Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control

Zhou, Wenya; Yin, Kuilong; Wang, Rui; Wang, Yue E.

doi:10.1155/2014/492680

Cited by 23 publications

(19 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first step consists of a classical pole placement method, followed by partially linearized inner loops and a robust H ∞ outer loop controller. On the feedback linearization subject, (Zhou et al, 2014) propose an attitude controller which also uses adaptive control to stabilize the decoupled model of the UAV. Furthermore, in (Rezende et al, 2018) a robust control law, based on a reference model with constrained inputs and states, is proposed to navigate the UAV inside a smooth vector field in R 2 and R 3 .…”

Section: Related Workmentioning

confidence: 99%

MPC Based Feedback-Linearization Strategy of a Fixed-Wing UAV

Pereira

Pimenta

Raffo

2020

Anais Do Congresso Brasileiro De Automática 2020

View full text Add to dashboard Cite

This work proposes a xed-wing UAV (Ummaned Aerial Vehicle) control strategy based on feedback-linearization and model predictive control (MPC). The strategy makes use of the relationship between the applied control inputs of the UAV and the generalized forces and moments actuating on it. A linear model is obtained by the exact feedback-linearization technique, followed by the use of MPC to solve the trajectory tracking and the control allocation problems. The proposed controller is capable of actuating on the 6 DOF (Degrees of Freedom) of the UAV, avoiding inherited restrictions when the model is decoupled. The proposed strategy is applied in a curve tracking task. Simulations are performed using MATLAB software, and the results show the eciency of the proposed control strategy.

show abstract

Section: Related Workmentioning

confidence: 99%

MPC Based Feedback-Linearization Strategy of a Fixed-Wing UAV

Pereira

Pimenta

Raffo

2020

Anais Do Congresso Brasileiro De Automática 2020

View full text Add to dashboard Cite

show abstract

“…The control vector u consists of elevator, aileron and rudder control surfaces plus propeller thrusting force. The expanded form of (1) is as below (Zhou et al, 2014):…”

Section: Parameter Uncertainty Modellingmentioning

confidence: 99%

Lateral control of an Aerosonde facing tolerance in parameters and operation speed: performance robustness study

Seyedtabaii

Zaker

2018

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

The aim is to acquire low variance roll responses (performance robustness) of control of an Aerosonde despite the high level of tolerances in aerodynamic parameters and working speed. In this respect, fractional-order proportional plus integral and derivative (FOPID) is a valuable option; others are H∞ and μ synthesis. FOPID can tolerate system uncertainty by maintaining a wide open-loop flat phase margin band. All three methods are worked out using the linearized system model and deliver (at least initially) high-integer-order controllers. The uncertainty level is not explicitly considered in H∞, but it may be presented in the μ synthesis and FOPID. The uncertainty presentation in the modified fractional-order controller (mFOC) design is through a Φd curve. The Φd curve is fitted to the mean of the upper and lower bands of the phase margins distribution map of the random systems. It is shown that the mFOC design perfectly secures the desired phase margin flatness. The controllers are applied to the roll of an unmanned aircraft vehicle with a 30% tolerance in the aerodynamic parameters, and operation speed and robustness in performance is evaluated. The simulation results indicate that the mFOC design renders more coherent responses than what H∞ and µ synthesis design deliver. This is confirmed through extensive simulations.

show abstract

“…For overcoming the flight control difficulties, many algorithms have been designed. Such as, nonlinear dynamic inversion technique (Stein et al, 1994), quasi-continuous highorder sliding mode controller (Tian et al, 2013), non-linear and adaptive flight control based on invariant manifolds (Karagiannis and Astolfi., 2010), control based on adaptation and feedback linearization (Zhou et al, 2014), optimal control (Arifianto and Farhood, 2015), robust control (Hoffer et al, 2014), model prediction techniques (Kang and Hedrick, 2009), H 1 technique, support vector regression (Shin et al, 2011), active disturbance rejection control (Wang et al, 2015) and anti-windup compensator (Wang et al, 2011). For pathtracking of UAV in the presence of wind, different solutions are presented in (Liu et al, 2013;Sarras and Siguerdidjane, 2014); the purpose of these solutions is to improve the preciseness and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Disturbances rejection based on sliding mode control

Taimoor

Amin

2019

AEAT

View full text Add to dashboard Cite

Purpose The purpose of this paper aims to investigate an effective algorithm for different types of disturbances rejection. New dynamics are designed based on disturbance. Observer-based sliding mode control (SMC) technique is used for approximation the disturbances as well as to stabilize the system effectively in presence of uncertainties. Design/methodology/approach This research work investigates the disturbances rejection algorithm for fixed-wing unmanned aerial vehicle. An algorithm based on SMC is introduced for disturbances rejection. Two types of disturbances are considered, the constant disturbance and the sinusoidal disturbance. The comprehensive lateral and longitudinal models of the system are presented. Two types of dynamics, the dynamics without disturbance and the new dynamics with disturbance, are presented. An observer-based algorithm is presented for the estimation of the dynamics with disturbances. Intensive simulations and experiments have been performed; the results not only guarantee the robustness and stability of the system but the effectiveness of the proposed algorithm as well. Findings In previous research work, new dynamics based on disturbances rejection are not investigated in detail; in this research work both the lateral and longitudinal dynamics with different disturbances are investigated. Practical implications As the stability is always important for flight, so the algorithm proposed in this research guarantees the robustness and rejection of disturbances, which plays a vital role in practical life for avoiding any kind of damage. Originality/value In the previous research work, new dynamics based on disturbances rejection are not investigated in detail; in this research work both the lateral and longitudinal dynamics with different disturbances are investigated. An observer-based SMC not only approximates the different disturbances and also these disturbances are rejected in order to guarantee the effectiveness and robustness.

show abstract

Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control

Cited by 23 publications

References 24 publications

MPC Based Feedback-Linearization Strategy of a Fixed-Wing UAV

MPC Based Feedback-Linearization Strategy of a Fixed-Wing UAV

Lateral control of an Aerosonde facing tolerance in parameters and operation speed: performance robustness study

Disturbances rejection based on sliding mode control

Contact Info

Product

Resources

About