Processes with recycle are quite common in the process industries. It has been pointed out that such processes often exhibit unusual dynamics, such as open-loop instability, which makes the task of designing a model-based controller for them challenging. In this work, a design procedure for rapid parametrization of simple internal model control (IMC)-based proportional integral/derivative (PI/D) controller for open-loop unstable recycle processes is proposed. In the proposed strategy, first, the recycle process model is decomposed into the forward and recycle path. Thereafter, a perfect/approximate compensator is designed for the global process. The final controllers for the global process are then parametrized using the compensated system model and implemented on the system. It was observed that the compensator was able to restore stability to the open-loop unstable recycle process model and simplifies its model order considerably. Simulation results obtained revealed that the recycle compensated system displays better closed-loop performance, in terms of set-point tracking and disturbance rejection. Furthermore, the proposed method results in a closed-loop system that is less sensitive to disturbance and has a smoother control signal with higher gain and phase margins.
This paper proposes an offset-free Quasi-Infinite Horizon Nonlinear Model Predictive Controller (QIH-NMPC) using online parameter adaptation. In the proposed method, the adaptation law is modeled by a first order differential equation as a function of the tracking error and subsequently combined with a QIH-NMPC algorithm for online updating of the unknown parameter. The effectiveness of the proposed control scheme is demonstrated on a continuous stirred tank reactor (CSTR) and an experimental cascaded three-tank system with uncertain model parameters, structural plant/model mismatch and noisy measurements. For the purpose of comparison, the state-of-the-art online state and parameter estimators such as Moving Horizon Estimation (MHE) and Extended Kalman Filter (EKF) were also incorporated into QIH-NMPC algorithm. The simulation and experimental results obtained showed the efficacy of the proposed adaptation scheme as it demonstrated a comparable performance to standard estimators (MHE and EKF) although with a lesser computational time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.