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Full-Text Articles in Engineering
Robust Control Techniques Enabling Duty Cycle Experiments Utilizing A 6-Dof Crewstation Motion Base, A Full Scale Combat Hybrid Electric Power System, And Long Distance Internet Communications, Marc Compere, Jarrett Goodell, Miguel Simon, Wilford Smith, Mark Brudnak
Robust Control Techniques Enabling Duty Cycle Experiments Utilizing A 6-Dof Crewstation Motion Base, A Full Scale Combat Hybrid Electric Power System, And Long Distance Internet Communications, Marc Compere, Jarrett Goodell, Miguel Simon, Wilford Smith, Mark Brudnak
Publications
The RemoteLink effort supports the U.S. Army's objective for developing and fielding next generation hybrid-electric combat vehicles. It is a distributed soldierin- the-Ioop and hardware-in-the-Ioop environment with a 6-DOF motion base for operator realism, a full-scale combat hybrid electric power system, and an operational context provided by OneSAF. The driver/gunner crewstations rest on one of two 6-DOF motion bases at the U.S. Army TARDEC Simulation Laboratory (TSL). The hybrid power system is located 2,450 miles away at the TARDEC Power and Energy System Integration Laboratory (P&E SIL). The primary technical challenge in the RemoteLink is to operate both laboratories together …
Nanomanipulation Using Atomic Force Microscope With Drift Compensation, Qinmin Yang, Jagannathan Sarangapani
Nanomanipulation Using Atomic Force Microscope With Drift Compensation, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes an atomic force microscope (AFM) based force controller to push nanoparticles on the substrates since it is tedious for human. A block phase correlation-based algorithm is embedded into the controller for compensating the thermal drift during nanomanipulation. Further, a neural network (NN) is employed to approximate the unknown nanoparticle and substrate contact dynamics including the roughness effects. Using the NN-based adaptive force controller the task of pushing nanoparticles is demonstrated. Finally, using the Lyapunov-based stability analysis, the uniform ultimately boundedness (UUB) of the closed-loop signals is demonstrated
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; …