This paper presents the design of a now controller for the read/write head of a hard disk drive. The general controller for seeking is the time-optimal control. However if we use only the time optimal control law, this could be vulnerable to chattering effect. To solve this problem, we propose a modified controller design algorithm in this paper. The proposed controller consists of bang-bang control for seeking and sliding-mode control for tracking. Moreover, to test the robustness and stability of control system, a bounded disturbance is selected to maximize a severity index. Simulation results show the superiority of the proposed controller through comparison with time optimal VSC(variable structure control).

The major concern of networked control system is network uncertainties such as time delay and data loss. Because these uncertainties may degrade the performance of networked control system and destabilize the entire system. Therefore, the performance and the stability variation of networked control system due to network uncertainties must be considered first in designing networked control system. In particular, the stability analysis of networked control system is most important issue since time delay and data loss can make the overall systems unstable. In this paper, we present a new stability analysis method of networked control system with time delay and data loss, which is impossible in previous works. The proposed method can determine maximum time delay and allowable transmission rate that preserve stability performance of networked control system. The results of the simulation validate effectiveness of our stability analysis method.

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

In this paper, we proposed that the problem of controlling induction motor with magnetic saturation, is studied from an input-output feedback linearization with adaptive algorithm. is considered. An adaptive input-output feedback linearizing controller is considered under the assumption of known motor parameters and unknown load torque. In order to achieve the speed regulation with the consideration of improving power efficiency, rotor angular speed and flux amplitude tracking objectives are formulated. Simulation results are provided for illustration.

This paper presents the new obstacle avoidance method that is composed of vision and sonar sensors, also a navigation algorithm is proposed. Sonar sensors provide poor information because the angular resolution of each sonar sensor is not exact. So they are not suitable to detect relative direction of obstacles. In addition, it is not easy to detect the obstacle by vision sensors because of an image disturbance. In This paper, the new obstacle direction measurement method that is composed of sonar sensors for exact distance information and vision sensors for abundance information. The modified splitting/merging algorithm is proposed, and it is robuster for an image disturbance than the edge detecting algorithm, and it is efficient for grouping of the obstacle. In order to verify our proposed algorithm, we compare the proposed algorithm with the edge detecting algorithm via experiments. The direction of obstacle and the relative distance are used for the inputs of the fuzzy controller. We design the angular velocity controllers for obstacle avoidance and for navigation to center in corridor, respectively. In order to verify stability and effectiveness of our proposed method, it is apply to a vision and sonar based mobile robot navigation system.

The Controller Area Network (CAN) is being widely used in real-time control applications such as automobiles, aircraft, and automated factories. Unfortunately, CAN, in its current form, is not able to either share out the system bandwidth among the different devices fairly or to grant an upper bound on the transmission times experienced by the nodes connected to the communication medium as it happens, for instance, in the token-based networks. In this paper. we present the message scheduling for CAN, based on the distributed control scheme to integrate actuators and sensors in a humanoid robot. Besides introducing the new algorism, this paper also presents some performance figures obtained using a specially developed software simulator, while the behavior of the new algorism is compared with the traditional CAN systems, in order to see how effective they are.

The data transmission over MVB(Multifunction Vehicle Bus) of TCN(Train Communication Network) is divided into the periodic transmission phase and the sporadic transmission phase. TCN standard recommends the event-polling method as the message transfer in the sporadic phase. However, since the event-polling method does not use pre-scheduling to the priority of the messages, it is inefficient for the real-time systems. To schedule message transmission, a master node should know the priority of message to be transmitted by a slave node prior to the sporadic phase, but the existing TCN standard does not support any protocol for this. This paper proposes the slave frame bit-stuffing algorithm, with which a master node gets the necessary information for scheduling and includes the simulation results of the event-polling method and the proposed algorithm.

TCXO is one of the most important component in communication systems. We present a analytic method approach to design the Temperature Compensated Circuit. The conventional method for extracting the circuit parameters, which are for thermistor, Colpitts and frequency control circuit is the trial and error correction. In this paper, we analyse the temperature compensating circuit to extract TCXO circuit parameter. In order to show the validity of this paper, we have designed and implemented the 10MHz TCXO. The fabricated TCXO shows 1ppm frequency drift characteristic over the temperature range of -40℃∼85℃.

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) is proposed. The whole system consists of two-lead electrocardiogram acquisition (lead Ⅰ and Ⅱ), baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problem of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we proposed a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 subjects, and we could obtain satisfactory respiration signals that shows high correlation (r>0.9) with the signal acquired from the chest-belt respiration sensor.