Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Closed Loop Systems (1)
- Closed-Loop Signals (1)
- Cycle-To-Cycle Dispersion (1)
- Discrete Time Systems (1)
- Feedback (1)
-
- Heat Release (1)
- Internal Combustion Engines (1)
- Learning (Artificial Intelligence) (1)
- Lyapunov Analysis (1)
- Lyapunov Methods (1)
- NOx Emission (1)
- Neural Network Controllers (1)
- Neural Network Observer (1)
- Neurocontrollers (1)
- Nonlinear Behavior (1)
- Nonlinear Control Systems (1)
- Nonlinear Cyclic Dispersion (1)
- Observers (1)
- Output Feedback Controller (1)
- Reinforcement Learning (1)
- Spark Ignition Engines (1)
- Stability (1)
- Unburned Hydrocarbon (1)
- Uniform Ultimate Boundedness (1)
Articles 1 - 2 of 2
Full-Text Articles in Mechanical Engineering
Torque Ripple Sensor And Mitigation Mechanism, Jason Neely, Steven Pekarek, Jason M. Banaskavich, Daniel S. Stutts
Torque Ripple Sensor And Mitigation Mechanism, Jason Neely, Steven Pekarek, Jason M. Banaskavich, Daniel S. Stutts
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A torque ripple sensor and method for torque ripple sensing and/or mitigation. A piezoelectric sensor is positioned relative to a motor so that torque fluctuations due to torque ripple of the motor are transmitted to the sensor, resulting in strain of a piezoelectric element. A resulting signal can be amplified and conditioned for determining a magnitude of the torque ripple and/or fed into a feedback loop for applying current control or a counter-torque to the motor for torque ripple mitigation.
Neural Network-Based Output Feedback Controller For Lean Operation Of Spark Ignition Engines, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier, Jonathan B. Vance, Pingan He
Neural Network-Based Output Feedback Controller For Lean Operation Of Spark Ignition Engines, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier, Jonathan B. Vance, Pingan He
Electrical and Computer Engineering Faculty Research & Creative Works
Spark ignition (SI) engines running at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle dispersion of heat release even though such operation can significantly reduce NOx emissions and improve fuel efficiency by as much as 5-10%. A suite of neural network (NN) controller without and with reinforcement learning employing output feedback has shown ability to reduce the nonlinear cyclic dispersion observed under lean operating conditions. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; …