• Journal of Mechanical Science and Technology
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• v.16 no.12
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• pp.1613-1626
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• 2002

This paper presents hybrid control of an active suspension system with a full-car model by using H$\sub$$\infty$/ and nonlinear adaptive control methods. The full-car model has seven degrees of freedom including heaving, pitching and rolling motions. In the active suspension system, the controller shows good performance: small gains from the road disturbances to the heaving, pitching and rolling accelerations of the car body. Also the controlled system must be robust to system parameter variations. As the control method, H$\sub$$\infty$/ controller is designed so as to guarantee the robustness of a closed-loop system in the presence of uncertainties and disturbances. The system parameter variations are taken into account by multiplicative uncertainty model and the system robustness is guaranteed by small gain theorem. The active system with H$\sub$$\infty$/ controller can reduce the accelerations of the car body in the heaving, pitching and rolling directions. The nonlinearity of a hydraulic actuator is handled by nonlinear adaptive control based on the back-stepping method. The effectiveness of the controllers is verified through simulation results in both frequency and time domains.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.9
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• pp.929-938
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• 2009

This paper presents a novel control methodology for communication network based nonlinear systems with time delay nature. We construct a nominal nonlinear control law for representing a linear model and a reset control system which is aimed for corrective control strategy to compensate system error due to uncertain time delay through wireless communication network. Next, online neural control approach is proposed for overcoming nonstationary statistical nature in the network topology. Additionally, DBN (Dynamic Bayesian Network) technique is accomplished for modeling of its dynamics in terms of casuality, which is then utilized for estimating prediction of system output. We evaluate superiority and reliability of the proposed control approach through numerical simulation example in which a nonlinear inverted pendulum model is employed as a networked control system.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.42-47
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• 1993

Importance of separation of a nonlinear dynamical system into nonlinear static part and linear dynamical part was insisted in designing a controller for the nonlinear system. We further proposed compensation techniques for oscillation of controlled variables caused by system time delay and compensation of steady state errors caused by modelling errors of the systems. The proposed principle of designing procedure and the compensation methods were discussed by applying them for temperature and level control of an actual tanked water system.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.33-35
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• 2000

We propose a intelligent controller for FACTS(Flexible AC Transmission System) device to stabilize a power system. In order to identify the nonlinear characteristics of the power system and to estimate a control signal, an artificial neural network is utilized. The control signal which is provided for FACTS device installed in the network is produced. The proposed controller is applied to Unified Power Flow Controller(UPFC) to verified the effectiveness of the proposed control system. The results show that the proposed nonlinear FACTS controller is able to enhance the transient stability of three machine nine bus power system.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.82-85
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• 1987

The objective of this paper is to develop computer programs to aid in the design and analysis of control systems in which nonlinear characteristics exist. Control systems are dynamic systems, which can be described using various mathematical models. A convenient model for digital computer simulation is the state model in which described using a set of linear and non linear first order differential equations. The digital simulation was performed on a IBM PC/XT personal computer, and the computer language was BASIC. There are four possible configurations from which a user may choose. When running a program, the user is asked to enter the system parameters according to a specified control system configurations are; 1. A control system with a nonlinear element followed by a plant in a feedback configurations(NLSVF1). 2. A control system with a nonlinear device situated between two plants in a feedback configurations(NLSVF2). 3. A control system with a nonlinear element followed by a plant, followed by a the dealy in feedback configurations(TLAG). 4. A motor and load with a backlash nonlinearity between dynamic portions of the motor/load configurations (BACKLASH). The matrix from state equations are integrated using combination the trapezoidal method and fixed point iteration. Several cases which have nonlinearity were implemented on the computer and the results were discussed.

• Journal of Power System Engineering
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• v.6 no.3
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• pp.49-56
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• 2002

This study suggests a trajectory tracking controller composed of an input output linearization compensator and a linear controller. The input output linearization compensator is derived from the nonlinear equations of a pneumatic control system and it algebraically transforms a nonlinear system dynamics into a linear one, so that input output characteristics of the control system is linearized regardless of the variation of the operating point and linear control techniques can be applied. The results of nonlinear simulations show that the proposed controller tracks the given trajectories more accurately than a state feedback controller does.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1165-1168
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Network(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Radial Basis Function Networks(RBFN). The learning with RBFN is fast and precise. This paper discusses RBFN as identification procedure is based on a nonlinear dynamical systems. and A design method of model follow control system based on RBFN controller is developed. As a result of applying this method to inverted pendulum, the simulation has shown that RBFN can be used as identification and control of nonlinear dynamical systems effectively.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.10
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• pp.555-562
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• 2000

We propose an adaptive nonlinear control algorithm for compensation of the stick-slip friction in a dynamic system. The friction force and mass of the system are estimated and compensated by adaptive control law. Especially, as the nonlinear control input in a small tracking error zone is enlarged by the nonlinear function, the steady state error is significantly reduced. The proposed algorithm is a direct adaptive control method based on the Laypunov stability theory, and its convergence is guaranteed under the bounded noise or torque disturbance. We verified the performance of the proposed algorithm by computer simulation on one-DOF mechanical system with friction.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.528-530
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• 1998

This paper addresses analysis and design of a fuzzy model-based-controller for the control of uncertain SISO nonlinear systems. In the design procedure, we represent the nonlinear system by using a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller via parallel distributed compensation and sliding mode control. Unlike other parallel distributed controllers. this globally stable fuzzy controller is designed without finding a common positive definite matrix for a set of Lyapunov equations, and has good tracking performance. Furthermore, stability analysis is conducted not for the fuzzy model but for the real underlying nonlinear system. A simulation is included for the control of the Duffing forced-oscillation system, to show the effectiveness and feasibility of the proposed fuzzy control method.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.371-374
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• 1999

This paper present the nonlinear control and the Lyapunov analysis of the nonlinear electro-hydraulic system for steering control of UCT. Electro-hydraulic system itself has the high nonlinearities arisen from the nonlinear characteristics of the pressure-fluid flow in valve and friction in cylinder. These nonlinearities are unmodeled terms in the transfer function. This paper presents the system modeling, analysis of stability based on the Lyapunov function and simulation of the nonlinear hydraulic servo system.