A Review of Control Algorithms for  Autonomous Quadrotors

Zulu, Andrew; John, Samuel

doi:10.4236/ojapps.2014.414053

Cited by 159 publications

(66 citation statements)

References 26 publications

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“…where Γ σ = [k 11 k 12 k 21 k 22 k 31 k 32 µ 1 µ 2 µ 3 α 1 α 2 α 3 ], σ = {x, y, z, φ, θ, ψ} while for the LPID controller (25) where Γ σ = [Kp Kd Ki], σ = {x, y, z, φ, θ, ψ} The ITAE and USQR are defined in Table 3. The weighting variables must satisfy w 1i + w 2i = 1, They are defined as the relative emphasis of one objective as compared to the other.…”

Section: Step Reference Trackingmentioning

confidence: 99%

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Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Najm

Ibraheem

2019

Engineering Science and Technology, an International Journal

110

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A Nonlinear PID (NLPID) controller is proposed to stabilize the translational and rotational motion of a 6-DOF UAV quadrotor system and enforce it to track a given trajectory with minimum energy and error. The complete nonlinear model of the 6-DOF quadrotor system are obtained using Euler-Newton formalism and used in the design process, taking into account the velocity and acceleration vectors resulting in a more accurate 6-DOF quadrotor model and closer to the actual system. Six NLPID controllers are designed, each for Roll, Pitch, Yaw, Altitude, and the Position subsystems, where their parameters are tuned using GA to minimize a multi-objective Output Performance Index (OPI). The stability of the 6-DOF UAV subsystems has been analyzed in the sense of Hurwitz stability theorem under certain conditions on the gains of the NLPID controllers. The simulations have been accomplished under MATLAB/SIMULINK environment and included three different trajectories, i.e., circular, helical, and square. The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the Linear PID (LPID) one and the simulations showed the effectiveness of the proposed NLPID controller in terms of speed, control energy, and steady-state error.

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Section: Step Reference Trackingmentioning

confidence: 99%

“…Other used controllers like: NLPID control [17] and [18], LQR [19], geometric control [20], nonlinear model predictive control [21], L1 control [22], fuzzy control [23,24]. A further studies for control algorithms with quadrotor systems can be found in [25].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Najm

Ibraheem

2019

Engineering Science and Technology, an International Journal

110

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“…The optimal control theory is widely applied to design both LQR and LQG controllers for the UAV applications. In [11,12,14,17,19], the dynamic controller synthesis based on LQtechniques is presented. The design criteria applied in this article leans on [15,16].…”

Section: Literature Reviewmentioning

confidence: 99%

Robust LQG Controller Design for the Small Unmanned Aerial Vehicle

Szabolcsi¹

2018

Review of the Air Force Academy

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This paper deals with the dynamic optimal control of unmanned aerial vehicles (UAVs) in the presence of the stochastic plant disturbances and sensor noises. The LQR static optimal control technique of the dynamical system is used for an ideal dynamics of the aircraft, determined under conditions of no external or internal disturbance, which can be rarely met in the practice. The more realistic dynamical model applied for the controller synthesis is the random dynamical model allowing consideration of both plant disturbances and sensor noises simultaneously. The purpose of the research is to propose and implement the LQG design method for the fixed-wing UAV controller synthesis procedure.

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“…Due to the utility and growing popularity of unmanned aerial vehicles (UAV), especially in military field [1], various methods of control have been developed [2][3][4][5]. One important aspect of controlling these platforms is waypoint following, or trajectory tracking.…”

Section: Introductionmentioning

confidence: 99%

Linear controller design with the use of PSO algorithm for UAV trajectory tracking

Pawłowski¹,

Konatowski²

2020

Radioelectronic Systems Conference 2019

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This paper uses the PSO (Particle Swarm Optimization) algorithm to linearly control a BSP unmanned aircraft, enabling accurate tracking of a predefined trajectory. In the process of control optimization, the PSO algorithm was implemented with a quadratic cost function. During the study, unstable algorithm behavior was observed, as a result of which a modification was made by introducing a coefficient suppressing the movement of particles in the search space. The main task of the coefficient is to ensure the convergence of the solution. The effect of the coefficient value on population behavior was tested. In the simulation research on tracking, the previously generated trajectory was used, which is a reference for the location, velocity, and course for the linear dynamic BSP model. The results of these tests were presented in the form of waveforms of control signals and waveforms of state variables. By selecting the appropriate parameters, the proposed algorithm enabled the repeatability of results, as well as the proper mapping of the trajectory at the time of the operation of the algorithm not exceeding 0.03 seconds.

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A Review of Control Algorithms for Autonomous Quadrotors

Cited by 159 publications

References 26 publications

Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Robust LQG Controller Design for the Small Unmanned Aerial Vehicle

Linear controller design with the use of PSO algorithm for UAV trajectory tracking

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