Development of a mechanized transformer test procedure

Issued as Progress report no. 1-2, and Final report, Project no. E-21-66

Artificial neural network control of a nonminimum phase, single-flexible-link

©1996 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 1996 IEEE International Conference on Robotics and Automation (ICRA), April 22-28, 1996, Minneapolis, MN.DOI: 10.1109/ROBOT.1996.506994A single-link flexible manipulator with a rotary actuator at one end and a mass at the other is modeled using the Lagrangian method coupled with an assumed modes vibration model. A SIMO state space model is developed by linearizing the equations of motion and simplified by neglecting natural damping. Laplace domain pole-zero plots between torque input and tip position show nonmzmmum phase behavior. Nonminimum phase behavior causes difficulty for both conventional and artificial neural network (ANN) inversemodel control. The most promising ANN method for the control of flexible manipulators does not appear to converge to a solution when the system is lightly damped. To overcome this limitation, a modified cost junction is proposed. Simulations show that the ANN is able to converge to a solution even in the case of no damping. The modified approach fails, however, for beams exceeding some critical length measure. Identification of the critical length and proposals for extending the result are discussed

The application of digital computers to root-locus analysis

M.S.Frank O. Nottingham, Jr

Distributed computer architecture for the control of cooperative robots

Issued as Final report, Project no. E-21-68

Microstructure logic arrays

Issued as Final report, Project no. E-21-66

Computer control for an automatic washer

Issued as Final report, Project no. E-21-649Final report has title: Computer control for an automatic washe

Dryer motor protector/nuisance trip test

Issued as Final report, Project no. E-21-603Final report has title: Dryer motor protector/nuisance trip tes

Neural network control of non minimum phase systems based on a noncausal inverse

©1996 ASMEPresented at the 1996 International Mechanical Engineering Congress and Exposition (IMECE), November 17-22, 1996, Atlanta, Georgia.A new approach for feedforward ANN control of nonminimum phase mechanical systems is proposed. A standard backpropagation-of-errors ANN is used to form an inverse model controller which is applied to simulated nonminimum phase systems. Learning in the new approach is based on the convolution between a noncausal impulse response and a desired tip trajectory. Selection of the proper input set, input scaling and the ANN structure are investigated. Once the input and structure are specified, the ANN is trained over a single trajectory. After training, the ANN is used to drive the system in an open-loop configuration. Plots of the system states resulting from the ideal excitation and from ANN excitation are compared. The results obtained by varying both the number of units and the input set are presented. The results demonstrate the effectiveness of the proposed ANN inverse model approach