• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.510-512
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• 1999

In this paper, we present a method for the analysis and design of fuzzy controller for nonlinear systems. In the design procedure, we represent the dynamics of nonlinear systems using a Takagi-Sugeno fuzzy model and formulate the controller rules, which shares the same fuzzy sets with the fuzzy system, using parallel distributed compensation method. Then, after the feedback gain of each local state feedback controller is obtained using the existing optimal pole-placement scheme, we construct an overall fuzzy logic controller by blending all local state feedback controller. Finally, the effectiveness and feasibility of the proposed fuzzy-model-based controller design method has been evaluated through an inverted pendulum system.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.9
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• pp.108-115
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• 1997

This paper presents a robust controller design for uncertain nonlinear systems with input saturation. In actual application, the robust controller may require a high input torque so that it faces input saturation due to power limitation of the system. The satruation problem may cause instability of the system. To improve this problem, a robust controller using a fuzzy logic control is proposed. The proposed controller keeps state errors bounded. To validate the proposed controller, an invert pendulum and its control system is set up. The experimental result shows bounded angular position errors under saturated input torques.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.263-272
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• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.188-193
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• 1989

This paper is proposed of multirate nonlinear controller for robot manipulator. The proposed controller is obtained by structure changes of feedback controller with C.T.M and for time differences commanded in caculating each term of controller, multirate sampling is used. And more robust controller is proposed by considering one-step ahead predictive action. In order to evaluate proposed controller, computer simulation is performed for a 3 D.O.F robot manipulator with varying load.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.797-802
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• 2010

In this paper, a systematic procedure to design a nonlinear controller for nonlinear active magnetic bearing (AMB) systems is presented. To do this, we effectively convert the AMB system into a polytopic quasi-linear parameter varying (LPV) system, which is a representation of nonlinear state-space models and is described by the convex combination of a set of precisely known vertices. Unlike the existing quasi-LPV systems, the nonlinear weighting functions, which construct the polytopic quasi-LPV model of the AMB system by connecting the vertices, include not only state variables but also the input ones. This allows us to treat the input nonlinearity effectively. By means of the derived polytopic quasi-LPV model and linear matrix inequality (LMI) conditions, nonlinear controller that stabilizes the AMB system is obtained. The effectiveness of the proposed controller design methodology is finally demonstrated through numerical simulations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.2
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• pp.52-60
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• 2009

This paper proposes a digital controller for a nonlinear multi-input/multi-output(MIMO) system with time-delayed input. A nonlinear system with multi-input time delay is discretized using Taylor's discretization method, and the discretized system can be converted into a general nonlinear system. Consequently, general nonlinear controller synthesis can be applied to the discretized time-delay system We adopted MAC controller synthesis and verified the performance of the proposed method by conducting computer simulations. The results of the simulation showed that the proposed controller synthesis performs well and the proposed method is useful for controlling a nonlinear time-delay system.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1863-1871
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• 2016

This paper presents an application of fractional order controller for the control of multi input multi output twin rotor aerodynamic system. Dynamics of the considered system are highly nonlinear and there exists a significant cross-coupling between the horizontal and vertical axes (pitch & yaw). In this paper, a fractional order model of twin rotor aerodynamic system is identified using input output data from nonlinear system. Based upon identified fractional order model, a fractional order PID controller is designed to control the angular position of level bar of twin rotor aerodynamic system. The parameters of controller are tuned using Nelder-Mead optimization and compared with particle swarm optimization techniques. Simulation results on the nonlinear model show a significant improvement in the performance of fractional order PID controller as compared to a classical PID controller.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1133-1145
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• 2015

A new neural network (NN) predictive controller (NNPC) algorithm has been developed and tested in the computer simulation of active control of a nonlinear structure. In the present method an NN is used as a predictor. This NN has been trained to predict the future response of the structure to determine the control forces. These control forces are calculated by minimizing the difference between the predicted and desired responses via a numerical minimization algorithm. Since the NNPC is very time consuming and not suitable for real-time control, it is then used to train an NN controller. To consider the effectiveness of the controller on probability of damage, fragility curves are generated. The approach is validated by using simulated response of a 3 story nonlinear benchmark building excited by several historical earthquake records. The simulation results are then compared with a linear quadratic Gaussian (LQG) active controller. The results indicate that the proposed algorithm is completely effective in relative displacement reduction.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.110-112
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• 2000

A new robust digital fuzzy controller design methodology is proposed for load frequency of nonlinear power system with valve position limits of governor in the presence of parametric uncertainties. The Takagi-Sugeno (TS) fuzzy model is adopted for fuzzy modeling of the nonlinear power system. A sufficient condition of robust stability for robust fuzzy control with parametric uncertainties is presented in the sense of Lyapunov. The controller that designed by preposed robust fuzzy controller design method is based compounding condition between continues system and discrete system. The effectiveness of controller that designed by the proposed robust fuzzy controller design method is demonstrated through simulation example.