Missouri University of Science and Technology

About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Artificial neural network & Control theory. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.

Optical fiber magnetic field sensor based on single-mode-multimode-single-mode structure and magnetic fluid

Abstract: An optical fiber magnetic field sensor based on the single-mode-multimode-single-mode (SMS) structure and magnetic fluid (MF) is proposed and demonstrated. By using a piece of no-core fiber as the multimode waveguide in the SMS structure and MF sealed in a capillary tube as the magnetic sensitive media, which totally immersing the no-core fiber, an all-fiber magnetic sensor was fabricated. Interrogation of the magnetic field strength can be achieved either by measuring the dip wavelength shift of the transmission spectrum or by detecting the transmission loss at a specific wavelength. A demonstration sensor with sensitivities up to 905 pm/mT and 0.748 dB/mT was fabricated and investigated. A theoretical model for the design of the proposed device was developed and numerical simulations were performed.

Online Optimal Control of Affine Nonlinear Discrete-Time Systems With Unknown Internal Dynamics by Using Time-Based Policy Update

Abstract: In this paper, the Hamilton-Jacobi-Bellman equation is solved forward-in-time for the optimal control of a class of general affine nonlinear discrete-time systems without using value and policy iterations. The proposed approach, referred to as adaptive dynamic programming, uses two neural networks (NNs), to solve the infinite horizon optimal regulation control of affine nonlinear discrete-time systems in the presence of unknown internal dynamics and a known control coefficient matrix. One NN approximates the cost function and is referred to as the critic NN, while the second NN generates the control input and is referred to as the action NN. The cost function and policy are updated once at the sampling instant and thus the proposed approach can be referred to as time-based ADP. Novel update laws for tuning the unknown weights of the NNs online are derived. Lyapunov techniques are used to show that all signals are uniformly ultimately bounded and that the approximated control signal approaches the optimal control input with small bounded error over time. In the absence of disturbances, an optimal control is demonstrated. Simulation results are included to show the effectiveness of the approach. The end result is the systematic design of an optimal controller with guaranteed convergence that is suitable for hardware implementation.

A switched reluctance machine-based starter/alternator for more electric cars

Abstract: Switched reluctance machines (SRMs) are considered as serious candidates for starter/alternator (S/A) systems in more electric cars. Robust performance in the presence of high temperature, safe operation, offering high efficiency, and a very long constant power region, along with a rugged structure contribute to their suitability for this high impact application. To enhance these qualities, we have developed key technologies including sensorless operation over the entire speed range and closed-loop torque and speed regulation. The present paper offers an in-depth analysis of the drive dynamics during motoring and generating modes of operation. These findings will be used to explain our control strategies in the context of the S/A application. Experimental and simulation results are also demonstrated to validate the practicality of our claims.