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A NEURAL NET APPROACH TO SPACE VEHICLE GUIDANCE

Award Information

Agency:
National Aeronautics and Space Administration
Branch:
N/A
Award ID:
11940
Program Year/Program:
1991 / SBIR
Agency Tracking Number:
11940
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Charles River Analytics Inc.
625 Mount Auburn Street Cambridge, MA 02138-4555
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 2
Fiscal Year: 1991
Title: A NEURAL NET APPROACH TO SPACE VEHICLE GUIDANCE
Agency: NASA
Contract: N/A
Award Amount: $496,069.00
 

Abstract:

THE ON-LINE IMPLEMENTATION OF NUMERICAL ALGORITHMS FOR SOLVING THE OPTIMUM TRAJECTORY/GUIDANCE PROBLEM FOR ADVANCED SPACE VEHICLES SUCH AS ALS, HLLV, AOTV, TRANSATMOSPHERIC VEHICLES AND INTERPLANETARY SPACECRAFT IS NOT POSSIBLE DUE TO THEIR COMPLEXITY. HENCE, THE CURRENT APPROACH TO THE DEVELOPMENT OF REAL-TIME GUIDANCE LAWS FOR THESE ADVANCED SPACE VEHICLES IS TO USE APPROXIMATION THEORY TO OBTAIN CLOSED-LOOP GUIDANCE LAWS. NEURAL NETWORKS OFFER AN ALTERNATIVE TO THE DERIVATION AND IMPLEMENTATION OF GUIDANCE LAWS. IN THIS STUDY, WE PROPOSE TO FORMULATE THE SPACE VEHICLE GUIDANCE PROBLEM USING A NEURAL NETWORK APPROACH, FIND THE APPROPRIATE NEURAL NET ARCHITECTURE FOR MODELLING OPTIMUM GUIDANCE TRAJECTORIES, TRAIN THE DEVELOPED NETWORK WITH A DATABASE OF OPTIMUM GUIDANCE TRAJECTORIES, AND DEMONSTRATE ITS PERFORMANCE AS AN ON-LINE CLASSIFIER. SUCH A NEURAL NETWORK BASED GUIDANCE APPROACH CAN READILY ADAPT TO ENVIRONMENT UNCERTAINTIES SUCH AS THOSE ENCOUNTERED BY AN AOTV DURING ATMOSPHERIC MANEUVERS.

Principal Investigator:

Alper K. Caglayan
Principal Scientist
6174913474

Business Contact:

Alper k. caglayan
PRESIDENT
6174913474
Small Business Information at Submission:

Charles River Analytics, Inc.
55 Wheeler Street Cambridge, MA 02138

EIN/Tax ID:
DUNS: N/A
Number of Employees: N/A
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No