Model-based strategies for sensor fault accommodation in uncertain dynamic processes with multi-rate sampled measurements

Abstract This work presents model-based strategies for the accommodation of sensor faults in dynamic processes with multi-rate sampled state measurements. The developed strategies account explicitly for both closed-loop stability and performance considerations. Initially, a model-based feedback control system, in which a model predictor compensates for the unavailability of state measurements between sampling times, is designed. The stability and performance characteristics of the multi-rate sampled-data closed-loop system are analyzed and explicitly characterized in terms of the state sampling rates, the fault parameters, and the various process, model and controller design parameters. The resulting characterizations provide insight into the robustness and margins of tolerable faults that can be accommodated, and are used to develop both stability-based and performance-based fault accommodation schemes that aim to maintain closed-loop stability and minimize the degradation of closed-loop performance in the presence of sensor faults. Two types of sensor faults are considered. These include faults that manifest themselves as improper sensor readings, as well as faults that cause drift in the sensor sampling rate. The first type of fault introduces errors in the model state updates at the sampling times, while the second type of fault alters the rate at which the sensors sample the process states. The developed methods are illustrated through a case study involving a cascade of two non-isothermal continuous-stirred tank reactors with plant-model mismatch and access to full state measurements that are sampled at different rates. The case study gives some insight into the effects of sensor faults on the stability and performance of the sampled-data state feedback control system, and how these effects can be mitigated through use of fault-tolerant control.

[1]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2006 .

[2]  Nael H. El-Farra,et al.  Fault Detection and Accommodation in Particulate Processes with Sampled and Delayed Measurements , 2013 .

[3]  Jie Bao,et al.  Multi-rate dissipativity based control of process networks , 2014 .

[4]  Nael H. El-Farra,et al.  Quasi-decentralized model-based networked control of process systems , 2008, Comput. Chem. Eng..

[5]  Panos J. Antsaklis,et al.  Performance evaluation for Model-Based Networked Control systems , 2006 .

[6]  Nael H. El-Farra,et al.  Performance-Based Sensor Reconfiguration for Fault-Tolerant Control of Uncertain Spatially Distributed Processes , 2014 .

[7]  P. Frank,et al.  Survey of robust residual generation and evaluation methods in observer-based fault detection systems , 1997 .

[8]  Niket S. Kaisare,et al.  Distributed model predictive control of a system with multi-rate and delayed measurements , 2018 .

[9]  Youmin Zhang,et al.  Bibliographical review on reconfigurable fault-tolerant control systems , 2003, Annu. Rev. Control..

[10]  Nael H. El-Farra,et al.  Model-based Fault-Tolerant Control of Uncertain Particulate Processes: Integrating Fault Detection, Estimation and Accommodation , 2015 .

[11]  Panagiotis D. Christofides,et al.  Handling sensor malfunctions in control of particulate processes , 2008 .

[12]  Nael H. El-Farra,et al.  Networked Control of Distributed Energy Resources: Application to Solid Oxide Fuel Cells , 2009 .

[13]  Nael H. El-Farra,et al.  A model-based framework for fault estimation and accommodation applied to distributed energy resources , 2016 .

[14]  Rolf Isermann,et al.  Fault-diagnosis systems : an introduction from fault detection to fault tolerance , 2006 .

[15]  Li Sheng,et al.  Iterative Learning Fault-Tolerant Control for Networked Batch Processes with Multirate Sampling and Quantization Effects , 2017 .

[16]  Nael H. El-Farra,et al.  Model-Based Fault Detection and Fault-Tolerant Control of Process Systems with Sampled and Delayed Measurements , 2011 .

[17]  Fuad E. Alsaadi,et al.  Detection of intermittent faults for nonuniformly sampled multi-rate systems with dynamic quantisation and missing measurements , 2018, Int. J. Control.

[18]  Mohsen Montazeri,et al.  Controllability and stabilizability of multi-rate sampled data systems , 2018, Syst. Control. Lett..

[19]  Silvio Simani,et al.  Model-based fault diagnosis in dynamic systems using identification techniques , 2003 .

[20]  Ali Cinar,et al.  System identification and distributed control for multi-rate sampled systems , 2015 .

[21]  Nael H. El-Farra,et al.  A predictor-corrector approach for multi-rate sampled-data control of spatially distributed systems , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[22]  Nael H. El-Farra,et al.  Sensor Fault Accommodation Strategies in Multi-rate Sampled-Data Control of Particulate Processes , 2013 .