• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.865-871
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• 2009

In this paper, a novel feedback linearization is proposed for discrete-time nonlinear systems described by discrete-time T-S fuzzy models. The local linear models of a T-S fuzzy model are transformed to a controllable canonical form respectively, and their T-S fuzzy combination results in a feedback linearizable Tagaki-Sugeno fuzzy model. Based on this model, a nonlinear state feedback linearizing input is determined. Nonlinear state transformation is inferred from the linear state transformations for the controllable canonical forms. The proposed method of this paper is more intuitive and easier to understand mathematically compared to the well-known feedback linearization technique which requires a profound mathematical background. The feedback linearizable condition of this paper is also weakened compared to the conventional feedback linearization. This means that larger class of nonlinear systems is linearizable compared to the case of classical linearization.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.8
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• pp.1188-1194
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• 1997

In this paper, we design output feedback nonlinear dynamic control law by using state feedback nonlinear dynamic compensator and PI observer and show that the controller can stabilize globally and asymptotically a class of nonlinear systems with mismatched uncertainties. We also show that it is possible for a nonlinear system to use the output of PI observer in place of state variables in case that the nonlinear dynamic control law is used, similarly as in the linear system. The effectiveness of the proposed control law is demonstrated by a numerical simulation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.8
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• pp.1184-1184
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• 1997

In this paper, we design output feedback nonlinear dynamic control law by using state feedback nonlinear dynamic compensator and PI observer and show that the controller can stabilized globally and asymptotically a class of nonlinear systems with mismatched uncertainties. We also show that it is possible for a nonlinear system to use the output of PI observer in place of state variables in case that the nonlinear dynamic control law is used, similarly as in the linear system. The effectiveness of the proposed control law is demonstrated by a numerical simulation.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1843-1850
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• 2015

Controlling nonlinear systems with linear feedback control methods can lead to chaotic behaviors. Order increase in system dynamics due to integral control and control parameter variations in PID controlled nonlinear systems are studied for possible chaos regions in the closed-loop system dynamics. The Lur’e form of the feedback systems are analyzed with Routh’s stability criterion and describing function analysis for chaos prediction. Several novel chaotic systems are generated from second-order nonlinear systems including the simplest continuous-time chaotic system. Analytical and numerical results are provided to verify the existence of the chaotic dynamics.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.238-239
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• 2008

In this paper, active noise control (ANC) of a Volterra system with a nonlinear secondary path is proposed in the presence of a linear acoustic feedback, whereby the conventional ANC of a linear system with online acoustic feedback-path modeling is further extended to ANC of a Volterra system with a linear acoustic feedback path. In particular, the proposed ANC system consists of two adaptive Volterra filters (for nonlinear noise control and nonlinear adaptive noise cancellation) and one feedback-path modeling filter. Simulation results show that the proposed approach yields more effective reduction of disturbances arising from the acoustic feedback, in addition to high nonlinear ANC performance.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.19-27
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• 2003

It is well known that one can easily design a high-gain adaptive output feedback control for a class of nonlinear systems which satisfy a certain condition called output feedback exponential passivity (OFEP). The designed high-gain adaptive controller has simple structure and high robustness with regard to bounded disturbances and unknown order of the controlled system. However, from the viewpoint of practical application, it is important to consider a robust control scheme for controlled systems for which some of the assumptions of output feedback stabilization are not valid. In this paper, we design a robust high-gain adaptive output feedback control for the OFEP nonlinear systems with uncertain nonlinearities and/or disturbances. The effectiveness of the proposed method is shown by numerical simulations.

• Journal of IKEEE
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• v.18 no.2
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• pp.206-213
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• 2014

In this note, a new robust nonlinear output feedback variable structure controller is first systematically and generally designed for the output control of more affine uncertain nonlinear systems with mismatched uncertainties and matched disturbance. A transformed integral output feedback sliding surface with a most simple form is applied in order to remove the reaching phase problems. The closed loop exponential stability and the existence condition of the sliding mode on the integral output feedback sliding surface is investigated with a corresponding output feedback control input in Theorem 1. For practical application the continuous implementation of the control input is made by the modified saturation function. The effectiveness of the proposed controller is verified through a design example and simulation study.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.494-499
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• 2013

Based on the state-feedback adaptive neuro-control algorithm for a SISO nonaffine pure-feedback nonlinear system proposed in [15], an output-feedback controller is proposed in this paper. The output-feedback adaptive neural-net controller for the considered nonlinear system has not been previously proposed in any other literatures yet. The proposed output-feedback controller inherits all the advantages of [15] such that it does not adopt backstepping and this results in relatively simple control and adapting laws. Only one neural network is required for the proposed adaptive controller. The proposed neural-net control scheme expands the applicable class of nonlinear systems.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.6 no.2
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• pp.1-9
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• 2014

This paper presents a study on nonlinear time varying systems based on differential geometry. A brief introduction about controllability and involutivity will be presented. As an example, the exact feedback linearization and the approximate feedback linearization are used in order to show some application examples.