An alternative inverse feedback structure for adaptive active control of periodic noise is introduced for systems with nonminimum phase cancellation path. To obtain the inverse model of the nonminimum phase cancellation path, the cancellation path model can be factorized into a minimum phase term and a maximum phase term. The maximum phase term containing unstable zeros makes the inverse model unstable. To avoid the instability, we alter the inverse model of the maximum phase system into an anti-causal FIR one. An LMS predictor estimates the future samples of the noise, which are necessary for causality of both anti-causal FIR approximation for the stable inverse of the maximum phase system and time-delay existing in the cancellation path. The proposed method has a faster convergence behavior and a better transient response than the conventional filtered-x LMS algorithms with the same internal model control structure since a filtered reference signal is not required. We compare the proposed methods with the conventional methods through simulation studies.

This study investigates the use of mixed $H_2/H_{$\infty}$ and $\mu$-synthesis to construct a robust controller for the benchmark problem. The model treated in the problem is a coupled three-inertial system that reflects the dynamics of mechanical vibrations. This kind of problem requires to be satisfied the robust performance (both in the time and frequency-domain specifications). We, first, adopt the mixed $H_2/H_{$\infty}$ theory to design a feedback controller K(s). Next, $\mu$-synthesis method is applied to the overall system to make use of structured parametric uncertainty. This process permits higher levels of controller authority and reduces the conservativeness of the controller. Finally, the feedforward controller is also used to improve the transient response of the output. We confirm that all design specifications except a complementary sensitivity condition can be achieved.

A broomstick swinging biped acrobatic controller is designed and simulated to show capability of the system of controllers: virtual model controller is employed for the robot\`s posture balancing control while a higher level fuzzy controller modulate the one of the virtual model controller\`s parameter for the pendulum swinging motion generation. The robot is of 7 degree-of-freedom, 8-link planar bipedal robot having two slim legs and a body. Each leg consists of a hip joint, a knee joint, an ankle joint and the body has a free joint at the top in the head at which a freely rotating broomstick is attached. We assume that the goal for the acrobat robot is to maintain a body balance in the sagittal plane while swinging up the freely up the freely rotating pendulum. We also assume that the actuators in the joints are all ideal torque generators. The proposed system of controllers satisfies the goal and the simulation results are presented.

In this study, we define the measuring method of crank angle precisely using an event and perform a study on the hardware structure and software algorithms which is applicable for the commercial engine. Also we developed a Computer-ECU(Personal computer based electronic control unit) using a computer and a microprocessor, for performing the ignition at a desire position(angle) and for controlling a duty ratio a pulse for ISC(Idle speed control). We applied these algorithms to the modeling which is induced a concept of event and got a better result than a conventional ECU in the state of transient as a result of performing air fuel ratio control in a commercial engine. This technique can be used for the back to improve ECU performance. It the present type of Hybrid I. C voltage regulator is altered to the new type of regulator, we will be surely able to reduce the production cost as well as simplify the design of alternator\`s rear bracket and rectifier part because of the removal of trio diode. Experiment is taken by MS-R004.

In this paper, intelligent control method using neural network as a nonlinear controller is presented. Simulation studies for three link rotary robot are performed. Neural network controller is implemented on DSP board in PC to make real time computing possible. On-line training algorithms for neural network control are proposed. As a test-bed, a large x-y table was build and interface with PC has been implemented. Experiments such as inverted pendulum control and large x-y table position control are performed. The results for different PD controller gains with neural network show excellent position tracking for circular trajectory compared with those for PD controller only. Neural control scheme also works better for controlling inverted pendulum on x-y table.

When people write a character with a pen stably, proper compliance planning is necessary. In this paper, after investigating the property of character writing task, we propose a fundamental grasp model for character writing and also analyze compliance characteristics for effective character writing using multi-fingered hands. For this, the general stiffness relation of multi-fingered hand is firstly described. Next, we investigate the grasp configurations for grasping a pen and then, we analyze the conditions of the specified stiffness matrix in the operational space to successfully and more effectively achieve the given character writing task. Through the analysis, an effective grasp modeling for successful character writing is shown. And also, we conclude that the operational compliance characteristics should be properly planned for character writing, stably and precisely.

In this paper, we describe a method for the mobile robot using images of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using the a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot`s position. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot. The Kalman filter scheme is applied to this method. Effectiveness of the proposed method is demonstrated by the simulation.

PLC(programmable Logic Controller)s essential components of modern automation systems encompassing almost every industry. Ladder Diagrams (LD) have been widely used in the design of such PLC since the LD is suitable for the modeling of the sequential control system. However, the synthesis of LD itself mainly depends on the experience of the industrial engineer, which may results in unstructured or inflexible design. Hence, in this paper, we propose a ladder diagram conversion algorithm which systematically produces LDs for PLCs based on discrete event models to enhance the structured and flexible design mechanism.

Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it is desired to combine INS with external aids such as GPS. However GPS informations have a randomly abrupt jump due to a sudden corruption of the received satellite signals and environment, and moreover GPS can\`t provide navigation solutions. In this paper, smoothing and prediction schemes are proposed for GPS`s jump or unavailable GPS. The smoothing algorithm which is designed as a scalar adaptive filter, smooths abrupt jump. The prediction algorithm which is proved by Schuler error model of INS, estimates INS error in appropriate time. The outputs of proposed algorithm apply stable measurements to GPS aided INS Kalman filter. Simulations show that the proposed algorithm can effectively remove measurement jump and predict INS error.

This paper is concerned with a transfer alignment method for the SDINS under ship motions. Major error sources of transfer alignment are data transfer time-delay, lever-arm velocity and ship body flexure. Specifically, to reduce alignment errors induced by measurement time-delay effects, the error compensation method through delay state augmentation is suggested. A linearized error model for the velocity and attitude matching transfer alignment system is first derived by linearizing the nonliner measurement equation with respect to its time delay and augmenting the delay state into the conventional linear state equations. And then it is shown via observability analysis and computer simulations that the delay state can be estimated and compensated during ship motions resulting in considerably less alignment errors.

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.

The major problem at a branch point for point-to-multipoint available bit rate(ABR) services in asynchronous transfer mode (ATM) networks is how to consolidate backward resource management(BRM) cells from each branch for a multicast connection. In this paper, we propose an efficient feedback consolidation algorithm based on an adaptive dynamic threshold(ADT) to eliminate the consolidation noise and the reduce the consolidation delay. The main idea of the ADT algorithm lies in that each branch point estimates the ABR traffic condition of the network through the virtual queue estimation and the transmission threshold of the queue level in branch points is adaptively controlled according to the estimation. Simulation results show that the proposed ADT algorithm can achieve a faster response in congestion status and a higher link utilization compared with the previous works.