Patents Assigned to Guided Systems Technologies, Inc.
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Publication number: 20170073071Abstract: Infrastructure is remotely inspected using a sensor pod such as an unmanned ground vehicle and sensors adapted to inspect a surface of the infrastructure and an unmanned aircraft adapted to interoperate with the sensor pod. The sensor pod drives along the surface of the infrastructure being inspected. A tether to the unmanned aircraft deploys and retrieves the sensor pod on the surface of the infrastructure. Electronic sensors of the sensor pod are deployable in a crevice of the surface of the infrastructure obstructed from view by the unmanned aircraft. The unmanned aircraft can comprise a radio repeater adapted to relay ground commands to the sensor pod.Type: ApplicationFiled: November 21, 2016Publication date: March 16, 2017Applicant: Guided Systems Technologies, Inc.Inventors: Jared David Salzmann, J Eric Corban
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Publication number: 20170066530Abstract: An interior length of a confined space is inspected by autonomously flying an unmanned aerial vehicle having a sensor pod. The sensor pod can be tethered to the unmanned aerial vehicle and lowered into the confined space from above perhaps by an electromechanical hoist. An altitude or heading of the sensor pod can be measured. The confined space can be the flue of a chimney.Type: ApplicationFiled: October 27, 2016Publication date: March 9, 2017Applicant: Guided Systems Technologies, Inc.Inventors: Jared David Salzmann, J Eric Corban
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Patent number: 7415311Abstract: A computer system for controlling a nonlinear physical process. The computer system comprises a linear controller and a neural network. The linear controller receives a command signal for control of the nonlinear physical process and a measured output signal from the output of the nonlinear physical process. The linear controller generates a control signal based on the command signal, a measured output signal, and a fixed linear model for the process. The neural network receives the control signal from the linear controller and the measured output signal from the output of the nonlinear physical process. The neural network uses the measured output signal to modify the connection weights of the neural network. The neural network also generates a modified control signal supplied to the linear controller to iterate a fixed point solution for the modified control signal used to control the nonlinear physical process.Type: GrantFiled: January 4, 2007Date of Patent: August 19, 2008Assignee: Guided Systems Technologies, Inc.Inventors: Anthony J. Calise, Byoung-Soo Kim, J. Eric Corban
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Patent number: 7177710Abstract: A computer system for controlling a nonlinear physical process. The computer system comprises a linear controller and a neural network. The linear controller receives a command signal for control of the nonlinear physical process and a measured output signal from the output of the nonlinear physical process. The linear controller generates a control signal based on the command signal, a measured output signal, and a fixed linear model for the process. The neural network receives the control signal from the linear controller and the measured output signal from the output of the nonlinear physical process. The neural network uses the measured output signal to modify the connection weights of the neural network. The neural network also generates a modified control signal supplied to the linear controller to iterate a fixed point solution for the modified control signal used to control the nonlinear physical process.Type: GrantFiled: June 7, 2005Date of Patent: February 13, 2007Assignee: Guided Systems Technologies, Inc.Inventors: Anthony J. Calise, Byoung-Soo Kim
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Patent number: 7039473Abstract: A computer system for controlling a nonlinear physical process. The computer system comprises a linear controller and a neural network. The linear controller receives a command signal for control of the nonlinear physical process and a measured output signal from the output of the nonlinear physical process. The linear controller generates a control signal based on the command signal, a measured output signal, and a fixed linear model for the process. The neural network receives the control signal from the linear controller and the measured output signal from the output of the nonlinear physical process. The neural network uses the measured output signal to modify the connection weights of the neural network. The neural network also generates a modified control signal supplied to the linear controller to iterate a fixed point solution for the modified control signal used to control the nonlinear physical process.Type: GrantFiled: March 22, 2004Date of Patent: May 2, 2006Assignee: Guided Systems Technologies, Inc.Inventor: J. Eric Corban
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Publication number: 20040176860Abstract: The invention comprises apparatuses and methods for providing the capability to stabilize and control a non-minimum phase, nonlinear plant with unmodeled dynamics and/or parametric uncertainty through the use of adaptive output feedback. A disclosed apparatus can comprise a reference model unit for generating a reference model output signal ym. The apparatus can comprise a combining unit that combines and differences a plant output signal y of a non-minimum phase plant for which not all of the states can be sensed, and a plant output signal y, to generate an output error signal {tilde over (y)}. The apparatus can further comprise an adaptive control unit for generating an adaptive control signal uad used to control the plant.Type: ApplicationFiled: December 9, 2003Publication date: September 9, 2004Applicant: Guided Systems Technologies, Inc.Inventors: Naira Hovakimyan, C. Anthony Calise, Bong-Jun Yang
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Patent number: 6757570Abstract: A process and neural network architecture for on-line adjustment of the weights of the neural network in a manner that corrects errors made by a nonlinear controller designed based on a model for the dynamics of a process under control. A computer system is provided for controlling the dynamic output response signal of a nonlinear physical process, where the physical process is represented by a fixed model of the process. The computer system includes a controlled device for responding to the output response signal of the system. The computer system also includes a linear controller for providing a pseudo control signal that is based on the fixed model for the process and provides a second controller, connected to the linear controller, for receiving the pseudo control signal and for providing a modified pseudo control signal to correct for the errors made in modeling the nonlinearities in the process. A response network is also included as part of the computer system.Type: GrantFiled: May 31, 2000Date of Patent: June 29, 2004Assignee: Guided Systems Technologies, Inc.Inventors: Anthony J. Calise, Byoung-Soo Kim, J. Eric Corban
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Patent number: 6092919Abstract: A process and neural network architecture for on-line adjustment of the weights of the neural network in a manner that corrects errors made by a nonlinear controller designed based on a model for the dynamics of a process under control. A computer system is provided for controlling the dynamic output response signal of a nonlinear physical process, where the physical process is represented by a fixed model of the process. The computer system includes a controlled device for responding to the output response signal of the system. The computer system also includes a linear controller for providing a pseudo control signal that is based on the fixed model for the process and provides a second controller, connected to the linear controller, for receiving the pseudo control signal and for providing a modified pseudo control signal to correct for the errors made in modeling the nonlinearities in the process. A response network is also included as part of the computer system.Type: GrantFiled: August 1, 1995Date of Patent: July 25, 2000Assignee: Guided Systems Technologies, Inc.Inventors: Anthony J. Calise, Byoung-Soo Kim