Time-Varying Formation Tracking With Prescribed Performance for Uncertain Nonaffine Nonlinear Multiagent Systems

Yang, Yang; Si, Xuefeng; Yue, Dong; Tian, Yu-Chu

doi:10.1109/tase.2020.3019346

Cited by 57 publications

(35 citation statements)

References 52 publications

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“…In this setting, the tedious work of constructing an precision mathematical model of the system is avoided, and the anti‐interference ability is improved. Yang et al 18 focused on a formation tracking problem in consensus control of nonaffine MASs, where the uncertainties of each follower were estimated by the ESOs in ADRC. Meanwhile, the TD is commonly utilized to approximate the derivatives of complex functions in nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Active disturbance rejection‐based event‐triggered bipartite consensus control for nonaffine nonlinear multiagent systems

Cao

Niu

Zong

et al. 2023

Intl J Robust & Nonlinear

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This article investigates the active disturbance rejection‐based distributed event‐triggered bipartite consensus problem of nonaffine nonlinear multiagent systems with input saturation. To reduce the update frequency of the control signal, an event‐triggered mechanism is employed for each follower. Additionally, the active disturbance rejection technology, as a combination of the extended state observer and the tracking differentiator, is introduced to estimate the uncertainties of the system and address the problem of explosion of complexity in the backstepping design process. Compared with the traditional distributed bipartite consensus schemes using neural networks/fuzzy logic systems, the design complexity of the control law is reduced in the proposed scheme. Meanwhile, the tracking error is maintained within a predefined range via the funnel function. Finally, the stability of the system is proved under the Lyapunov stability analysis theory, and the effectiveness of the proposed strategy is verified via simulation examples.

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Section: Introductionmentioning

confidence: 99%

Active disturbance rejection‐based event‐triggered bipartite consensus control for nonaffine nonlinear multiagent systems

Cao

Niu

Zong

et al. 2023

Intl J Robust & Nonlinear

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“…Remark Main advantages of this NTD over the first‐order filter in the CDSC approach are its convergence in finite‐time, its ability in tracking the generalized derivatives signals, its smaller phase shift, and its lower computation complexity in deriving the derivatives of virtual inputs 45,51 . These advantages make it attractive in controller design and synthesis.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Adaptive neural network observer‐based filtered backstepping control for nonlinear systems with fuzzy dead‐zone and uncertainty

Shahriari-kahkeshi

Jahangiri‐heidari

Shi

2023

Adaptive Control & Signal

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This study proposes a high gain observer-based filtered backstepping control scheme for nonlinear systems with unknown dynamics and dead-zone constraint. Majority of the previously papers describe dead-zone by the conventional certain models, assume that all state variables are available and invoke the backstepping or conventional dynamic surface control (CDSC) approaches for controller design. This work describes the dead-zone by the fuzzy model, invokes radial basis function neural network (RBFNN) to model the unknown functions and proposes a RBFNN-based high gain observer to estimate unmeasurable states. To avoid the complexity explosion in the backstepping approach and eliminate the sensitivity to the time-constant of the first-order filters in the CDSC, a nonlinear tracking differentiator (NTD) with finite time convergence property is used instead of the first-order filters in the CDSC to obtain derivative of the virtual inputs. Moreover, the error compensation mechanism and auxiliary signal are proposed to eliminate the influence of the filtering error and input nonlinearity, respectively. Despite the fuzzy dead-zone, the proposed scheme makes the closed-loop signals uniformly ultimately bounded. In comparison with the existing results, (i) some assumptions like certain model of dead-zone and full state measurement are removed, (ii) "explosion of complexity" and sensitivity to the time constant of the first-order filters are eliminated, (iii) effect of the filtering error and input nonlinearity are compensated, (iv) number of adjustable parameters and online computational burden are decreased effectively. Simulation results on the two well-known examples verify the effectiveness and applicability of the proposed new design technique.

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“…Let

{\overset{x}{true}}_{italicei}

( i = 2, 3) denotes the estimates of the state x ei and

{\overset{x}{true}}_{italicei}

represents the estimation errors, that is,

{\overset{x}{true}}_{italicei} = x_{italicei} - {\overset{x}{true}}_{italicei}

. By referring to References 24,25, the structures of composite observer with parameter adaptation can be constructed as:

\begin{array}{l} ({, \begin{array}{l} {\dot{\overset{x}{true}}}_{1} goodbreak= {\overset{true^}{x}}_{2} goodbreak+ {\overset{true^}{D}}_{1} goodbreak+ 3 ω_{e 1} ((), x_{1} goodbreak- {\overset{true^}{x}}_{1}) \\ {\dot{\overset{x}{true}}}_{2} goodbreak= x_{3} goodbreak+ {\overset{true^}{boldθ}}_{π 1}^{T} φ_{2 d} goodbreak+ {\overset{true^}{x}}_{e 2} goodbreak+ 3 ω_{e 1}^{2} ((), x_{1} goodbreak- {\overset{true^}{x}}_{1}) \\ {\dot{\overset{x}{true}}}_{e 2} goodbreak= ω_{e 1}^{3} ((), x_{1} goodbreak- {\overset{true^}{x}}_{1}) \end{array}) \\ ({, \begin{array}{l} {\dot{\overset{x}{true}}}_{3} goodbreak= {\overset{true^}{boldθ}}_{π 2}^{T} φ_{3 d} goodbreak+ {\overset{true^}{x}}_{e 3} goodbreak+ 3 ω_{e 2} ((), x_{3} goodbreak- {\overset{true^}{x}}_{3}) \\ {\dot{\overset{x}{true}}}_{e 3} goodbreak= ω_{e} \end{array}) \end{array}

…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…Further approaches have been more focused on the impacts from time‐varying unknown disturbances caused by the variability of TEOGSP flight environment 24 . However, due to mismatching uncertainties input restrictions, the methods proposed in Reference 25 cannot effectively solve the problem of system with large modeling uncertainties in the form of time‐varying unknown disturbances. For convenience of theoretical analysis, the extended state observer (ESO) was developed as an alternative solution, which also displayed simplicity of parameter tuning 26 .…”

Section: Introductionmentioning

confidence: 99%

Composite‐observer‐based adaptive robust control of electrical‐optical gyro‐stabilized platform with largely unknown modeling uncertainties and partial state feedback

Yang

2022

Adaptive Control & Signal

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In this article, a desired compensation version of the output feedback controller (DOFCESO) without using velocity measurement signal is proposed for precise tracking control of the three-axis electrical-optical gyro-stabilized platform in the presence of largely unknown matched and mismatched modeling uncertainties. The proposed controller takes into account not only the system parametric deviations as well as the unmeasurable signal and external disturbances. To further handle the unmeasurable signal and the external disturbances, the composite extended state observer and nonlinear disturbance observer are constructed simultaneously via integrating adaptive nonlinear feedback tracking control design. To address the uncertainties arising from parametric deviations and external disturbances, the parameter adaptation mechanism is incorporated into the composite observer design to estimates of both the unmeasurable signal and the mismatched disturbance in the adaptive backstepping design. The tracking differentiator design is introduced here to estimate the derivative of the virtual control, leading to a much simpler control structure and reduced implementation costs. Furthermore, the proposed controller guarantees final tracking accuracy in the presence of time-invariant modeling uncertainties and preserves the performance results of both control methods while overcoming their practical performance limitations. Extensive comparative experimental results are obtained to verify the high-performance nature of the proposed control strategy.

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Time-Varying Formation Tracking With Prescribed Performance for Uncertain Nonaffine Nonlinear Multiagent Systems

Cited by 57 publications

References 52 publications

Active disturbance rejection‐based event‐triggered bipartite consensus control for nonaffine nonlinear multiagent systems

Active disturbance rejection‐based event‐triggered bipartite consensus control for nonaffine nonlinear multiagent systems

Adaptive neural network observer‐based filtered backstepping control for nonlinear systems with fuzzy dead‐zone and uncertainty

Composite‐observer‐based adaptive robust control of electrical‐optical gyro‐stabilized platform with largely unknown modeling uncertainties and partial state feedback

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