[vc_row][vc_column][vc_column_text]Guided Systems Technologies has refiled for Chapter 11 bankruptcy protection after its first filing was dismissed for failing to file a plan and disclosure statement within 300 days (Order to show cause – https://ecf.ganb.uscourts.gov/doc1/054168942430) (Order dismissing case – https://ecf.ganb.uscourts.gov/doc1/054169100568). It is worth noting that the new filing has a number of deficiencies (https://ecf.ganb.uscourts.gov/doc1/054169622976).
Guided Systems is focused primarily on the application of its patented neural network adaptive control methods in the aerospace sector. Its technology is used to dramatically reduce the time and money required to complete guidance and control system design, development and validation, and also offers the potential to significantly increase a system’s tolerance of faults or failures when redundant means for actuation are available. While applicable to manned flight systems, missiles, munitions and spacecraft, the technology is particularly well suited to the development programs typical of unmanned flight vehicles, and is especially effective in application to rotorcraft and other types of complex vertical take-off and landing vehicle designs. The technology also offers great benefits for control of flexible structures, including the flexible airframes that are characteristic of next generation High Altitude Long Endurance (HALE) aircraft designs.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space][vc_tta_tabs][vc_tta_section title=”Company Info” tab_id=”1485966886423-b3803678-c7ec5ac5-4518fd03-b29a”][vc_column_text]
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Address 630 Red Oak Rd
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Ownership
Private Industry Aerospace Products |
[/vc_column_text][/vc_tta_section][vc_tta_section title=”Patent-Backed Value Estimation ” tab_id=”1485966886486-c8105d58-76bb5ac5-4518fd03-b29a”][vc_column_text]Katy Industries Debt-Asset Leverage Estimate
| Chapter Type | Case Number | Assets | Liabilities | Industry/Description |
| 11 | 18-61243 | $10 – 50 Thousand | $1 – 5 Million | Aerospace Products |
| Portfolio Valuation Range | Asset Valuation Range | Total Assets Valuation Range | Liability Range | Leverage Ratio Range |
| $500,000.00 – $1,750,000.00 | $5,000.00 – $50,000.00 | $505,000.00 – $1,800,000.00 | $250,000.00 – $2,500,000.00 | 0.202 – 7.2 |
Patent Portfolio
15 assets
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[/vc_column_text][/vc_tta_section][vc_tta_section title=”Featured Assets” tab_id=”1485967825251-b275f45c-d8b55ac5-4518fd03-b29a”][vc_column_text]Guided Systems Tech Portfolio Summary
1. US7218973 B2
Title: Adaptive control system having hedge unit and related apparatus and methods
Abstract: The invention includes an adaptive control system used to control a plant. The adaptive control system includes a hedge unit that receives at least one control signal and a plant state signal. The hedge unit generates a hedge signal based on the control signal, the plant state signal, and a hedge model including a first model having one or more characteristics to which the adaptive control system is not to adapt, and a second model not having the characteristic(s) to which the adaptive control system is not to adapt. The hedge signal is used in the adaptive control system to remove the effect of the characteristic from a signal supplied to an adaptation law unit of the adaptive control system so that the adaptive control system does not adapt to the characteristic in controlling the plant.
First Claim:
A method comprising the step of:
a) generating a hedge signal with an adaptive control system to avoid adaptation to at least one characteristic of the adaptive control system, the characteristic having an effect to which adaptation would be detrimental to control of a plant with the adaptive control system.
2. US7177710 B2
Title: System and method for adaptive control of uncertain nonlinear processes
Abstract: 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.
First Claim:
A computer system for controlling a non-linear physical process, the computer system comprising:
a linear controller connected to receive a command signal for control of the non-linear physical process and an output signal from the non-linear physical process, the linear controller generating a control signal based on the command signal and the output signal, the linear controller designed using a fixed linear model for the process; and
a neural network connected to receive a combined control signal and the output signal from the non-linear physical process, the neural network using at least one of the combined control signal and the output signal from the non-linear physical process to modify the connection weights of the neural network on-line as the neural network and linear controller are used to control the non-linear physical process, the neural network generating a modified control signal based on the modified connection weights and at least one of the combined control signal and the output signal from the non-linear physical process, the modified control signal combining with the control signal to form the combined control signal used to control the non-linear physical process and to correct for errors inherent in controlling the physical process using the linear controller designed using the fixed linear model.
3. US7039473 B2
Title: System and method for adaptive control of uncertain nonlinear processes
Abstract: 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.
First Claim:
A computer system for controlling a non-linear physical process, the computer system comprising:
a linear controller connected to receive a command signal for control of the non-linear physical process and an output signal from the non-linear physical process, the linear controller generating a control signal based on the command signal, the output signal, and a fixed linear model for the process; and
a neural network connected to receive the control signal from the linear controller and the output signal from the non-linear physical process, the neural network receiving the control signal as an input and using the output signal to modify the connection weights of the neural network on-line as the neural network and linear controller are used to control the non-linear physical process, the neural network generating a modified control signal that combines with the control signal output from the linear controller to control the non-linear physical process to correct for errors inherent in modeling the physical process using the fixed linear model.
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