Least squares based iterative identification for multivariable integrating and unstable processes in closed loop

Inspired by the fact that those multivariable integrating and unstable processes are usually operated in a closed loop manner for safety and economic reasons, an improved iterative least squares identification method is proposed, which is detailed for a second-order plus dead-time (SOPDT) model. The iterative computation process is able to availably weaken the effect of errors caused by first-order Taylor series approximation for time delay items. And the least squares based iterative identification algorithm has fast convergence rates and effectively improves the accuracy of the process parameter estimates in noisy environments. Also, the proposed algorithm can be further extended to multivariable closed loop systems via the equivalent inputs and outputs. Simulation examples verify the validation of the proposed method for multivariable integrating and unstable processes in closed loop.