Optimal robust motion controller design using multi-objective genetic algorithm

Andrej Sarjaš (Author), Rajko Svečko (Author), Amor Chowdhury (Author)

Abstract

This paper describes the use of a multi-objective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with non-negativity conditions. Regional pole placement method is presented with the aims of controllers% structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multi-objective function is composed of different unrelated criteria such as, robust stability, controllers% stability and time performance indexes of closed loops. The design of controllers and multi-objective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm - Differential evolution.

Keywords

disturbance observer-DOB;uncertaintry systems;optimal robust control;multi-objective optimization;differential evolution;

Data

Language:	English
Year of publishing:	2014
Typology:	1.01 - Original Scientific Article
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
UDC:	61:004.5
COBISS:	17704982
ISSN:	1537-744X
Views:	850
Downloads:	313
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	negotovost;optimalno robustno vodenje;večciljna optimizacija;diferencialna evolucija;
URN:	URN:SI:UM:
Type (COBISS):	Scientific work
Pages:	str. 1-14
Chronology:	Accepted 18 March 2014
DOI:	10.1155/2014/978167
ID:	10842816