Adaptive Control of Actuator Lifetime

Gokdere, L.U.; Богданов, А. А.; Chiu, S.; Keller, K.; Vian, John

doi:10.1109/aero.2006.1656096

Cited by 23 publications

(20 citation statements)

References 4 publications

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“…It is assumed hereafter that the control input u(t) and its variations participate in the evolution of the actuator's degradation. This viewpoint agrees with [5,19] where it is explained that there is a physical relation between u(t) and the level of actuator's degradation. In these papers, the studied system is an electrical motor where the current input u(t) both impacts the winding temperature and the bearing load.…”

Section: A Degradation Process For the Actuatorsupporting

confidence: 88%

A modeling framework for deteriorating control system and predictive maintenance of actuators

Langeron

Grall

Barros

2015

Reliability Engineering & System Safety

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Section: A Degradation Process For the Actuatorsupporting

confidence: 88%

A modeling framework for deteriorating control system and predictive maintenance of actuators

Langeron

Grall

Barros

2015

Reliability Engineering & System Safety

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“…In contrast, the resultant temperature without active temperature control was much higher than the maximum reference temperature, and the peak temperature was over 200 °C. Given that the average life expectancy of winding insulation is reduced approximately by half for every 10 °C rise in temperature [6], the temperature difference shown in Fig. 8 will significantly affect the lifetime of the motor winding when the excessive winding temperature is not managed with the proposed active temperature management scheme.…”

Section: Simulation Studymentioning

confidence: 99%

“…In applications where a large contribution to the machine torque is due to acceleration and deceleration, a trade-off between drive control effort (motor torque) and performance (position or speed tracking error) may be possible [6]. When the machine operates at high temperature, relaxation in drive performance requirements makes it possible to reduce control effort (torque) and hence machine temperature.…”

Section: Introductionmentioning

confidence: 99%

“…When the machine operates at high temperature, relaxation in drive performance requirements makes it possible to reduce control effort (torque) and hence machine temperature. The trade-off can be formulated in the form of the linear quadratic regulator (LQR) based on measured or estimated machine temperature [6]. However, the technique is effective only if the majority of the torque is for acceleration/deceleration.…”

Section: Introductionmentioning

confidence: 99%

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Active Thermal Management for Interior Permanent Magnet Synchronous Machine (IPMSM) Drives Based on Model Predictive Control

Sun

Wang

Griffo

et al. 2018

IEEE Trans. on Ind. Applicat.

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Abstract-This paper proposes an active thermal management scheme for Interior Permanent Magnet Synchronous Machine (IPMSM) drives based on the model predictive control concept. The proposed control scheme can adaptively set torque limit based on the thermal state of the machine to limit the machine winding and end-winding temperatures. The proposed control scheme is assessed by experiments on a laboratory machine drive system and simulated for traction drives over Worldwide Harmonized Lightduty Test Cycle (WLTC). Compared with conventional traction control scheme, the proposed scheme can effectively reduce peak temperature and hence thermal stress of the machine for improving its lifetime.

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“…The potential consequence may be a reduction of its lifetime. On the contrary, the actuator is smoothly solicited with a large magnitude of R. The actuator health is preserved but at the expense of reduced performances Gokdere et al (2006).…”

Section: Lqr Controllermentioning

confidence: 99%

Joint maintenance and controller reconfiguration policy for a gradually deteriorating control system

Langeron

Grall

Barros

2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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This paper tackles with the maintenance decision-making of gradually deteriorating control systems. The main feature of these systems is their ability within a closedloop to drive actuators in order to set the controlled process in a given state.Usually, the literature deals with the diagnosis of faults of a control system and the means for recovering system performances after their appearance. The controller reconfiguration is one of these means. The root cause of a fault is rarely argued nor its occurrence time. Before designing maintenance policies, this paper proposes a stochastic modeling framework of a degrading control system focusing on the actuator deterioration. It is assumed a close relationship between the controller setting, the actuator deterioration and finally its faulty situations. These latter are related to given degradation thresholds. Due to the stochastic nature of the deterioration, the corresponding hitting times are thus random. The obtained models allow to assess the conditional reliability of the actuator and then the prognosis of its residual useful life namely the RUL. This RUL is finally used to achieve two maintenance policies based upon the reconfiguration of the controller considered as a new maintenance action.Prepared using sagej.cls

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Adaptive Control of Actuator Lifetime

Cited by 23 publications

References 4 publications

A modeling framework for deteriorating control system and predictive maintenance of actuators

A modeling framework for deteriorating control system and predictive maintenance of actuators

Active Thermal Management for Interior Permanent Magnet Synchronous Machine (IPMSM) Drives Based on Model Predictive Control

Joint maintenance and controller reconfiguration policy for a gradually deteriorating control system

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