Fault-Tolerant Control for Systems With Model Uncertainty and Multiplicative Faults

Zhao, Zhengen; Chen, Xiaolu; Ding, Steven X.; Li, Linlin

doi:10.1109/tsmc.2017.2759144

Cited by 33 publications

(10 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is only state information in formulae (10) and (11). One can obtain the optimal action only using the current state, but without knowing the system dynamics.…”

Section: Reinforcement Learning Methodsmentioning

confidence: 99%

“…2 As a possible solution to ensure safe and reliable operation of the system, the model-based fault detection and isolation (FDI) and FTC techniques have achieved fruitful results. [7][8][9][10][11][12] The sliding mode control, 8 adaptive decentralized control, 9 and coprime factorization techniques 10 have been applied successfully. Youla parameterization-based FTC was developed in Ding et al 11 and Yin et al 12 to solve a nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reinforcement learning–based fault-tolerant control with application to flux cored wire system

Zhang

2018

Measurement and Control

View full text Add to dashboard Cite

Background: Processes and systems are always subjected to faults or malfunctions due to age or unexpected events, which would degrade the operation performance and even lead to operation failure. Therefore, it is motivated to develop fault-tolerant control strategy so that the system can operate with tolerated performance degradation. Methods: In this paper, a reinforcement learning -based fault-tolerant control method is proposed without need of the system model and the information of faults. Results and Conclusions: Under the real-time tolerant control, the dynamic system can achieve performance tolerance against unexpected actuator or sensor faults. The effectiveness of the algorithm is demonstrated and validated by the rolling system in a test bed of the flux cored wire.

show abstract

“…There is only state information in formulae (10) and (11). One can obtain the optimal action only using the current state, but without knowing the system dynamics.…”

Section: Reinforcement Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement learning–based fault-tolerant control with application to flux cored wire system

Zhang

2018

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…is can be traced from some valuable survey papers [1][2][3] and books [4,5]. Different methods have been developed and implemented in different directions and for several systems [6][7][8][9][10][11] such as model-based method [12,13], observer method [14][15][16], parameter estimation method [17], parity space method [18], and a combination of these methods with artificial intelligent [8,19]. e three-tank system (3TS) is considered an important and effective prototype of many applications in industrial processes, such as water treatment, food industry, chemical and petrochemical plants, oil, and gas systems.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Control for Three-Tank System in Case of Sensor Faults

Amor

Ladhari

Said

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This research paper would be devoted to the application of a fault-tolerant control (FTC) for a benchmark system composed of three interconnected tanks in case of sensor faults. The control scheme includes two blocks: fault detection and isolation (FDI) block and a control law reconfiguration block. The strategy of the FDI method is based on a bank of high gain observers; each of them is constructed to estimate the system state vector. Thus, the diagnostic signal-residuals are generated by the comparison of measured and estimated outputs and the faulty sensor is isolated. The reconfiguration block performs an update of the controller parameters according to the operating mode. The application of this method to a pilot plant demonstrates that the hydrographic system maintains quite performances after sensor faults occurrence.

show abstract

“…Since there exists a stabilizing controller which can maintain the maglev system a good performance under ideal condition, the PnP controller can be adopted with a performance enhancement module plugged in to tackle the track irregularity problem. For configuration of the plugged in module, a simple realization can be made by offline designing and online switching [16]. However, this method only deals with specific known disturbances.…”

Section: Introductionmentioning

confidence: 99%

Track Irregularity Disturbance Rejection for Maglev Train Based on Online Optimization of PnP Control Architecture

Wang

2019

IEEE Access

View full text Add to dashboard Cite

Track irregularities, caused by reasons such as track deformation and installation error, are common in real maglev line. These disturbances brought by tracks exert an adverse influence on the performance of a maglev train levitation system. It takes a lot of maintenance costs and time to keep the tracks in a good condition. In another way, a disturbance rejection through a controller optimization method is proposed in this paper. First, the influences of the track irregularity disturbances on levitation performance are illustrated in detail. Then, an online optimization of the PnP control architecture method is adopted to reject the disturbances caused by track irregularities. The effectiveness of this method is verified through the MATLAB simulation. This method makes the levitation system adaptive to known and unknown time-varying track irregularities, saving time, and human resources. INDEX TERMS Maglev train, online optimization, PnP control architecture, track irregularity.

show abstract

Fault-Tolerant Control for Systems With Model Uncertainty and Multiplicative Faults

Cited by 33 publications

References 32 publications

Reinforcement learning–based fault-tolerant control with application to flux cored wire system

Reinforcement learning–based fault-tolerant control with application to flux cored wire system

Fault-Tolerant Control for Three-Tank System in Case of Sensor Faults

Track Irregularity Disturbance Rejection for Maglev Train Based on Online Optimization of PnP Control Architecture

Contact Info

Product

Resources

About