Dynamical Modelling and Robust Control for an Unmanned Aerial Robot Using Hexarotor with 2-DOF Manipulator

Ding, Li; Wu, Hongtao

doi:10.1155/2019/5483073

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…The joint angular velocity and angular acceleration of the manipulator are set to 0, and the simulation time lasting 10 s with 50 Hz sampling time. Meanwhile, two other controllers are considered, including the SMC-ESO (sliding mode based on extended state observer) in the reference [46], and the LADRC (linear active disturbance rejection controller) proposed in the reference [48]. These controllers are introduced as comparisons to investigate the performance of the proposed controller, and are referred to as controller 1 and controller 2, respectively.…”

Section: Casementioning

confidence: 99%

Cable-Driven Unmanned Aerial Manipulator Systems for Water Sampling: Design, Modeling, and Control

Ding

Zhu

et al. 2023

Drones

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The unmanned aerial manipulator (UAM) is a kind of aerial robot that combines a quadrotor aircraft and an onboard manipulator. This paper focuses on the problems of structure design, system modeling, and motion control of an UAM applied for water sampling. A novel, light, cable-driven UAM has been designed. The drive motors installed in the base transmit the force and motion remotely through cables, which can reduce the inertia ratio of the manipulator. The Newton–Euler method and Lagrangian method are adopted to establish the quadrotor model and manipulator model, respectively. External disturbances, model uncertainty, and joint flexibility are also accounted for in the two submodels. The quadrotor and manipulator are controlled separately to ensure the overall accurate aerial operation of the UAM. Specifically, a backstepping control method is designed with the disturbance observer (BC-DOB) technique for the position loop and attitude loop control of the quadrotor. A backstepping integral fast terminal sliding mode control based on the linear extended state observer (BIFTSMC-LESO) has been developed for the manipulator to provide precise manipulation. The DOB and LESO serve as compensators to estimate the external disturbances and model uncertainty. The Lyapunov theory is used to ensure the stability of the two controllers. Three simulation cases are conducted to test the superior performance of the proposed quadrotor controller and manipulator controller. All the results show that the proposed controllers provide better performances than other traditional controllers, which can complete the task of water quality sampling well.

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Section: Casementioning

confidence: 99%

Cable-Driven Unmanned Aerial Manipulator Systems for Water Sampling: Design, Modeling, and Control

Ding

Zhu

et al. 2023

Drones

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“…This designed quadcopter aerial manipulator is developed for remote inspecting, manipulating, and transporting. Another example of a two degree of freedom ARA can be found in [12], where the designed aerial manipulator is developed for aerial manipulations using a hexarotor. In [13], a winged aerial manipulator of dual two degrees of freedom ARAs is designed for assisting flying and manipulating objects.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

et al. 2021

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Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.

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“…In addition, the problem of designing autonomous flight controllers for Q-UAVs is equally challenging because these controllers are closely connected with the dynamic models [11][12][13]. Hence, the Q-UAV controller can be considered to be a model that combines discrete and continuous behaviors named the Hybrid Dynamic System (HDS), and it can be modeled by hybrid automata (HA) [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Object-Oriented Systems Engineering Point of View to Develop Controllers of Quadrotor Unmanned Aerial Vehicles

Hien

Truong

Bui

2020

International Journal of Aerospace Engineering

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The aerospace industry needs to be provided with system solutions to technologically challenging and mission-critical problems. Based on the industrial control point of view, development engineers must take costs and existing standards into account in order to effectively design, implement, and deploy control systems with reasonable costs. The customization and reusability are important factors associated with the production of new applications in order to reduce their costs, resources, and development time. In this work, the Model-Driven Architecture (MDA)/Model-Based Systems Engineering (MBSE) approach combined with the real-time Unified Modeling Language (UML)/Systems Modeling Language (SysML), Unscented Kalman Filter (UKF) algorithm, and hybrid automata is specialized to obtain a hybrid control model in order to conveniently deploy controllers of Quadrotor Unmanned Aerial Vehicles (Q-UAVs). This hybrid control model also provides a real-time capsule pattern, which allows the designed elements to be customizable and reusable in new applications of various multirotor UAVs of the Vertical Take-Off and Landing (VTOL) type. The Q-UAV dynamics and control architecture are combined with the MDA/MBSE specialization as follows: the Computation Independent Model (CIM) is defined by specifying the use-case model together with the UKF algorithm and hybrid automata to intensively gather the control requirements. The Platform Independent Model (PIM) is then designed by specializing the real-time UML/SysML’s features to obtain the main control capsules, ports, and protocols, together with their dynamic evolution. The detailed PIM is subsequently transformed into the PSM by open-source platforms to rapidly implement and deploy the Q-UAV controller. The paper ends with trial flights and deployment results that show good feasibility to be used for a trajectory-tracking controller of a low-cost Q-UAV. In this case study, the Q-UAV controller is implemented with the simulation model in the OpenModelica tool. The obtained simulation results then allow the main control elements and their properties to be defined, as well as building the implementation libraries in the Arduino environment based on C++ language to quickly perform the realization model in the ATMEGA32-U2 and STM32 Cortex-M4 microcontrollers.

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Dynamical Modelling and Robust Control for an Unmanned Aerial Robot Using Hexarotor with 2-DOF Manipulator

Cited by 4 publications

References 28 publications

Cable-Driven Unmanned Aerial Manipulator Systems for Water Sampling: Design, Modeling, and Control

Cable-Driven Unmanned Aerial Manipulator Systems for Water Sampling: Design, Modeling, and Control

Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

An Object-Oriented Systems Engineering Point of View to Develop Controllers of Quadrotor Unmanned Aerial Vehicles

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