Design of mixed static/dynamic output feedback controller using multi-objective distribute particle swarm optimization

2017 ◽  
Author(s):  
Semin Chun ◽  
Gihoon Choi ◽  
Tae-Hyoung Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Output Feedback ◽  
Particle Swarm ◽  
Feedback Controller ◽  
Dynamic Output Feedback ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Output Feedback Controller ◽  
Dynamic Output ◽  
Dynamic Output Feedback Controller

scholarly journals Semiglobal Sampled-Data Dynamic Output Feedback Controller for the Glucose–Insulin System

2020 ◽  
Vol 28 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Mario Di Ferdinando ◽  
Pierdomenico Pepe ◽  
Pasquale Palumbo ◽  
Simona Panunzi ◽  
Andrea De Gaetano
Keyword(s):  
Output Feedback ◽  
Feedback Controller ◽  
Dynamic Output Feedback ◽  
Sampled Data ◽  
Output Feedback Controller ◽  
Dynamic Output ◽  
Dynamic Output Feedback Controller

scholarly journals A 2D system approach to the design of a robust modified repetitive-control system with a dynamic output-feedback controller

2014 ◽  
Vol 24 (2) ◽  
pp. 325-334 ◽  
Author(s):  
Lan Zhou ◽  
Jinhua She ◽  
Shaowu Zhou
Keyword(s):  
Control System ◽  
Output Feedback ◽  
Repetitive Control ◽  
Feedback Controller ◽  
Linear Matrix ◽  
Control Input ◽  
Dynamic Output Feedback ◽  
Output Feedback Controller ◽  
Dynamic Output ◽  
Dynamic Output Feedback Controller

Abstract This paper is concerned with the problem of designing a robust modified repetitive-control system with a dynamic output feedback controller for a class of strictly proper plants. Employing the continuous lifting technique, a continuous-discrete two-dimensional (2D) model is built that accurately describes the features of repetitive control. The 2D control input contains the direct sum of the effects of control and learning, which allows us to adjust control and learning preferentially. The singular-value decomposition of the output matrix and Lyapunov stability theory are used to derive an asymptotic stability condition based on a Linear Matrix Inequality (LMI). Two tuning parameters in the LMI manipulate the preferential adjustment of control and learning. A numerical example illustrates the tuning procedure and demonstrates the effectiveness of the method.

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