Global stabilization for a class of stochastic nonlinear systems with SISS‐like conditions and time delay

Abstract

Summary The global stabilization problem for a class of stochastic time‐delay nonlinear systems with stochastic‐input‐to‐state‐stable–like conditions is investigated. Different from the existing results, the nonlinear growing conditions are more general, and the existences of the state and input time delays make the work more challenging in the control design and stability analysis. By introducing an appropriate gain‐scaling method and using a homogeneous domain control strategy, a delay‐independent controller is constructed to ensure that the equilibrium at the origin of the closed‐loop system is globally asymptotically stable in probability. Examples are given to show the validness of the proposed method.

A Hierarchical Approach for Joint Parameter and State Estimation of a Bilinear System with Autoregressive Noise

“…Moreover, the state estimator-based F-TS-RGLS algorithm could greatly improve the computational efficiency. The methods proposed in this paper could be combined the particle Monte Carlo methods [56,57] and some statistical methods to study the model selection and parameter estimation [58][59][60] for different systems [61][62][63][64][65][66] and could be applied to other fields [67][68][69][70][71][72] such as communication networks [73][74][75].…”

Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems

“…Compared with the least-squares-based iterative identification algorithm, the proposed algorithms achieve highly accurate parameter estimates and improve the performance the algorithms. The proposed decomposition least-squares-based iterative identification algorithms for multivariable equation-error autoregressive moving average systems can combine other estimation methods [71][72][73][74] and the mathematical techniques [75][76][77] to explore the parameter identification methods of other scalar, multivariable linear, nonlinear systems with colored noises [78][79][80], and can be extended to other scientific fields [81][82][83][84][85][86][87][88] such as signal modeling and communication networked systems [89][90][91][92].…”

Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems

“…In order to provide a comparison, we drive the basic multivariable generalized extended stochastic gradient (M-GESG) algorithm for the M-CARARMA system in (3).…”

“…Parameter estimation has wide application in many areas such as controller designs [1,2] and stochastic systems [3,4], signal processing [5,6,7,8] and other practical projects [9,10]. Exploring valid methods is the eternal theme of the parameter estimation [11,12,13,14].…”