Robust hybrid internal model control based smith predictor for MIMO systems with multiple time delays

Abstract

Internal Model Control based Smith Predictor for MIMO processes with multiple time delays is reported in this paper. The proposed structure combines the advantages of Smith predictor and Internal model control while providing a simplified tuning strategy for the Internal Model Controller (IMC) of linear MIMO systems with time delays, based on a specific inversion method. The main novelty of this work lies in the development a unified IMC-based Smith Predictor structure that is initially proposed for linear MIMO systems with multiple time delays and further extended to nonlinear MIMO processes. In the proposed extension, nonlinear dynamics are represented using a set of local linear models, each associated with a dedicated controller, while a switching mechanism selects the appropriate control law according to the operating conditions. Simulation results demonstrate the effectiveness of the proposed approaches compared with conventional control strategies. For the linear MIMO case, the proposed IMC-SP reduces the settling time by 57%, decreases the overshoot by 97%, and improves the tracking performance by reducing the error criteria by 58% compared with the conventional IMC controller. For the nonlinear MIMO case, the proposed IMMC-SP achieves a 64% reduction in settling time and a 70% decrease in tracking error compared with a decentralized PI controller. Furthermore, robustness evaluations considering model parameter variations and time-delay uncertainties, including delay perturbations of up to ± 50% around thenominal value, show that the proposed controller maintains satisfactory closed-loop performance under the investigated uncertainty scenarios. These results demonstrate the potential of the proposed IMC-SP and IMMC-SP frameworks for improving tracking performance and robustness in MIMO systems affected by time delays.