Plug-and-play fault diagnosis and control-reconfiguration for a class of nonlinear large-scale constrained systems

TL;DR

This paper introduces a novel Plug-and-Play framework combining distributed Model Predictive Control and Fault Detection for nonlinear large-scale systems, enabling autonomous fault management and subsystem reconfiguration.

Contribution

It presents a new PnP architecture integrating distributed MPC and FD for nonlinear large-scale systems, ensuring stability and constraints during fault handling.

Findings

Effective fault detection and reconfiguration demonstrated in simulations

Maintains system stability and constraints during plug-and-play operations

Potential for autonomous fault management in complex systems

Abstract

This paper deals with a novel Plug-and-Play (PnP) architecture for the control and monitoring of Large-Scale Systems (LSSs). The proposed approach integrates a distributed Model Predictive Control (MPC) strategy with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use the FD scheme as an autonomous decision support system: once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. PnP design of local controllers and detectors allow these operations to be performed safely, i.e. without spoiling stability and constraint satisfaction for the whole LSS. The PnP distributed MPC is derived for a class of nonlinear LSS and an integrated PnP distributed FD architecture is proposed. Simulation results show the effectiveness and the potential of the general methodology.