Model predictive control: theory and practice—a survey
Automatica (Journal of IFAC)
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
A MPC and genetic algorithm based approach for multiple UAVs cooperative search
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
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Research on unmanned aircraft is improving constantly the autonomous flight capabilities of these vehicles in order to provide performance needed to employ them in even more complex tasks. UAV path planning PP system plans the best path to perform the mission and then it uploads this path on the flight management system FMS providing reference to the aircraft navigation. Tracking the path is the way to link kinematic references related to the desired aircraft positions with its dynamic behaviours, to generate the right command sequence. This paper presents a non-linear model predictive control NMPC system that tracks the reference path provided by PP and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control system solves online i.e., at each sampling time a finite horizon state horizon open loop optimal control problem with a genetic algorithm. This algorithm finds the command sequence that minimises the tracking error with respect to the reference path, driving the aircraft far from sensed obstacles and towards the desired trajectory.