This paper deals with speed control of DC servo motor using a Adaptive high gain obserber. In this parer, the gain of the observer is properly set up using the fuzzy control and adaptive high gain observer that have a superior transient characteristic and is easy to implement compared the existing method is designed. In order to verify the performance of the Adaptive high gain observer which is proposed in this paper, it is compared estimate performance of High-gain Observer and Adaptive High Gain Observer with the computer simulation. Effectiveness of the proposed high gain observer is proved from the experiment to compare the case with a speed sensor to the case with Adaptive high gain observer in the speed control of DC servo motor.

This paper proposes an expert system to generate automatically the test table of test system which can highly enhance the quality and productivity of product by inspecting quickly and accurately the defect device on the electronic circuit board tested. The expert system identifies accurately the tested components and the circuit patterns by tracing automatically the connectivity of circuit from electronic circuit database. And it generates automatically the test table to detect accurately the missing components, the misplaced components, and the wrong components for analog components such as resistance, coil, condenser, diode, and transistor, based on the experience knowledge of veteran expert. It is implemented in C computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique, the interface with the electronic circuit database and the hardware direct control. And, the validity of the builded expert system is proved by simulating for a typical electronic board model.

In order to navigate the mobile robots safely in unknown environments, many researches have been studied to devise navigational algorithms for the mobile robots. In this paper, we propose a navigational algorithm that consists of an obstacle-avoidance behavior module, a goal-approach behavior module and a radial basis function network(RBFN) supervisor. In the obstacle-avoidance behavior module and goal-approach behavior module, the fuzzy-artificial immune networks are used to select a proper steering angle which makes the autonomous mobile robot(AMR) avoid obstacles and approach the given goal. The RBFN supervisor is employed to combine the obstacle-avoidance behavior and goal-approach behavior for reliable and smooth motion. The outputs of the RBFN are proper combinational weights for the behavior modules and velocity to steer the AMR appropriately. Some simulations and experiments have been conducted to confirm the validity of the proposed navigational algorithm.

Fault-tolerance is an important design criterion for robotic systems operating in hazardous or remote environments. This paper addresses the issue of tolerating a locked joint failure in gait planning for hexapod walking machines which have symmetric structures and legs in the form of an articulated arm with three revolute joints. A locked joint failure is one for which a joint cannot move and is locked in place. If a failed joint is locked, the workspace of the resulting leg is constrained, but hexapod walking machines have the ability to continue static walking. A strategy of fault-tolerant tripod gait is proposed and, as a specific form, a periodic tripod gait is presented in which hexapod walking machines have the maximum stride length after a locked failure. The adjustment procedure from a normal gait to the proposed fault-tolerant gait is shown to demonstrate the applicability of the proposed scheme.

This paper presents a systematic method of probing channel characteristics and communication reliabilities of home power line communication network applied to the Internet accessed control of home appliances. The effects of the three performance deterioating factors, i.e., additive noise, channel attenuation, and intersymbol interference, can be systematically measured by applying the channel probing waveform in the frequency range from 100KHz to 450KHz. Probability of bit error is derived with the probed channel parameters of the signal attenuation, noise and signal-to-interference ratio read in the frequency domain. The agreement between the derived probability of bit error and the measured probability of bit error supports the validity of the proposed approach of probing home power line channel characteristics. The experimental results performed with the constructed test-bed applying the proposed channel probing method also support the feasibility of commercially deploying the PLC modem installed home appliances and their services for the Internet accessed home automation.

This study describes a new technique for human sensibility evaluation using electroencephalogram(EEG). Production of EEG is assumed to be linear. The linear predictor coefficients and the linear cepstral coefficients of EEG are used as the feature parameters of sensibility and pattern classification performances of them are compared. Using the better parameter, a human sensibility evaluation algorithm is designed. The obtained results are as follows. The linear predictor coefficients showed the better performance in pattern classification than the linear cepstral coefficients. Then, using the linear predictor coefficients as the feature parameter, a human sensibility evaluation algorithm is developed at the base of a multi-layer neural network. This algorithm showed 90% of accuracy in comfortableness evaluation in spite of fluctuations in statistics of EEG signal.

The surface EMG signal detected from voluntarily activated muscles can be used as a control signal for functional electrical stimulation. To use the voluntary EMG signal, it is necessary to eliminate the muscle response evoked by the electrical stimulation and enable to process the algorithm in real time. In this paper, we propose the Gram-Schmidt(GS) algorithm and implement it in FPGA(field programmable gate array). GS algorithm is efficient to eliminate periodic signals like muscle response, and is more stable and suitable to FPGA implementations than the conventional least-square approach, due to the systolic array structure.