Journal: Sensors (Basel, Switzerland)

Article Title: Fault Injection with Multiple Fault Patterns for Experimental Evaluation of Demand-Controlled Ventilation and Heating Systems

doi: 10.3390/s22218180

Figure Lengend Snippet: The exterior view of the Stateflow diagram.

Article Snippet: A simulation-based multiple FI framework for DCV and heating systems was developed according to the defined fault model and implemented in MATLAB/Simulink using Stateflow diagrams.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Fault Injection with Multiple Fault Patterns for Experimental Evaluation of Demand-Controlled Ventilation and Heating Systems

doi: 10.3390/s22218180

Figure Lengend Snippet: The interior view of the Stateflow diagram with two faulty and healthy states.

Article Snippet: A simulation-based multiple FI framework for DCV and heating systems was developed according to the defined fault model and implemented in MATLAB/Simulink using Stateflow diagrams.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

doi: 10.3390/s23135799

Figure Lengend Snippet: Figure 6. Diagram of MATLAB/Simulink model block for the proposed PMSM sensored control system using a robust controller.

Article Snippet: Block diagram of the MATLAB/Simulink implementation of the proposed PMSM sensored control system using a robust controller plus RL-TD3 agent. sing the elements and structure from the MATLAB Reinforcement Learning toolbox, Figure 12 shows the RL-TD3 agent implementation subsystem.

Techniques: Blocking Assay, Control

Journal: Sensors (Basel, Switzerland)

Article Title: Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

doi: 10.3390/s23135799

Figure Lengend Snippet: Figure 11. Block diagram of the MATLAB/Simulink implementation of the proposed PMSM sensored control system using a robust controller plus RL-TD3 agent.

Article Snippet: Block diagram of the MATLAB/Simulink implementation of the proposed PMSM sensored control system using a robust controller plus RL-TD3 agent. sing the elements and structure from the MATLAB Reinforcement Learning toolbox, Figure 12 shows the RL-TD3 agent implementation subsystem.

Techniques: Blocking Assay, Control

Journal: Sensors (Basel, Switzerland)

Article Title: Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

doi: 10.3390/s23135799

Figure Lengend Snippet: Figure 12. MATLAB/Simulink subsystem implementation of the RL-TD3 agent for improvement in robust controller ud and uq command.

Article Snippet: Block diagram of the MATLAB/Simulink implementation of the proposed PMSM sensored control system using a robust controller plus RL-TD3 agent. sing the elements and structure from the MATLAB Reinforcement Learning toolbox, Figure 12 shows the RL-TD3 agent implementation subsystem.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

doi: 10.3390/s23135799

Figure Lengend Snippet: Figure 22. MATLAB/Simulink subsystem implementation of multiple NN for load torque estimation and selection using stateflow. “*” is specific to the multi switch block in Simulink and represents the default selection.

Article Snippet: Block diagram of the MATLAB/Simulink implementation of the proposed PMSM sensored control system using a robust controller plus RL-TD3 agent. sing the elements and structure from the MATLAB Reinforcement Learning toolbox, Figure 12 shows the RL-TD3 agent implementation subsystem.

Techniques: Selection, Blocking Assay

Journal: Sensors (Basel, Switzerland)

Article Title: Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

doi: 10.3390/s23135799

Figure Lengend Snippet: Figure 23. Implementation of an NN for load torque estimation in Neural Network Fitting toolbox from MATLAB: (a) block diagram of a NN for load torque estimation; (b) Simulink NN exported model for load torque estimation.

Article Snippet: Block diagram of the MATLAB/Simulink implementation of the proposed PMSM sensored control system using a robust controller plus RL-TD3 agent. sing the elements and structure from the MATLAB Reinforcement Learning toolbox, Figure 12 shows the RL-TD3 agent implementation subsystem.

Techniques: Blocking Assay

Journal: Sensors (Basel, Switzerland)

Article Title: Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

doi: 10.3390/s23135799

Figure Lengend Snippet: Figure 29. MATLAB/Simulink subsystem implementation of the RL-TD3 agent for improvement in PMSM rotor speed estimation.

Article Snippet: Block diagram of the MATLAB/Simulink implementation of the proposed PMSM sensored control system using a robust controller plus RL-TD3 agent. sing the elements and structure from the MATLAB Reinforcement Learning toolbox, Figure 12 shows the RL-TD3 agent implementation subsystem.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

doi: 10.3390/s23135799

Figure Lengend Snippet: Figure 31. MATLAB/Simulink implementation of the proposed PMSM sensorlesss control system based on a robust controller combined with an RL-TD3 agent for improvement in ud and uq command, using improved ESO-types version observers for speed estimation. “*” is specific to the multi switch block in Simulink and represents the default selection.

Article Snippet: Block diagram of the MATLAB/Simulink implementation of the proposed PMSM sensored control system using a robust controller plus RL-TD3 agent. sing the elements and structure from the MATLAB Reinforcement Learning toolbox, Figure 12 shows the RL-TD3 agent implementation subsystem.

Techniques: Control, Blocking Assay, Selection

Journal: Sensors (Basel, Switzerland)

Article Title: Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

doi: 10.3390/s23135799

Figure Lengend Snippet: Figure 32. MATLAB/Simulink subsystem implementation for an ESO-type basic version observer for PMSM rotor speed estimation. The red dotted lines highlight the output quantities of the observer.

Article Snippet: Block diagram of the MATLAB/Simulink implementation of the proposed PMSM sensored control system using a robust controller plus RL-TD3 agent. sing the elements and structure from the MATLAB Reinforcement Learning toolbox, Figure 12 shows the RL-TD3 agent implementation subsystem.

Techniques: