• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.42-47
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• 2014

This paper investigates an adaptive approximation design problem for the tracking control of output-constrained non-affine pure-feedback systems. To satisfy the desired performance without constraint violation, we employ a barrier Lyapunov function which grows to infinity whenever its argument approaches some limits. The main difficulty in dealing with pure-feedback systems considering output constraints is that the system has a non-affine appearance of the constrained variable to be used as a virtual control. To overcome this difficulty, the implicit function theorem and mean value theorem are exploited to assert the existence of the desired virtual and actual controls. The function approximation technique based on adaptive neural networks is used to estimate the desired control inputs. It is shown that all signals in the closed-loop system are uniformly ultimately bounded.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.9
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• pp.1319-1329
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• 2012

Globally stabilizing adaptive fuzzy state- and output-feedback controllers for the fully nonaffine pure-feedback nonlinear system are proposed in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controllers require no backstepping design procedures. Avoiding backstepping makes the controller structure and stability analysis to be considerably simplified. For the global stabilty of the clossed-loop system, the self-structuring fuzzy system whose memebership functions and fuzzy rules are automatically generated and tuned is adopted. The proposed controllers employ only one fuzzy logic system to approximate unknown nonlinear function, which highlights the simplicity of the proposed adaptive fuzzy controller. Moreover, the output-feedback controller of the considered system proposed in this paper have not been dealt with in any literature yet.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.47-56
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• 1994

The dynamic feedback is well-known to be much more powerful tool in control than the static one. This paper deals with the dynamic feedback linearization of the nonlinear systems which are not (static) feedback linearizable. The dynamic feedback linearization problem is however too difficult to solve at momemt. Thus we introduce a restricted class of the dynamic feedback (pure integrators followed by the static feedback) which is often used to study the problems using dynamic feedback and obtain the necessary and sufficient conditions of the linearization problem using this class of the dynamic feedback.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.153-158
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• 2017

This paper investigates a robust low-complexity design problem for tracking control of uncertain switched pure-feedback systems in the presence of unknown control direction. The completely unknown non-affine nonlinearities are assumed to be arbitrarily switched. By combining the nonlinear error transformation technique and Nussbaum-type functions, a robust tracking controller is designed without using any adaptive function approximators. Thus, compared with existing results, the proposed control scheme has the low-complexity property. From Lyapunov stability theory, it is shown that the tracking error remains within the preassigned transient and steady-state error bounds.

• 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].

• Journal of IKEEE
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• v.23 no.2
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• pp.716-721
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• 2019

A novel switching differentiator-based backstepping controller for uncertain pure-feedback nonlinear systems is proposed. Using asymptotically convergent switching differentiator, time-derivatives of the virtual controls are directly estimated in every backstepping design steps. As a result, the control law has an extremely simple form and asymptotical stability of the tracking error is guaranteed regardless of parametric or unstructured uncertainties and unmatched disturbances in the considered system. It is required no universal approximators such as neural networks or fuzzy logic systems that are adaptively tuned online to cope with system uncertainties. Simulation results show the simplicity and performance of the proposed controller.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.523-525
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• 2006

Adaptive neural state-feedback controllers for the fully nonaffine pure-feedback nonlinear system are presented in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controllers require no backstepping design procedures. Avoiding backstepping makes the controller structure and stability analysis considerably to be simplified. The proposed controllers employ only one neural network to approximate unknown ideal controllers, which highlights the simplicity of the proposed neural controller.

• Journal of IKEEE
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• v.14 no.3
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• pp.182-189
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• 2010

A new Adaptive neural state-feedback controller for the fully nonaffine pure-feedback nonlinear system are presented in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controller requires no backstepping design procedure. Avoiding backstepping makes the controller structure and stability analysis considerably simple. The proposed controller employs only one neural network to approximate unknown ideal controllers, which highlights the simplicity of the proposed neural controller. Simulation examples demonstrate the efficiency and performance of the proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1604-1607
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• 2004

In this paper, we consider a problem of asymptotic output regulation of a class of uncertain nonlinear systems by output feedback. The system under consideration is in the Parametric-Pure-Feedback Form, which does not satisfy the existing conditions such as the triangularity condition or the Lipschitz condition. We propose a linear output feedback controller with a scaling factor, which asymptotically regulates the output of the considered system.