Digital Control Design Via Convex Optimization

1993 ◽  
Vol 115 (4) ◽  
pp. 579-586 ◽  
Author(s):  
R. E. Avedon ◽  
B. A. Francis
Keyword(s):  
Convex Optimization ◽  
Operator Theory ◽  
Design Method ◽  
Numerical Algorithms ◽  
Optimization Techniques ◽  
Data Systems ◽  
Sampled Data ◽  
Design Heuristics ◽  
Stability Robustness ◽  
Data Design

A design method for sampled-data systems is presented incorporating robust control theory and convex optimization techniques. The technique is developed using operator theory applied to the relevant input-output operators for both performance and stability robustness. Numerical algorithms and an example for the SISO case are presented to explore some traditional sampled-data design heuristics.

Balancing of Flexible Rotors Using Convex Optimization Techniques: Optimum Min-Max LMI Influence Coefficient Balancing

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Costin D. Untaroiu ◽  
Paul E. Allaire ◽  
William C. Foiles
Keyword(s):  
Convex Optimization ◽  
Optimization Theory ◽  
Numerical Algorithms ◽  
Optimization Methods ◽  
Industrial Applications ◽  
Optimization Techniques ◽  
Linear Matrix ◽  
Influence Coefficient ◽  
Flexible Rotors ◽  
Optimum Solution

In some industrial applications, influence coefficient balancing methods fail to find the optimum vibration reduction due to the limitations of the least-squares optimization methods. Previous min-max balancing methods have not included practical constraints often encountered in industrial balancing. In this paper, the influence coefficient balancing equations, with suitable constraints on the level of the residual vibrations and the magnitude of correction weights, are cast in linear matrix inequality (LMI) forms and solved with the numerical algorithms developed in convex optimization theory. The effectiveness and flexibility of the proposed method have been illustrated by solving two numerical balancing examples with complicated requirements. It is believed that the new methods developed in this work will help in reducing the time and cost of the original equipment manufacturer or field balancing procedures by finding an optimum solution of difficult balancing problems. The resulting method is called the optimum min-max LMI balancing method.

scholarly journals Periodic Switched Control of Dual-Rate Sampled-Data Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-16
Author(s):  
Dawei Zhang ◽  
Wei Bai ◽  
Xinchun Jia
Keyword(s):  
Design Method ◽  
Switched System ◽  
Data Systems ◽  
State Variables ◽  
Sampled Data ◽  
Data Control ◽  
Switched Control ◽  
Rate Design ◽  
Input Delays ◽  
Delay Dependent

This paper is concerned with the periodic switched control of a linear dual-rate sampled-data system. The state variables of the continuous-time plant are sampled by two types of sensors. The ratio of two sampling rates is assumed to be a rational number. Depending on whether the sampled-data of state variables at two sampling rates is available simultaneously or separately, a periodic switched controller is constructed. Applying an input delay approach, the closed-loop system is modeled as a switched system with subsystems having different input delays. Some delay-dependent criteria for theH∞performance of the switched system and the existence of the switched controller are derived by employing a Lyapunov-Krasovskii functional that includes information about two sampling periods. The dual-rate sampled-data control of a vehicle dynamic system is given to show that the proposed method is effective and it can achieve a betterH∞control performance than the single-rate design method.

scholarly journals Security Enhancement of Sampled-Data Systems: Zero Assignment via Generalized Sampler

2020 ◽  
Vol 53 (2) ◽  
pp. 3482-3487
Author(s):  
Daehan Kim ◽  
Kunhee Ryu ◽  
Juhoon Back
Keyword(s):  
Data Systems ◽  
Sampled Data ◽  
Security Enhancement
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