Lyapunov-based nonlinear controller for quadrotor position and attitude tracking with GA optimization

Safaei, Ali; Mahyuddin, Muhammad Nasiruddin

doi:10.1109/ieacon.2016.8067402

Cited by 10 publications

(15 citation statements)

References 16 publications

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“…Moreover, by referring to Lemma 1, we can useÂ instead of A in (43). Thus, we reach to (20). This completes the proof.…”

Section: Designing Procedures For Omfc and Regressor-free Adaptive Lawsmentioning

confidence: 54%

“…It should be noted that, for under-actuated dynamic systems, where z > m, the number of control inputs can be increased virtually in an initial step to make all the system states be controllable, either directly or indirectly. 20 The desired values for the internal states, which are not considered as the system outputs, are set to be the values of the corresponding virtual controllers. Then, one can define…”

Section: Definitionmentioning

confidence: 99%

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Optimal model‐free control for a generic MIMO nonlinear system with application to autonomous mobile robots

Safaei

Mahyuddin

2018

Adaptive Control & Signal

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In this paper, the design procedure for optimal model-free control algorithm is presented for the tracking problem of completely unknown nonlinear dynamic systems operating under unknown disturbances. The procedure includes a new structure in the context of model-free control and data-driven control algorithms. In the new structure, the unknown nonlinear functions are segmented into 1 unknown linear-in-states part and another unknown nonlinear part. The adaptive laws proposed for estimating the unknown system dynamics are regressor-free estimation methods in which there is no need for regressor parameters and, consequently, the persistent excitation condition is not required anymore. Moreover, the main controller gains are updated online, incorporating the adapted values of linear terms in the system dynamics. A comparative study is presented to show that the proposed optimal model-free control outperforms the state-of-the-art model-free control algorithms. In addition, the simulation results for the application of the algorithm on autonomous mobile robots are provided.

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“…Moreover, by referring to Lemma 1, we can useÂ instead of A in (43). Thus, we reach to (20). This completes the proof.…”

Section: Designing Procedures For Omfc and Regressor-free Adaptive Lawsmentioning

confidence: 54%

Section: Definitionmentioning

confidence: 99%

Optimal model‐free control for a generic MIMO nonlinear system with application to autonomous mobile robots

Safaei

Mahyuddin

2018

Adaptive Control & Signal

Self Cite

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show abstract

“…Proposition In the following simulation case studies, the dynamic model of a quadrotor is considered as follows :

\begin{align} {\overset{\overset{p}{true}}{true}}_{q} & = \overset{v_{q}}{true} \\ {\overset{\overset{normalΦ}{true}}{true}}_{q} & = R_{q t}^{prefix- 1} \overset{w_{q}}{true} \\ {\overset{\overset{v}{true}}{true}}_{q} & = \frac{1}{M_{q}} false[R_{q} \overset{F_{q}}{true} prefix- K_{d} \overset{v_{q}}{true} prefix- M_{q} \overset{F_{g}}{true} false] + \overset{f_{q}}{true} \\ {\overset{\overset{w}{true}}{true}}_{q} & = J_{q}^{prefix- 1} false[\overset{τ_{q}}{true} prefix- K_{a} \overset{w_{q}}{true} prefix- \overset{w_{q}}{true} prefix\times J_{q} \overset{w_{q}}{true} false] + \overset{t_{q}}{true}, \end{align}

where

{\vec{p}}_{q} = [x_{q}; y_{q}; z_{q}]

and

{\vec{Φ}}_{q} =

…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Also,

{\vec{v}}_{q}

and

{\vec{w}}_{q}

are the vectors of linear and angular velocities. Two matrices R q and R qt defined in

R^{3 \times 3}

are the transformation matrices used to transform the coordinates between the body frame and the inertial frame . M q is the quadrotor mass and

J_{q} \in R^{3 \times 3}

is its inertia matrix .…”

Section: Simulation Resultsmentioning

confidence: 99%

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Adaptive relative velocity estimation algorithm for autonomous mobile robots using the measurements on acceleration and relative distance

Safaei

2020

Adaptive Control & Signal

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Summary In this article, an adaptive algorithm is proposed for online velocity estimation of the autonomous mobile robots (AMRs) without positioning data received from a Global Positioning System (GPS) module or other means for odometry. Unlike the popular Kalman and particle filters that use the measurements on vectors of global (or local) position and acceleration of a mobile robot, the proposed adaptive relative velocity estimation (ARVE) algorithm requires the scalar value of measured distance to a beacon agent and also the measurement on acceleration vector, in order to generate an online estimation of the global velocity vector of a mobile robot. Combining the ARVE algorithm with the recently proposed adaptive relative position estimation (ARPE) algorithm provides a solution for online estimation of the translational states of a mobile robot without accessing the GPS data, which makes the package applicable in both indoor and outdoor environments. The stability of the ARVE algorithm is analyzed with LaSalle‐Yoshizawa theorem. In addition, two simulation studies are provided to show the application of the proposed estimation package (ARVE+ARPE) for aerial AMRs in two cases corresponding to the stationary and moving beacon agents. In the simulation results, it is shown that the estimation package can be used in conjunction with the recently proposed adaptive model‐free control (AMFC) algorithm to achieve desired tracking objective in autonomous movement of a quadrotor, without requiring the information on the internal dynamics of the robot.

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Parametric Optimization for Nonlinear Quadcopter Control Using Stochastic Test Signals

Matus-Vargas

Rodríguez-Gómez

Martínez-Carranza

2019

Studies in Computational Intelligence

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Lyapunov-based nonlinear controller for quadrotor position and attitude tracking with GA optimization

Cited by 10 publications

References 16 publications

Optimal model‐free control for a generic MIMO nonlinear system with application to autonomous mobile robots

Optimal model‐free control for a generic MIMO nonlinear system with application to autonomous mobile robots

Adaptive relative velocity estimation algorithm for autonomous mobile robots using the measurements on acceleration and relative distance

Parametric Optimization for Nonlinear Quadcopter Control Using Stochastic Test Signals

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