Parameter identification based fault tolerant control against actuator failures applied to UUV dynamics

Uney, Emre; Hajiyev, Chingiz

doi:10.1109/ecai.2014.7090193

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Different ways of producing residual signals are described by Frank [4], parity space approach [5], dedicated observer approach and innovation-based approach [6], fault detection filter [7] and parameter identification [8]. These methods can be applied to the continuous-time systems and discretetime systems [9].…”

Section: Introductionmentioning

confidence: 99%

An adaptive fault tolerant PI controller for an information-poor, nonlinear system

Zhou

2015

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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In this paper, an adaptive PI controller is proposed to achieve fault tolerant control. Information-poor systems are normally nonlinear, and difficult to be described via physical models. Hence fuzzy relational model is used to describe their behaviours with only input/output information. An aircooling subsystem in HVAC (Heating, Ventilating and AirConditioning) system is used as a case study. The neurofuzzy based adaptive PI controller is designed to accommodate the faults in the system with fault tolerance achievement. Simulation experiments have been performed to prove the efficacy of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

An adaptive fault tolerant PI controller for an information-poor, nonlinear system

Zhou

2015

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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“…This zero and non-zero property of the residual is used to determine the existence of faults (Patton et al, 2000). The residual signals can be generated via different methods (Frank, 1990; Saif, 1998), such as the parity space approach (Berriri and Slama-Blekhodja, 2010), dedicated observer approach and innovation-based approach (Kamal et al, 2012), fault detection filter (Jamouli et al, 2004) and parameter identification (Uney and Hajiyev, 2014).…”

Section: Introductionmentioning

confidence: 99%

An adaptive fault tolerant neuro-fuzzy based proportional-integral controller for a nonlinear system

Zhou

2018

Transactions of the Institute of Measurement and Control

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In this paper, an adaptive proportional-integral controller based on a fuzzy relational model is developed for the purpose of fault tolerant control in a nonlinear, information-poor system. First, the methods of fault tolerant control are briefly introduced. An air-cooling subsystem in a heating, ventilating and air-conditioning system is used as an example to describe the fuzzy relational modelling procedure. Then a proportional-integral (PI) observer is established for fault identification and a PI-based adaptive controller is designed for fault tolerance with varied parameter adjusting. By introducing the fault estimation, an adaptive mechanism is adopted to update the parameter selection in the control scheme. Sensor noise is also considered in the method and simulation experiments are performed to verify the effectiveness of the proposed scheme.

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Fault Classification for Unmanned Surface Vehicles using Supervised Learning Methods

Singh

Bhushan

2019

2019 International Conference on Power Electronics, Control and Automation (ICPECA)

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Parameter identification based fault tolerant control against actuator failures applied to UUV dynamics

Cited by 4 publications

References 17 publications

An adaptive fault tolerant PI controller for an information-poor, nonlinear system

An adaptive fault tolerant PI controller for an information-poor, nonlinear system

An adaptive fault tolerant neuro-fuzzy based proportional-integral controller for a nonlinear system

Fault Classification for Unmanned Surface Vehicles using Supervised Learning Methods

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