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Duong, Quynh Nga
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

 1 
Formation Control of Multiple Unmanned Aerial Vehicle Systems using Integral Reinforcement Learning Dang, Ngoc Trung; Duong, Quynh Nga
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23505

Abstract

Formation control of Unmanned Aerial Vehicles (UAVs), especially quadrotors, has many practical applications in contour mapping, transporting, search and rescue. This article solves the formation tracking requirement of a group of multiple UAVs by formation control design in outer loop and integral Reinforcement Learning (RL) algorithms in position sub-system. First, we present the formation tracking control structure, which uses a cascade description to account for the model separation of each UAV. Second, based on value function of inner model, a modified iteration algorithm is given to obtain the optimal controller in the presence of discount factor, which is necessary to employ due to the finite requirement of infinite horizon based cost function. Third, the integral RL control is developed to handle dynamic uncertainties of attitude sub-systems in formation UAV control scheme with a discount factor to be employed in infinite horizon based cost function. Specifically, the advantage of the proposed control is pointed out in not only formation tracking problem but also in the optimality effectiveness. Finally, the simulation results are conducted to validate the proposed formation tracking control of a group of multiple UAV system.
Model-free Optimal Control for Underactuated Quadrotor Aircraft via Reinforcement Learning Duong, Quynh Nga; Dang, Ngoc Trung
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23585

Abstract

The control of Unmanned Aerial Vehicles (UAVs), especially quadrotor aircraft, has many practical applications such as transporting, mapping, rescue, and agricultural applications. This paper investigates solving the optimal tracking control problem for a quadrotor system. First, an underactuated quadrotor system is considered a highly nonlinear system with six degrees of freedom and four inputs. Second, a hierarchical control structure consisting of position and attitude controller is adopted to address the underactuated problem, the position controller to achieve the desired tracking and generates the references for the attitude controller, and the attitude controller to achieve the reference attitude tracking. Third, to achieve optimal trajectory tracking, two Data- based Reinforcement Learning (RL) algorithms are applied to both position and attitude controllers to find the optimal control input by using the input- output quadrotor system data. Compared with the traditional optimal algorithms which require directly solving the Algebraic Ricatti Equation (ARE) or the Hamilton-Jacobi-Bellman (HJB) equation. It is impossible or difficult to implement due to the high nonlinear dynamic nature of the quadrotor system. By using RL in the proposed method, optimal policies can be learned without the knowledge of quadrotor dynamic information. Applying the learning control policies to the quadrotor system, the vehicle achieves optimal trajectory tracking. Finally, a simulation result is conducted to verify the optimal trajectory tracking for quadrotor with the proposed controller.
Formation Control of Multiple Unmanned Aerial Vehicle Systems using Integral Reinforcement Learning Dang, Ngoc Trung; Duong, Quynh Nga
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23505

Abstract

Formation control of Unmanned Aerial Vehicles (UAVs), especially quadrotors, has many practical applications in contour mapping, transporting, search and rescue. This article solves the formation tracking requirement of a group of multiple UAVs by formation control design in outer loop and integral Reinforcement Learning (RL) algorithms in position sub-system. First, we present the formation tracking control structure, which uses a cascade description to account for the model separation of each UAV. Second, based on value function of inner model, a modified iteration algorithm is given to obtain the optimal controller in the presence of discount factor, which is necessary to employ due to the finite requirement of infinite horizon based cost function. Third, the integral RL control is developed to handle dynamic uncertainties of attitude sub-systems in formation UAV control scheme with a discount factor to be employed in infinite horizon based cost function. Specifically, the advantage of the proposed control is pointed out in not only formation tracking problem but also in the optimality effectiveness. Finally, the simulation results are conducted to validate the proposed formation tracking control of a group of multiple UAV system.
Model-free Optimal Control for Underactuated Quadrotor Aircraft via Reinforcement Learning Duong, Quynh Nga; Dang, Ngoc Trung
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23585

Abstract

The control of Unmanned Aerial Vehicles (UAVs), especially quadrotor aircraft, has many practical applications such as transporting, mapping, rescue, and agricultural applications. This paper investigates solving the optimal tracking control problem for a quadrotor system. First, an underactuated quadrotor system is considered a highly nonlinear system with six degrees of freedom and four inputs. Second, a hierarchical control structure consisting of position and attitude controller is adopted to address the underactuated problem, the position controller to achieve the desired tracking and generates the references for the attitude controller, and the attitude controller to achieve the reference attitude tracking. Third, to achieve optimal trajectory tracking, two Data- based Reinforcement Learning (RL) algorithms are applied to both position and attitude controllers to find the optimal control input by using the input- output quadrotor system data. Compared with the traditional optimal algorithms which require directly solving the Algebraic Ricatti Equation (ARE) or the Hamilton-Jacobi-Bellman (HJB) equation. It is impossible or difficult to implement due to the high nonlinear dynamic nature of the quadrotor system. By using RL in the proposed method, optimal policies can be learned without the knowledge of quadrotor dynamic information. Applying the learning control policies to the quadrotor system, the vehicle achieves optimal trajectory tracking. Finally, a simulation result is conducted to verify the optimal trajectory tracking for quadrotor with the proposed controller.
Co-Authors Dang, Ngoc Trung