WP
W. Plaetinck
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2 records found
1
Identifying the time-varying, adapting human operator online could enable adaptive human-machine support systems and attention monitoring for human-in-the-loop vehicle control tasks. A validated, online identification method, including adaptation detection is missing, however.
In this MSc thesis project, online human operator identification using low-order ARX models with recursive least squares estimation was implemented. Operator delay estimation was attempted in simulations using the parallel recursive ARX method. Two online adaptation detection algorithms were evaluated on experimental data from eight subjects who performed a compensatory tracking task with time-varying controlled element. Participants were instructed to explicitly indicate when they detected this change with a button push, as comparison with the performance of the detection methods.
The experiment validated the online identification method, but time-varying delay estimation was not sufficiently accurate. The best detection method was based on recognizing the deviation of the human error rate response gain from a priori measured non-adapted behavior, and had a detection accuracy of 57%. A more practical method based on the parameter moving average was also developed and tested, but had a lower accuracy of 43%. For both methods, the lag in detection was found to be equivalent to the human operator detection lags measured in the experiment. Thus, the developed methods open the door for further development of identification-driven adaptive operator support systems in manual vehicle control tasks.
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In this MSc thesis project, online human operator identification using low-order ARX models with recursive least squares estimation was implemented. Operator delay estimation was attempted in simulations using the parallel recursive ARX method. Two online adaptation detection algorithms were evaluated on experimental data from eight subjects who performed a compensatory tracking task with time-varying controlled element. Participants were instructed to explicitly indicate when they detected this change with a button push, as comparison with the performance of the detection methods.
The experiment validated the online identification method, but time-varying delay estimation was not sufficiently accurate. The best detection method was based on recognizing the deviation of the human error rate response gain from a priori measured non-adapted behavior, and had a detection accuracy of 57%. A more practical method based on the parameter moving average was also developed and tested, but had a lower accuracy of 43%. For both methods, the lag in detection was found to be equivalent to the human operator detection lags measured in the experiment. Thus, the developed methods open the door for further development of identification-driven adaptive operator support systems in manual vehicle control tasks.
...
Identifying the time-varying, adapting human operator online could enable adaptive human-machine support systems and attention monitoring for human-in-the-loop vehicle control tasks. A validated, online identification method, including adaptation detection is missing, however.
In this MSc thesis project, online human operator identification using low-order ARX models with recursive least squares estimation was implemented. Operator delay estimation was attempted in simulations using the parallel recursive ARX method. Two online adaptation detection algorithms were evaluated on experimental data from eight subjects who performed a compensatory tracking task with time-varying controlled element. Participants were instructed to explicitly indicate when they detected this change with a button push, as comparison with the performance of the detection methods.
The experiment validated the online identification method, but time-varying delay estimation was not sufficiently accurate. The best detection method was based on recognizing the deviation of the human error rate response gain from a priori measured non-adapted behavior, and had a detection accuracy of 57%. A more practical method based on the parameter moving average was also developed and tested, but had a lower accuracy of 43%. For both methods, the lag in detection was found to be equivalent to the human operator detection lags measured in the experiment. Thus, the developed methods open the door for further development of identification-driven adaptive operator support systems in manual vehicle control tasks.
In this MSc thesis project, online human operator identification using low-order ARX models with recursive least squares estimation was implemented. Operator delay estimation was attempted in simulations using the parallel recursive ARX method. Two online adaptation detection algorithms were evaluated on experimental data from eight subjects who performed a compensatory tracking task with time-varying controlled element. Participants were instructed to explicitly indicate when they detected this change with a button push, as comparison with the performance of the detection methods.
The experiment validated the online identification method, but time-varying delay estimation was not sufficiently accurate. The best detection method was based on recognizing the deviation of the human error rate response gain from a priori measured non-adapted behavior, and had a detection accuracy of 57%. A more practical method based on the parameter moving average was also developed and tested, but had a lower accuracy of 43%. For both methods, the lag in detection was found to be equivalent to the human operator detection lags measured in the experiment. Thus, the developed methods open the door for further development of identification-driven adaptive operator support systems in manual vehicle control tasks.
E-SPARC: Electrically, Sustainably Propelled Aerobatic Racing Aircraft
Make Aerobatic Racing Innovative and Eco-friendly for the Future
Bachelor thesis
(2015)
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J. Hoogendoorn, Paul Hulsman, D.A.J. Peschier, O.F. Pfeifle, W. Plaetinck, H.C. Prins, B. Röell, L.N. de Ruiter, E.T.B. Smeets, N. Weber, S. Shroff, A.C. in 't Veld, M.F.M. Hoogreef, H.J. van Overvest
Following in the footsteps of the automotive industry with the successful implementation of Formula E, the E-SPARC design is the world’s first all-electric racing aircraft. E-SPARC’s mission is to proof the feasibility of a sustainable and high performance alternative for the current state-of-the-art in aerobatic racing. Thereby, the aim is to present a design worthy of competing in the popular Red Bull Air Races. Given the combination of being the world’s fastest growing international motorsport with the commitment towards reducing the carbon footprint [1], Red Bull Air Races provide the optimal platformfor the E-SPARC design. The leading design question is therefore whether an all-electric racing aircraft can be designed with performance characteristics equal to or exceeding the performance characteristics of the current competition. This report describes the design decisions and outcomes taken during the preliminary design phase, continuing upon the pusher canard configuration that was selected during the conceptual design phase...
...
Following in the footsteps of the automotive industry with the successful implementation of Formula E, the E-SPARC design is the world’s first all-electric racing aircraft. E-SPARC’s mission is to proof the feasibility of a sustainable and high performance alternative for the current state-of-the-art in aerobatic racing. Thereby, the aim is to present a design worthy of competing in the popular Red Bull Air Races. Given the combination of being the world’s fastest growing international motorsport with the commitment towards reducing the carbon footprint [1], Red Bull Air Races provide the optimal platformfor the E-SPARC design. The leading design question is therefore whether an all-electric racing aircraft can be designed with performance characteristics equal to or exceeding the performance characteristics of the current competition. This report describes the design decisions and outcomes taken during the preliminary design phase, continuing upon the pusher canard configuration that was selected during the conceptual design phase...