Predefined-time robust control with formation constraints and saturated controls

Yang, Xiaowu; Fan, Xiaoping; Long, Fei; Li, Gan-rong

doi:10.1007/s11071-022-07670-6

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…In the second stage t ≥ (1 − ω)T, the time derivative of V can be obtained from ( 19), ( 23), (25), and Condition 2 as…”

Section: Proof Consider a Lyapunov Function Candidate Asmentioning

confidence: 99%

“…Recently, there has been a great deal of attention on the analysis of a class of systems known as predefined-time stable systems [21][22][23][24][25][26], since the settling time boundary of such systems is an exactly known design parameter. In the view of the advantage of predefinedtime stability, predefined-time system identification should be also investigated for the sake of estimation time presetting.…”

Section: Introductionmentioning

confidence: 99%

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Concurrent Learning-Based Two-Stage Predefined-Time System Identification

Liu,

Zhang,

2024

Electronics

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This paper proposes a novel two-stage predefined-time system identification algorithm for uncertain nonlinear systems based on concurrent learning. The main feature of the algorithm is that the convergence time of estimation error is an exact predefined parameter, which can be known and adjusted directly by users. Historic identification data are stored in the first stage to guarantee that a finite-rank condition is satisfied. In the second stage, the estimation error converges to zero for linearly parameterized uncertain systems, or it is regulated into the neighborhood of zero for unknown systems modeled by neural networks. The identification algorithm takes effect without the restrictive requirement of the persistent excitation condition. Simulation examples verify the effectiveness of the proposed method.

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“…In the second stage t ≥ (1 − ω)T, the time derivative of V can be obtained from ( 19), ( 23), (25), and Condition 2 as…”

Section: Proof Consider a Lyapunov Function Candidate Asmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Concurrent Learning-Based Two-Stage Predefined-Time System Identification

Liu,

Zhang,

2024

Electronics

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“…In some recent works, [14][15][16][17] the predefined-time control was utilized in combination with the SMC method to improve the robustness for first-order systems, but these controllers only consider the predefined-time stability in the reaching phase. Furthermore, some works [18][19][20][21][22][23] made an attempted to extend the predefined-time stability to second-order dynamic systems, even high-order systems. 24,25 In the work of Sánchez-Torres et al 18 and Aldana-López et al, 19 the predefined-time SMCers for the second-order system can guarantee the predefined-time convergence of the reaching phase and the sliding phase, but their upper bounds of settling time are often too conservative.…”

Section: Introductionmentioning

confidence: 99%

Nonconservative adaptive practical predefined‐time sliding mode tracking of uncertain robotic manipulators

Sai,

Xu,

Cui

2023

Intl J Robust & Nonlinear

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In this article, a nonconservative predefined‐time sliding mode control (SMC) scheme and an adaptive practical predefined‐time SMC scheme are proposed for trajectory tracking of uncertain robotic manipulators. The nonconservative predefined‐time SMCer can achieve strong predefined‐time stability of a class of second‐order systems without dynamic uncertainties and disturbances. Nonetheless, even in the dynamic uncertainties and disturbances, we show that the studied scheme provides a nonconservative upper bound of settling time. Moreover, considering the unknown coupling uncertainty of the robotic system, adaptive laws are proposed to estimate the upper bound of the coupling uncertainty. Based on the proposed practical predefined‐time stability criterion, the designed adaptive practical predefined‐time SMCer is shown that the tracking error of the system can converge to a neighborhood of the origin within a predefined time, and the proposed controller weakens the chattering and no prior knowledge of the upper bound of the system uncertainty is required. Several simulation examples are conducted to show the feasibility of the proposed controller, especially evaluating the robustness and the nonconservative settling time.

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“…Many formation strategies have been proposed to address the cooperative control problem of multiagent systems under the practical constraints [16][17][18][19][20]. In [21], a fractional-order proportional-integral scheme based robust controller is presented to address the formation control problem of multiagent systems subject to constant disturbances, and convergence speed of the proposed controller is discussed.…”

mentioning

confidence: 99%

Adaptive Learning Gain based Resilient Control of Multiagent Systems with Partial Uncertain Control Coefficients Under FDI Attacks

Yang,

Long,

Lei

2023

Preprint

Self Cite

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This paper presents a novel approach to security resilient control in collective multiagent systems, incorporating an adaptive learning gain mechanism. The proposed method addresses challenges posed by bounded external disturbances, partial false-data-injection (FDI) attacks on the actuators, and partial unknown time-varying control coefficients. The presented synthesized control consists of following parts. Specifically, a robust adaptive control law with implicit learning is proposed to address the modeling parameterized uncertainties and disturbances. An adaptive learning gain-based control technique is designed to reduce the adversarial effect caused by mismatched disturbances with time-varying parameters, which curbs the cooperative errors to the assigned field. A resilient control approach is established to mitigate the detrimental impact of malicious attacks on actuators. An adaptive control strategy, utilizing a novel congelation of variables, is implemented to address the control issue pertaining to partially unknown control coefficients. This technique ensures that control inputs align with the practical actuators by establishing feasible control bounds. The property of the resulting closed-loop error multiagent systems is analyzed. Finally, the efficacy of the designed controller is illustrated by providing an illustrative example.

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Predefined-time robust control with formation constraints and saturated controls

Cited by 5 publications

References 39 publications

Concurrent Learning-Based Two-Stage Predefined-Time System Identification

Concurrent Learning-Based Two-Stage Predefined-Time System Identification

Nonconservative adaptive practical predefined‐time sliding mode tracking of uncertain robotic manipulators

Adaptive Learning Gain based Resilient Control of Multiagent Systems with Partial Uncertain Control Coefficients Under FDI Attacks

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