scholarly journals Recent Advances on Recursive Filtering and Sliding Mode Design for Networked Nonlinear Stochastic Systems: A Survey

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jun Hu ◽  
Zidong Wang ◽  
Hongli Dong ◽  
Huijun Gao
Keyword(s):  
Stochastic Systems ◽  
Sliding Mode ◽  
Future Research ◽  
Missing Measurements ◽  
Nonlinear Stochastic Systems ◽  
Design Problems ◽  
Recursive Filtering ◽  
Mode Design ◽  
Recent Advances ◽  
Randomly Occurring Uncertainties

Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.

scholarly journals A Review on Analysis and Synthesis of Nonlinear Stochastic Systems with Randomly Occurring Incomplete Information

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Hongli Dong ◽  
Zidong Wang ◽  
Xuemin Chen ◽  
Huijun Gao
Keyword(s):  
Incomplete Information ◽  
Stochastic Systems ◽  
Future Research ◽  
Missing Measurements ◽  
Parameter Uncertainties ◽  
Nonlinear Stochastic Systems ◽  
Randomly Occurring Uncertainties ◽  
Analysis And Synthesis ◽  
System States ◽  
And Control

In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out.

A new integral sliding mode design method for nonlinear stochastic systems

Automatica ◽  
2018 ◽  
Vol 90 ◽  
pp. 304-309 ◽  
Author(s):  
Yueying Wang ◽  
Yuanqing Xia ◽  
Hongyi Li ◽  
Pingfang Zhou
Keyword(s):  
Stochastic Systems ◽  
Sliding Mode ◽  
Design Method ◽  
Nonlinear Stochastic Systems ◽  
Integral Sliding Mode ◽  
Mode Design

scholarly journals Recent Advances on Filtering and Control for Nonlinear Stochastic Complex Systems with Incomplete Information: A Survey

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Bo Shen ◽  
Zidong Wang ◽  
Jinling Liang ◽  
Yurong Liu
Keyword(s):  
Complex Systems ◽  
Incomplete Information ◽  
Stochastic Systems ◽  
Future Research ◽  
Control Problems ◽  
Missing Measurements ◽  
Recent Advances ◽  
Future Research Directions ◽  
Control Technologies ◽  
And Control

Some recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.

scholarly journals Variance-Constrained Multiobjective Control and Filtering for Nonlinear Stochastic Systems: A Survey

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Lifeng Ma ◽  
Zidong Wang ◽  
Hongli Dong ◽  
Guoliang Wei
Keyword(s):  
Stochastic Systems ◽  
Practical Method ◽  
Future Research ◽  
Engineering Practice ◽  
Nonlinear Stochastic Systems ◽  
Design Problems ◽  
Multiobjective Control ◽  
Multi Objective ◽  
Objective Control ◽  
Future Research Directions

The multiobjective control and filtering problems for nonlinear stochastic systems with variance constraints are surveyed. First, the concepts of nonlinear stochastic systems are recalled along with the introduction of some recent advances. Then, the covariance control theory, which serves as a practical method for multi-objective control design as well as a foundation for linear system theory, is reviewed comprehensively. The multiple design requirements frequently applied in engineering practice for the use of evaluating system performances are introduced, including robustness, reliability, and dissipativity. Several design techniques suitable for the multi-objective variance-constrained control and filtering problems for nonlinear stochastic systems are discussed. In particular, as a special case for the multi-objective design problems, the mixedH2/H∞control and filtering problems are reviewed in great detail. Subsequently, some latest results on the variance-constrained multi-objective control and filtering problems for the nonlinear stochastic systems are summarized. Finally, conclusions are drawn, and several possible future research directions are pointed out.

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