Attack-Resilience Distributed Model Predictive Control of Vehicular Platoon Systems using Moving Horizon Attack Estimation

Chen, Jicheng; Qi, Zhi; Zhang, Hui

doi:10.1109/icit58465.2023.10143095

“…The discrete-time situation and the continuous-time case have each been studied in [16][17][18] in turn. The set-valued estimating technique seeks to construct ellipsoids around state estimations that are both consistent with the observations and subject to particular norm constraints with regard to the noise disturbances [19].…”

Section: ░ 2 Literature Reviewmentioning

confidence: 99%

SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

Bashir,

Queen,

Habib

2024

IJEER

0

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Sensor data is an integral component of internet of things (IoT) and edge computing environments and initiatives. In IoT, almost any entity imaginable can be outfitted with a unique identifier and the capacity to transfer data over a network. The estimate problem was formulated as a min-max problem subject to system dynamics and limitations on states and disturbances within the moving horizon strategy framework. In this paper, a novel Sand Cat Swarm Optimization Based MHEF for Nonlinear LTI IOT Sensor Data Enhancement (SCSO-MHEF) is proposed. In the proposed method the MHEF is optimized using Sand Cat Swarm Optimization to enhance sensor data stability tuned by initial parameters. Simulation experiments were conducted on various and unique scenarios in various orders LTI system with IOT sensor data in order to validate the suggested approach. This method can be used to analyze systems with dynamically changing systems. The proposed SCSO-MHEF technique overall accuracy of 84.5%, 87.3 %, and 99.5 % better than Kalman Filter (KF), EKF and Moving Horizon Filter (MHEF) respectively.

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Reputation-based DNMPC Design for a Heterogeneous Platoon under False Data Injection Attack

Akbari,

Naraghi,

Sharifi

2023

2023 11th RSI International Conference on Robotics and Mechatronics (ICRoM)

0

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SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

Bashir,

Queen,

Habib

2024

IJEER

0

View full text Add to dashboard Cite

Sensor data is an integral component of internet of things (IoT) and edge computing environments and initiatives. In IoT, almost any entity imaginable can be outfitted with a unique identifier and the capacity to transfer data over a network. The estimate problem was formulated as a min-max problem subject to system dynamics and limitations on states and disturbances within the moving horizon strategy framework. In this paper, a novel Sand Cat Swarm Optimization Based MHEF for Nonlinear LTI IOT Sensor Data Enhancement (SCSO-MHEF) is proposed. In the proposed method the MHEF is optimized using Sand Cat Swarm Optimization to enhance sensor data stability tuned by initial parameters. Simulation experiments were conducted on various and unique scenarios in various orders LTI system with IOT sensor data in order to validate the suggested approach. This method can be used to analyze systems with dynamically changing systems. The proposed SCSO-MHEF technique overall accuracy of 84.5%, 87.3 %, and 99.5 % better than Kalman Filter (KF), EKF and Moving Horizon Filter (MHEF) respectively.

show abstract

Attack-Resilience Distributed Model Predictive Control of Vehicular Platoon Systems using Moving Horizon Attack Estimation

Cited by 3 publications

References 17 publications

SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

Reputation-based DNMPC Design for a Heterogeneous Platoon under False Data Injection Attack

SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

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