scholarly journals On Output Feedback Multiobjective Control for Singularly Perturbed Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-28
Author(s):  
Mehdi Ghasem Moghadam ◽  
Mohammad Taghi Hamidi Beheshti
Keyword(s):  
Numerical Simulations ◽  
Output Feedback ◽  
Design Procedure ◽  
Perturbation Parameter ◽  
Singularly Perturbed ◽  
Dynamic Output Feedback ◽  
Singularly Perturbed Systems ◽  
Dynamic Output ◽  
Perturbed Systems ◽  
And Performance

A new design procedure for a robust and control of continuous-time singularly perturbed systems via dynamic output feedback is presented. By formulating all objectives in terms of a common Lyapunov function, the controller will be designed through solving a set of inequalities. Therefore, a dynamic output feedback controller is developed such that and performance of the resulting closed-loop system is less than or equal to some prescribed value. Also, and performance for a given upperbound of singular perturbation parameter are guaranteed. It is shown that the -dependent controller is well defined for any and can be reduced to an -independent one so long as is sufficiently small. Finally, numerical simulations are provided to validate the proposed controller. Numerical simulations coincide with the theoretical analysis.

scholarly journals Control of Discrete-Time Singularly Perturbed Systems via Static Output Feedback

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Dan Liu ◽  
Lei Liu ◽  
Ying Yang
Keyword(s):  
Discrete Time ◽  
Output Feedback ◽  
Perturbation Parameter ◽  
Singularly Perturbed ◽  
Static Output Feedback ◽  
Singularly Perturbed Systems ◽  
Closed Loop System ◽  
Perturbed Systems ◽  
Norm Bound ◽  
Static Output

This paper concentrates on control problems of discrete-time singularly perturbed systems via static output feedback. Two methods of designing an controller, which ensures that the resulting closed-loop system is asymptotically stable and meets a prescribed norm bound, are presented in terms of LMIs. Though based on the same matrix transformation, the two approaches are turned into different optimal problems. The first result is given by an -independent LMI, while the second result is related to . Furthermore, a stability upper bound of the singular perturbation parameter is obtained. The validity of the proposed two results is demonstrated by a numerical example.

scholarly journals Synchronization via output feedback for multi-agent singularly perturbed systems with guaranteed cost

Automatica ◽  
2021 ◽  
Vol 128 ◽  
pp. 109549
Author(s):  
Eduardo S. Tognetti ◽  
Taís R. Calliero ◽  
Irinel-Constantin Morărescu ◽  
Jamal Daafouz
Keyword(s):  
Output Feedback ◽  
Singularly Perturbed ◽  
Singularly Perturbed Systems ◽  
Guaranteed Cost ◽  
Perturbed Systems ◽  
Multi Agent

Discrete-Time Nonlinear Singularly Perturbed System Identification Using Coupled Multimodel Representation

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Asma Ben Rajab ◽  
Nesrine Bahri ◽  
Majda Ltaief
Keyword(s):  
Discrete Time ◽  
Perturbation Parameter ◽  
Singularly Perturbed ◽  
System Model ◽  
Model Parameters ◽  
Singularly Perturbed Systems ◽  
Perturbed System ◽  
Marquardt Algorithm ◽  
Perturbed Systems ◽  
Full Knowledge

Abstract Many control and observability theories for singularly perturbed systems require the full knowledge of system model parameters exceptionally if the system is considered as black box. To overcome this problem and to obtain an accurate and faithful model, this paper describes a new identification method for discrete-time nonlinear singularly perturbed systems (NLSPS) using the coupled state multimodel representation. The Levenberg–Marquardt algorithm is used to identify not only the submodels parameters but also the perturbation parameter ε. Two cases are considered to identify these systems. The first one supposes that the perturbation parameter ε of the real system is known and thus only the submodels parameters are identified. The second case supposes that this perturbation parameter is unknown and has to be identified with the other submodels parameters. The simulation example demonstrates the effectiveness of the proposed identification.

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