Authors:
Ivan Ryzhikov
;
Christina Brester
and
Eugene Semenkin
Affiliation:
Siberian State Aerospace University, Russian Federation
Keyword(s):
Linear Time Invariant Systems, System Identification, Order Reduction, Multi-objective Optimization, Evolution-based Algorithms, Meta-heuristic, Restart Operator.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Evolutionary Computation and Control
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
;
System Modeling
Abstract:
An order reduction problem for linear time invariant models brought to the multi-objective optimization problem is considered. Each criterion is multi-extremum and complex, requires an efficient tool for estimating the parameters of the lower order system and characterizes the model adequacy for the unit-step and Dirac function inputs. A common problem definition is to estimate the lower order model coefficients by minimizing the distance between the output of this model and the initial one. We propose an evolution-based multi-objective stochastic optimization algorithm with a restart operator implemented. The algorithm performance was estimated on two order reduction problems for a single input-single output system and a multiple input-multiple output one. The effectiveness of the algorithm increased sufficiently after implementing a meta-heuristic restart operator. It is shown that the proposed approach is comparable to other approaches, but allows a Pareto-front approximation to b
e found and not just a single solution.
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