Forecast-Triggered Model Predictive Control of Constrained Nonlinear Processes with Control Actuator Faults
AbstractThis paper addresses the problem of fault-tolerant stabilization of nonlinear processes subject to input constraints, control actuator faults and limited sensor–controller communication. A fault-tolerant Lyapunov-based model predictive control (MPC) formulation that enforces the fault-tolerant stabilization objective with reduced sensor–controller communication needs is developed. In the proposed formulation, the control action is obtained through the online solution of a finite-horizon optimal control problem based on an uncertain model of the plant. The optimization problem is solved in a receding horizon fashion subject to appropriate Lyapunov-based stability constraints which are designed to ensure that the desired stability and performance properties of the closed-loop system are met in the presence of faults. The state-space region where fault-tolerant stabilization is guaranteed is explicitly characterized in terms of the fault magnitude, the size of the plant-model mismatch and the choice of controller design parameters. To achieve the control objective with minimal sensor–controller communication, a forecast-triggered communication strategy is developed to determine when sensor–controller communication can be suspended and when it should be restored. In this strategy, transmission of the sensor measurement at a given sampling time over the sensor–controller communication channel to update the model state in the predictive controller is triggered only when the Lyapunov function or its time-derivative are forecasted to breach certain thresholds over the next sampling interval. The communication-triggering thresholds are derived from a Lyapunov stability analysis and are explicitly parameterized in terms of the fault size 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, a simulation case study involving a chemical process example is presented to illustrate the implementation and evaluate the efficacy of the developed fault-tolerant MPC formulation. View Full-Text
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Xue, D.; El-Farra, N.H. Forecast-Triggered Model Predictive Control of Constrained Nonlinear Processes with Control Actuator Faults. Mathematics 2018, 6, 104.
Xue D, El-Farra NH. Forecast-Triggered Model Predictive Control of Constrained Nonlinear Processes with Control Actuator Faults. Mathematics. 2018; 6(6):104.Chicago/Turabian Style
Xue, Da; El-Farra, Nael H. 2018. "Forecast-Triggered Model Predictive Control of Constrained Nonlinear Processes with Control Actuator Faults." Mathematics 6, no. 6: 104.
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