Abstract
This paper presents Hardware-in-the-Loop (HIL) simulation results of a novel configuration for guidance and tracking control laws for unmanned air vehicles (UAV). The proposed adaptive system is based on an extended non-linear dynamic inversion (NLDI) approach augmented with an artificial immune system mechanism that relies on a direct compensation inspired primarily by the biological immune system response. The implementation of the control laws is illustrated through HIL simulation using a mathematical model of an UAV research platform developed at Embry-Riddle Aeronautical University (ERAU) to support the design, testing and validation of bio-inspired fault tolerant adaptive control algorithms. The main objective of the control laws is to minimize forward, lateral, and vertical distances with respect to a desired trajectory, and maintain stability and adequate performance in the presence of sub-system failures. The performance of the control laws is evaluated during autonomous flight in terms of trajectory tracking errors, real-time execution on board the flight computer, and control activity at nominal and abnormal conditions. The results obtained with the ERAU-UAV HIL environment show that for all cases investigated the extended NLDI approach augmented with the immunity-based mechanism has desirable fault tolerant capabilities and is reliable for in-flight testing operation as a next step towards the validation and verification of this adaptive configuration.
Original language | American English |
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DOIs | |
State | Published - Aug 2013 |
Event | AIAA Modeling and Simulation Technologies (MST) Conference 2013 - Boston, MA Duration: Aug 1 2013 → … |
Conference
Conference | AIAA Modeling and Simulation Technologies (MST) Conference 2013 |
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Period | 8/1/13 → … |
Keywords
- Artificial Immune System
- Fault tolerance
- Unmanned aerial vehicles
- UAV
- Control theory
- Nonlinear dynamic inversion
- Trajectory tracking errors
Disciplines
- Aeronautical Vehicles
- Systems Engineering and Multidisciplinary Design Optimization