several control algorithms including the time-delayed controller.

A new dynamic balancing algorithm has been proposed to minimize the number of sensors necessary for the horizontal balancing of the mobile inverted pendulum while maintaining the same level of the commercial performance. The inverted pendulum technique is getting attention and there have been many researches on the Segway since the US inventor Dean Kamen commercialized. One of the major problems of the Segway is that many sensors are required for the control of the Segway, which results in the high price. In this research, a single gyro and a tilt sensor are fused to obtain the absolute tilt information, which is applied for the control of the mobile inverted pendulum. A dynamic balancing technique has been developed and applied for a robust control system against disturbances. The intelligent handling and stable curving of the Segway as a next generation mobile tool are verified with a human loading.

This study deals with image-based visual servoing for automatic recharging of mobile robot. Because mobile robot must be recharged periodically, it is necessary to detect and move to docking station. Generally, laser scanner is used for detect of position of docking station. CCD Camera is also used for this purpose. In case of using cameras, the position-based visual servoing method is widely used. But position-based visual servoing method requires the accurate calibration and it is hard and complex work. Another method using cameras is image-based visual servoing. Recently, image based visual servoing is widely used for robotic application. But it has a problem that cannot have linear trajectory in the 3-dimensional space. Because of this weak point, image-based visual servoing has a limit for real application. In case of 2-dimensional movement on the plane, it has also similar problem. In order to solve this problem, we point out the main reason of the problem of the resolved rate control method that has been generally used in the image-based visual servoing and we propose an image-based visual servoing method that can reduce the curved trajectory of mobile robot in the cartesian space.

In this paper, a novel neuro-fuzzy controller is presented. The generic fuzzy controller is compensated by a neural network controller so that an overall control structure forms a neuro-fuzzy controller. The proposed neuro-fuzzy controller solves the difficulty of selecting optimal fuzzy rules by providing the similar effect of modifying fuzzy rules simply by changing crisp input values. The performance of the proposed controller is tested by controlling humanoid robot arms. The humanoid robot arm is analyzed and implemented. Experimental studies have shown that the performance of the proposed controller is better than that of a PID controller and of a generic fuzzy PD controller.

Concurrent competition for finite resources by multiple parts in flexible manufacturing systems(FMS's) and inappropriate initial marking or net structure of Petri net with share resources results in deadlock. This is an important issue to be addressed in the operation of the systems. Deadlock is a system state so that some working processes can never be finished. Deadlock situation is due to a wrong resource allocation policy. In fact, behind a deadlock problem there is a circular wait situation for a set of resources. Deadlock can disable an entire system and make automated operation impossible. Particularly, an unmanned system cannot recover from such a status and a set of jobs waits indefinitely for never-to-be-released resources. In this paper, we proposed a deadlock prevention method using siphon and trap of Petri net. It is based on potential deadlock which are siphon that eventually became empty. This method prevents the deadlock by the control of transition fire and initial marking in the Petri net. An given example of FMS is shown to illustrate our results with deadlock-free.

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and Closed-Form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-Form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a Self-Tuning Weighted Least Square AOA algorithm that is a modified version of the conventional Closed-Form solution. In order to estimate the error covariance matrix as a weight, a two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

The INS/GPS integration system is designed by employing an adaptive filter that can estimate the measurement noise variance using the residual of the filter. To verify the efficiency of the proposed loosely-coupled INS/GPS integration system, simulation is performed by assuming that GPS information has large position errors. Simulation results show that the proposed integration system with the adaptive filter is more effective in estimating the position and attitude errors than those with the Extended Kalman Filter.

The SSWM(Software Switching Mechanism) of I-processor concept using non-real time in-house software simulation program is an effective method in order to develop the flight control law in desktop or HQS environment. And, this system has some advantages compare to HSWM(Hardware Switching Mechanism) such as remove the time delay effectiveness and reduce the costs of development. But, if this system loading to the OFP(Operational Flight Program), the OFP guarantee the enough throughput in order to calculate the two control law at once. Therefore, the HSWM(Hardware Switching Mechanism) of 2-processor concept is necessary. This paper addresses the concept of HSWM of the HQS-PC interface using TCP/IP(Transmission Control Protocol/Internet Protocol) communication based on flight control law of advanced supersonic trainer. And, the fader logic of TFS(Transient Free Switch) and stand-by mode of reset '0' type are designed in order to reduce the abrupt transient response and minimize the integrator effect in pitch axis. The result of the analysis based on HQS pilot simulation using HSWM reveals that the flight control systems are switching between two computers without any problem.

This paper presents an attitude determination scheme for the Science and Technology Satellite-2(STSAT-2) using Fine Digital Sun Sensor(FDSS). The FDSS has been developed for STSAT-2, and exhibits the accuracy of 0.032degree in $1{\sigma}$. To be specific, the attitude information from the sensor is exploited to compensate for Fiber Optic Gyro(FOG) mounted on STSAT-2, and Kalman filter model is derived and implemented. To show the effectiveness of the present compensation scheme, computer simulations have been carried out resulting in the attitude errors within a bound.

Location information will become increasingly important for future Pervasive Computing applications. Location tracking system of a moving device can be classified into two types of architectures: an active mobile architecture and a passive mobile architecture. In the former, a mobile device actively transmits signals for estimating distances to listeners. In the latter, a mobile device listens signals from beacons passively. Although the passive architecture such as Cricket location system is inexpensive, easy to set up, and safe, it is less precise than the active one. In this paper, we present a passive location system using Cricket Mote sensors which use RF and ultrasonic signals to estimate distances. In order to improve accuracy of the passive system, the transmission speed of ultrasound was compensated according to air temperature at the moment. Upper and lower bounds of a distance estimation were set up through measuring minimum and maximum distances that ultrasonic signal can reach to. Distance estimations beyond the upper and the lower bounds were filtered off as errors in our scheme. With collecting distance estimation data at various locations and comparing each distance estimation with real distance respectively, we proposed an equation to compensate the deviation at each point. Equations for proposed algorithm were derived to calculate relative coordinates of a moving device. At indoor and outdoor tests, average location error and average location tracking period were 3.5 cm and 0.5 second, respectively, which outperformed Cricket location system of MIT.