MUL TIVARJABLE CLOSED-LOOP SYSTEM IDENTIFICATION USING ITERATIVE LEAKY LEAST MEAN SQUARES METHOD

Channel isolation usmg partial correlation analysis and estimation of model parameters in the conventional multivariable closed-loop system identification approaches use the method of Least Square (LS) with the limitations, viz. (I) large estimation bias due to the highly correlated input signals, low Signal to Noise Ratio (SNR) and correlated noise signals; and (2) inconsistent parameter estimates due to noise and unmeasured disturbances. In this thesis. iterative Leaky Least Mean Squares (LLMS) based methods are proposed to address the limitations ofLS method in MultiInput Multi-Output (MIMO) closed-loop system identification. In this research. novel algorithms have been developed to: (I) isolate the less interacting channe Is using a modified partial correlation algorithm. (2) achieve unbiased and consistent parameter estimates using an iterative LLMS algorithm and (3) develop parsimonious models for closed-loop MIMO systems. All the proposed algorithms are demonstrated with the help of extensive simulation case studies and a real pilot-scale distillation column with appropriate validation procedure