A novel observer design methodology for linear time-invariant systems named the dynamic observer is presented. Efficient and plausible design algorithms are also provided. The dynamic observer is an extension of the usual static observer. The concept of the dynamic observer enlarges the capacity of observer design so much that well-developed advanced linear controller design techniques can be utilized to design observers. Both the dynamic observers in the form of standard and Luenburger types are considered. The essential characteristics of the dynamic observer to be qualified as an effective observer design scheme are addressed.

In this paper, an improved method for magnetic inspection to detect surface of a conductor is presented. The presented method is based on the technique of ECP(Electric Current Perturbation), which is to measure the variation of current flow due to defects with a magnetic sensor. The inspection performance is improved by using high frequency current behaviors in order to concentrate the current near the defect and employing the resonant frequency of a search coil as an operating frequency. By analytical results and experiments of the test specimens, the feasibility of the inspection method is shown.

In this paper, Wiener-Hopf design of the two-degree-of-freedom(2DOF) controller configuration is treated for the standard plant model. It is shown that the 2DOF structure makes it possible to treat the design of feedback properties and reference tracking problem separately. Wiener-Hopf factorization technique is used to obtain the optimal controller which minimizes a given quadratic cost index. The class of all stabilizing controllers that yield finite cost index is also characterized. An illustrative example is given for the step reference tracking problem which can not be treated by the conventional H2 controller formula.

In this paper, we presented a new algebraic iterative algorithm for the optimal control of the nonlinear systems. The algorithm is based on tow steps. The first step transforms optimal control problem into a sequence of linear optimal control problem using the quasilinearization method. In the second step, TPB(two point boundary condition problem) is solved by algebraic equations instead of differential equations using BPF(block pulse functions). The proposed algorithm is simple and efficient in computation for the optimal control of nonlinear systems. In computer simulation, the algorithm was verified through the optimal control design of Van del pole system and Volterra Predatory-prey system.

A new control method for precision robust position control of a PMSM (Permanent Magnet Synchronous Motor) using asymptotically stable adaptive load torque observer is presented in the paper. Precision position control is obtained for the PMSM system approximately linearized using the field-orientation method. Recently, many of these drive systems use the PMSM to avoid backlashes. However, the disadvantages of the motor are high cost and complex control because of nonlinear characteristics. Also, the load torque disturbance directly affects the motor shaft. The application of the load torque observer is published in [1] using fixed gain. However, the motor flux linkage is not exactly known for a load torque observer. There is the problem of uncertainty to obtain very high precision position control. Therefore, a model reference adaptive observer is considered to overcome the problem of unknown parameter and torque disturbance in this paper. The system stability analysis is carried out using Lyapunov stability theorem. As a result, asymptotically stable observer gain can be obtained without affecting the overall system response. The load disturbance detected by the asymptotically stable adaptive observer is compensated by feedforwarding the equivalent current which gives fast response. The experimental results are presented in the paper using DSP TMS320c31.

In this paper, presented are an active surface-micromachined silicon accelerometer, force rebalance loop using parametric robust control method, and experimental results with a real micromachined accelerometer. And finally, a robust controller of the form of PID compensator was designed to construct force rebalance loop. Through the frequency response analysis, it is shown that the loop guarantees appropriate stability and robustness. Experiments with a real accelerometer demonstrated that the proposed loop effectively controls the position of the accelerometer's proof mass. It also demonstrated that the resolution of the fabricated accelerometer is better than 1mg. Compared with a commercial accelerometer the proposed force rebalance silicon accelerometer showed better performances.

Genetic Algorithm(GA) has been known as a method of solving large-scaled optimization problems with complex constraints in various applications. Since a major drawback of the GA is that it needs a long computation time, the hardware implementations of Genetic Algorithm Processors(GAP) are focused on in recent studies. In this paper, a hardware-oriented GA was proposed in order to save the hardware resources and to reduce the execution time of GAP. Based on steady-state model among continuos generation model, the proposed GA used modified tournament selection, as well as special survival condition, with replaced whenever the offspring's fitness is better than worse-fit parent's. The proposed algorithm shows more than 30% in convergence speed over the conventional algorithm in simulation. Finally, by employing the efficient pipeline parallelization and handshaking protocol in proposed GAP, above 30% of the computation speed-up can be achieved over survival-based GA which runs one million crossovers per second (1㎒), when device speed and size of application are taken into account on prototype. It would be used for high speed processing such of central processor of evolvable hardware, robot control and many optimization problems.

In this paper, we propose a new methodology which includes the optimal design procedure of Polynomial Neural Networks(PNN) structure for model identification of complex and nonlinear system. The proposed PNN algorithm is based on GMDA(Group Method of Data handling) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. In other words, the PNN uses high-order polynomial as extended type besides quadratic polynomial used in GMDH, and the number of input of its node in each layer depends on that of variables used in the polynomial. The design procedure to obtain an optimal model structure utilizing PNN algorithm is shown in each stage. The study is illustrated with the aid of pH neutralization process data besides representative time series data for gas furnace process used widely for performance comparison, and shows that the proposed PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and prediction capabilities of model is evaluated and also discussed.

In this paper, an Evolving Neural Network Controller(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algoithm(RVEGA) was presented for stabilization of an Inverter Pendulum(IP) system with nonlinearity. This proposed ENNC was described by a simple genetic chromosome. And the deletion of neuron, the determinations of input or output neuron, the deleted neuron and the activation functions types are given according to the various flag types. Therefore, the connection weights, its structure and the neuron types in the given ENNC can be optimized by the proposed evolution strategy. Through the simulations, we showed that the finally acquired optimal ENNC was successfully applied to the stabilization control of an IP system.