Improving Long-Term Learning of Model Reference Adaptive Controllers for Flight Applications: A Sparse Neural Network Approach AIAA Guidance, Navigation, and Control Conference January 2017 Scott A. Nivison Pramod P. Khargonekar Department of Electrical and Computer Engineering University of Florida Distribution A: Approved for public release; distribution is unlimited.
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Improving Long-Term Learning of Model Reference Adaptive
Controllers for Flight Applications: A Sparse Neural Network Approach
AIAA Guidance, Navigation, and Control Conference January 2017
Scott A. Nivison Pramod P. Khargonekar
Department of Electrical and Computer Engineering University of Florida
Distribution A: Approved for public release; distribution is unlimited.
Distribution A: Approved for public release; distribution is unlimited.
Prior Research
Develop a MRAC architecture that improves long-term learning and tracking performance of flight vehicles with consistent uncertainties over regions of the flight envelope while utilizing small to moderate learning rates and significant processing constraints.
Research Goals
Enhancements to the MRAC architecture πΏ1 Adaptive Control Concurrent Learning
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ποΏ½Μ = ππππ(2Ξπ((π(ποΏ½ππ)-οΏ½ΜοΏ½(ποΏ½ππ) ποΏ½ππ)ππππ΅) ποΏ½Μ = ππππ(2Ξππππππ΅ποΏ½ ποΏ½ΜοΏ½(ποΏ½ππ)) Distribution A: Approved for public release; distribution is unlimited.
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Sparse Neural Network (SNN)
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Simulation
Adaptive Controller: Longitudinal Short-Period Dynamics for High-Speed Flight Vehicle:
Adaptive Controller: Flight Condition:
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Results - LQR
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Results - SHL
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Results - RBF
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Results β RBF vs SHL
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Results - SNN
Adaptive Controller: Longitudinal Short-Period Dynamics for High-Speed Flight Vehicle:
Adaptive Controller:
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Since the disturbance, π(π₯) , was designed based on a single input variable, πΌ , only the 1-D SNN architecture with T=91 segments was employed for simulation results.
Results β SNN
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Results and Conclusions
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Conclusions
Traditional Neural Network schemes typically update adaptive weights based solely on the current state vector which leads to poor long-term learning Sparse Neural Network (SNN) adaptive controllers only update a small portion of neurons at each point in the flight envelope Better memory for uncertainty estimates and weights
from previously visited segments Superior tracking performance and uncertainty
estimates for tasks that have consistent uncertainties and disturbances over regions of the flight envelope
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Future Work
Develop standard analysis tools to explore trade-offs between variations of neural network adaptive controllers Explore effectiveness of high dimensional sparse
neural network (SNN) adaptive controllers against numerous uncertainties
Investigate structured sparse neural networks (SNN) for adaptive control of flight vehicles
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Questions?
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