Attitude and Altitude Control of a Quadcopter Using Neural Network Based Direct Inverse Control Scheme

2017 ◽  
Vol 23 (5) ◽  
pp. 4060-4064 ◽  
Author(s):  
M. Ary Heryanto ◽  
Herwin Suprijono ◽  
Bhakti Yudho Suprapto ◽  
Benyamin Kusumoputro
Keyword(s):  
Neural Network ◽  
Inverse Control ◽  
Control Scheme ◽  
Altitude Control

Neural Network-Based Speed Control of A Two-Mass-Model System

1999 ◽  
Vol 3 (5) ◽  
pp. 427-430 ◽  
Author(s):  
Rached Dhaouadi ◽  
◽  
Khaled Nouri
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Mass Model ◽  
Neural Network Learning ◽  
Inverse Control ◽  
Network Learning ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Neural Network Structure

We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.

PID Adaptive Control in the Application of the Induction Motor System Based on the RBF Neural Network Inverse

2014 ◽  
Vol 556-562 ◽  
pp. 2393-2396
Author(s):  
Zhang Li ◽  
Yu Bo
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Fault Tolerant ◽  
Rbf Neural Network ◽  
External Disturbance ◽  
External Disturbances ◽  
Inverse Control ◽  
Control Scheme ◽  
Adaptive Control Algorithm ◽  
Self Learning

In view of the nonlinear mapping ability of artificial neural network, the ability of self-learning and adaptive uncertainty system dynamic characteristic, fault tolerant and generalization ability and parallel processing ability, etc., the article puts forward a PID adaptive control algorithm based on neural network inverse as well, introducing RBF neural network to the inverse control, and operating PID integration. In a sudden external disturbance and model parameter change, the control scheme can significantly reduce resistance perturbation influence on speed, and strong robustness on parameter variations and external disturbances of the system.

Inverse Control of Cable-driven Parallel Mechanism Using Type-2 Fuzzy Neural Network

2010 ◽  
Vol 36 (3) ◽  
pp. 459-464 ◽  
Author(s):  
Cheng-Dong LI ◽  
Jian-Qiang YI ◽  
Yi YU ◽  
Dong-Bin ZHAO
Keyword(s):  
Neural Network ◽  
Parallel Mechanism ◽  
Fuzzy Neural Network ◽  
Inverse Control ◽  
Fuzzy Neural

scholarly journals Robust Quadrotor Control through Reinforcement Learning with Disturbance Compensation

2021 ◽  
Vol 11 (7) ◽  
pp. 3257
Author(s):  
Chen-Huan Pi ◽  
Wei-Yuan Ye ◽  
Stone Cheng
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Control Strategy ◽  
External Disturbance ◽  
Control Agent ◽  
Network Control ◽  
Outdoor Environment ◽  
Disturbance Compensation ◽  
Tracking Accuracy ◽  
Control Scheme

In this paper, a novel control strategy is presented for reinforcement learning with disturbance compensation to solve the problem of quadrotor positioning under external disturbance. The proposed control scheme applies a trained neural-network-based reinforcement learning agent to control the quadrotor, and its output is directly mapped to four actuators in an end-to-end manner. The proposed control scheme constructs a disturbance observer to estimate the external forces exerted on the three axes of the quadrotor, such as wind gusts in an outdoor environment. By introducing an interference compensator into the neural network control agent, the tracking accuracy and robustness were significantly increased in indoor and outdoor experiments. The experimental results indicate that the proposed control strategy is highly robust to external disturbances. In the experiments, compensation improved control accuracy and reduced positioning error by 75%. To the best of our knowledge, this study is the first to achieve quadrotor positioning control through low-level reinforcement learning by using a global positioning system in an outdoor environment.

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