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Volume 146, Issue 3

May 2024

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ISSN 0022-0434

EISSN 1528-9028

In this Issue

Research Papers

Research Papers

Synthesizing Negative-Imaginary Systems With Closed-Loop $H$ _∞-Performance Via Static Output Feedback Control

Pravin Behera, Arnab Dey, Sourav Patra

J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031001. doi: https://doi.org/10.1115/1.4064175

Abstract

View articletitled, Synthesizing Negative-Imaginary Systems With Closed-Loop <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mi mathvariant="script">H</mi></mrow></math>∞-Performance Via Static Output Feedback Control

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Topics: Algorithms , Control equipment , Design , Feedback , Relaxation (Physics) , Optimization , Uncertainty , Closed loop systems

Hierarchical Iterative Learning Control for a Class of Distributed Hierarchical Systems

Spencer Igram, Andrew G. Alleyne

J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031002. doi: https://doi.org/10.1115/1.4064174

Abstract

View articletitled, Hierarchical Iterative Learning Control for a Class of Distributed Hierarchical Systems

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Topics: Errors , Iterative learning control , Stability

High-Gain-Observer-Based Predictive Output Feedback for Nonlinear Systems With Large Input-Delays

Jing Lei

J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031003. doi: https://doi.org/10.1115/1.4064349

Abstract

View articletitled, High-Gain-Observer-Based Predictive Output Feedback for Nonlinear Systems With Large Input-Delays

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Topics: Delays , Feedback , Nonlinear systems , Simulation , Control equipment , Stability , Pendulums

Exergy Management Strategies for Hybrid Electric Ground Vehicles: A Dynamic Programming Solution

Matteo Acquarone, Gabriele Pozzato, Corey James, Simona Onori

J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031004. doi: https://doi.org/10.1115/1.4063610

Abstract

View articletitled, Exergy Management Strategies for Hybrid Electric Ground Vehicles: A Dynamic Programming Solution

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Topics: Exergy , Generators , Heat , Vehicles , Temperature , Batteries , Emissions , Dynamic programming

Electrohydraulic System Analysis of Variable Recruitment Fluidic Artificial Muscle Bundles With Interaction Effects

Jeong Yong Kim, Matthew Bryant

J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031005. doi: https://doi.org/10.1115/1.4064092

Abstract

View articletitled, Electrohydraulic System Analysis of Variable Recruitment Fluidic Artificial Muscle Bundles With Interaction Effects

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Topics: Actuators , Displacement , Engines , Motors , Pressure , Pumps , Artificial muscles , System efficiency , Modeling

Artificial Flow Guidance Method for Combined Off-Tracking and Stability Improvement for Automated Articulated Vehicles

Shammi Rahman, Timothy Gordon, Qingwei Liu, Yangyan Gao, Leon Henderson, Leo Laine

J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031006. doi: https://doi.org/10.1115/1.4064173

Abstract

View articletitled, Artificial Flow Guidance Method for Combined Off-Tracking and Stability Improvement for Automated Articulated Vehicles

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Topics: Control equipment , Flow (Dynamics) , Stability , Vehicles , Traffic circles , Boundary layers , Roads

A New Physics-Informed Condition Indicator for Cost-Effective Direct Current Solenoid Valves Using Significant Points of the Excitation Current

Luca Maurelli, Mirko Mazzoleni, Fabio Previdi, Andrea Camisani

J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031007. doi: https://doi.org/10.1115/1.4064602

Abstracts

View articletitled, A New Physics-Informed Condition Indicator for Cost-Effective Direct Current Solenoid Valves Using Significant Points of the Excitation Current

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Topics: Solenoids , Valves , Excitation , Algorithms

What Observables Are Needed for Precision Data-Enabled Learning of Inverse Operators?

Liangwu Yan, Santosh Devasia

J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031008. doi: https://doi.org/10.1115/1.4064655

Abstracts

View articletitled, What Observables Are Needed for Precision Data-Enabled Learning of Inverse Operators?

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Topics: Algorithms , Dynamics (Mechanics) , Errors , Noise (Sound) , Simulation , Trains , Trajectories (Physics) , Artificial neural networks

Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems

Kaustav Jyoti Borah

J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031009. doi: https://doi.org/10.1115/1.4064601

Abstracts

View articletitled, Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems

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Topics: Actuators , Algorithms , Computer simulation , Control equipment , Design , Errors , Filtration , Flaw detection , Multi-agent systems , Noise (Sound)

Gas Path Fault Diagnosis of Turboshaft Engine Based on Novel Transfer Learning Methods

Yong-Ping Zhao, Hui-Jie Jin, Hao Liu

J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031010. doi: https://doi.org/10.1115/1.4064846

Abstracts

View articletitled, Gas Path Fault Diagnosis of Turboshaft Engine Based on Novel Transfer Learning Methods

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Topics: Aircraft engines , Algorithms , Engines , Fault diagnosis , Machinery

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Research Papers

Synthesizing Negative-Imaginary Systems With Closed-Loop $H$ _∞-Performance Via Static Output Feedback Control

Hierarchical Iterative Learning Control for a Class of Distributed Hierarchical Systems

High-Gain-Observer-Based Predictive Output Feedback for Nonlinear Systems With Large Input-Delays

Exergy Management Strategies for Hybrid Electric Ground Vehicles: A Dynamic Programming Solution

Electrohydraulic System Analysis of Variable Recruitment Fluidic Artificial Muscle Bundles With Interaction Effects

Artificial Flow Guidance Method for Combined Off-Tracking and Stability Improvement for Automated Articulated Vehicles

A New Physics-Informed Condition Indicator for Cost-Effective Direct Current Solenoid Valves Using Significant Points of the Excitation Current

What Observables Are Needed for Precision Data-Enabled Learning of Inverse Operators?

Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems

Gas Path Fault Diagnosis of Turboshaft Engine Based on Novel Transfer Learning Methods

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