Design of a Quadrotor Roll Controller Using System Identification to Improve Empirical Results

Batmaz, Anil Ufuk; Elbir, Ovunc; Kasnakoğlu, Coşku

doi:10.7763/ijmmm.2013.v1.75

Cited by 4 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All simulations were carried out on an Intel Core i7 3.4GHz, 8 GB DDR3 RAM, 500 GB Hard Disk Drive, Windows 7 64bits, Matlab R R-2014b and represent a time of 300 s. A sampling frequency of f s = 30 Hz was selected, within the feasible sample time limit for a small quadrotor with a diameter around 50 cm [37].…”

Section: Simulations and Discussionmentioning

confidence: 99%

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Oliveira

Boaventura‐Cunha

et al. 2020

Robotics

View full text Add to dashboard Cite

The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

show abstract

Section: Simulations and Discussionmentioning

confidence: 99%

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Oliveira

Boaventura‐Cunha

et al. 2020

Robotics

View full text Add to dashboard Cite

show abstract

“…We are also investigating the possibility of applying AMPC methods to other test platforms developed by our research group. These include improving the performance of flight stabilizer systems of fixed-wing aircraft [48][49][50] and rotorcraft, 51 building better software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testbeds, 52,53 investigating novel approaches to provide robustness against parametric uncertainties, 54 and constructing specialized autopilots for control loss scenarios. 55,56…”

Section: Discussionmentioning

confidence: 99%

Adaptive Model Predictive Control of a Two-wheeled Robot Manipulator with Varying Mass

Önkol

Kasnakoğlu

2018

Measurement and Control

View full text Add to dashboard Cite

This paper presents the adaptive model predictive control approach for a two-wheeled robot manipulator with varying mass. The mass variation corresponds to the robot picking and placing objects or loads from one place to another. A linear parameter varying model of the system is derived consisting of local linear models of the system at different values of the varying parameter. An adaptive model predictive control controller is designed to control the fast-varying center of gravity angle in the inner loop. The reference for the inner loop is generated by a slower outer loop controlling the linear position using a linear quadratic Gaussian regulator. This adaptive model predictive control/linear quadratic Gaussian control system is simulated on the nonlinear model of the robot, and the closed-loop performance of the proposed scheme is compared with a system having inner/outer loop controllers as proportional integral derivative/ proportional integral derivative, feedback linearization/linear quadratic Gaussian, and linear quadratic Gaussian/linear quadratic Gaussian. It is seen that adaptive model predictive control shows mostly superior and otherwise very good performance when compared to these benchmarks in terms of reference tracking and robustness to mass parameter variations.

show abstract

“…In spite of being relatively old fashion ones, PID-controllers still widely used both in classical and modern control engineering. Past decades modern robot applications like ground and air robots due to its privileges the PID-controller based platforms gained special attention, which are thoroughly examined in [4,5,6,7,8,9,11,12,15,16,17]. The affordability, the price and, finally, experiences captured in several applications of modern robotics, allowed to keep this technique at top level, and development of PIDcontrollers still is ongoing [21,22].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Pid Controller Based Autopilot Design and System Modelling for Small Unmanned Aerial Vehicle

Szabolcsi¹

2018

Review of the Air Force Academy

View full text Add to dashboard Cite

The Proportional-Integral-Derivative (PID) controller is often considered for an old fashion technique. However, from the early 1910's it has been deeply penetrated in many control applications. The impressive history of PID controllers and experiences gained from their wide-broad control applications assign to emphasize that it is still a promising solution in a given set of control applications. Most of the commercial autopilots available for use unmanned aerial vehicle (UAV) aboard must be hand-tuned when it is installed in the UAV. This requires a welltrained, experienced UAV operator, or, ground maintenance staff able to set the PID controller parameters proper to given UAV flight mission and proper for the flight conditions during the entire flight time. The early commercial-off-the-shelves (COTS) UAV autopilots used the PID controller to steer the UAV. Recently, the PID controller is not forgotten, and, there is an intensive spread and deep penetration of the PID controllers applied for UAV automatic flight control. The purpose of the author is to highlight the control problem of the PID controller tuning, and, to introduce a new enhanced PI controller. The MATLAB scripts are created by the author supporting controller parameters' tuning activity of the UAV operators, or of the ground maintenance staff to minimize time required, and to maximize readiness of the UAV for flights.

show abstract

Design of a Quadrotor Roll Controller Using System Identification to Improve Empirical Results

Cited by 4 publications

References 11 publications

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Adaptive Model Predictive Control of a Two-wheeled Robot Manipulator with Varying Mass

Optimal Pid Controller Based Autopilot Design and System Modelling for Small Unmanned Aerial Vehicle

Contact Info

Product

Resources

About