Solving multiobjective random interval programming problems by a capable neural network framework

In this paper, the stability of a class of nonlinear control systems is analyzed. We first construct an optimal control problem by inserting a suitable performance index, which this problem is referred to as an infinite horizon problem. By a suitable change of variable, the infinite horizon problem...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Applied intelligence (Dordrecht, Netherlands) Ročník 49; číslo 4; s. 1566 - 1579
Hlavní autoři: Arjmandzadeh, Ziba, Nazemi, Alireza, Safi, Mohammadreza
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2019
Springer Nature B.V
Témata:
Artificial Intelligence
Computer Science
Control stability
Control systems design
Controllers
Dynamic stability
Feedback control
Machine learning
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Neural networks
Nonlinear analysis
Nonlinear control
Nonlinear programming
Nonlinear systems
Optimal control
Performance indices
Processes
Stability analysis
Random interval parameters
Neural network models
Satisficing solution
Stability
Fractile model
Convergence
ISSN:0924-669X, 1573-7497
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, the stability of a class of nonlinear control systems is analyzed. We first construct an optimal control problem by inserting a suitable performance index, which this problem is referred to as an infinite horizon problem. By a suitable change of variable, the infinite horizon problem is reduced to a finite horizon problem. We then present a feedback controller designing approach for the obtained finite horizon control problem. This approach involves a neural network scheme for solving the nonlinear Hamilton Jacobi Bellman (HJB) equation. By using the neural network method, an analytic approximate solution for value function and suboptimal feedback control law is achieved. A learning algorithm based on a dynamic optimization scheme with stability and convergence properties is also provided. Some illustrative examples are employed to demonstrate the accuracy and efficiency of the proposed plan. As a real life application in engineering, the stabilization of a micro electro mechanical system is studied.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-018-1344-6