Disturbance-Rejection-Based Optimized Robust Adaptive Controllers for UAVs

Kazim, Muhammad; Azar, Ahmad Taher; Koubâa, Anis; Zaidi, Adeel

doi:10.1109/jsyst.2020.3006059

Cited by 44 publications

(16 citation statements)

References 45 publications

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“…This section puts our proposed methodology into context, focusing on the most related work. The cumulative complexity of the subproblems, i.e., object detection and recognition [6], non-linear control [1], and path planning [7] makes drone racing an exciting and challenging problem. The rapid development in AI has contributed to broader use in the robotics community of many novel technologies, such as in deep learning [8] and reinforcement learning [9].…”

Section: B Related Workmentioning

confidence: 99%

“…In recent years, autonomous Unmanned Aerial Vehicles (UAVs) or drones in diverse scenarios have gained much attention. UAVs are attracting increased interest across various communities such as defense, emergency response, disaster relief, healthcare, agriculture, mining, infrastructure development, sports, education, and many other areas [1]. One of the challenging tasks for UAVs is competitive drone racing.…”

Section: Introduction a Motivationmentioning

confidence: 99%

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Perception Action Aware-Based Autonomous Drone Race in a Photorealistic Environment

et al. 2022

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The development of autonomous, fast, agile small Unmanned Aerial Vehicles (UAVs) brings up fundamental challenges in dynamic environments with fast and agile maneuvers, unreliable state estimation, imperfect sensing, coupling action, and perception in real-time under severe resource constraints. However, autonomous drone racing is a challenging research problem at the intersection of computer vision, planning, state estimation, and control. To bridge this, we propose an approach in the context of autonomous, perception-action aware vision-based drone racing in a photorealistic environment. Our approach integrates a deep convolutional neural network (CNN) with state-of-the-art path planning, state estimation, and control algorithms. The developed deep learning method is based on computer vision approaches to detecting the gates and estimating the flyable area. The planner and controller then use this information to generate a short, minimum-snap trajectory segment and send corresponding motor commands to reach the desired goal. A thorough evaluation of our proposed methodology has been carried out using the Gazebo and FlightGoggles (photorealistic sensor) environments. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art methods and flies the drone more consistently than many human pilots. Moreover, we demonstrated that our proposed system successfully guided the drone through tight race courses, reaching speeds up to 7m/s of the 2019 AlphaPilot Challenge.

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Section: B Related Workmentioning

confidence: 99%

Section: Introduction a Motivationmentioning

confidence: 99%

Perception Action Aware-Based Autonomous Drone Race in a Photorealistic Environment

et al. 2022

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“…The main advantage of using the optimal control approach over classical approaches to MAS, such as a consensus, is the ability to provide a solution for time-varying systems, where the dynamics of the agent and the environment can vary due to disturbances or noises [22], [23]. The optimal control approach also allows the optimization of a desired performance metric by minimizing the specific cost function.…”

Section: Introductionmentioning

confidence: 99%

HDP Algorithms for Trajectory Tracking and Formation Control of Multi-Agent Systems

2022

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A heuristic dynamic programming (HDP) algorithm for trajectory tracking and formation control of multi-agent systems (MAS) is presented in this paper. The selected HDP method allows for an online optimal control design. The multi-agent control problem is formulated as a leader-follower, where it is necessary for followers to maintain the assigned formation, while the leader follows the specified trajectory. Developed QR-Solver and RLS µ -QR-HDP-DLQR algorithms provide a new methodology to obtain the solution of the Hamilton-Jacobi-Bellman (HJB) equation. These proposed solutions are based on QR factorization to decrease the computational cost and avoid issues associated with numerical stability of conventional least squares (LS) method. The algorithms' performances such as convergence, numerical stability, and computational metrics, were experimentally evaluated for two control systems: aerial altitude control (one degree of freedom) and terrestrial robot (two degrees of freedom).

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“…While designing the controller for quadrotor vehicle, the major challenge is to deal with the complexity, un-stability and nonlinearity of the dynamical model of the quadrotor. Since, the dynamical model of quadrotor is extremely vulnerable to external disturbances [4], various control strategies have been investigated and proposed for a single quadrotor such as backstepping control [5], sliding mode control [6], proportional-integral derivative (PID) control [7], and linear quadratic regulator (LQR) control [8] etc. More challenging task is to deal with the swarm of drones and recently some methods are proposed such as containment control for multiple quadrotors [9] and leader-follower approach with robust observer-based control for dynamic trajectory tracking [10].…”

Section: Introductionmentioning

confidence: 99%

A Robust Backstepping Sliding Mode Controller with Chattering-Free Strategy for a Swarm of Drones

Zaidi

Kazim

Wang

2022

J. Phys.: Conf. Ser.

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This paper presents a robust controller for a swarm of drones that are subjected to external disturbances. Integrating a fuzzy-based system in the sliding mode control technique, a chattering-free controller is proposed for the trajectory tracking of a drone. Furthermore, the formation controller for a group of drones is developed using the Backstepping control design technique. The integration of a fuzzy rule-based system in the sliding mode tracking controller enables overcoming the inherent chattering problem due to discontinuous switching function in the sliding mode control technique and provides smooth and chattering-free control signals. The asymptotic stability of the overall system is shown using the Lyapunov stability theory. Finally, the control design performance is evaluated using numerical simulations.

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Disturbance-Rejection-Based Optimized Robust Adaptive Controllers for UAVs

Cited by 44 publications

References 45 publications

Perception Action Aware-Based Autonomous Drone Race in a Photorealistic Environment

Perception Action Aware-Based Autonomous Drone Race in a Photorealistic Environment

HDP Algorithms for Trajectory Tracking and Formation Control of Multi-Agent Systems

A Robust Backstepping Sliding Mode Controller with Chattering-Free Strategy for a Swarm of Drones

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