Engineering topics

Choi, Benjamin B.

Publications and source records attributed to Choi, Benjamin B..

24 records · Page 2

NASA NTRS2002

Linear-Quadratic-Gaussian Regulator Developed for a Magnetic Bearing

Linear-Quadratic-Gaussian (LQG) control is a modern state-space technique for designing optimal dynamic regulators. It enables us to trade off regulation performance and control effort, and to take into account process and measurement noise. The Structural Mechanics and Dynamics Branch at the NASA Glenn Research Center has developed an LQG control for a fault-tolerant magnetic bearing suspension rig to optimize system performance and to reduce the sensor and processing noise. The LQG regulator consists of an optimal state-feedback gain and a Kalman state estimator. The first design step is to seek a state-feedback law that minimizes the cost function of regulation performance, which is measured by a quadratic performance criterion with user-specified weighting matrices, and to define the tradeoff between regulation performance and control effort. The next design step is to derive a state estimator using a Kalman filter because the optimal state feedback cannot be implemented without full state measurement. Since the Kalman filter is an optimal estimator when dealing with Gaussian white noise, it minimizes the asymptotic covariance of the estimation error.

Choi, Benjamin B.↗

NASA NTRS2002

Fail-Safe Operation of a High-Temperature Magnetic Bearing Investigated for Gas Turbine Engine Applications

The Structural Mechanics and Dynamics Branch at the NASA Glenn Research Center has developed a three-axis high-temperature magnetic bearing suspension rig to enhance the safety of the bearing system up to 1000 F. This test rig can accommodate thrust and radial bearings up to a 22.84 cm (9 in.) diameter with a maximum axial loading of 22.25 kN (5000 lb) and a maximum radial loading up to 4.45 kN (1000 lb). The test facility was set up to test magnetic bearings under high-temperature (1100 F) and high-speed (20,000 rpm) conditions. The magnetic bearing is located at the center of gravity of the rotor between two high-temperature grease-packed mechanical ball bearings. The drive-end duplex angular contact ball bearing, which is in full contact, acts as a moment release and provides axial stability. The outboard end ball bearing has a 0.015-in. radial clearance between the rotor to act as a backup bearing and to compensate for axial thermal expansion. There is a 0.020-in. radial air gap between the stator pole and the rotor. The stator was wrapped with three 1-kW band heaters to create a localized hot section; the mechanical ball bearings were outside this section. Eight threaded rods supported the stator. These incorporated a plunger and Bellville washers to compensate for radial thermal expansion and provide rotor-to-stator alignment. The stator was instrumented with thermocouples and a current sensor for each coil. Eight air-cooled position sensors were mounted outside the hot section to monitor the rotor. Another sensor monitored this rotation of the outboard backup bearing. Ground fault circuit interrupts were incorporated into all power amplifier loops for personnel safety. All instrumentation was monitored and recorded on a LabView-based data acquisition system. Currently, this 12-pole heteropolar magnetic bearing has 13 thermal cycles and over 26 hr of operation at 1000 F.

Choi, Benjamin B.↗

NASA NTRS2001

Fail-Safe Magnetic Bearing Controller Demonstrated Successfully

The Structural Mechanics and Dynamics Branch has successfully demonstrated a fail-safe controller for the Fault-Tolerant Magnetic Bearing rig at the NASA Glenn Research Center. The rotor is supported by two 8-pole redundant radial bearings, and coil failing situations are simulated by manually shutting down their control current commands from the controller cockpit. The effectiveness of the controller was demonstrated when only two active coils from each radial bearing could be used (that is, 14 coils failed). These remaining two coils still levitated the rotor and spun it without losing stability or desired position up to the maximum allowable speed of 20,000 rpm.

Choi, Benjamin B.↗

NASA NTRS1998

Neural Network Control of a Magnetically Suspended Rotor System

Magnetic bearings offer significant advantages because they do not come into contact with other parts during operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. At the NASA Lewis Research Center, a neural network was selected as a nonlinear controller because it generates a neural model without any detailed information regarding the internal working of the magnetic bearing system. It can be used even for systems that are too complex for an accurate system model to be derived. A feed-forward architecture with a back-propagation learning algorithm was selected because of its proven performance, accuracy, and relatively easy implementation.

Choi, Benjamin B.↗

NASA NTRS1994

Intelligent control of a multi-degree-of freedom reaction compensating platform system using fuzzy logic

This paper presents the development of a general-purpose fuzzy logic (FL) control methodology for isolating the external vibratory disturbances of space-based devices. According to the desired performance specifications, a full investigation regarding the development of an FL controller was done using different scenarios, such as variances of passive reaction-compensating components and external disturbance load. It was shown that the proposed FL controller is robust in that the FL-controlled system closely follows the prespecified ideal reference model. The comparative study also reveals that the FL-controlled system achieves significant improvement in reducing vibrations over passive systems.

Choi, Benjamin B.↗

NASA NTRS1992

Inverse kinematics problem in robotics using neural networks

In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods.

Choi, Benjamin B.↗