• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.95-105
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• 2001

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control(AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model(SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied for the control of an uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control(CMFC).

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.44-49
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• 2001

For minimum phase systems, the conventional fuzzy logic controllers (FLCs) use the error and the change-of-error as fuzzy input variables. Then the control rule table is a skew symmetric type, that is, it has UNLP (Upper Negative and Lower Positive) or UPLN property. This property allowed to design a single-input FLC (SFLC) that has many advantages. But its control parameters are not automatically adjusted to the situation of the controlled plant. That is, the adaptability is still deficient. We here design a single-input direct adaptive FLC (SDAFLC). In the AFLC, some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules are adjusted by an adaptive law. The SDAFLC is designed by a stable error dynamics. We prove that its closed-loop system is globally stable in the sense that all signals involved are bounded and its tracking error converges to zero asymptotically. We perform computer simulations using a nonlinear plant and compare the control performance between the SFLC and the SDAFLC.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.62.4-62
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• 2001

In this paper, we propose a delay-dependent guaranteed cost controller design method for uncertain linear systems with time delay. The uncertainty is norm bounded and time-varying. A quadratic cost function is considered as the performance measure for the given system. Based on the Lyapunov method, sufficient condition, which guarantees that the closed-loop system is asymptotically stable and the upper bound value of the closed-loop cost function is not more than a specied one, is derived in terms of Linear Matrix Inequalities(LMIs) that can be solved sufficiently. A convex optimization problem can be formulated to design a guaranteed cost controller, which minimizes the upper bound value of the cost function. Numerical examples show the activeness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.85.4-85
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• 2001

A learning controller is presented for repetitive walking motion of biped robot. The learning control scheme learns the approximate inverse dynamics input of biped walking robot and uses the learned input pattern to generate an input profile of different walking motion from that learnt. In the learning controller, the PID feedback controller takes part in stabilizing the transient response of robot dynamics while the feedforward learning controller plays a role in computing the desired actuator torques for feedforward nonlinear dynamics compensation in steady state. It is shown that all the error signals in the learning control system are bounded and the robot motion trajectory converges to the desired one asymptotically. The proposed learning control scheme is ...

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.271-274
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• 1995

This paper is concerned with the problem of robust stabilization of uncertain single-input and single-output nonlinear systems. Based on the input/output linearization approach for nonlinear state feedback synthesis in conjunction with Lyapunov methods, a stabilizing state feedback controller is proposed. Compared with the controllers reported in the control literature, instead of uniform ultimate boudedness, the controller proposed in this paper can guarantee uniform asymptotic stability of nonlinear systems in the presence of uncertainties. The required information about uncertain dynamics in the system is only that the uncertainties are bounded in Euclidean norm by known functions of the system state.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.98-103
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• 2000

This paper is concerned with a delay-dependent sliding mode scheme for the robust stabilization of input-delay systems with bounded unknown uncertainties. A sliding surface based ona predictor is proposed to minimize the effect of the input delay. Then, a robust control law is derived to ensure the existence of a sliding mode on the surface. In input-delay systems, uncertainties given during te delayed time are not directly controlled by the switching control because of causality prolem of them. They can influence the stability of the system in the sliding mode. Hence, a delay-dependent stability analysis for reduced order dynamics is employed to estimate maximum delay bound such that the system is globally asymptotically stable in the sliding mode. A numerical example is given to illustrate the design procedure.

• Journal of Mechanical Science and Technology
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• v.18 no.3
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• pp.337-346
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• 2004

The control scheme using fuzzy modeling and Parallel Distributed Compensation (PDC) concept is proposed to provide asymptotic tracking of a reference signal for the flexible joint manipulators with uncertain parameters. From Lyapunov stability analysis and simulation results, the developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop multi-input/multi-output system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.233-236
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• 2002

We consider a single-input-single-output nonlinear system which can be represented in a normal form. The nonlinear system has a modeling uncertainties including the input coefficient uncertainties. A high-gain observer is used to estimate the states variables to reject a modeling uncertainty. A globally bounded output feedback integral sliding mode control is proposed to stabilize the closed loop system. The proposed integral sliding mode control can asymptotically stabilize the closed loop system in the it presence of input coefficient uncertainty.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.577-582
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• 1999

Using a sole input variable simplifies the design process for the fuzzy logic controller(FLC). This is called single-input fuzzy logic controller(SFLC). However it is still deficient in the capability of adapting to the varying operating conditions. We here design a single-input adaptive fuzzy logic controller(AFLC) using a switching function of the sliding mode control. The AFLC can directly incorporate linguistic fuzzy control rules into the controller. Hence some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules can be adjusted by an adaptive law. In the proposed AFLC center values of fuzzy sets are directly adjusted by a fuzzy logic system. We prove that 1) its closed-loop system is globally stable in the sense that all signals involved are bounded and 2)its tracking error converges to zero asymptotically. We perform computer simulation using a nonlinear plant.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1597-1600
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• 2010

In this paper, a direct controller for nonlinear plants using a neural network is presented. The composed of the control input by using RBF neural networks and auxiliary input to compensate for effects of the approximation errors and disturbances. In the results, using this scheme, the output tracking error between the plant and the reference model can asymptotically converge to zero in the presence of bounded disturbances and approximation errors. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.