Robust adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems via small-gain approach

Yang, Yiling; Zhou, Changjiu

doi:10.1016/j.ins.2004.02.022

Cited by 92 publications

(57 citation statements)

References 50 publications

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“…To handle the control problem of uncertain nonlinear systems without satisfying matching conditions, in recent years, many backstepping-based adaptive fuzzy controllers have been developed for nonlinear systems in strict-feedback form (Yang et al, 2005;Wang et al, 2007;Zou et al, 2008;Chen et al, 2005;Tong et al, 2010a;2010b). Among them, are those for singleinput and single-output (SISO) nonlinear systems (Yang et al, 2005;Wang et al, 2007;Zou et al, 2008;Tong et al, 2010a;2010b), those for multiple-input and multipleoutput (MIMO) nonlinear systems (Chen et al, 2005;, and the ones for SISO nonlinear systems with dynamics and dynamical disturbances (Tong et al, 2010a;2010b).…”

Section: S Tong Et Almentioning

confidence: 99%

“…Among them, are those for singleinput and single-output (SISO) nonlinear systems (Yang et al, 2005;Wang et al, 2007;Zou et al, 2008;Tong et al, 2010a;2010b), those for multiple-input and multipleoutput (MIMO) nonlinear systems (Chen et al, 2005;, and the ones for SISO nonlinear systems with dynamics and dynamical disturbances (Tong et al, 2010a;2010b).…”

Section: S Tong Et Almentioning

confidence: 99%

“…Fuzzy logic systems are utilized to approximate unknown nonlinear functions, K-filters are used for estimating unmeasured states, and, combining the backstepping technique and the smallgain theorem, a new stable adaptive fuzzy output feedback robust control scheme is developed. The main advantages of the proposed control scheme are as follows: (i) by designing K-filters as a state observer, the proposed control method does not require that all the states of the system be measured directly, which is a common assumption in the existing adaptive fuzzy backstepping controller (Yang et al, 2005;Wang et al, 2007;Tong et al, 2010a;2010b); (ii) by combining backstepping design with input-to-state practically stability (ISpS) and the small-gain theorem, the proposed control method has a strong robustness to the modeled dynamics and dynamical disturbances, and the stability of entire closed-loop systems can be guaranteed by the small-gain theorem.…”

Section: S Tong Et Almentioning

confidence: 99%

“…It is noted that, in recent years, several adaptive fuzzy backsteping control approaches have also been developed by Yang et al (2005) and Tong et al (2010a;2010b) for some strict-feedback nonlinear systems based on smallgain theorem. However, the approach of Yang et al (2005) can only control a class of nonlinear systems without unmodeled dynamics or dynamical disturbances and requires that the states of the controlled systems must be measured.…”

Section: S Tong Et Almentioning

confidence: 99%

“…However, the approach of Yang et al (2005) can only control a class of nonlinear systems without unmodeled dynamics or dynamical disturbances and requires that the states of the controlled systems must be measured. Although the approaches of Tong et al (2010a;2010b) have addressed the same class of nonlinear systems as this paper, they also require that the states of the nonlinear systems must be measured.…”

Section: S Tong Et Almentioning

confidence: 99%

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Robust adaptive fuzzy filters output feedback control of strict-feedback nonlinear systems

Tong¹,

Liu²,

Li³

2010

International Journal of Applied Mathematics and Computer Science

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In this paper, an adaptive fuzzy robust output feedback control approach is proposed for a class of single input single output (SISO) strict-feedback nonlinear systems without measurements of states. The nonlinear systems addressed in this paper are assumed to possess unstructured uncertainties, unmodeled dynamics and dynamic disturbances, where the unstructured uncertainties are not linearly parameterized, and no prior knowledge of their bounds is available. In recursive design, fuzzy logic systems are used to approximate unstructured uncertainties, and K-filters are designed to estimate unmeasured states. By combining backstepping design and a small-gain theorem, a stable adaptive fuzzy output feedback control scheme is developed. It is proven that the proposed adaptive fuzzy control approach can guarantee the all the signals in the closed-loop system are uniformly ultimately bounded, and the output of the controlled system converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by a simulation example and some comparisons.

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Section: S Tong Et Almentioning

confidence: 99%

Section: S Tong Et Almentioning

confidence: 99%

Section: S Tong Et Almentioning

confidence: 99%

Section: S Tong Et Almentioning

confidence: 99%

Section: S Tong Et Almentioning

confidence: 99%

See 3 more Smart Citations

Robust adaptive fuzzy filters output feedback control of strict-feedback nonlinear systems

Tong¹,

Liu²,

Li³

2010

International Journal of Applied Mathematics and Computer Science

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Practical Robust Neural Path Following Control for Underactuated Marine Vessels with Actuators Uncertainties

Zhang

2016

Asian Journal of Control

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This note tackles the problem of path‐following control for underactuated ships with actuators uncertainties and unknown environmental disturbances. A novel dynamical virtual ship (DVS) guidance principle is initially developed to deal with the assumption that “the reference path could be generated by a virtual ship”, which is normal but not workable in the current literature. In addition, a practical robust control scheme is proposed using feedforward approximation, dynamic surface control, and robust neural damping techniques. In the algorithm, the strong couplings among state variables, underactuated characteristic and the actuators uncertainties are considered to guarantee semiglobal uniform ultimate boundedness of the closed‐loop system. The outstanding advantages are that the control law has a concise form and is easy to implement in practice.The control inputs of interest are two actuator‐steerable variables: the main engine speed and the rudder angle. Although the uncertainties are compensated by NNs, no adaptive parameters are derived for merits of the robust neural damping technique. Finally, the experiment has demonstrated the effectiveness of the proposed scheme, comparing with the LOS self‐tuning fuzzy control.

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Dynamic inversion control for a class of pure‐feedback systems

Gao

Sun

Liu

2010

Asian Journal of Control

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A dynamic inversion control method is proposed for a class of purefeedback nonlinear systems. By combing the back-steeping control method with the singular perturbation theory, the virtual and the final actual control inputs are derived from the solutions of a series of fast dynamical equations. Stability analysis shows that the system output tracks the desired trajectory with bounded errors, which can be made arbitrarily small by choosing appropriate design parameters. Tracking performance is illustrated by simulation results.

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Robust adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems via small-gain approach

Cited by 92 publications

References 50 publications

Robust adaptive fuzzy filters output feedback control of strict-feedback nonlinear systems

Robust adaptive fuzzy filters output feedback control of strict-feedback nonlinear systems

Practical Robust Neural Path Following Control for Underactuated Marine Vessels with Actuators Uncertainties

Dynamic inversion control for a class of pure‐feedback systems

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