Adaptive Fixed-Time Fuzzy Control for Uncertain Nonlinear Systems with Asymmetric Time-Varying Full-State Constraints

Liu, Ruixia; Liu, Ming; Shi, Yan; Qu, Junsuo

doi:10.1007/s40815-023-01461-w

Cited by 3 publications

(9 citation statements)

References 43 publications

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“…This section includes a practical illustration to illustrate the effectiveness of the proposed control algorithm. A pulled car system is considered, which is modeled as [38]:…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…To further validate the effectiveness of the algorithm proposed in this article, we conducted a set of comparative simulation verifications in the revised manuscript. To ensure a fair comparison, we have designed the same parameters for the system model (92) in both the fixed-time controller presented in [38] and this paper proposed fixed-time controller. The simulation results obtained from the fixed-time controller proposed in [38] and this article introduced fixed-time controller are illustrated in Figures 6, 7 Based on Figures 6, 7, 10 and 11, it is apparent that the system state x 1 can track the desired signal within 50 s and 20 s, respectively, with the fixed-time controller proposed in [38] and the proposed fixed-time controller.…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…To ensure a fair comparison, we have designed the same parameters for the system model (92) in both the fixed-time controller presented in [38] and this paper proposed fixed-time controller. The simulation results obtained from the fixed-time controller proposed in [38] and this article introduced fixed-time controller are illustrated in Figures 6, 7 Based on Figures 6, 7, 10 and 11, it is apparent that the system state x 1 can track the desired signal within 50 s and 20 s, respectively, with the fixed-time controller proposed in [38] and the proposed fixed-time controller. From Figures 6 and 10, it is evident that the system state can track the desired signal within 50 s using the fixed-time controller in reference [38], and within 20 s using our controller.…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…Hence, the fixed-time control approach has emerged as a progression of the finite-time control algorithm, specifically designed to calculate the settling time of uncertain nonlinear systems, regardless of the initial conditions [33][34][35][36]. So far, considerable research has been dedicated to exploring fixed-time control techniques suitable for intricate engineering systems, such as airplanes, racing vehicles, and multiagent systems [37][38][39][40][41][42]. In [37], two innovative adaptive fault-tolerant fixed-time control schemes were introduced: the distributed fixed-time control (DFTC) method and the priority-based fixed-time control (PFTC) method.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed scheme ensures that the tracking error remains bounded within fixed time. (2) Unlike the fixed-time control algorithm discussed in [38], this paper takes into account the impact of unmodeled dynamics. The challenge posed by the presence of unmodeled dynamics is effectively resolved by the derived fixed-time controller.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics

Luo,

Zhang,

et al. 2024

Symmetry

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This article concentrates on the problem of fixed-time tracking control for a certain class of nonlinear systems with unmodeled dynamics. Unmodeled dynamics are prevalent in practical engineering systems, such as axially symmetric systems like robotic arms, spacecraft, and missiles. In this paper, the fuzzy-logic systems (FLSs) are implemented to address the challenge of accurately approximating the unknown nonlinear terms that arise during the derived control algorithm process. By employing fixed-time command filters (FTCF), the “explosion of complexity” issues encountered in traditional backstepping methods will be effectively resolved. Moreover, error compensation mechanisms are derived to effectively mitigate the filtering errors that may arise from the FTCFs. The computational burden associated with FLSs is reduced through the utilization of the weight vector estimation method based on the maximal norm and an adaptive approach. A fixed-time adaptive fuzzy tracking controller is developed within the backstepping control framework to ensure the boundedness of all signals and achieve fixed-time convergence of the tracking error for the controlled system. Illustrative examples are conducted to illustrate the viability of the derived controller.

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“…This section includes a practical illustration to illustrate the effectiveness of the proposed control algorithm. A pulled car system is considered, which is modeled as [38]:…”

Section: Illustrative Examplesmentioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics

Luo,

Zhang,

et al. 2024

Symmetry

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Adaptive Finite-Time Fuzzy Control for Uncertain Nonlinear Systems with Asymmetric Full-State Constraints

Hou,

Xu,

Liu

et al. 2023

Mathematics

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This paper studies the adaptive finite-time fuzzy control issue associated with uncertain nonlinear systems that exhibit asymmetric constraints on the full state. A distinct function, constrained by nonlinear states, is designed to mitigate the excessive breach of these full-state boundaries. Unlike the standard barrier Lyapunov function (BLF) method, this approach solves symmetric and asymmetric full-state constraints without modifying the controller structure, and it does not require any additional assumptions about virtual control to be met. Simultaneously employing approximating functions using fuzzy logic systems and incorporating dynamic surface control technology integrated with a first-order filter, the unknown nonlinear functions emanating from the suggested controller strategy are estimated. Additionally, this approach addresses the prevalent problem of complexity explosion observed in conventional backstepping techniques. An adaptive finite-time fuzzy tracking control strategy is introduced, ensuring that all signals and tracking errors of the controlled system remain bounded in finite time. Finally, two simulation examples are given to illustrate the effectiveness of the proposed control scheme, confirming that all states remain within the predefined regions.

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Adaptive Neural Network Prescribed Time Control for Constrained Multi-Robotics Systems with Parametric Uncertainties

Tang,

Lin,

Liu

et al. 2024

Mathematics

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This study designed an adaptive neural network (NN) control method for a category of multi-robotic systems with parametric uncertainties. In practical engineering applications, systems commonly face design challenges due to uncertainties in their parameters. Especially when a system’s parameters are completely unknown, the unpredictability caused by parametric uncertainties may increase control complexity, and even cause system instability. To address these problems, an adaptive NN compensation mechanism is proposed. Moreover, using backstepping and barrier Lyapunov functions (BLFs), guarantee that state constraints can be ensured. With the aid of the transform function, systems’ convergence speeds were greatly improved. Under the implemented control strategy, the prescribed time control of multi-robotic systems with parametric uncertainties under the prescribed performance was achieved. Finally, the efficacy of the proposed control strategy was verified through the application of several cases.

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Adaptive Fixed-Time Fuzzy Control for Uncertain Nonlinear Systems with Asymmetric Time-Varying Full-State Constraints

Cited by 3 publications

References 43 publications

Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics

Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics

Adaptive Finite-Time Fuzzy Control for Uncertain Nonlinear Systems with Asymmetric Full-State Constraints

Adaptive Neural Network Prescribed Time Control for Constrained Multi-Robotics Systems with Parametric Uncertainties

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