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Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031009.
Paper No: DS-22-1309
Published Online: March 13, 2024
Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Dyn. Sys., Meas., Control. July 2024, 146(4): 041002.
Paper No: DS-23-1245
Published Online: March 13, 2024
Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Dyn. Sys., Meas., Control. July 2024, 146(4): 041001.
Paper No: DS-23-1231
Published Online: March 13, 2024
Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Dyn. Sys., Meas., Control. May 2024, 146(3): 031010.
Paper No: DS-23-1031
Published Online: March 13, 2024
Image
in Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 1 States and their corresponding action set in a grid More about this image found in States and their corresponding action set in a grid
Image
in Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 2 Simulation setup More about this image found in Simulation setup
Image
in Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 3 Leader–follower MAS communication More about this image found in Leader–follower MAS communication
Image
in Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 4 ( a ) Adaptive and ( b ) constant beta for fault detection More about this image found in ( a ) Adaptive and ( b ) constant beta for fault detection
Image
in Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 5 Position and velocity estimation error using AUKF ( a )–( d ) More about this image found in Position and velocity estimation error using AUKF ( a )–( d )
Image
in Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 6 Position and velocity estimation error using RUKF ( a )–( d ) More about this image found in Position and velocity estimation error using RUKF ( a )–( d )
Image
in Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 7 Performance matrices under incipient fault More about this image found in Performance matrices under incipient fault
Image
in Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 8 Performance matrices under control command fault More about this image found in Performance matrices under control command fault
Image
in Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 9 Performance matrices under degraded control command More about this image found in Performance matrices under degraded control command
Image
in Predictive Control Co-Design: A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive Model Predictive Control in Control Co-Design
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 1 Online implementation of pCCD controller, Eq. (2) . Thetext provides descriptions for various signals and also indicates quantities and variables optimized within the pCCD algorithm. More about this image found in Online implementation of pCCD controller, Eq. (2) . Thetext provides descr...
Image
in Predictive Control Co-Design: A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive Model Predictive Control in Control Co-Design
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 2 Notional dual-tank aircraft thermal management system schematic More about this image found in Notional dual-tank aircraft thermal management system schematic
Image
in Predictive Control Co-Design: A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive Model Predictive Control in Control Co-Design
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 3 Block diagram of online control implementation of system co-designed using OL-CCD. The text provides descriptions for various signals and also indicates quantities and variables optimized within the pCCD algorithm. More about this image found in Block diagram of online control implementation of system co-designed using ...
Image
in Predictive Control Co-Design: A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive Model Predictive Control in Control Co-Design
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 4 Online control implementation for nested CCD with receding-horizon MPC. The text provides descriptions for various signals and also indicates quantities and variables optimized within the pCCD algorithm. More about this image found in Online control implementation for nested CCD with receding-horizon MPC. The...
Image
in Predictive Control Co-Design: A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive Model Predictive Control in Control Co-Design
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 5 Case study 1 , perfect knowledge of disturbances: each CCD algorithm's plant/controller design meets the constraints given perfect knowledge of the load profile. ( a ) Disturbance input signals: exiting mass flow (gray curve with axis on right) and pulsed heat load (black curve with axis o... More about this image found in Case study 1 , perfect knowledge of disturbances: each CCD algorithm's plan...
Image
in Predictive Control Co-Design: A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive Model Predictive Control in Control Co-Design
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 6 Case study 2 , unexpected load profiles: the pCCD and CCDwMPC systems are robust to uncertainty in the loading profile whereas the OL-CCD system is not. ( a ) Disturbance input signals: exiting mass flow (gray curve with axis on right) and pulsed heat load (black curve with axis on left). ... More about this image found in Case study 2 , unexpected load profiles: the pCCD and CCDwMPC systems are r...
Image
in A Graph-Based Technique for the Automated Control-Oriented Modeling of District Heating Networks
> Journal of Dynamic Systems, Measurement, and Control
Published Online: March 13, 2024
Fig. 1 Sample six user DHN More about this image found in Sample six user DHN
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