Trajectory Tracking of Quad-Rotor UAV Using Fractional Order $$\textit{PI}^{\mu }D^{\lambda }$$PIμDλ Controller

Maurya, Heera Lal; Behera, Laxmidhar; Verma, Nishchal K.

doi:10.1007/978-981-13-1132-1_14

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Such flying abilities make it possible for quadrotors to take off and land in any environment, making them an ideal and relevant platform for emulating a planetary lander. It has four rotors and their arrangement relative to coordinate system has two configurations: the "x" and "+" [21]. The quadrotor has four inputs controlling six degrees of freedom output states as given by Equation (1).…”

Section: Uav Test Platformmentioning

confidence: 99%

Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

et al. 2021

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It is imperative to find new places other than Earth for the survival of human beings. Mars could be the alternative to Earth in the future for us to live. In this context, many missions have been performed to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing on Mars. Mars landing consists of different phases (hypersonic entry, parachute descent, terminal descent comprising gravity turn, and powered descent). However, the focus of this work is the powered descent phase of landing. Firstly, the main objective of this study is to minimize the landing error during the powered descend landing phase. The second objective involves constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID and LQR have been developed for the stated problem; however, the predictive control algorithm with constraint handling’s ability has not been explored much. This research discusses the Model Predictive Control algorithm for the powered descent phase of landing. Model Predictive Control (MPC) considers input/output constraints in the calculation of the control law and thus it is very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of Explicit MPC, which gives comparatively less computational time than MPC. A comparison is done among MPC variants in terms of feasibility, constraints handling, and computational time. Moreover, other conventional control algorithms like PID and LQR are compared with the proposed predictive algorithm. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB.

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Section: Uav Test Platformmentioning

confidence: 99%

Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

et al. 2021

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“…For example [29], proposed a variable structure control with neural network and optimized fractional-order selection policy for the sensorless telerobotic. As a powerful technique, the genetic algorithm is used for the tuning of the parameters of several controllers and has been used significantly to tune the parameters of fractional order controllers specially the PID [30], [31] and sliding mode [32], [33] controllers.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Flight Control of a Fleet of Quadrotors Using Fractional Sliding Mode With Potential Field Algorithms

Alabsari,

Saif,

El-Ferik

et al. 2024

IEEE Access

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In this paper, a leader-follower-based cooperative and formation flight control is designed for a group of quadrotors using new forms of fractional sliding mode control (FSMC) strategy accompanied by the potential field algorithm. The FSMC strategy is employed as an inner controller to stabilize each individual UAV quadrotor while the potential field function is used as a formation algorithm (output controller) in order to keep a desired shape formation during the navigation of the fleet. Firstly, the stability of a single quadrotor subjected to external disturbances is addressed by designing an FSMC in addition to a classical PID controller. The FSMC is responsible for stabilizing the altitude and attitude of the vehicle while the responsibility of the PID is to control the two-dimensional (x-y) position of the UAV. Lyapunov-based sliding condition is used in the design of the fractional control strategy to guarantee system stability. The controller's parameters are tuned using the genetic algorithm (GA) to obtain better performance and more robustness against external disturbances. The potential field algorithm is used to generate the paths that are required for the fleet of vehicles to navigate while keeping a prescribed formation shape. This is done by using the attractive potential field to attract the followers toward the leader and the repulsive potential field to repulse every two neighboring followers in order to keep a required distance between them. Simulation results have proved the efficiency of the proposed control system for both the single and multi-agent cases.

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Applications of Fractional Operators in Robotics: A Review

Chávez-Vázquez

Gómez-Aguilar

Lavín-Delgado

et al. 2022

J Intell Robot Syst

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Trajectory Tracking of Quad-Rotor UAV Using Fractional Order $$\textit{PI}^{\mu }D^{\lambda }$$PIμDλ Controller

Cited by 4 publications

References 16 publications

Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

Cooperative Flight Control of a Fleet of Quadrotors Using Fractional Sliding Mode With Potential Field Algorithms

Applications of Fractional Operators in Robotics: A Review

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