By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the S...By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting （i.e., the data product moment matrix has a bounded condition number） and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persis- tent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.展开更多
This paper deals with nonholonomic systems in chained form with unknown covariance stochastic disturbances The objective is to design the almost global adaptive asymptotical controllers in probability Uo and u1 for th...This paper deals with nonholonomic systems in chained form with unknown covariance stochastic disturbances The objective is to design the almost global adaptive asymptotical controllers in probability Uo and u1 for the systems by using discontinuous control. A switching control law Uo is designed to almost globally asymptotically stabilize the state x0 in both the singular Xo（t0）=0 case and the non-singular Xo（to）≠O case. Then the state scaling technique is introduced for the discontinuous feedback into the （x1, x2,…, xn）-subsystem. Thereby, by using backstepping technique the global adaptive asymptotical control law u1 has been presented for （x1, x2, …, xn） -subsystem for both different Uo in non-singular x0 （t0）≠0 case and the singular case X0 （t0）=0. The control algorithm validity is proved by simulation.展开更多
In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sa...In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors＇ states are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for a connected network, the static mean square error between the individual state and the average of the initial states of all agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise intensity, network topology and the number of network nodes.展开更多
基金Supported by the National Natural Science Foundation of China (Grant Nos. 60574051 and 60674092) the Natural Science Foundation of Jiangsu Province, China (Grant No. BK2007017) and by Program for Innovative Research Team of Jiangnan University
文摘By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting （i.e., the data product moment matrix has a bounded condition number） and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persis- tent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.
文摘This paper deals with nonholonomic systems in chained form with unknown covariance stochastic disturbances The objective is to design the almost global adaptive asymptotical controllers in probability Uo and u1 for the systems by using discontinuous control. A switching control law Uo is designed to almost globally asymptotically stabilize the state x0 in both the singular Xo（t0）=0 case and the non-singular Xo（to）≠O case. Then the state scaling technique is introduced for the discontinuous feedback into the （x1, x2,…, xn）-subsystem. Thereby, by using backstepping technique the global adaptive asymptotical control law u1 has been presented for （x1, x2, …, xn） -subsystem for both different Uo in non-singular x0 （t0）≠0 case and the singular case X0 （t0）=0. The control algorithm validity is proved by simulation.
基金Supported by Singapore Millennium Foundationthe National Natural Science Foundation of China (Grant Nos. 60821091, 60674308)
文摘In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors＇ states are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for a connected network, the static mean square error between the individual state and the average of the initial states of all agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise intensity, network topology and the number of network nodes.
