A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Verifiable control of a swarm of unmanned aerial vehicles

This article considers the distributed control of a swarm of unmanned aerial vehicles (UAVs) investigating autonomous pattern formation and reconfigurability. A behaviour-based approach to formation control is considered with a velocity field control algorithm developed through bifurcating potential fields. This new approach extends previous research into pattern formation using potential field theory by considering the use of bifurcation theory as a means of reconfiguring a swarm pattern through a free parameter change. The advantage of this kind of system is that it is extremely robust to individual failures, is scalpable, and also flexible. The potential field consists of a steering and repulsive term with the bifurcation of the steering potential resulting in a change of the swarm pattern. The repulsive potential ensures collision avoidance and an equally spaced final formation. The stability of the system is demonstrated to ensure that desired behaviours always occur, assuming that at large separation distances the repulsive potential can be neglected through a scale separation that exists between the steering and repulsive potential. The control laws developed are applied to a formation of ten UAVs using a velocity field tracking approach, where it is shown numerically that desired patterns can be formed safely ensuring collision avoidance

An Innovative Mission Management System for Fixed-Wing UAVs

This paper presents two innovative units linked together to build the main frame of a UAV Mis- sion Management System. The first unit is a Path Planner for small UAVs able to generate optimal paths in a tridimensional environment, generat- ing flyable and safe paths with the lowest com- putational effort. The second unit is the Flight Management System based on Nonlinear Model Predictive Control, that tracks the reference path and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control system solves on-line (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 min- imizes the tracking error with respect to the ref- erence path, driving the aircraft far from sensed obstacles and towards the desired trajectory

Decentralized Hybrid Formation Control of Unmanned Aerial Vehicles

This paper presents a decentralized hybrid supervisory control approach for a team of unmanned helicopters that are involved in a leader-follower formation mission. Using a polar partitioning technique, the motion dynamics of the follower helicopters are abstracted to finite state machines. Then, a discrete supervisor is designed in a modular way for different components of the formation mission including reaching the formation, keeping the formation, and collision avoidance. Furthermore, a formal technique is developed to design the local supervisors decentralizedly, so that the team of helicopters as whole, can cooperatively accomplish a collision-free formation task

Tightly Coupled GNSS and Vision Navigation for Unmanned Air Vehicle Applications

This paper explores the unique benefits that can be obtained from a tight integration of a GNSS sensor and a forward-looking vision sensor. The motivation of this research is the belief that both GNSS and vision will be integral features of future UAV avionics architectures, GNSS for basic aircraft navigation and vision for obstacle-aircraft collision avoidance. The paper will show that utilising basic single-antenna GNSS measurements and observables, along with aircraft information derived from optical flow techniques creates unique synergies. Results of the accuracy of attitude estimates will be presented, based a comprehensive Matlab® Simulink® model which re-creates an optical flow stream based on the flight of an aircraft. This paper establishes the viability of this novel integrated GNSS/Vision approach for use as the complete UAV sensor package, or as a backup sensor for an inertial navigation system

Aerial navigation in obstructed environments with embedded nonlinear model predictive control

We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAV) using nonlinear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A C89 implementation of PANOC solves the NMPC problem at a rate of 20Hz on board a lab-scale MAV. The MAV performs smooth maneuvers moving around an obstacle. For increased autonomy, we propose a simple method to compensate for the reduction of thrust over time, which comes from the depletion of the MAV's battery, by estimating the thrust constant