Abstract
This 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.
Highlights
Model predictive control (MPC), known as receding horizon control, refers to a class of optimization-based control algorithms that utilize an explicit process model to predict the future response of the plant
The comparison shows that stabilization using the forecast-triggered model predictive control (MPC) requires only 14% of the model state updates over the same time interval, and is achieved with a significant reduction in the sensor–controller communication frequency
A forecast-triggered fault-tolerant Lyapunonv-based MPC scheme is developed for constrained nonlinear systems with sensor–controller communication constraints
Summary
Model predictive control (MPC), known as receding horizon control, refers to a class of optimization-based control algorithms that utilize an explicit process model to predict the future response of the plant. With the increasing demand over the past few decades for meeting stringent stability and performance specifications in industrial operations, fault-tolerance capabilities have become an increasingly important requirement in the design and implementation of modern day control systems. This is especially the case for safety-critical applications, such as chemical processes, where malfunctions in the control devices or process equipment can cause instabilities and lead to safety hazards if not appropriately mitigated through the use of fault-tolerant control approaches (see, for example, References [5,6,7] for some results and references on fault-tolerant control). The need for fault-tolerant control is further underscored by the increasing calls in recent times to achieve zero-incident plant operations as part of enabling the transition to smart plant operations ([8])
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