Second order sliding mode control for a quadrotor UAV

Zheng, Enhui; Xiong, Jing-Jing; Luo, Jiliang

doi:10.1016/j.isatra.2014.03.010

Cited by 414 publications

(222 citation statements)

References 19 publications

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“…After the linearization around the equilibrium points, the new cascaded form is obtained as denotes the real part of the leftmost eigenvalues of the matrix A in the negative half plane, the matrix A is Hurwitz, the system is asymptotically stable near the desired equilibrium points [1,5,6]. Thus, it is only necessary to consider the stability of…”

Section: Controller Designmentioning

confidence: 99%

“…In the controller design, the sliding manifold is constructed by combining the position and velocity tracking errors of two states in a nominal linear form [5,6], which brings four coefficients associated with two states. The coefficients can be calculated via Hurwitz condition, and the corresponding controller is derived by Lyapunov theory.…”

Section: Introductionmentioning

confidence: 99%

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Second Order Sliding Mode Control for a Class of Underactuated Systems

Xiong¹,

Zhang²

2017

dtetr

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Abstract. This paper presents a second order sliding mode control method to stabilize a class of underactuated systems. In the controller design, the sliding manifold is designed by the nominal linear combination of position and velocity tracking errors of two system states, this can stabilize the indirectly controlled modes. The four coefficients of sliding manifold are obtained via Hurwitz condition in the system stability analysis process. In addition, simulation results are presented for a translator oscillator with rotational actuator (TORA) and an inverted pendulum as two examples of a class of underactuated systems. In both cases, the proposed control method is shown to be effective in the presence of stabilization and tracking control.

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Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Second Order Sliding Mode Control for a Class of Underactuated Systems

Xiong¹,

Zhang²

2017

dtetr

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“…In recent years, numerous authors, such as Bouadi et al [5]; Dydek et al [6]; Jafarnejadsani et al [7]; Loukianov [8]; Mohammadi & Shahri [9]; Zheng et al [10], to name a few, employed nonlinear robust control techniques, such as sliding mode control, model reference adaptive control (MRAC), adaptive sliding mode control, and L 1 adaptive control, to design autopilots for quadrotors that are able to account for inaccurate modeling assumptions and compensate failures in the propulsion system. These autopilots are generally designed assuming perfect knowledge of the location of the quadrotor's center of mass, supposing that the vehicle's Euler angles are small at all times, and neglecting the inertial counter-torque.…”

Section: Introductionmentioning

confidence: 99%

“…Theorem 7.1 Consider the nonlinear dynamical system given by (43) and (44), the nonlinear dynamical system given by (50) and (51), the reference signals (53) and (54), the augmented dynamical system (8), the reference dynamical model (9), the feedback control law γ(Á, Á , Á) given by (10), and the adaptation laws (11)- (13). If there exist K x ∈ R 18 Â 6 and K cmd ∈ R 6 Â 6 such that (15) and ( …”

mentioning

confidence: 99%

Robust Adaptive Output Tracking for Quadrotor Helicopters

Mohammadi¹,

L’Afflitto²

2018

Adaptive Robust Control Systems

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Quadrotor helicopters are drawing considerable attention both for their mobility and their potential to perform multiple tasks in complete autonomy. Moreover, the numerous limitations characterizing these aircraft, such as their underactuation, make quadrotors ideal testbeds for innovative theoretical approaches to the problem of controlling autonomous mechanical systems. In this chapter, we propose a robust model reference adaptive control architecture and design an autopilot for quadrotors, which guarantees satisfactory output tracking despite uncertainties in the vehicle's mass, matrix of inertia, and location of the center of mass. The feasibility of our results is supported by a detailed analysis of the quadrotor's equations of motion. Specifically, considering the vehicle's equations of motion as a time-varying nonlinear dynamical system and avoiding the common assumption that the vehicle's Euler angles are small at all times, we prove that the proposed autopilot guarantees satisfactory output tracking and verifies sufficient conditions for a weak form of controllability of the closed-loop system known as strong accessibility. A numerical example illustrates the applicability of the theoretical results presented and clearly shows how the proposed autopilot outperforms in strong wind conditions autopilots designed using a commonly employed proportional-derivative control law and a conventional model reference adaptive control law.

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“…At present, the flight control technology is mainly divided into two categories: linear control techniques such as proportional-integral-derivative (PID) control, 10 H 1 control, 11,12 and H 2 control 13 ; nonlinear control techniques such as fuzzy control, [14][15][16][17][18] feedback linearization control, 19 backstepping control, [20][21][22] sliding model control, 1,4,23 and neural network control. 5,[24][25][26] Linear control method is very suitable for practical applications, since its design process is simple and the controller is easy to be implemented, which is also the reason for UAVHs dominated by linear control methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy tracking control for non-affine nonlinear yaw channel of unmanned aerial vehicle helicopter

Yan

Huang

2016

International Journal of Advanced Robotic Systems

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The article deals with the problem of the yaw control of the unmanned aerial vehicle helicopter which is non-affine nonlinear by use of a novel projection-based adaptive fuzzy control approach. First, principle model and control design model of the yaw channel of the unmanned aerial vehicle helicopter are described. Then, a dynamic approximation technique is introduced to approach the non-affine model of the unmanned aerial vehicle helicopter into an affine model with variable parameters, which is applied to facilitate the design of nonlinear control scheme. Next, in the proposed controller, fuzzy controller is designed to deal with the unknown uncertainties and disturbances. Meanwhile, the projection-based adaptive law applied in fuzzy controller bounds the parameter estimation function and can also guarantee the robustness of the designed control scheme against uncertain disturbances. Moreover, the convergence and stability of the designed controller are proved by Lyapunov stability theory. Finally, the simulation results of the yaw channel of an unmanned aerial vehicle helicopter are performed to illustrate that the designed controller has good tracking performance, stability, and robustness under the condition of the time-vary uncertain disturbances.

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Second order sliding mode control for a quadrotor UAV

Cited by 414 publications

References 19 publications

Second Order Sliding Mode Control for a Class of Underactuated Systems

Second Order Sliding Mode Control for a Class of Underactuated Systems

Robust Adaptive Output Tracking for Quadrotor Helicopters

Adaptive fuzzy tracking control for non-affine nonlinear yaw channel of unmanned aerial vehicle helicopter

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