A novel networked online recursive identification method for multivariable systems with incomplete measurement information

Network based identification of multivariable systems plays a key role in future smart manufacturing systems in achieving the goals of industry 4.0. The incomplete information caused by network traffic congestion or cyber-attack in the networked environment will inevitably deteriorate the performance of system identification, and in the extreme cases, it will cause non-convergence of the identifier. Unlike the traditional recursive least-squares (RLS) algorithms based on the complete data, this paper investigates a novel networked online recursive identification method for multivariable systems with incomplete information. In this new algorithm, the characteristics of data packet dropouts are firstly formulated as a Bernoulli process, and the lost data is compensated by an auxiliary model. A new information set including networked parameters is then constructed, and the corresponding networked online identification algorithm for multivariable systems is proposed. The proposed algorithm can overcome the negative effect of data packet losses on the identification performance and can be updated recursively. Furthermore, using the Lyapunov and martingale methods, the convergence rate of the proposed algorithm as well as its computational complexity are analysed in detail. Simulation examples confirm the feasibility and efficiency of the proposed method.