• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1821-1826
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• 2009

An adaptive neural controller for perturbed strict-feedback nonlinear system is proposed. All the previous adaptive neural (or fuzzy) controllers are based on the backstepping scheme where the universal approximators are employed in every design steps. These schemes involve virtual controls and their time derivatives that make the stability analysis and implementation of the controller very complex. This fact is called 'explosion of complexty ' since the complexity grows exponentially as the system dynamic order increases. The proposed adaptive neural control scheme adopt the backstepping design procedure only for determining ideal control law and employ only one neural network to approximate the finally selected ideal controller, which makes the controller design procedure and stability analysis considerably simple compared to the previously proposed controllers. It is shown that all the time-varing signals containing tracking error are stable in the Lyapunov viewpoint.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2009.05a
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• pp.128-132
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• 2009

Digital control system for active combustion control of lab-scale combustor equipped with secondary fuel injection is designed. Controllability with adaptive law is revealed with the Cambridge Combustor model and the requirement for control system is derived. The input and output requirements of frequency estimator and fuel supply actuator for the adaptive control law is verified with cold tests. The system can be used as digital based active combustion controller having 150Hz combustion instability.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1924-1929
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• 1991

The conventional sliding mode control and variable structure control (VSC) of nonlinear uncertain system are well known for their robust property and simplity of control law. However, the use of them is only pardonable on the assumption that the upper-bound of parameter variation or nonlinearity is known and that the complete information about state is available. Though the former has been solved with adaptive robust control theory recently, the latter seems not to be solved. In this paper, we try to solve this problem using the technique of VSS adaptive robust control theory. That is, we propose a VSS adaptive observer and a sliding mode control incorporated with this observer. We can prove the robust stability of the closed system applying the Lyapunov's second method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.852-857
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• 2008

A new adaptive neuro-control algorithm for a SISO strict-feedback nonlinear system is proposed. All the previous adaptive neural control algorithms for strict-feedback nonlinear systems are based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semi-global sense.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2441-2443
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• 2003

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. 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. The computer simulation results for an inverted pendulum system show the performance of the proposed robust adaptive fuzzy controller.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.443-443
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• 2000

A systematic scheme is developed fer the design of new adaptive feedback linearizing controllers for nonlinear systems. The developed adaptation law estimates the uncertain time-varying parameters using the structure of diffeomorphisrn. Our scheme is applicable to a class of nonlinear systems which violates the restrictive parametric-pure-feedback condition [4]-[6].

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.445-445
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• 2000

This paper proposes the MIMO indirect adaptive fuzzy controller to control the two-link manipulators. The input-output linearization technique, equivalent control input plus integral term, augmented error model and recursive least square adaptive law are used fer the controller. The linear type of fuzzifier-defuzzifier fuzzy logic system used for nonlinear function makes easy to farm the error model and able to follow the adaptive system approach. Such that control approach, the control system is not required joint speed and accerelation measurement and easy to implement and tune. The simulation results showed that the proposed controller has good control performance, stability, very small tracking error, decoupling, fast convergence, robust to parameter variation and load.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1492-1495
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• 1996

In this paper, an approach to autopilot design based on the robust nonlinear dynamic inversion method is proposed. Both unknown parameters and uncertainty bounds are estimated and parameter estimates are used in the fast inversion. Furthermore, to get more robustness slow inversion is incorporated with MRAC(Model Reference Adaptive Control) and sliding mode control where the estimates of uncertainty bounds are used. The proposed method is applied to the pitch autopilot design of a missile system and excellent performance is shown via computer simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• Smart Structures and Systems
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• v.29 no.4
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• pp.617-624
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• 2022

A new intelligent adaptive control scheme was proposed that combines the control based on interference observer and fuzzy adaptive s-curve for flight path tracking control of unmanned aerial vehicle (UAV). The most important contribution is that the control configurations don't need to know the uncertainty limit of the vehicle and the influence of interference is removed. The proposed control law is an integration of fuzzy control estimator and adaptive proportional integral (PI) compensator with input. The rated feedback drive specifies the desired dynamic properties of the closed control loop based on the known properties of the preferred acceleration vector. At the same time, the adaptive PI control compensate for the unknown of perturbation. Additional terms such as s-surface control can ensure rapid convergence due to the non-linear representation on the surface and also improve the stability. In addition, the observer improves the robustness of the adaptive fuzzy system. It has been proven that the stability of the regulatory system can be ensured according to linear matrix equality based Lyapunov's theory. In summary, the numerical simulation results show the efficiency and the feasibility by the use of the robust control methodology.