This work is concerned with the assignment of the desired eigenstructure for linear time-varying systems such as missiles, rockets, fighters, etc. Despite its well-known limitations, gain scheduling control appeared to be the focus of the research efforts. Scheduling of frozen-time, frozen-state controller for fast time-varying dynamics is known to be mathematically fallacious, and practically hazardous. Therefore, recent research efforts are being directed towards applying time-varying controllers. In this paper, ⅰ) we introduce a differential algebraic eigenvalue theory for linear time-varying systems, and ⅱ) we also propose an eigenstructure assignment scheme for linear time-varying systems via the differential Sylvester equation based upon the newly developed notions. The whole design procedure of the proposed eigenstructure assignment scheme is very systematic, and the scheme could be used to determine the stability of linear time-varying systems easily as well as provides a new horizon of designing controllers for the linear time-varying systems. The presented method is illustrated by a numerical example.

A self-sensing magnetic suspension system utilizing a LC resonant circuit is proposed by using the characteristic that the inductance of the magnetic system is varied with respect to the air gap displacement. An external capacitor is added into the electric system to make the levitation system be statically stable system, which much relieves the control effort required to stabilize the magnetic suspension system of haying an intrinsic unstable nature. For the realization of the self- sensing magnetically levitated system, an amplitude modulation / demodulation method is used with a positive position feedback controller Experimental results are presented to validate the proposed method.

This study deals with robustness bounds estimation for uncertain linear systems with structured perturbations where the eigenvalues of the perturbed systems are guaranteed to stay in a prescribed region. Based upon the Lyapunov approach, new theorems to estimate allowable perturbation parameter bounds are derived. The theorems are referred to as the zero-order or first-order asymmetric robustness measure depending on the order of the P matrix in the sense of Taylor series expansion of perturbed Lyapunov equation. It is proven that Gao's theorem for the estimation of stability robustness bounds is a special case of proposed zero-order asymmetric robustness measure for eigenvalue assignment. Robustness bounds of perturbed parameters measured by the proposed techniques are asymmetric around the origin and less conservative than those of conventional methods. Numerical examples are given to illustrate proposed methods.

A Periodic disturbance canceller is proposed to compensate for the periodic disturbance due to cutting process in a CNC machining center. The periodic disturbance canceller estimates the Periodic disturbance without phase delay. This is achieved by using linear phase low-pass filter and frequency response reciprocal filter of plant at the frequency of the periodic disturbance. This method is implemented in the position control system of the CNC machining center with general disturbance compensators in order to compensate for both the frictional force and the periodic disturbance. The experimental results are described to show its effectiveness.

A fuzzy learning method to control an unstable and multivariable system is presented in this paper, Because the multivariable system has generally a coupling effect between the inputs and outputs, it is difficult to find its modeling equation or parameters. If the system is unstable, initial condition rules are needed to make it stable because learning is nearly impossible. Therefore, this learning method uses the initial rules and introduces a cost function composed of the actual error and error-rate of each output without the modeling equation. To minimize the cost function, we experimentally got the Jacobian matrix in the operating point of the system. From the Jacobian matrix, we can find the direction of the convergence in the learning, and the optimal control rules are finally acquired when the fuzzy rules are updated by changing the portion of the errors and error rates.

We propose a target identification algorithm for visual tracking. Target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrical relationship between model segments and extracted line segments.

This paper proposes a face recognition method using feature spaces and image vectors in the image plane. We obtain the 2-D feature space using the self-organizing map which has two inputs from the axis of the given image. The image vector consists of its weights and the average gray levels in the feature space. Also, we can reconstruct an normalized face by using the image vector having no connection with the size of the given face image. In the proposed method, each face is recognized with the best match of the feature spaces and the maximum match of the normally retrieval face images, respectively. For enhancing recognition rates, our method combines the two recognition methods by the feature spaces and the retrieval images. Simulations are conducted on the ORL(Olivetti Research laboratory) images of 40 persons, in which each person has 10 facial images, and the result shows 100% recognition and 14.5% rejection rates for the 20$\times$20 feature sizes and the 24$\times$28 retrieval image size.

In this paper, a dead reckoning navigation system for differential drive mobile robots is presented. The navigation system consists of two incremental encoders and a gyroscope. We have built a third order polynomial function for compensating the nonlinear scale factor errors of the gyroscope. We utilize an indirect Kalman filter that feeds back estimated errors to the main navigation system. Also, the observability of the filter is analyzed in order to systematically evaluate the filter's performance. Experimental results show that the proposed navigation system provides a reliable position and heading angle by mutually compensating the encoder and the gyroscope errors. The proposed filter also reduces the computational burden and enhances the navigation system's reliability. The observability analysis confirms the characteristics of inevitably unbounded position error growth in dead reckoning navigation systems.

Linear motion and angular motion in task space are handled separately in joint velocity planning for redundant robot manipulators. In solving inverse kinematic equations with given joint velocity limits, we consider the order of priority for linear motion and angular motion. The proposed method will be useful in such applications where only linear motions are important than angular motions or vice versa.

For successful assembly of flexible parts, informations about their deformation as well as possible misalignments between the holes and their mating parts are essential. Such informations can be acquired from visual sensors. For robotic assembly, the corrective assembly motion to compensate for such misalignments has to be determined from the measured informations. However, this may not be simply derived from the measured misalignment alone because the part deformation progressively occurs during misalignment compensation. Based on the analysis of flexible parts assembly process, this paper presents a neural net-based inference system that can infer the complex relationship between the corrective motion and the measured information of parts deformation and misalignments. And it verifies the performance of the implemented inference system. The results show that the proposed neural net-based misalignment compensation algorithm Is effective in compensating for the lateral misalignment, and that it can be extended to the assembly tasks under more general conditions.

In this paper, we designed and implemented a precise pulse motor controller chip that generates the pulse needed to control step motor, DC servo and AC servo motors. This chip generates maximum pulse output rate of 5Mpps and has the quasi-S driving capability and speed and moving distance override capability during driving. We designed this chip with VHDL and executed a logic simulation and synthesis using Synopsys tool. The pre-layout simulation and post-layout simulation was executed by Compass tool. This chip was produced with 100 pins, PQFP package by 0.8${\mu}{\textrm}{m}$ gate array process and implemented by completely digital logic. We developed the test hardware board of performance and the CAMC(Computer Aided Motor Controller) Agent softwate to test the performance of the pulse motor controller chip produced. CAMC Agent enables user to set parameters needed to control motor with easy GUI(Graphic User Interface) environment and to display the output response of motor graphically.

In this paper, we present new application for the auction method which has been based on one-chip microprocessors. We develop the portable wired terminal for market blocker. And, using that, the recommended price of products is able to push into and show the information of action status. Through our research, using EAS(Electronic Auction System), we can prevent supplier from blocker's rigging the market, because anyone who have a qualification for the action blocker is able to participate in action.

In general, the realization of high performance brushless permanent magnet motors which are widely used in servo drive is focused on the linear control for ripple-free torque. This is also the main problem that should be solved in all AC motors including induction motor to achieve high performance control, and recent papers deal with this problem. In this paper, the novel optimal excitation scheme of brushless permanent magnet motor producing loss-minimized ripple-free torque based on the d-q-0 reference frame is presented including 3 phase unbalanced condition. The optimized phase current waveforms that are obtained by the proposed method can be a reference values and the motor winding currents are forced to track it by delta modulation technique. As a results, it can be shown that the proposed work can minimize the torque ripple by the optimal excitation current for brushless permanent magnet motor with any arbitrary phase back EMF waveform. Simulation and experimental results prove the validity and practical applications of the proposed control scheme.

In this paper, digital motor control center using protection relay is developed in order to protect power systems by means of timely fault detection and diagnosis during operation for induction motor which have various load environments and capacities in power systems. Digital motor control center is employed by power supervisory control systems without separate remote terminal unit and transducers adding communicational ability. Also we develope a maximum demand controller to control the load effectively at peak status and a power factor controller to minimize real power losses and improve the power factor. Therefore, when using the developed controller, real time computation is possible by loading DSP in hardware and applying real-time kernel which can convert each algorithm to task module.

It is difficult to determine the feeding rate of coagulant in the water treatment process, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the genetic-fuzzy system was used in determining the feeding rate of the coagulant. The genetic algorithms are excellently robust in complex optimization problems. Since it uses randomized operators and searches for the best chromosome without auxiliary informations from a population consists of codings of parameter set. To apply this algorithms, we made the lookup table and membership function from the actual operation data of the water treatment process. We determined optimum dosages of coagulant(LAS) by the fuzzy operation, and compared it with the feeding rate of the actual operation data.