A NEURAL NET APPROACH TO SPACE VEHICLE GUIDANCE

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$496,069.00
Award Year:
1991
Program:
SBIR
Phase:
Phase II
Contract:
n/a
Award Id:
11940
Agency Tracking Number:
11940
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
55 Wheeler Street, Cambridge, MA, 02138
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Alper K. Caglayan
Principal Scientist
(617) 491-3474
Business Contact:
ALPER K. CAGLAYAN
PRESIDENT
(617) 491-3474
Research Institute:
n/a
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.

* information listed above is at the time of submission.

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