Particle filtering-based recursive identification for controlled auto-regressive systems with quantised output

• Published in: IET control theory & applications 2019-09, Vol.13 (14), p.2181-2187
• Main Authors: Ding, Jie, Chen, Jiazhong, Lin, Jinxing, Jiang, Guoping
• Format: Article
• Language: English
• Subjects: autoregressive processes; auxiliary model principle; auxiliary model‐based; controlled auto‐regressive systems; discrete random sampling points; filtering theory; gradient methods; invalid particles; least squares approximations; linear output estimates; main tools; novel particle filtering technique; novel recursive identification algorithm; parameter estimation; particle filtering (numerical methods); particle filtering technique‐based algorithm; particle filtering‐based recursive identification; posterior probability density function; probability; quantised output; recursive estimation; recursive prediction error method; Research Article; standard stochastic gradient algorithm; stochastic processes
• ISSN: 1751-8644; 1751-8652; 1751-8652
• DOI: 10.1049/iet-cta.2019.0028

Recursive prediction error method is one of the main tools for analysis of controlled auto-regressive systems with quantised output. In this study, a recursive identification algorithm is proposed based on the auxiliary model principle by modifying the standard stochastic gradient algorithm. To improve the convergence performance of the algorithm, a particle filtering technique, which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilised to correct the linear output estimates. It can exclude those invalid particles according to their corresponding weights. The performance of the particle filtering technique-based algorithm is much better than that of the auxiliary model-based one. Finally, results are verified by examples from simulation and engineering.