Robust state estimator design for uncertain linear systems using optimization techniques

• Published in: Neural computing & applications Vol. 23; no. 5; pp. 1395 - 1406
• Main Authors: Sheikhan, Mansour, Bagheri, Mohammad Mahdi
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2013; Springer
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Original Article; Probability and Statistics in Computer Science; Theoretical computing; Uncertain system; Optimization algorithm; Learning automata; State estimator; Modified genetic algorithm; Particle swarm optimization; Performance evaluation; Kalman filter; Linear programming; Linear time; Dynamical system; Modeling; Experimental result; Genetic algorithm; Discrete time; Swarm intelligence; Learning algorithm; State estimation; Artificial intelligence; Mathematical programming
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-012-1089-9

The estimation of state variables of dynamic systems in noisy environments has been an active research field in recent decades. In this way, Kalman filtering approach may not be robust in the presence of modeling uncertainties. So, several methods have been proposed to design robust estimators for the systems with uncertain parameters. In this paper, an optimized filter is proposed for this problem considering an uncertain discrete-time linear system. After converting the subject to an optimization problem, three algorithms are used for optimizing the state estimator parameters: particle swarm optimization (PSO) algorithm, modified genetic algorithm (MGA) and learning automata (LA). Experimental results show that, in comparison with the standard Kalman filter and some related researches, using the proposed optimization methods results in robust performance in the presence of uncertainties. However, MGA-based estimation method shows better performance in the range of uncertain parameter than other optimization methods.