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Journal Articles
Publisher: ASME
Article Type: Research Papers
Letters Dyn. Sys. Control. April 2025, 5(2): 021006.
Paper No: ALDSC-24-1050
Published Online: February 5, 2025
Journal Articles
Publisher: ASME
Article Type: Research Papers
Letters Dyn. Sys. Control. April 2025, 5(2): 021007.
Paper No: ALDSC-24-1044
Published Online: February 5, 2025
Journal Articles
Publisher: ASME
Article Type: Technical Briefs
Letters Dyn. Sys. Control. April 2025, 5(2): 024502.
Paper No: ALDSC-24-1049
Published Online: February 5, 2025
Image
in Frequency Domain Adjoint-Based Iterative Learning Control for MIMO Systems
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 1 Schematic of the experiment procedures of the time domain adjoint ILC algorithm More about this image found in Schematic of the experiment procedures of the time domain adjoint ILC algor...
Image
in Frequency Domain Adjoint-Based Iterative Learning Control for MIMO Systems
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 2 Schematic of the experiment procedures of the proposed frequency domain adjoint ILC algorithm More about this image found in Schematic of the experiment procedures of the proposed frequency domain adj...
Image
in Frequency Domain Adjoint-Based Iterative Learning Control for MIMO Systems
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 3 Complex plane visualization of the propagation factor D = 1 − ρ G ^ G : ( a ) when Δ θ ^ ≤ π / 2 , ρ * = cos Δ θ ^ / | G ^ | max 2 produces the fastest convergence and ( b ) when Δ θ ^ > π / 2 , | ... More about this image found in Complex plane visualization of the propagation factor D = 1 − ρ G...
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in Frequency Domain Adjoint-Based Iterative Learning Control for MIMO Systems
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 4 ILC convergence of the frequency and time domain adjoint algorithms of the SISO system (38) from tracking a 200 Hz triangular waveform (reference trajectory of axis 1 in Fig. 6 ) More about this image found in ILC convergence of the frequency and time domain adjoint algorithms of the ...
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in Frequency Domain Adjoint-Based Iterative Learning Control for MIMO Systems
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 5 System magnitude | G ( e j ω n ) | , the ideal learning gain 1 / | G ( e j ω n ) | 2 of noise-free adjoint ILC, the frequency domain adjoint learning gain ρ ( n ) from Eq. (37) , and time domain adjoint learning gain from the ratio o... More about this image found in System magnitude | G ( e j ω n ) | , the ideal learnin...
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in Frequency Domain Adjoint-Based Iterative Learning Control for MIMO Systems
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 6 The desired reference trajectories decomposed from a raster scanning pattern for the two motion axes of the galvanometer More about this image found in The desired reference trajectories decomposed from a raster scanning patter...
Image
in Frequency Domain Adjoint-Based Iterative Learning Control for MIMO Systems
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 7 ILC convergence of the frequency and time domain adjoint algorithms of the MIMO system from tracking the trajectories presented in Fig. 6 . Note that the horizontal axis denotes the number of experiments rather than ILC iterations to demonstrate the reduced interleaving trials. More about this image found in ILC convergence of the frequency and time domain adjoint algorithms of the ...
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in Frequency Domain Adjoint-Based Iterative Learning Control for MIMO Systems
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 8 Error progression of the frequency domain adjoint ILC for the MIMO galvanometer system More about this image found in Error progression of the frequency domain adjoint ILC for the MIMO galvanom...
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in Reinforcement Learning-Based Event-Triggered Model Predictive Control for Electric Vehicle Active Battery Cell Balancing
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 1 Battery pack configuration for active cell balancing, where the power converter circuit is capable of moving electricity from any cell to any cell More about this image found in Battery pack configuration for active cell balancing, where the power conve...
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in Reinforcement Learning-Based Event-Triggered Model Predictive Control for Electric Vehicle Active Battery Cell Balancing
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 2 Q -diff for λ = 0.9 and various ρ : ( a ) ρ = 2 , ( b ) ρ = 0.2 , ( c ) ρ = 0.02 , and ( d ) ρ = 0.002 More about this image found in Q -diff for λ = 0.9 and various ρ : ( a ) ρ = 2 ,...
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in Reinforcement Learning-Based Event-Triggered Model Predictive Control for Electric Vehicle Active Battery Cell Balancing
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 3 Q -diff for ρ = 0.002 and λ = 0.95 , where the region to the left of the dividing line corresponds to not to trigger MPC and the region to the right corresponds to trigger, if a greedy policy is used More about this image found in Q -diff for ρ = 0.002 and λ = 0.95 , where the region to t...
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in Reinforcement Learning-Based Event-Triggered Model Predictive Control for Electric Vehicle Active Battery Cell Balancing
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 4 Final results for ρ = 0.002 and λ = 0.95: ( a ) Cell Voltage, ( b ) Balancing Currents, ( c ) Trigger counter, and ( d ) σSOC More about this image found in Final results for ρ = 0.002 and λ = 0.95: ( a ) Cell Voltage, (...
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in Exploiting Stochastic Resonance Principles to Influence the Efficiency of a Torsional-Flutter Energy Harvester in Turbulent Winds
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 1 Schematics of the torsional-flutter harvester—cross-sectional view on the X Y horizontal plane More about this image found in Schematics of the torsional-flutter harvester—cross-sectional view on the ...
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in Exploiting Stochastic Resonance Principles to Influence the Efficiency of a Torsional-Flutter Energy Harvester in Turbulent Winds
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 2 Schematics of the torsional-flutter harvester—cross-sectional view on the X Y horizontal plane More about this image found in Schematics of the torsional-flutter harvester—cross-sectional view on the ...
Image
in Exploiting Stochastic Resonance Principles to Influence the Efficiency of a Torsional-Flutter Energy Harvester in Turbulent Winds
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 3 Λ Υ ( 2 ) versus time τ for the linear model and apparatus at mean flow speeds: ( a ) U = 12.0 m / s and ( b ) U = 16.6 m / s More about this image found in Λ Υ ( 2 ) versus time τ for the linear model and appara...
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in Exploiting Stochastic Resonance Principles to Influence the Efficiency of a Torsional-Flutter Energy Harvester in Turbulent Winds
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 4 Λ Υ ( 2 ) versus time τ for the nonlinear Duffing model and apparatus at mean flow speeds: ( a ) U = 12.0 m / s and ( b ) U = 14.8 m / s More about this image found in Λ Υ ( 2 ) versus time τ for the nonlinear Duffing model...
Image
in Exploiting Stochastic Resonance Principles to Influence the Efficiency of a Torsional-Flutter Energy Harvester in Turbulent Winds
> ASME Letters in Dynamic Systems and Control
Published Online: February 5, 2025
Fig. 5 Λ Υ ( 2 ) versus time τ for the hybrid Duffing–van der Pol model at a mean flow speed U = 19.8 m / s More about this image found in Λ Υ ( 2 ) versus time τ for the hybrid Duffing–van der ...
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