This paper studies the consensus control of multiagent systems with binary-valued observations.An algorithm alternating estimation and control is proposed.Each agent estimates the states of its neighbors based on a pr...This paper studies the consensus control of multiagent systems with binary-valued observations.An algorithm alternating estimation and control is proposed.Each agent estimates the states of its neighbors based on a projected empirical measure method for a holding time.Based on the estimates,each agent designs the consensus control with a constant gain at some skipping time.The states of the system are updated by the designed control,and the estimation and control design will be repeated.For the estimation,the projected empirical measure method is proposed for the binary-valued observations.The algorithm can ensure the uniform boundedness of the estimates and the mean square error of the estimation is proved to be at the order of the reciprocal of the holding time(the same order as that in the case of accurate outputs).For the consensus control,a constant gain is designed instead of the stochastic approximation based gain in the existing literature for binary-valued observations.And,there is no need to make modification for control since the uniform boundedness of the estimates ensures the uniform boundedness of the agents’states.Finally,the systems updated by the designed control are proved to achieve consensus and the consensus speed is faster than that in the existing literature.Simulations are given to demonstrate the theoretical results.展开更多
This paper focuses on the state estimate for a class of systems with both process noise and measurement noise under binary-valued observations,in which the Gaussian assumption on the predicted density of the state is ...This paper focuses on the state estimate for a class of systems with both process noise and measurement noise under binary-valued observations,in which the Gaussian assumption on the predicted density of the state is not required.A recursive projected filter algorithm with time-varying thresholds is constructed to estimate the state under binary-valued observations.The time-varying thresholds are designed as the prediction value of the measurement,which can provide more information about the system state.The convergence property is established with some suitable stability,boundedness and observability conditions.In particular,the estimation error between state and estimate is proved to be asymptotically bounded in the mean-square sense,whose upper bound is related to the variance of process noise.Finally,the theoretical results are demonstrated via numerical examples of first-order and high-order systems.展开更多
Estimation and control problems with binary-valued observations exist widely in practical systems.However,most of the related works are devoted to finite impulse response(FIR for short)systems,and the theoretical prob...Estimation and control problems with binary-valued observations exist widely in practical systems.However,most of the related works are devoted to finite impulse response(FIR for short)systems,and the theoretical problem of infinite impulse response(IIR for short)systems has been less explored.To study the estimation problems of IIR systems with binary-valued observations,the authors introduce a projected recursive estimation algorithm and analyse its global convergence properties,by using the stochastic Lyapunov function methods and the limit theory on double array martingales.It is shown that the estimation algorithm has similar convergence results as those for FIR systems under a weakest possible non-persistent excitation condition.Moreover,the upper bound for the accumulated regret of adaptive prediction is also established without resorting to any excitation condition.展开更多
This paper considers the adaptive tracking problem for a class of first-order systems with binary-valued observations generated via fixed thresholds. A recursive projection algorithm is proposed for parameter estimati...This paper considers the adaptive tracking problem for a class of first-order systems with binary-valued observations generated via fixed thresholds. A recursive projection algorithm is proposed for parameter estimation based on the statistical properties of the system noise. Then, an adaptive control law is designed via the certainty equivalence principle. By use of the conditional expectations of the innovation and output prediction with respect to the estimates, the closed-loop system is shown to be stable and asymptotically optimal. Meanwhile, the parameter estimate is proved to be both almost surely and mean square convergent, and the convergence rate of the estimation error is also obtained. A numerical example is given to demonstrate the efficiency of the adaptive control law.展开更多
This paper investigates a distributed recursive projection identification problem with binaryvalued observations built on a sensor network,where each sensor in the sensor network measures partial information of the un...This paper investigates a distributed recursive projection identification problem with binaryvalued observations built on a sensor network,where each sensor in the sensor network measures partial information of the unknown parameter only,but each sensor is allowed to communicate with its neighbors.A distributed recursive projection algorithm is proposed based on a specific projection operator and a diffusion strategy.The authors establish the upper bound of the accumulated regrets of the adaptive predictor without any requirement of excitation conditions.Moreover,the convergence of the algorithm is given under the bounded cooperative excitation condition,which is more general than the previously imposed independence or persistent excitations on the system regressors and maybe the weakest one under binary observations.A numerical example is supplied to demonstrate the theoretical results and the cooperative effect of the sensors,which shows that the whole network can still fulfill the estimation task through exchanging information between sensors even if any individual sensor cannot.展开更多
With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security ...With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security threats to the systems.From the perspective of system identification with binary-valued observations,we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering attacks.The packet loss rate and the data tampering rate caused by the attack is given,and the estimation error is derived.Then the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with constraints.The explicit expression of the optimal attack strategy is obtained.Simulation examples are presented to verify the effectiveness of the main conclusions.展开更多
基金supported by the National Natural Science Foundation of China(61803370,61622309)the China Postdoctoral Science Foundation(2018M630216)the National Key Research and Development Program of China(2016YFB0901902)
文摘This paper studies the consensus control of multiagent systems with binary-valued observations.An algorithm alternating estimation and control is proposed.Each agent estimates the states of its neighbors based on a projected empirical measure method for a holding time.Based on the estimates,each agent designs the consensus control with a constant gain at some skipping time.The states of the system are updated by the designed control,and the estimation and control design will be repeated.For the estimation,the projected empirical measure method is proposed for the binary-valued observations.The algorithm can ensure the uniform boundedness of the estimates and the mean square error of the estimation is proved to be at the order of the reciprocal of the holding time(the same order as that in the case of accurate outputs).For the consensus control,a constant gain is designed instead of the stochastic approximation based gain in the existing literature for binary-valued observations.And,there is no need to make modification for control since the uniform boundedness of the estimates ensures the uniform boundedness of the agents’states.Finally,the systems updated by the designed control are proved to achieve consensus and the consensus speed is faster than that in the existing literature.Simulations are given to demonstrate the theoretical results.
基金supported by the National Natural Science Foundation of China under Grant Nos.62025306,62122083,62303452,and T2293773CAS Project for Young Scientists in Basic Research under Grant No.YSBR-008+1 种基金China Postdoctoral Science Foundation under Grant No.2022M720159Guozhi Xu Postdoctoral Research Foundation.
文摘This paper focuses on the state estimate for a class of systems with both process noise and measurement noise under binary-valued observations,in which the Gaussian assumption on the predicted density of the state is not required.A recursive projected filter algorithm with time-varying thresholds is constructed to estimate the state under binary-valued observations.The time-varying thresholds are designed as the prediction value of the measurement,which can provide more information about the system state.The convergence property is established with some suitable stability,boundedness and observability conditions.In particular,the estimation error between state and estimate is proved to be asymptotically bounded in the mean-square sense,whose upper bound is related to the variance of process noise.Finally,the theoretical results are demonstrated via numerical examples of first-order and high-order systems.
基金supported by the National Natural Science Foundation of China(No.12288201)。
文摘Estimation and control problems with binary-valued observations exist widely in practical systems.However,most of the related works are devoted to finite impulse response(FIR for short)systems,and the theoretical problem of infinite impulse response(IIR for short)systems has been less explored.To study the estimation problems of IIR systems with binary-valued observations,the authors introduce a projected recursive estimation algorithm and analyse its global convergence properties,by using the stochastic Lyapunov function methods and the limit theory on double array martingales.It is shown that the estimation algorithm has similar convergence results as those for FIR systems under a weakest possible non-persistent excitation condition.Moreover,the upper bound for the accumulated regret of adaptive prediction is also established without resorting to any excitation condition.
基金supported by the National Natural Science Foundation of China under Grant Nos.60934006, 61174042,and 61120106011
文摘This paper considers the adaptive tracking problem for a class of first-order systems with binary-valued observations generated via fixed thresholds. A recursive projection algorithm is proposed for parameter estimation based on the statistical properties of the system noise. Then, an adaptive control law is designed via the certainty equivalence principle. By use of the conditional expectations of the innovation and output prediction with respect to the estimates, the closed-loop system is shown to be stable and asymptotically optimal. Meanwhile, the parameter estimate is proved to be both almost surely and mean square convergent, and the convergence rate of the estimation error is also obtained. A numerical example is given to demonstrate the efficiency of the adaptive control law.
基金National Key R&D Program of China under Grant No.2018YFA0703800the National Natural Science Foundation of China under Grant Nos.61877057 and 62025306Open Fund Program of Beijing National Research Center for Information Science and Technology。
文摘This paper investigates a distributed recursive projection identification problem with binaryvalued observations built on a sensor network,where each sensor in the sensor network measures partial information of the unknown parameter only,but each sensor is allowed to communicate with its neighbors.A distributed recursive projection algorithm is proposed based on a specific projection operator and a diffusion strategy.The authors establish the upper bound of the accumulated regrets of the adaptive predictor without any requirement of excitation conditions.Moreover,the convergence of the algorithm is given under the bounded cooperative excitation condition,which is more general than the previously imposed independence or persistent excitations on the system regressors and maybe the weakest one under binary observations.A numerical example is supplied to demonstrate the theoretical results and the cooperative effect of the sensors,which shows that the whole network can still fulfill the estimation task through exchanging information between sensors even if any individual sensor cannot.
文摘With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security threats to the systems.From the perspective of system identification with binary-valued observations,we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering attacks.The packet loss rate and the data tampering rate caused by the attack is given,and the estimation error is derived.Then the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with constraints.The explicit expression of the optimal attack strategy is obtained.Simulation examples are presented to verify the effectiveness of the main conclusions.
