Robust Fault-Tolerant Controllers for Unmanned Aircraft Vehicles

Dear Colleagues,

An unmanned aerial vehicle (UAV), commonly known as a drone, is defined as an aircraft without a pilot, and it is typically controlled from the ground or by an onboard computer (controller).  The applications of UAVs range from military tasks to entertainment, photography, product transportation, inspection and surveillance, agriculture, wireless communication networks, and more. Given their remarkable development over the past few years, UAVs have become an essential field of research. A UAV swarm (a formation of multiple UAVs) is also of great interest.

Controllers play a key role in the UAV control system. The reliability and fault tolerance of controllers directly affect the survivability of the UAV and the success of the mission as a whole. Sources of failure in UAVs can include sensors, communications, mechanisms, rotors, external disturbances, cyberattacks, etc. The problem of developing reliable, fault-tolerant controllers is particularly acute when UAVs are exposed to electronic warfare interference.

Controllers in UAVs are used to track trajectories and control flight, altitude, sliding mode, UAV swarms, autonomous flight, speed, stability, autopilot, and super-twisting. In addition, controllers are used to control yaw, pitch, and roll; to transmit data; to mitigate gust attenuation; and to handle external disturbances.

There is a wide variety of controller types for UAVs, and they are based on different principles. The following types of UAV controllers can be distinguished: adaptive, PID (proportional–integral–derivative), PD (proportional–derivative), nonlinear, fuzzy-logic-based, neural, optimal, learning, hybrid, intelligent, Lyapunov theory-based, and Kalman filter-based. In addition, the following controllers are found in UAV control systems: inverse, neuro-adaptive, decentralized, asynchronous, singular perturbation theory-based, modular, hierarchical, multi-level, mixed, switching, convergent, etc. The wide variety of controller designs for UAVs indicates the complexity of UAV control and the tasks solved with UAVs.

There are many methods for designing robust controllers for UAVs. However, there are not many methods for designing fault-tolerant and safe UAV controllers. Some controllers are developed using finite-state machines (FSMs) and implemented in field-programmable gate arrays (FPGAs). At the same time, there are no methods for designing UAV controllers implemented on a system on a chip (SoC).

The reliability and fault-tolerance problems of UAV controllers can be addressed at various levels: algorithmic, structural, functional–logical, and at the level of hardware description language (HDL). The speed and functional power of UAV controllers can be increased by implementing them in FPGAs and SoCs rather than microprocessors and microcontrollers.

The following tasks are considered promising in the development of UAV controllers: obstacle avoidance, evasion of attacks by other drones, countering cyberattacks, neutralizing or mitigating external disturbances, switching to autonomous control in the event of external disturbances, using machine learning and swarm intelligence, and integration with artificial intelligence.

This Special Issue aims to present new ideas and experimental results in the field of UAV controller design. The topics of this Special Issue are not limited to the keywords listed below. Authors are welcome to propose their own topics related to the development and use of UAV control systems.

Sincerely,

Prof. Dr. Valery Salauyou
Guest Editor

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• actuator faults
• adaptive controller
• attitude controller
• disturbance rejection controller
• distributed controller
• external disturbances
• failure recovery system
• fault-tolerant controller
• field-programmable gate array (FPGA)
• finite-state machine (FSM)
• flight controller
• formation maintenance
• fuzzy logic controller
• hardware description language (HDL)
• hierarchical controller
• machine learning
• multi-drone system
• neural controller
• path tracking controller
• pose tracking
• proportional integral-derivative (PID) controller
• robust controller
• sliding mode controller
• speed controller
• swarm intelligence
• UAV swarm
• system on a chip (SoC)
• trajectory tracking
• tracking controller
• UAV network
• unmanned aerial vehicles (UAVs)