2013
DOI: 10.4304/jcp.8.5.1286-1291
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Adaptive Actuator Failure Compensation of Uncertain Nonlinear Systems with Dead-Zone Inputs

Abstract: As is well known, dead-zone nonlinearity often exists in practical actuators and unknown actuator failures may occur during system operation. However, available results based on adaptive approaches to compensate unknown failures of such actuators are still very limited. In this paper, we address such a problem by considering a class of uncertain nonlinear systems with dead-zone hysteresis nonlinearities inputs. An adaptive control scheme is proposed by using backstepping technology to compensate the uncertaint… Show more

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Cited by 2 publications
(1 citation statement)
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References 18 publications
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“…Different faulty cases may happen for each actuator, which may be caused by mechanical imperfections or driver laws of actuator. Static non‐smooth nonlinearities such as friction, dead zone, saturation, or dynamic non‐smooth nonlinearities such as backlash, hysteresis, or partial or total actuator capacity loss refer to mechanical imperfections (Wang and Hu, ; Cai et al, ; Strano and Terzo, ). For evaluating the FDD part two scenario of faults in actuators are considered, dead zone fault in actuator groups III and IV and driver fault in actuator groups V and VI.…”
Section: Occurring Fault In Actuator and Fault Detection And Diagnosismentioning
confidence: 99%
“…Different faulty cases may happen for each actuator, which may be caused by mechanical imperfections or driver laws of actuator. Static non‐smooth nonlinearities such as friction, dead zone, saturation, or dynamic non‐smooth nonlinearities such as backlash, hysteresis, or partial or total actuator capacity loss refer to mechanical imperfections (Wang and Hu, ; Cai et al, ; Strano and Terzo, ). For evaluating the FDD part two scenario of faults in actuators are considered, dead zone fault in actuator groups III and IV and driver fault in actuator groups V and VI.…”
Section: Occurring Fault In Actuator and Fault Detection And Diagnosismentioning
confidence: 99%