Stochastic Approximation Methods and Their Finite-Time Convergence Properties

2014 ◽  
pp. 179-206 ◽  
Author(s):  
Saeed Ghadimi ◽  
Guanghui Lan
Keyword(s):  
Stochastic Approximation ◽  
Finite Time ◽  
Approximation Methods ◽  
Convergence Properties ◽  
Finite Time Convergence

scholarly journals A New Control Method for Second-Order Multiple Models Control System Based on Global Sliding Mode

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Dan-xu Zhang ◽  
Yang-wang Fang ◽  
Peng-fei Yang ◽  
You-li Wu ◽  
Tong-xin Liu
Keyword(s):  
Control System ◽  
Sliding Mode Control ◽  
Finite Time ◽  
Sliding Mode ◽  
Second Order ◽  
Multiple Models ◽  
Finite Time Convergence ◽  
Mode Control ◽  
Global Sliding Mode Control ◽  
Control Scheme

This paper proposed a finite time convergence global sliding mode control scheme for the second-order multiple models control system. Firstly, the global sliding surface without reaching law for a single model control system is designed and the tracking error finite time convergence and global stability are proved. Secondly, we generalize the above scheme to the second-order multimodel control system and obtain the global sliding mode control law. Then, the convergent and stable performances of the closed-loop control system with multimodel controllers are proved. Finally, a simulation example shows that the proposed control scheme is more effective and useful compared with the traditional sliding mode control scheme.

Time-varying sliding-mode control for finite-time convergence

2010 ◽  
Vol 92 (7-8) ◽  
pp. 257-268 ◽  
Author(s):  
Yu-Sheng Lu ◽  
Chien-Wei Chiu ◽  
Jian-Shiang Chen
Keyword(s):  
Sliding Mode Control ◽  
Finite Time ◽  
Sliding Mode ◽  
Time Varying ◽  
Finite Time Convergence ◽  
Mode Control

scholarly journals Exponential Mean-Square Stability of Numerical Solutions to Stochastic Differential Equations

2003 ◽  
Vol 6 ◽  
pp. 297-313 ◽  
Author(s):  
Desmond J. Higham ◽  
Xuerong Mao ◽  
Andrew M. Stuart
Keyword(s):  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Numerical Method ◽  
Finite Time ◽  
Mean Square ◽  
Convergence Conditions ◽  
Finite Time Convergence ◽  
Mean Square Stability ◽  
Exponential Mean Square Stability ◽  
Positive Results

AbstractPositive results are proved here about the ability of numerical simulations to reproduce the exponential mean-square stability of stochastic differential equations (SDEs). The first set of results applies under finite-time convergence conditions on the numerical method. Under these conditions, the exponential mean-square stability of the SDE and that of the method (for sufficiently small step sizes) are shown to be equivalent, and the corresponding second-moment Lyapunov exponent bounds can be taken to be arbitrarily close. The required finite-time convergence conditions hold for the class of stochastic theta methods on globally Lipschitz problems. It is then shown that exponential mean-square stability for non-globally Lipschitz SDEs is not inherited, in general, by numerical methods. However, for a class of SDEs that satisfy a one-sided Lipschitz condition, positive results are obtained for two implicit methods. These results highlight the fact that for long-time simulation on nonlinear SDEs, the choice of numerical method can be crucial.

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