The Experts below are selected from a list of 33 Experts worldwide ranked by ideXlab platform
Carlos Silvestre - One of the best experts on this subject based on the ideXlab platform.
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Set-based fault detection and isolation for detectable linear parameter-varying systems
International Journal of Robust and Nonlinear Control, 2017Co-Authors: Daniel Silvestre, Paulo Rosa, Joao P. Hespanha, Carlos SilvestreAbstract:Summary In the context of fault detection and isolation of linear parameter-varying systems, a challenging task appears when the dynamics and the available measurements render the Model Unobservable, which invalidates the use of standard set-valued observers. Two results are obtained in this paper, namely, using a left-coprime factorization, one can achieve set-valued estimates with ultimately bounded hyper-volume and convergence dependent on the slowest Unobservable Mode; and by rewriting the set-valued observer equations and taking advantage of a coprime factorization, it is possible to have a low-complexity fault detection and isolation method. Performance is assessed through simulation, illustrating, in particular, the detection time for various types of faults. Copyright © 2017 John Wiley & Sons, Ltd.
Daniel Silvestre - One of the best experts on this subject based on the ideXlab platform.
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Set-based fault detection and isolation for detectable linear parameter-varying systems
International Journal of Robust and Nonlinear Control, 2017Co-Authors: Daniel Silvestre, Paulo Rosa, Joao P. Hespanha, Carlos SilvestreAbstract:Summary In the context of fault detection and isolation of linear parameter-varying systems, a challenging task appears when the dynamics and the available measurements render the Model Unobservable, which invalidates the use of standard set-valued observers. Two results are obtained in this paper, namely, using a left-coprime factorization, one can achieve set-valued estimates with ultimately bounded hyper-volume and convergence dependent on the slowest Unobservable Mode; and by rewriting the set-valued observer equations and taking advantage of a coprime factorization, it is possible to have a low-complexity fault detection and isolation method. Performance is assessed through simulation, illustrating, in particular, the detection time for various types of faults. Copyright © 2017 John Wiley & Sons, Ltd.
Joao P. Hespanha - One of the best experts on this subject based on the ideXlab platform.
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Set-based fault detection and isolation for detectable linear parameter-varying systems
International Journal of Robust and Nonlinear Control, 2017Co-Authors: Daniel Silvestre, Paulo Rosa, Joao P. Hespanha, Carlos SilvestreAbstract:Summary In the context of fault detection and isolation of linear parameter-varying systems, a challenging task appears when the dynamics and the available measurements render the Model Unobservable, which invalidates the use of standard set-valued observers. Two results are obtained in this paper, namely, using a left-coprime factorization, one can achieve set-valued estimates with ultimately bounded hyper-volume and convergence dependent on the slowest Unobservable Mode; and by rewriting the set-valued observer equations and taking advantage of a coprime factorization, it is possible to have a low-complexity fault detection and isolation method. Performance is assessed through simulation, illustrating, in particular, the detection time for various types of faults. Copyright © 2017 John Wiley & Sons, Ltd.
Paulo Rosa - One of the best experts on this subject based on the ideXlab platform.
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Set-based fault detection and isolation for detectable linear parameter-varying systems
International Journal of Robust and Nonlinear Control, 2017Co-Authors: Daniel Silvestre, Paulo Rosa, Joao P. Hespanha, Carlos SilvestreAbstract:Summary In the context of fault detection and isolation of linear parameter-varying systems, a challenging task appears when the dynamics and the available measurements render the Model Unobservable, which invalidates the use of standard set-valued observers. Two results are obtained in this paper, namely, using a left-coprime factorization, one can achieve set-valued estimates with ultimately bounded hyper-volume and convergence dependent on the slowest Unobservable Mode; and by rewriting the set-valued observer equations and taking advantage of a coprime factorization, it is possible to have a low-complexity fault detection and isolation method. Performance is assessed through simulation, illustrating, in particular, the detection time for various types of faults. Copyright © 2017 John Wiley & Sons, Ltd.
William Singhose - One of the best experts on this subject based on the ideXlab platform.
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Improving trajectory tracking for systems with Unobservable Modes using command generation
Proceedings of the 2005 American Control Conference 2005., 1Co-Authors: Erika Biediger, Jason Lawrence, William SinghoseAbstract:Trajectory tracking with flexible systems is extremely difficult. This difficulty is increased when there are Unobservable Modes. Optimal PID control can be used for standard trajectory tracking, whereas sliding Mode control can be used in systems with parametric uncertainties. Command shaping has been proven to be beneficial in eliminating unwanted vibration. This paper shows that command generation can be utilized to eliminate unwanted vibration from a system with an Unobservable Mode. Both optimal PID and sliding Mode control can be used in conjunction with command generation for enhanced system performance.
