Optimal path tracking control of a quadrotor UAV

Suicmez, Emre Can; Kutay, Ali Türker

doi:10.1109/icuas.2014.6842246

Cited by 40 publications

(20 citation statements)

References 6 publications

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“…Myriad advanced methods, considered quadrotor attitude and altitude control design, have been proposed in literature such as backstepping (Kobilarov (2013(Kobilarov ( ), huo et al (2014, Jasim and Gu (2015)), feedback linearization (Voos (2009), Choi and Ahn (2015)), optimal control (Navabi and Mirzaei (2016)), Suicmez and Kutay (2014)), and robust control (Xiong and Zheng (2014)). This list is, of course, far from being exhaustive.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimal Adaptive Trajectory Tracking Control of Quadrotor Helicopter

Navabi

Mirzaei

2017

Lat. Am. j. solids struct.

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This paper focuses on robust optimal adaptive control strategy to deal with tracking problem of a quadrotor unmanned aerial vehicle (UAV) in presence of parametric uncertainties, actuator amplitude constraints, and unknown time-varying external disturbances. First, Lyapunov-based indirect adaptive controller optimized by particle swarm optimization (PSO) is developed for multi-input multi-output (MIMO) nonlinear quadrotor to prevent input constraints violation, and then disturbance observer-based control (DOBC) technique is aggregated with the control system to attenuate the effects of disturbance generated by an exogenous system. The performance of synthesis control method is evaluated by a new performance index function in time-domain, and the stability analysis is carried out using Lyapunov theory. Finally, illustrative numerical simulations are conducted to demonstrate the effectiveness of the presented approach in altitude and attitude tracking under several conditions, including large time-varying uncertainty, exogenous disturbance, and control input constraints.

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Section: Introductionmentioning

confidence: 99%

Robust Optimal Adaptive Trajectory Tracking Control of Quadrotor Helicopter

Navabi

Mirzaei

2017

Lat. Am. j. solids struct.

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“…where C is the output matrix and (k) is the ouput vector. Several different control techniques may be used to control the system in Equations (5) and (6). Among them is the MPC, which is discussed in the next section.…”

Section: Vehicle Modelmentioning

confidence: 99%

“…In particular, the weights of the objective function are selected in order to meet the desired performance of the system. This selection is typically done through trial and error 6 or by taking into consideration the dynamics of the optimization problem. 5 However, when the problem becomes more complex, the number of parameters to be selected increases and their selection may be counterintuitive.…”

Section: Introductionmentioning

confidence: 99%

Adaptive predictive control of a differential drive robot tuned with reinforcement learning

Jardine

Kogan

Givigi

et al. 2018

Adaptive Control & Signal

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One of the most important steps in designing a model predictive control strategy is selecting appropriate parameters for the relative weights of the objective function. Typically, these are selected through trial and error to meet the desired performance. In this paper, a reinforcement learning technique called learning automata is used to select appropriate parameters for the controller of a differential drive robot through a simulation process. Results of the simulation show that the parameters always converge, although to different values. A controller chosen by the learning process is then ported to a real platform. The selected controller is shown to control the robot better than a standard model predictive control. KEYWORDS feedback linearization, machine learning, model predictive control, reinforcement learning Abbreviations: FALA, finite action-set learning automata; LA, learning automata; MPC, model predictive control; RL, reinforcement learning.410

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“…Considering harsh scenarios characterized by limited area [2], the presence of obstacles and possible dynamic constraints, rotary-wing UAVs have been shown to give the best performance [3]. This is why we consider these specific types in this paper.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs)

Salamat

Tonello

2017

Aerospace

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Abstract:The aim of this paper is to provide a realistic stochastic trajectory generation method for unmanned aerial vehicles that offers a tool for the emulation of trajectories in typical flight scenarios. Three scenarios are defined in this paper. The trajectories for these scenarios are implemented with quintic B-splines that grant smoothness in the second-order derivatives of Euler angles and accelerations. In order to tune the parameters of the quintic B-spline in the search space, a multi-objective optimization method called particle swarm optimization (PSO) is used. The proposed technique satisfies the constraints imposed by the configuration of the unmanned aerial vehicle (UAV). Further particular constraints can be introduced such as: obstacle avoidance, speed limitation, and actuator torque limitations due to the practical feasibility of the trajectories. Finally, the standard rapidly-exploring random tree (RRT*) algorithm, the standard (A*) algorithm and the genetic algorithm (GA) are simulated to make a comparison with the proposed algorithm in terms of execution time and effectiveness in finding the minimum length trajectory.

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Optimal path tracking control of a quadrotor UAV

Cited by 40 publications

References 6 publications

Robust Optimal Adaptive Trajectory Tracking Control of Quadrotor Helicopter

Robust Optimal Adaptive Trajectory Tracking Control of Quadrotor Helicopter

Adaptive predictive control of a differential drive robot tuned with reinforcement learning

Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs)

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