Novel observer-based fault-tolerant tracking control of input-constrained polymerization reactor with statistical analysis

Abstract

The polymerization reaction within a continuous stirred tank reactor is modeled as a multivariable, nonlinear control process with input constraints. This study proposes a novel optimization-based approach for fault diagnosis and compensation, despite the uncertainties and disturbances present in the dynamic model of the polymerization reactor. This approach facilitates the design of a reliable model-based controller through the estimation of system perturbations. The proposed strategy mitigates external disturbances, time-varying uncertainties, and faults by incorporating complementary terms, calculated in real-time from output measurements, into the initial process model. To ensure robust performance of the fault detection mechanism, the threshold bounds for external disturbances and other uncertainties are determined stochastically using the Monte Carlo simulation approach. A continuous predictive controller is designed in closed form based on the updated reactor model, accounting for the presence of control input limitations. The constrained controller is formulated by solving an optimization problem using the Karush–Kuhn–Tucker (KKT) conditions. The boundedness of the tracking errors is established under the constrained multivariable controller. The results demonstrate that the proposed method exhibits high sensitivity, accuracy, and robustness in fault detection and isolation for a nonlinear uncertain reactor. Simulations confirm the superior performance of the proposed observer-based fault-tolerant control system over existing passive and active actuator fault-tolerant control methods.