Encrypted Decentralized Model Predictive Control of Nonlinear Processes with Input Delays

This work focuses on enhancing the operational safety, cybersecurity, computational efficiency, and closed-loop performance of large-scale nonlinear processes with input delays. This is achieved by employing a decentralized model predictive controller (MPC) with encrypted networked communication. Within this decentralized setup, the nonlinear process is partitioned into multiple subsystems, each controlled by a distinct Lyapunov-based MPC. These controllers take into account the interactions between subsystems by utilizing full state feedback. To address the performance degradation associated with input delays, we integrate a predictor with each LMPC to compute the states after the input delay period. The LMPC process model is initialized with these predicted states. Furthermore, to enhance cybersecurity, all signals transmitted to and received from each subsystem are encrypted. Guidelines are established to implement this proposed control structure in any nonlinear system with input delays. The simulation results, conducted on a nonlinear chemical process network, illustrate the effective closed-loop performance of the decentralized MPCs alongside the predictor with encrypted communication when dealing with input delays in a large process.