Forecast-Triggered Fault-Tolerant Model Predictive Control of Nonlinear Processes

Abstract

In this work, we consider the problem of fault-tolerant stabilization of constrained nonlinear processes controlled over resource-constrained sensor-controller communication networks and subject to control actuator faults. A methodology for the design of resource-aware Lyapuonv-based model predictive control (MPC) systems that achieve the fault-tolerant stabilization objective with reduced sensor-controller communication is presented. In this approach, the control action is computed by solving on-line a finite-horizon optimal control problem based on an uncertain model of the plant subject to appropriate Lyapuonv-based stability constraints. The stability constraints are designed to ensure the desired closed-loop stability and performance properties in the presence of faults, and an explicit characterization of the state space region where fault-tolerant stabilization is guaranteed is obtained in terms of the fault size, the choice of the controller design parameters and the size of the plant-model mismatch. To keep sensor-controller communication to a minimum, a forecast-triggered communication strategy is used to determine when communication should be suspended or restored over the network. In this strategy, an update of the model state in the predictive controller using the sensor measurements at a given sampling time is triggered only when the Lyapunov function is forecasted to breach a certain threshold over the next sampling interval. The update-triggering threshold is derived using Lyapunov techniques and is explicitly parameterized in terms of the fault and a suitable fault accommodation parameter. Based on this characterization, fault accommodation strategies that guarantee closed-loop stability while simultaneously optimizing control and communication system resources are devised. Finally, the developed MPC formulation is illustrated using a chemical process example.