Neural network fuzzy control for enhancing the stability performance of quad-rotor helicopter

Abstract

Recently, the quad-rotor helicopter has gained increasing attention owing to its very good flexibility, its ability to execute various flight missions even in harsh environments. The quad-rotor helicopter can implement different fight attitudes, which is attributed to the effective control of the motor speed about four propellers. In order to make the quad-rotor helicopter can better finish flight mission, the performance of flight stability then becomes particularly important. A neural network fuzzy control algorithm is proposed in this paper so as to guarantee the stability performance of the quad-rotor helicopter. The proposed algorithm is based on the neural network, which keeps the self-organization and self-learning ability, besides this, it utilizes the strong impression ability of constitutive knowledge as to the fuzzy logic. The proposed control scheme aims to implement good abilities such as describing qualitative knowledge, strong learning mechanism and direct processing about quantitative data of the quad-rotor helicopter. In the practical flight process of the quad-rotor helicopter, while the deviation of position and attitude information become larger, fuzzy control is adopted so as to shorten the overshoot and adjustment time. On the other hand, if the deviation of position and attitude become relatively smaller, neural network PID control will be used so as to reduce the error. Experimental results show that the proposed neural network fuzzy control algorithm exhibits good performance in the flight process of the quad-rotor helicopter.

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

“…The simulation results are shown in Figures 11 and 12. It can be seen from the figure that the three control algorithms can effectively reduce the position error Ex, Ey and the distance error d. The robots controlled by the literature 11,12 showed significant fluctuations in the left and right direction, and slight fluctuations in the up and down direction. However, the proposed algorithm to control the position error and the distance error of the robot target point are the smallest, which can show that the robot controlled by the algorithm is relatively stable and smooth, and the effectiveness of the proposed algorithm can be further verified.…”

“…On the simulation platform, the trajectory curve of the basketball robot moving from the current pose S to the given target point G is simulated. Figures 8, 9, and 10, respectively, show the trajectory diagram of controlling the basketball robot to reach the target point by using the literature 11,12 and the algorithm of the present invention.…”

Application of artificial neural networks for prediction of sinter quality based on process parameters control

“…ANNs are massively interconnected parallel network of simple elements, linked by weighted connections (Xiao et al, 2011). One of the advantages of using the neural network approach is that a model can be constructed very easily based on the given input and output and trained to accurately predict dynamics sinter process (Wang et al, 2018). This technique is especially valuable in processes where a complete understanding on the physical mechanisms is very difficult or even impossible to acquire, as in the case of the sintering process.…”

Smart backstepping control using revised recurrent fuzzy neural network and revised ant colony optimization for linear permanent magnet synchronous motor drive system

“…The training procedure with large number of iterations was the cause of slower convergence and higher computational complexity. Recently, the notion of combining fuzzy logic with neural network has been become a popular study field (Liao et al, 2018; Lin, 2004; Liu et al, 2019; Wang et al, 2018). The fuzzy neural network possesses some merits of both neural network and fuzzy system such as it integrates the property of fuzzy reasonings in processing uncertainities and the ability of neural networks in learning phases.…”

“…Lin (2004) proposed the adaptive fuzzy neural network control system with on-line training method for controlling synchronous reluctance motor drive. Wang et al (2018) proposed the neural network fuzzy controller to implement good abilities such as describing qualitative knowledge, strong learning mechanism and direct processing about quantitative data of the quad-rotor helicopter. Liao et al (2018) proposed the distributed adaptive dynamic surface formation control with interval type-2 fuzzy neural networks for uncertain multiple quadrotor systems.…”