Observer and Command-Filter-Based Adaptive Fuzzy Output Feedback Control of Uncertain Nonlinear Systems

Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Yu, Haisheng

doi:10.1109/tie.2015.2418317

Cited by 338 publications

(140 citation statements)

References 35 publications

Supporting

Mentioning

140

Contrasting

Order By: Relevance

“…Remark 2.3: Assumption 2.1 is reasonable and realistic since the energy of a desired trajectory (leader) in practical applications is always limited. It is also worth mentioning that Assumption 2.1 is commonly employed in many relevant literatures such as Chen, Liu, Ge, and Lin (2012), Chen et al (2015a) and Yu, Shi, Dong, and Yu (2015).…”

Section: Remark 22mentioning

confidence: 99%

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

Wang

Yan

et al. 2016

International Journal of Systems Science

View full text Add to dashboard Cite

This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series (FS) to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy.

show abstract

Section: Remark 22mentioning

confidence: 99%

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

Wang

Yan

et al. 2016

International Journal of Systems Science

View full text Add to dashboard Cite

show abstract

“…In reference [20], authored by Yu et al, adaptive fuzzy output feedback control is considered for a class of strict-feedback systems with parametric uncertainties and unmeasured states using the observer and command filter. Fuzzy logic systems are used to approximate the unknown and nonlinear functions and fuzzy state observer is to estimate the unmeasurable states.…”

Section: E Practical/emerging Estimation Techniquesmentioning

confidence: 99%

Advances in Disturbance/Uncertainty Estimation and Attenuation [Guest editors' introduction]

Chen

Ohnishi

Guo

2015

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

“…The main idea of this methodology is to employ a universal approximator() to model the unknown uncertainties in system dynamics, and a stable controller is constructed by fusing adaptive technique with backstepping. () Many significant results on output‐feedback control, quantized control, output tracking,() and sampled‐data control have been reported.…”

Section: Introductionmentioning

confidence: 99%

Robust approximation‐based adaptive control of multiple state delayed nonlinear systems with unmodeled dynamics

Shi

Lim

et al. 2018

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

Summary This paper addresses the problem of tracking control for a class of uncertain nonstrict‐feedback nonlinear systems subject to multiple state time‐varying delays and unmodeled dynamics. To overcome the design difficulty in system dynamical uncertainties, radial basis function neural networks are employed to approximate the black‐box functions. Novel continuous functions that deal with whole states uncertainties are introduced in each step of the adaptive backstepping to make the controller design feasible. The robust problem caused by unmodeled dynamics when constructing a stable controller is solved by employing an auxiliary signal to regulate its boundedness. A novel Lyapunov‐Krasovskii functional is developed to compensate for the delayed nonlinearity without requiring the priori knowledge of its upper bound functions. On the basis of the proposed robust adaptive neural controller, all the closed‐loop signals are semiglobal uniformly ultimately bounded with good tracking performance.

show abstract

Observer and Command-Filter-Based Adaptive Fuzzy Output Feedback Control of Uncertain Nonlinear Systems

Cited by 338 publications

References 35 publications

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

Advances in Disturbance/Uncertainty Estimation and Attenuation [Guest editors' introduction]

Robust approximation‐based adaptive control of multiple state delayed nonlinear systems with unmodeled dynamics

Contact Info

Product

Resources

About