Tracking Control of Quadrotor Using Static Output Feedback with Modified Command-Generator Tracker

Agustinah, Trihastuti; Isdaryani, Feni; Nuh, Mohammad

doi:10.15866/ireaco.v9i4.9431

Cited by 6 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section presents a quadcopter dynamics model like those previously presented in [17]. Quanser Qdrone is the type of quadcopter used in this study (see Fig.…”

Section: A Quadcopter Dynamicsmentioning

confidence: 99%

Cooperative Position-based Formation-pursuit of Moving Targets by Multi-UAVs with Collision Avoidance

Nurjanah

Agustinah²,

Fuad³

2022

JAREE

View full text Add to dashboard Cite

The paper focuses on the issue of capturing a moving target for multiple unmanned aerial vehicles (UAVs). The problem involves a group of UAVs to create a formation-pursuit in the encirclement of a moving target. Dynamic task allocation algorithm is used in 3D dynamic environments to efficiently allocate the target to several existing UAVs. Target information is disseminated to neighbor UAVs by the temporary leader of UAVs. For the formation-pursuit using a position-based strategy, destination points to create formation are made at the sphere coordinates around a moving target. Then the destination points are tracked using a fuzzy state feedback controller. Optimized artificial potential field (APF) algorithm is used to avoid collisions with targets, other UAVs, and static obstacles. Each UAV can choose the optimal trajectory to avoid obstacles and reset the formation after passing them. The simulation results show that multi-UAVs successfully surrounded and formed formation-pursuit of a moving target without colliding with the closest Euclidean distance between UAVs of 1.32957 m. UAVs with a target is 1.94359 m, and UAVs with static obstacles within a range of 1.60632 m.Keywords—formation-pursuit, multi-UAVs, obstacle avoidance, task allocation, tracking control.

show abstract

“…This section presents a quadcopter dynamics model like those previously presented in [17]. Quanser Qdrone is the type of quadcopter used in this study (see Fig.…”

Section: A Quadcopter Dynamicsmentioning

confidence: 99%

Cooperative Position-based Formation-pursuit of Moving Targets by Multi-UAVs with Collision Avoidance

Nurjanah

Agustinah²,

Fuad³

2022

JAREE

View full text Add to dashboard Cite

show abstract

“…The drone has 40 cm × 40 cm ×15 cm dimensions and is equipped with propeller protectors. The quadcopter's system model [12] is represented in (1), where 𝑋, 𝑌, 𝑍 is the quadcopter's position while 𝑝, 𝑞, 𝑟 is the roll, pitch and yaw speed. The parameters used in the drone are shown in Table 1 depending on the type of drone.…”

Section: Quadcopter Systemmentioning

confidence: 99%

“…∆𝐸 𝑖,𝑗 = ∆𝐸 𝑝 + ∆𝐸 𝑘 (12) where ∆𝐸 𝑝 is the potential energy and ∆𝐸 𝑘 is the kinetic energy of the quadcopter.…”

Section: Energymentioning

confidence: 99%

Obstacle Avoidance in Quadcopter Navigation Using Modified Local Mean K-Nearest Centroid Neighbor Method

Prasetyo,

Agustinah

2022

kursor

View full text Add to dashboard Cite

Quadcopter is a type of Unmanned Aerial Vehicle (UAV) technology, characterized by simple mechanical structure, ease of flying and good maneuvering. In its usage, the quadcopter is required to evade obstacles in its path. Thus, an obstacle avoidance system in a 3D space with both static and dynamic obstacles is. Avoidance direction is determined by considering nearest distance based on the dimensions of the obstacle. Due to limited battery capacity, the quadcopter also needs to consider energy efficiency in obstacle avoidance. The obstacleâ€™s properties and movement direction are also needed in considering the correct avoidance direction. Using a modified Local Mean K-Nearest Centroid Neighbor (LMKNCN) algorithm results in a 97.5% accuracy for avoidance direction decision. The learning process between training data and testing data yielded a computation duration of 0.142341 seconds. The simulations showed that the quadcopter is able to avoid static and dynamic obstacles to reach its destination without collisions.

show abstract

“…The control performance or the performance index is measured using a quadratic cost function, which consists of the state and control input of the system. After the performance index is expressed, the optimal state feedback control gain is obtained by solving the state-dependent The LQR control strategy has been successfully implemented in a complex system such as tracking control of 2 DoF laboratory helicopter [1], double inverted pendulum [4,21], and also for tracking control of quadrotor [22]. The main reason the LQR is successfully implemented is the inherent robustness and stability properties, such as a gain margin of at least ( ) and a phase margin of ( ) degree.…”

Section: Linear Quadratic Regulatormentioning

confidence: 99%

“…The composition of the element in this weighting matrix has a significant influence to obtain the optimal performance of LQR [23]. Several works [1,4,21,22] have been selected for the diagonal form of weighting matrices, which make the performance index only as a weighted integral square error of the state and the control input. Conventionally, the weighted matrices of LQR tuned manually [4,21]; thus, it does not have the optimal performance.…”

Section: Linear Quadratic Regulatormentioning

confidence: 99%

Signature PSO: A novel inertia weight adjustment using fuzzy signature for LQR tuning

et al. 2021

View full text Add to dashboard Cite

Particle swarm optimization (PSO) is an optimization algorithm that is simple and reliable to complete optimization. The balance between exploration and exploitation of PSO searching characteristics is maintained by inertia weight. Since this parameter has been introduced, there have been several different strategies to determine the inertia weight during a train of the run. This paper describes the method of adjusting the inertia weights using fuzzy signatures called signature PSO. Some parameters were used as a fuzzy signature variable to represent the particle situation in a run. The implementation to solve the tuning problem of linear quadratic regulator (LQR) control parameters is also presented in this paper. Another weight adjustment strategy is also used as a comparison in performance evaluation using an integral time absolute error (ITAE). Experimental results show that signature PSO was able to give a good approximation to the optimum control parameters of LQR in this case.

show abstract

Tracking Control of Quadrotor Using Static Output Feedback with Modified Command-Generator Tracker

Cited by 6 publications

References 0 publications

Cooperative Position-based Formation-pursuit of Moving Targets by Multi-UAVs with Collision Avoidance

Cooperative Position-based Formation-pursuit of Moving Targets by Multi-UAVs with Collision Avoidance

Obstacle Avoidance in Quadcopter Navigation Using Modified Local Mean K-Nearest Centroid Neighbor Method

Signature PSO: A novel inertia weight adjustment using fuzzy signature for LQR tuning

Contact Info

Product

Resources

About