For a parametric uncertain system, there are many results on stability analysis, but only a few synthesis methods. In this paper, we proposed a new target polynomial decision method for the parametric uncertain system to stabilize the closed loop system with maximal parametric $l_2$ stability margin. To this, we used both Lipatov Theorem and coefficient diagram method(CDM). To show the effectiveness of the proposed method, we designed a robust controller for the inverted pendulum system with parametric uncertainties using fixed order pole assignment(FOPA) method and its performance was compared with that of the ${\mu}$ synthesis methods.

LMS algorithm has been used for plant identifier and noise cancellation. This algorithm has been researched for performance enhancement of filtering. The design and development of a reliable system has been becoming a key issue in industry field because the reliability of a system is considered as an important factor to perform the system's function successfully. And the computing with reliability and fault tolerance is a important factor in the case of aviation, system communication, and nuclear plant. This paper presents design of reliable adaptive filter with fault tolerance. Generally, redundancy is used for reliability. In this case it needs computing or circuit for voting mechanism, or fault detection. Therefore it has simple computing, and practicality for application. And in this paper, reliability of adaptive filter is analyzed. The effectiveness of the proposed adaptive filter is demonstrated to the case studies of plant identifier and noise cancellation by using DSP.

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. In indirect adaptive fuzzy control, based on the proved approximation capability of fuzzy systems, they are used to capture the unknown nonlinearities of the plant. Until now, most of the papers in the field of controller design for nonlinear system considers the affine system using fuzzy systems which have fixed grid-rule structure. We proposes a dynamic fuzzy rule insertion scheme where fuzzy rule-base grows as time goes on. With this method, the dynamic order of the controller reduces dramatically and an appropriate number of fuzzy rules are found on-line. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed-loop system is guaranteed.

This paper describes a neural network based fuzzy PID control scheme. The PID controller is being widely used in industrial applications. However, it is difficult to determine the appropriated PID gains in nonlinear systems and systems with long time delay and so on. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based self tuning fuzzy PID controller of which output gains were adjusted automatically. The tuning parameters of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods. Then they were adjusted by using proposed neural network learning algorithm. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The experiment on the magnetic levitation system, which is known to be heavily nonlinear, showed the proposed controller's excellent performance.

A robust adaptive technique for the longitudinal control of a platoon of automated vehicles is presented. A nonlinear model is used to represent the dynamics of each vehicle within the platoon. The external disturbances such as wind gust and a disturbance term due to engine transmission variations and so on are considered. The state observer is used to avoid direct measurement of the relative velocity or acceleration between the controlled and leading vehicles or the controlled vehicles's acceleration. The proposed controller guarantees to recover platoon stability in operation even if a speed dependent spacing policy is adopted, which incorporates a constant time headway in addition to the constant distance. It is shown that the proposed observer is exponentially stable, and the at the robust adaptive controller is stable. The simulation results demonstrate excellent tracking even in the presence of disturbances.

This paper investigates the direct adaptive control of nonlinear systems using RBFN(radial basis function networks). The structure of the controller consists of a fixed PD controller and a RBFN controller in parallel. An adaptation law for the parameters of RBFN is developed based on the Lyapunov stability theory to guarantee the stability of the overall control system. The filtered tracking error between the system output and the desired output is shown to be UUB(uniformly ultimately bounded). To evaluate the performance of the controller, the proposed method is applied to the trajectory contro of the two-link manipulator.

With increasing complexity of digital logic circuits, fast and accurate verification of functional behaviour becomes most critical bottleneck in meeting time-to-market requirement. This paper presents several techniques for accelerating a cycle-based logic simulation. The acceleration techniques include parallel pattern logic evaluation, circuit size reduction, and the partition of feedback loops in sequential circuits. Among all, the circuit size reduction plays a critical role in maximizing logic simulation speedup by reducing 50% of entire circuit nodes on the average. These techniques are incorporated into a levelized table-driven logic simulation system rather than a compiled-code simulation algorithm. Finally, experimental results are given to demonstrate the effectiveness of the proposed acceleration techniques. Experimental results show more than 27 times performance improvement over single pattern levelized logic simulation.