Chattering Reduction of Sliding Mode Control for Quadrotor UAVs Based on Reinforcement Learning

Wang, Qi; Namiki, Akio; Asignacion, Abner; Li, Ziran; Suzuki, Satoshi

doi:10.3390/drones7070420

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…However, the optimization offered by this method is usually non-convex, which leads to longer computational time and risks of solver non-convergence. In addition, the reinforcement learning-based control methods have been demonstrated to be effective for quadrotor-UAV control [10][11][12][13]. Han et al proposed a robust controller based on a hierarchical control framework and reinforcement learning [14].…”

Section: Introductionmentioning

confidence: 99%

A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory

Xu,

Fan,

Qiu

et al. 2023

Drones

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A robust controller for the waypoint tracking of a quadrotor unmanned aerial vehicle (UAV) is proposed in this paper, in which position control and attitude control are effectively decoupled. Model predictive control (MPC) is employed in the position controller. The constraints of motors are imposed on the state and input variables of the optimization equation. This design effectively mitigates the nonlinearity of the attitude loop and enhances the planning efficiency of the position controller. The attitude controller is designed using a nonlinear and robust control law based on SO(3) space, which enables continuous control on the SO(3) manifold. By extending the differential flatness of the quadrotor-UAV to the angular acceleration level, the mapping of the control reference from the position controller to the attitude controller is achieved. Simulations are carried out to demonstrate the capability of the proposed controller. In the simulations, multiple aggressive flight trajectories and severe external disturbances are designed. The results show that the controller is robust, with superior accuracy in tracking aggressive trajectories.

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Section: Introductionmentioning

confidence: 99%

A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory

Xu,

Fan,

Qiu

et al. 2023

Drones

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show abstract

“…However, none of these methods can effectively address the challenges posed by strong nonlinearity, uncertainty, and the need for fast solving in UAV control design. Hence, an important research direction is the achievement of synergistic benefits by combining different methods to effectively address these challenges [13,14].…”

Section: Introductionmentioning

confidence: 99%

Fast Tube-Based Robust Compensation Control for Fixed-Wing UAVs

Wang

Zheng

Wang³

et al. 2023

Drones

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When considering the robust control of fixed-wing Unmanned Aerial Vehicles (UAVs), a conflict often arises between addressing nonlinearity and meeting fast-solving requirements. In existing studies, the less nonlinear robust control methods have shown significant improvements that parallel computing and dimensionality reduction techniques in real-time applications. In this paper, a nonlinear fast Tube-based Robust Compensation Control (TRCC) for fixed-wing UAVs is proposed to satisfy robustness and fast-solving requirements. Firstly, a solving method for discrete trajectory tubes was proposed to facilitate fast parallel computation. Subsequently, a TRCC algorithm was developed that minimized the trajectory tube to enhance robustness. Additionally, considering the characteristics of fixed-wing UAVs, dimensionality reduction techniques such as decoupling and stepwise approaches are proposed, and a fast TRCC algorithm that incorporates the control reuse method is presented. Finally, simulations verify that the proposed fast TRCC effectively enhances the robustness of UAVs during tracking tasks while satisfying the requirements for fast solving.

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“…Several methods and techniques have been proposed in the literature to minimize the chattering effect when utilizing SMC, see e.g. [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Design and Control of an Innovative Overactuated Thrust Vectoring Six-DoF Quadrotor for Extreme and Challenging Environments

Kara-Mohamed,

Zhang,

Yan

2023

Un. Sys.

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The wide spectrum of recent applications for UAVs imposes further challenges to their abilities and control. This is especially true when operating in harsh and hostile environments where disturbances are huge and actuators are prone to failure. Conventional systems and traditional control techniques are not sufficient for stability and tracking under these circumstances. This paper proposes a unique innovative overactuated quadrotor system that has six DoFs. The vehicle has four rotors and each rotor can tilt independently in the [Formula: see text]-plane. It has the ability to correct its position and attitude in a decoupled way which is different from the conventional quadrotor configurations. Sliding-mode control associated with switching control mode is used for the control algorithm of the system. The system shows agility in the face of large disturbances and robustness against actuator failure which makes it a perfect fit for extreme and challenging environments. The concept of the system and its ability are illustrated in simulation with promising results.

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Chattering Reduction of Sliding Mode Control for Quadrotor UAVs Based on Reinforcement Learning

Cited by 8 publications

References 38 publications

A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory

A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory

Fast Tube-Based Robust Compensation Control for Fixed-Wing UAVs

Design and Control of an Innovative Overactuated Thrust Vectoring Six-DoF Quadrotor for Extreme and Challenging Environments

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