Stochastic model predictive control framework for resilient cyber-physical systems: review and perspectives

Chen, Jicheng; Shi, Yang

doi:10.1098/rsta.2020.0371

Cited by 21 publications

(5 citation statements)

References 73 publications

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“…where, , () i sa yk is the measurement output of the th i sensor after attacking and i s C is the corresponding system output matrix. Meanwhile, on the monitoring side, the state estimation equation, and the observation estimation equation for (17)…”

Section: Optimal State Estimation Based On Improved Kalman Filteringmentioning

confidence: 99%

“…Considering a secure reconstruction problem for linear CPS with both sparse actuator and sensor attacks, a descriptor switched sliding mode observer that effectively reconstructs the sparse FDI attacks and the system state is constructed [16] . In terms of secure control, an architectural framework for resilient CPS based on stochastic MPC is proposed to ensure robustness in the presence of stochastic uncertainty and to achieve resilient control against cyber-attacks [17] . In [18], by analyzing various cyber-attacks, a unified system model with uncertainty is developed and the robust control theory is applied to achieve secure control of the system after an attack.…”

Section: Introductionmentioning

confidence: 99%

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Research on Secure State Estimation and Recovery Control for CPS under Stealthy Attacks

Yang,

Xin,

Long

2023

Preprint

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As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

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Section: Optimal State Estimation Based On Improved Kalman Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Secure State Estimation and Recovery Control for CPS under Stealthy Attacks

Yang,

Xin,

Long

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The most prevalent path-tracking algorithms are pure pursuit [28], Stanley [29], Least Quadratic Regulator (LQR) [30], and Model Predictive Control (MPC) [31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

et al. 2022

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Navigating the AGV over the curve path is a difficult problem in all types of navigation (landmark, behavior, vision, and GPS). A single path tracking algorithm is required to navigate the AGV in a mixed environment that includes indoor, on-road, and agricultural terrain. In this paper, two types of proposed methods are presented. First, the curvature information from the generated trajectory (path) data is extracted. Second, the improved curve-aware MPC (C-MPC) algorithm navigates AGV in a mixed environment. The results of the real-time experiments demonstrated that the proposed curve finding algorithm successfully extracted curves from all types of terrain (indoor, on-road, and agricultural-land) path data with low type 1 (percentage of the unidentified curve) and type 2 (extra waypoints added to identified curve) errors, and eliminated path noise (hand-drawn line error over map). The AGV was navigated using C-MPC, and the real-time and simulation results reveal that the proposed path tracking technique for the mixed environment (indoor, on-road, agricultural-land, and agricultural-land with slippery error) successfully navigated the AGV and had a lower RMSE lateral and longitudinal error than the existing path tracking algorithm.

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“…One potential strategy involves implementing an architectural framework for resilient CPS through the utilization of stochastic model predictive control (MPC). This framework is designed with the objective of attaining robustness in the presence of stochastic uncertainty and ensuring resilient control against cyber attacks [24]. In reference [25], a comprehensive system model incorporating uncertainty is constructed through the analysis of diverse cyber attacks.…”

Section: Introductionmentioning

confidence: 99%

Research on Secure State Estimation and Recovery Control for CPS under Stealthy Attacks

Yang,

Xin,

Long

2023

Actuators

View full text Add to dashboard Cite

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation, or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

show abstract

Stochastic model predictive control framework for resilient cyber-physical systems: review and perspectives

Cited by 21 publications

References 73 publications

Research on Secure State Estimation and Recovery Control for CPS under Stealthy Attacks

Research on Secure State Estimation and Recovery Control for CPS under Stealthy Attacks

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

Research on Secure State Estimation and Recovery Control for CPS under Stealthy Attacks

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