Intelligent Controller Design for Quad-Rotor Stabilization in Presence of Parameter Variations

Liu

Journal of Advanced Transportation

et al. 2018

This paper proposes an effective approach for modelling and assessing the risks associated with unmanned aerial vehicles (UAVs) integrated into national airspace system (NAS). Two critical hazards with UAV operations are considered and analyzed, which are ground impacts and midair collisions. Threats to fatalities that result from the two hazards are the focus in the proposed method. In order to realize ground impact assessment, a multifactor risk model is designed by calculating system reliability required to meet a target level of safety for different UAV categories. Both fixed-wing and rotary-wing UAVs are taken into account under a real scenario that is further partitioned into different zones to make the evaluation more precise. Official territory and population data of the operation scenario are incorporated, as well as UAV self-properties. Casualty area of impacting debris can be obtained as well as the probability of fatal injuries on the ground. Sheltering factors are not neglected and defined as four types based on the real scenario. When midair collision fatality risk is estimated, a model of aircraft collisions based on the density of civil flight in different regions over Chinese airspace is proposed. In the model, a relative collision area and flying speed between UAVs and manned aircraft are constructed to calculate expected frequency of fatalities for each province correspondingly. Truthful data with different numbers of UAVs is incorporated in the model with the expected number of fatalities after a collision is included. Experimental simulations are made to evaluate the ground impacts and midair collisions when UAVs operate in the NAS.

Section: Model Analysismentioning

confidence: 99%

“…Unmanned aerial vehicles (UAVs) are a kind of aircraft without pilots onboard but can be remotely controlled or can fly autonomously based on preprogrammed flight plans [1,2], which is a viable and operational technology in the future [3,4]. UAVs are one of the fastest growing aviation sectors.…”

Section: Introductionmentioning

confidence: 99%

Safety Assessment and Risk Estimation for Unmanned Aerial Vehicles Operating in National Airspace System

Liu

Journal of Advanced Transportation

et al. 2018

“…UAV systems have been studied as a whole or in subsystems (for example, tilt control). The challenge of these vehicles is that they are nonlinear, subacted, and multivariable systems, subject to unpredictable disturbances such as the interaction of the rotor flow with the quadrotor chassis or atmospheric turbulence [26][27][28][29][30][31][32][33][34][35][36][37][38][39], so that classic control (linear and invariant over time) has limited results where instabilities can occur when the system moves away from equilibrium.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Based Tuning of Backstepping Controller for a Quadrotor-Type Unmanned Aerial Vehicle

et al. 2020

Backstepping is a control technique based on Lyapunov’s theory that has been successfully implemented in the control of motors and robots by several nonlinear methods. However, there are no standardized methods for tuning control gains (unlike the PIDs). This paper shows the tuning gains of the backstepping controller, using Genetic Algorithms (GA), for an Unmanned Aerial Vehicle (UAV), quadrotor type, designed for autonomous trajectory tracking. First, a dynamic model of the vehicle is obtained through the Newton‒Euler methodology. Then, the control law is obtained, and self-tuning is performed, through which we can obtain suitable values of the gains in order to achieve the design requirements. In this work, the establishment time and maximum impulse are considered as such. The tuning and simulations of the system response were performed using the MATLAB-Simulink environment, obtaining as a result the compliance of the design parameters and the correct tracking of different trajectories. The results show that self-tuning by means of genetic algorithms satisfactorily adjusts for the gains of a backstepping controller applied to a quadrotor and allows for the implementation of a control system that responds appropriately to errors of different magnitude.

Mehran Univ. res. j. eng. technol.

“…al. [12] designed a robust Self-Tuning PID controller based on fuzzy logic to adjust the external disturbances produced by the changes in the payload weight during the flight period.…”

Section: Introductionmentioning

confidence: 99%

Controller Design for the Rotational Dynamics of a Quadcopter

Rashdi

Ali

Larik

et al. 2019

Researchers have shown their interests in establishing miniature flying robots to be utilized for, both, commercial and research applications. This is due to that fact that there appears to be a huge advancement in miniature actuators and sensors which depend on the MEMS (Micro Electro-Mechanical Systems) NEMS (Nano-Electro Mechanical Systems). This research underlines a detailed mathematical model and controller design for a quadcopter. The nonlinear dynamic model of the quadcopter is derived from the Newton-Euler method and Euler Lagrange method. The motion of a quadcopter can be classified into two subsystems: a rotational subsystem (attitude and heading) and translational subsystem (altitude and x and y motion). The rotational system is fully actuated whereas translational subsystem is under actuated. However, a quadcopter is 6 DOF (Degrees of Freedom) under actuated system. The controller design of a quadcopter is difficult due to its complex and highly nonlinear mathematical model where the state variables are strongly coupled and contain under actuated property. Nonlinear controller such as SMC (Sliding Mode Controller) is used to control altitude, yaw, pitch, and roll angles.Simulation results show that the robustness of the SMC design gives a better way to design a controller with autonomous stability flight with good tracking performance and improved accuracy without any chattering effect. The system states are following the desired trajectory as expected.