Estimation and control for nonlinear stochastic systems

This paper presents an efficient online technique used for finite-horizon, nonlinear, stochastic, regulator, and tracking problems. This can be accomplished by the integration of the differential SDRE filter algorithm and the finite-horizon state dependent Riccati equation (SDRE) technique. Unlike the previous methods which deal with the linearized system, this technique provides finite-horizon estimation and control of the nonlinear stochastic systems. Further, the proposed technique is effective for a wide range of operating points. Simulation results of a missile guidance system are presented to illustrate the effectiveness of the proposed technique.

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Feedback Stabilization for a Class of Nonlinear Stochastic Systems with State- and Control-Dependent Noise

Mathematical Problems in Engineering ◽

10.1155/2014/484732 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8

Author(s):

Yu-Hong Wang ◽

Tianliang Zhang ◽

Weihai Zhang

Keyword(s):

State Feedback ◽

Stochastic Systems ◽

Sufficient Conditions ◽

Feedback Stabilization ◽

Linear Matrix ◽

Matrix Inequalities ◽

Global State ◽

Nonlinear Stochastic Systems ◽

Feedback Stabilizability ◽

And Control

This paper mainly studies the state feedback stabilizability of a class of nonlinear stochastic systems with state- and control-dependent noise. Some sufficient conditions on local and global state feedback stabilizations are given in linear matrix inequalities (LMIs) and generalized algebraic Riccati equations (GAREs). Some obtained results improve the previous work.

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Decomposition and control of a class of nonlinear stochastic systems: theory and its application

Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148) ◽

10.1109/acc.2001.945518 ◽

2001 ◽

Author(s):

A.S.C. Sinha ◽

S.E. Lyshevski

Keyword(s):

Systems Theory ◽

Stochastic Systems ◽

Nonlinear Stochastic Systems ◽

And Control

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

Mathematical Problems in Engineering ◽

10.1155/2012/416358 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 6

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.

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