Comparison of Three Data-Driven Networked Predictive Control Methods for a Class of Nonlinear Systems

Dear Editor,In this letter,in order to deal with random network delays and packet losses in a class of networked nonlinear systems,three data-driven networked predictive control methods are designed.Their closed-loop systems and control increments are derived,respectively.

Side Information-Based Stealthy False Data Injection Attacks Against Multi-Sensor Remote Estimation

Dear Editor,This letter investigates a novel stealthy false data injection(FDI)attack scheme based on side information to deteriorate the multi-sensor estimation performance of cyber-physical systems(CPSs).Compared with most existing works depending on the full system knowledge,this attack scheme is only related to attackers'sensor and physical process model.The design principle of the attack signal is derived to diverge the system estimation performance.Next,it is proven that the proposed attack scheme can successfully bypass the residual-based detector.Finally,all theoretical results are verified by numerical simulation.

Residual-Based False Data Injection Attacks Against Multi-Sensor Estimation Systems

This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.

Analysis of Stealthy False Data Injection Attacks Against Networked Control Systems:Three Case Studies

This paper mainly investigates the security problem of a networked control system based on a Kalman filter.A false data injection attack scheme is proposed to only tamper the measurement output,and its stealthiness and effects on system performance are analyzed under three cases of system knowledge held by an attacker and a defender.Firstly,it is derived that the proposed attack scheme is stealthy for a residual-based detector when the attacker and the defender hold the same accurate system knowledge.Secondly,it is proven that the proposed attack scheme is still stealthy even if the defender actively modifies the Kalman filter gain so as to make it different from that of the attacker.Thirdly,the stealthiness condition of the proposed attack scheme based on an inaccurate model is given.Furthermore,for each case,the instability conditions of the closed-loop system under attack are derived.Finally,simulation results are provided to test the proposed attack scheme.

Detection of Stealthy False Data Injection Attacks Against Cyber-Physical Systems:A Stochastic Coding Scheme

This paper,from the view of a defender,addresses the security problem of cyber-physical systems(CPSs)subject to stealthy false data injection(FDI)attacks that cannot be detected by a residual-based anomaly detector without other defensive measures.To detect such a class of FDI attacks,a stochastic coding scheme,which codes the sensor measurement with a Gaussian stochastic signal at the sensor side,is proposed to assist an anomaly detector to expose the FDI attack.In order to ensure the system performance in the normal operational context,a decoder is adopted to decode the coded sensor measurement when received at the controller side.With this detection scheme,the residual under the attack can be significantly different from that in the normal situation,and thus trigger an alarm.The design condition of the coding signal covariance is derived to meet the constraints of false alarm rate and attack detection rate.To minimize the trace of the coding signal covariance,the design problem of the coding signal is converted into a constraint non-convex optimization problem,and an estimation-optimization iteration algorithm is presented to obtain a numerical solution of the coding signal covariance.A numerical example is given to verify the effectiveness of the proposed scheme.

Data-Based Predictive Control for Networked Nonlinear Systems with Packet Dropout and Measurement Noise

In this paper, the data-based control problem is investigated for a class of networked nonlinear systems with measurement noise as well as packet dropouts in the feedback and forward channels. The measurement noise and the number of consecutive packet dropouts in both channels are assumed to be random but bounded. A data-based networked predictive control method is proposed, in which a sequence of control increment predictions are calculated in the controller based on the measured output error, and based on the control increment predictions received by the actuator, a proper control action is obtained and applied to the plant according to the real-time number of consecutive packet dropouts at each sampling instant. Then the stability analysis is performed for the networked closedloop system. Finally, the effectiveness of the proposed method is illustrated by a numerical example.

A Novel Networked Predictive Control Method for Systems with Random Communication Constraints

This paper presents a novel observer-based predictive control method for networked systems where random network-induced delays,packet disorders and packet dropouts in both feedback and forward channels are considered.The proposed method has three significant features:i)A concept of destination-based lumped(DBL)delay is introduced to represent the combined effects of random communication constraints in each channel;ii)in view of different natures of the random DBL delays in the feedback and forward channels,different compensation schemes are designed;and iii)it is actual control inputs rather than predicted ones that are employed to generate future control signals based on the latest system state estimate available in the controller.For the resulting closed-loop system,a necessary and sufficient stability condition is derived,which is less conservative and also independent of random communication constraints in both channels.Simulation results are provided to demonstrate the effectiveness of the proposed method.