In this thesis, a digital tracking controller is proposed for multi-axis position control system. Tracking and contouring error exist when the machine tool moves along a trajectory in multi-axis system. The proposed scheme enhances the tracking and contouring performance by reducing the errors. Also, an optimal tracking controller reduces the tracking error by the state feedback and the feedforward compensator reduces the effects of a nonlinear disturbance such as friction or dead zone. The proposed control scheme reduces the contour error which occurred when the tool tracks the reference trajectory. Finally, the performance of the proposed controller is exemplified by some simulations and by applying the real XY servo system.

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. First, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The control input for the system in the HFC combined PID controller with fuzzy controller is a convex combination of the FLC's output and PID's output by a fuzzy variable, namely, membership function of weighting coefficient. Second, an auto-tuning algorithms utilizing the simplified reasoning method and genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The proposed HFC is evaluated and discussed to show applicability and superiority with the and of three representative processes.

This paper presents an active noise control algorithm via Walsh transform domain controller learned by genetic algorithm. Typical active noise control algorithms such as the filtered-x lms algorithm are based on the gradient algorithm. Gradient algorithm have two major problems; local minima and eigenvalue ratio. To solve these problems, we propose a combined algorithm which consist of genetic learning algorithm and discrete Walsh transform called Walsh Transform Domain Genetic Algorithm(WTDGA). Analyses and computer simulations on the effect of Walsh transform to the genetic algorithm are performed. The results show that WTDGA increase convergence speed and reduce steady state errors.

The negative weight can be ofter seen in Hopfield neural network, which is difficult to implement negative conductance in circuits. Usually, the inverted output of amplifier is used to avoid negative resistors for expressing the negative weights in hardware implementation. However, there is some difference between using negative resistor and the inverted output of amplifier for representing the negative weight. This difference is discussed in this paper.

In designing fuzzy model, we encounter a major difficulty in the identification of an optimized fuzzy rule base, which is traditionally achieved by a tedious-and-error process. This paper presents an approach to automatic design of optimal fuzzy rule bases for modeling using evolutionary programming. Evolutionary programming evolves simultaneously the structure and the parameter of fuzzy rule base a given task. To check the effectiveness of the suggested approach, 3 examples for modeling are examined, and the performance of the identified models are demonstrated.

Fuzzy controllers show good performance in case of the systems being nonlinear and difficult to solve. But these fuzzy controllers have problems which have to decide suitable rules and membership functions. In general, we decide those using the heuristic methods or the experience of experts. Recently, G.A. have been studied in this field. The number of rules increase exponentially when the number of input and output increase. It also makes hard to decide the rules and membership functions even though we use G.A. In this paper, we suggest parallel fuzzy controllers, and also the method to decrease the number of rules. The excellent performance of these methods is confirmed through simulations.

IEEE 1149.1 boundary scan architecture is used as a standard in board-level system testing. The simplicity of this architecture is an advantage in system testing, but at the same time, it it makes a limitation of applications. Because of several problems such as 3-state net conflicts, or ambiguity issues, interconnect testing for multi-drop multi-board systems is more difficult than that of single board systems. A new approach using IEEE 1149.1 boundary scan architecture for multi-drop multi-board systems is developed in this paper. Adding boundary scan cells on backplane bus lines, each board has a complete scan-chain for interconnect test. This new scan-path insertion method on backplane bus using limited 1149.1 test bus less area overhead and mord efficient than previous approaches.