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A simple method to find a robust output feedback controller by random search approach.

¹Laboratoire de Tribologie et de Dynamique des Systèmes CNRS UMR5513, ECL/ENISE, Saint-Etienne, France. toscano@enise.fr

ISA Transactions

|February 17, 2006

Summary

This study introduces a random search algorithm for designing robust output feedback controllers. The method guarantees convergence and estimates solution probability, improving closed-loop system performance for various plants.

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Area of Science:

Control Engineering
Optimization Algorithms
Systems Theory

Background:

Output feedback control is crucial for system stability and performance.
Designing robust controllers that account for plant variations is challenging.
Existing methods may lack guaranteed convergence or performance estimation.

Purpose of the Study:

To present a novel, effective random search algorithm for robust output feedback controller design.
To guarantee the convergence of the random search algorithm.
To enable estimation of solution probability and required trials for practical application.

Main Methods:

A random search algorithm is employed to find an output feedback controller.
Controller robustness is achieved by minimizing a cost function across a set of plants.

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Convergence properties, solution probability, and trial counts are mathematically analyzed and estimated.

Main Results:

The proposed random search method effectively identifies robust output feedback controllers.
Guaranteed convergence of the algorithm is demonstrated.
The probability of finding a solution and the number of required trials can be estimated.

Conclusions:

The random search algorithm offers a simple yet effective approach to robust controller design.
The method enhances closed-loop system robustness by optimizing controller performance.
Simulation studies validate the effectiveness and practical applicability of the proposed technique.