Distributed Adaptive Fixed-Time Robust Platoon Control for Fully Heterogeneous Vehicles

Liu, Yang; Yao, Deyin; Wang, Lijie; Lu, Shejie

doi:10.1109/tsmc.2022.3179444

Cited by 92 publications

(29 citation statements)

References 53 publications

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“…Theorem 1. Consider the SISO strict-feedback system (1) with unknown dynamics, there are optimal virtual controllers ( 13) and ( 29), optimal actual controller (34), the identifier ( 14), ( 30), (35), critic ( 15), ( 31), (36), and actor ( 16), ( 32), (37) NNs adaptive laws to ensure that following conclusions hold.…”

Section: Stability Analysismentioning

confidence: 99%

Reinforcement learning‐based optimized backstepping control for strict‐feedback nonlinear systems subject to external disturbances

Yan

Cao

et al. 2023

Optim Control Appl Methods

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This article investigates a reinforcement learning‐based optimal backstepping control strategy for strict‐feedback nonlinear systems, which contain output constraints, external disturbances and uncertain unknown dynamics. The simplified reinforcement learning algorithm with the identifier‐critic‐actor architecture is constructed in the control design to build optimal virtual and actual controllers. To compensate for the disturbance, a lemma is adopted to transform external disturbances into an unknown “bounding functions‘’, which satisfy a triangular condition. Moreover, the unknown nonlinear functions, which composed of unknown dynamics and external disturbances, approximated by neural networks. Meanwhile, in order to avoid violating output constraints, a barrier‐type Lyapunov function approach is integrated into the optimal control strategy to satisfy output constraints requirements under the framework of backstepping technique. Furthermore, the presented optimal control strategy guarantees that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded. Finally, the effectiveness of the proposed optimal control approach is performed by a numerical example.

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Section: Stability Analysismentioning

confidence: 99%

Reinforcement learning‐based optimized backstepping control for strict‐feedback nonlinear systems subject to external disturbances

Yan

Cao

et al. 2023

Optim Control Appl Methods

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“…With the wide application of multiagent systems (MASs) in unmanned aerial vehicles (UAVs) formations, biological systems and robotic teams, more and more control methods for MASs are proposed in References 1–14. For example, in Reference 15, the path following problem for a quadcopter aircraft which exists unknown parameters and external disturbances was solved by using backstepping method and adaptive control.…”

Section: Introductionmentioning

confidence: 99%

A data‐based event‐triggered control for switching topology nonlinear multiagent systems with DoS attacks

Liu

Cao

Liang

et al. 2023

Intl J Robust & Nonlinear

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Summary In this paper, a secure cooperative control strategy which integrates the data‐driven control method and the backstepping framework is proposed for a class of discrete‐time nonlinear multiagent systems with denial‐of‐service (DoS) attacks. First, by designing special index functions, we overcome the algebraic loop problem in the backstepping framework to propose a data‐driven backstepping control method which does not use the Lyapunov theory to design the controller. Second, the problem of DoS attacks in communication channels is solved by switching topology strategy. Then, an event‐triggered mechanism based on synchronization error is used to save communication resources, which does not have a great negative impact on the control performance. Moreover, an event‐triggered cooperative strategy is constructed to ensure that all signals and tracking errors of the closed‐loop system are bounded. Finally, two simulation examples verify the effectiveness of the proposed control scheme.

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“…In recent several decades, NN-based techniques have gained great attention. The NNs are used as observers in [27], [28], adaptive NN is used to approximate unknown uncertainties in the system's dynamics [7], [21], [29], [30], and a variety of different types of nonlinear systems have been explored by using NNs-based adaptive backstepping techniques to deal with unknown nonlinearities [31]- [35].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Guo

Liu

2023

IEEE Access

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An adaptive neural network command-filtered backstepping design algorithm is proposed for the underactuated translational oscillator with a rotating actuator (TORA). The system dynamics are transformed into a nonlinear cascade system through a global change of coordinates. Considering the weak sinusoid-type nonlinear interaction and affine-free appearance in the cascade model, the TORA is looked at as a pure feedback system. Two neural networks are used to approximate the unknown functions and a command filter is used to produce the virtual control and its first and second-order derivatives to overcome the explosion of complexity problems in backstepping. A filter error compensation dynamic system is designed to overcome the error influence on control performance. Taking full account of the cascade model structure of the underactuated TORA, each backstepping step is designed for a second-order subsystem to reduce the design steps and simplify the design process. The system stability is proved through the Lyapunov stability theorem and verified by numerical simulations.

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Distributed Adaptive Fixed-Time Robust Platoon Control for Fully Heterogeneous Vehicles

Cited by 92 publications

References 53 publications

Reinforcement learning‐based optimized backstepping control for strict‐feedback nonlinear systems subject to external disturbances

Reinforcement learning‐based optimized backstepping control for strict‐feedback nonlinear systems subject to external disturbances

A data‐based event‐triggered control for switching topology nonlinear multiagent systems with DoS attacks

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

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