Identification of Model Parameters using Pseudo-Random-Binary-Sequence Signals in the Manipulated Variable in Closed-loop
Keywords:
Identification, Closed loop, PRBS, FOPDT, Tuning, PID ControllerAbstract
Identification of transfer functions using closed-loop input-output data is important as it not only improves loop performance through controller re-tuning (without affecting the ongoing process) but also helps in the early detection of fault and for various analyses of the process systems. Identification in closed-loop is preferred over that in open-loop in process industries. In conventional practice, pseudorandom binary sequence signal (PRBS) is used in open-loop mode as input to identify system parameters. However, in the present work, a simple method based on the least square technique is proposed in closed-loop mode, to estimate the process model parameters from collected input-output data in the time domain. The manipulated variable is externally perturbed by augmenting it with a PRBS in closed-loop (a PRBS is added) for a moment and closed-loop process output is measured without disturbing the continuous production. Both theoretical and real-time experimental results are obtained for low-order systems with PI controllers. A comparison of performances of the present method with existing ones suggests an implementable and feasible technique.
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