Adaptive sliding mode control for uncertain nonlinear switched system under probabilistic replay attacks: The output feedback approach

Zhang, Yujiao; Hu, Jun; Zhang, Hongxu; Yi, Xiaojian

doi:10.1002/acs.3635

Cited by 4 publications

(1 citation statement)

References 51 publications

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“…The main differences of sliding mode control from other control methods lie in its dynamic and intentional adaptation to the current state of the system. It offers several advantages, including rapid response speed and no need for online system identification [30][31][32]. However, due to estimation and linear idealization errors during the mathematical modeling process, a large switching gain to handle the uncertainty caused by these problems may lead to oscillations [33].…”

Section: State Of the Artmentioning

confidence: 99%

Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

Lai,

Wang

2023

Applied Sciences

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A pre-emptive braking control method is proposed to improve the stability of autonomous vehicles during emergency collision avoidance, aiming to imitate the realistic human driving experience. A linear model predictive control is used to derive the front wheel steering angle to track a predefined fifth-degree polynomial trajectory. Based on a two-degrees-of-freedom (DOF) vehicle dynamics model, the maximum stable vehicle speed during collision avoidance can be determined. If the actual vehicle speed exceeds the maximum stable vehicle speed, braking action will be applied to the vehicle. Furthermore, four-wheel steering (4WS) control and direct yaw moment control (DYC) are employed to further improve the stability of the vehicle during collision avoidance. Simulation results under a double lane change scenario demonstrate that the control system incorporating pre-emptive braking, 4WS, and DYC can enhance the vehicle stability effectively during collision avoidance. Compared to the 2WS system without pre-emptive braking control, the maximum stable vehicle speed of the integrated control system can be increased by at least 56.9%. The proposed integrated control strategy has a positive impact on the safety of autonomous vehicles, and it can also provide reference for the research and development of autonomous driving systems.

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Section: State Of the Artmentioning

confidence: 99%

Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

Lai,

Wang

2023

Applied Sciences

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Composite‐observer‐based asynchronous control for hidden Markov nonlinear systems with disturbances

Cheng,

He,

Wang

et al. 2024

Adaptive Control & Signal

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SummaryIn this article, an asynchronous adaptive tracking control approach is presented for a type of hidden Markov jump nonlinear systems with external disturbances. In this joint jump process model, hidden Markov model signifies the dynamics of the actual system, whereas the signal emits from the detector symbolizes the transmitted information. This leads to the phenomenon of asynchronization between the modes of the system and that of the controller. Accordingly, an asynchronous observer is developed by using the mode information from the detector to develop an asynchronous control approach. The observer contains a disturbance estimation part, to compensate the unknown external inputs. Utilizing the backstepping scheme, a strict‐feedback asynchronous tracking controller is formulated, guaranteeing that all signals within the closed‐loop system are semi‐globally uniformly ultimately bounded in probability. Finally, the validity of the presented methodology is illustrated by means of a simulation example.

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Spatiotemporal fault estimation for switched nonlinear reaction–diffusion systems via adaptive iterative learning

Peng,

Song,

Song

et al. 2024

Adaptive Control & Signal

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SummaryIn this paper, an iterative learning‐based spatiotemporal fault estimation issue in switched reaction–diffusion systems is investigated. Initially, average dwell‐time switching rules are utilized to describe a class of switched reaction–diffusion systems characterized by mode jumps. Then, different from the existing fault estimation methods, a fault estimator is designed for spatiotemporal faults to realize an accurate estimation of faults by using the iterative learning strategy. Subsequently, to improve the speed of fault estimation, an adaptive iterative learning‐based fault estimation law is proposed, which can achieve faster fault estimation by continuously adjusting the iterative learning gain. Moreover, sufficient conditions for the convergence of the fault estimation error are obtained by using the ‐norm and the mathematical induction methods. Finally, an illustrative example is presented to check the practicality and superiority of the proposed fault estimation scheme.

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Adaptive sliding mode control for uncertain nonlinear switched system under probabilistic replay attacks: The output feedback approach

Cited by 4 publications

References 51 publications

Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

Composite‐observer‐based asynchronous control for hidden Markov nonlinear systems with disturbances

Spatiotemporal fault estimation for switched nonlinear reaction–diffusion systems via adaptive iterative learning

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