Active disturbance rejection and predictive control strategy for a quadrotor helicopter

Ma, Dailiang; Xia, Yuanqing; Li, Tianya; Chang, Kai

doi:10.1049/iet-cta.2016.0125

Cited by 116 publications

(48 citation statements)

References 23 publications

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“…Proof. First, substituting the terminal controller (28) into the input constraint (25), it is obtained as follows:…”

Section: Preliminariesmentioning

confidence: 99%

“…The optimal control signal u c and the control torque are depicted in Figures 3C and 3D. It is shown that u c and are limited by the input constraint (25). To evaluate the input constraint (25), the curve | c |∕a + | c |∕b is presented in Figure 3E.…”

Section: Trajectory Tracking Testmentioning

confidence: 99%

“…It is shown that u c and are limited by the input constraint (25). To evaluate the input constraint (25), the curve | c |∕a + | c |∕b is presented in Figure 3E. It can be observed that the input constraint (25) is activated over the interval t ∈ [0, 0.5] s. During this interval, the constraint for control signal u c , ie, u c ∈ U c , is also satisfied, which presents the satisfaction of the input constraint (25).…”

Section: Trajectory Tracking Testmentioning

confidence: 99%

See 2 more Smart Citations

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

Yang

Guo

Xia

et al. 2019

Intl J Robust & Nonlinear

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Summary A dual closed‐loop tracking control is proposed for a wheeled mobile robot based on active disturbance rejection control (ADRC) and model predictive control (MPC). In the inner loop system, the ADRC scheme with an extended state observer (ESO) is proposed to estimate and compensate external disturbances. In the outer loop system, the MPC strategy is developed to generate a desired velocity for the inner loop dynamic system subject to a diamond‐shaped input constraint. Both effectiveness and stability analysis are given for the ESO and the dual closed‐loop system, respectively. Simulation results demonstrate the performances of the proposed control scheme.

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“…Proof. First, substituting the terminal controller (28) into the input constraint (25), it is obtained as follows:…”

Section: Preliminariesmentioning

confidence: 99%

Section: Trajectory Tracking Testmentioning

confidence: 99%

Section: Trajectory Tracking Testmentioning

confidence: 99%

See 1 more Smart Citation

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

Yang

Guo

Xia

et al. 2019

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [11], the MPC method was proposed to solve the pathtracking problem for the unmanned quadrotor, where a future control sequence was computed in a defined horizon, such that the prediction of the quadrotor output was driven close to the reference. In [12], a novel control strategy which combined the active disturbance rejection control and model predictive control was presented for the unmanned quadrotor. The proposed method improved the robustness for the modeling error and disturbances while performing a smooth trajectory tracking.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

Zhang

Sun

Deng

2019

International Journal of Aerospace Engineering

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This paper presents a dynamical recurrent neural network- (RNN-) based model predictive control (MPC) structure for the formation flight of multiple unmanned quadrotors. A distributed hierarchical control system with the translation subsystem and rotational subsystem is proposed to handle the formation-tracking problem for each quadrotor. The RNN-based MPC is proposed for each subsystem, where the RNN is introduced as the predictive model in MPC. And to improve the modeling accuracy, an adaptive updating law is developed to tune weights online for the RNN. Besides, the adaptive differential evolution (DE) algorithm is utilized to solve the optimization problem for MPC. Furthermore, the closed-loop stability is analyzed; meanwhile, the convergence of the DE algorithm is discussed as well. Finally, some simulation examples are provided to illustrate the validity of the proposed control structure.

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“…In recent decades, the use of flexible bodies in the manipulators robots structure has triggered a great interest in the control domain [5]. There has been an important progress in planning [6], [7], design [8] and control [9].…”

Section: Introductionmentioning

confidence: 99%

On the dynamic modelling and simulation of rigid-flexible manipulator robot using several inputs

Zribi

Tlijani

Knani

et al. 2017

2017 4th International Conference on Control, Decision and Information Technologies (CoDIT)

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Abstract-The purpose of this paper is to develop a dynamic model of a rigid-flexible manipulator robot with a load on its endpoint using Euler-Lagrange formulation. In order to test the performance of the studied system, several mathematical functions are used as motion profile. It choice is very important because it affects the robot's performance. Different factors intervene in this choice. However, the most important is the torque's continuity and the movement's smoothness. Numerical simulations show the robustness of the dynamic model of the studied system for several motions profiles.

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Active disturbance rejection and predictive control strategy for a quadrotor helicopter

Cited by 116 publications

References 23 publications

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

On the dynamic modelling and simulation of rigid-flexible manipulator robot using several inputs

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