Trajectory Control Method Of UAV In Autonomous Flight Mode Using Neural Network MELM Algorithm

2020 ◽  
Author(s):  
Oleksii Fesenko ◽  
Robert Bieliakov ◽  
Hrygorii Radzivilov ◽  
Volodymyr Hulii ◽  
Oleh Kovalchuk ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Trajectory Control ◽  
Autonomous Flight ◽  
Flight Mode

Hybrid intelligent adaptive controller for tiltrotor UAV

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jafar Tavoosi
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Hybrid Control ◽  
Adaptive Controller ◽  
Content Type ◽  
Flight Mode ◽  
Horizontal Flight ◽  
Aerial Vehicle ◽  
Model Reference Adaptive

PurposeIn this paper, an innovative hybrid intelligent position control method for vertical take-off and landing (VTOL) tiltrotor unmanned aerial vehicle (UAV) is proposed. So the more accurate the reference position signals tracking, the proposed control system will be better.Design/methodology/approachIn the proposed method, for the vertical flight mode, first the model reference adaptive controller (MRAC) operates and for the horizontal flight, the model predictive control (MPC) will operate. Since the linear model is used for both of these controllers and naturally has an error compared to the real nonlinear model, a neural network is used to compensate for them. So the main novelties of this paper are a new hybrid control design (MRAC & MPC) and a neural network-based compensator for tiltrotor UAV.FindingsThe proper performance of the proposed control method in the simulation results is clear. Also the results showed that the role of compensator is very important and necessary, especially in extreme speed wind conditions and uncertain parameters.Originality/valueNovel hybrid control method. 10;-New method to use neural network as compensator in an UAV.

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

Hexacopter trajectory control using a neural network

2013 ◽  
Author(s):  
V. Artale ◽  
M. Collotta ◽  
G. Pau ◽  
A. Ricciardello
Keyword(s):  
Neural Network ◽  
Trajectory Control

Active Noise Hybrid Feedforward/Feedback Control Using Neural Network Compensation*

2001 ◽  
Vol 124 (1) ◽  
pp. 100-104 ◽  
Author(s):  
Zhang Qizhi ◽  
Jia Yongle
Keyword(s):  
Neural Network ◽  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Equivalent Model ◽  
Gradient Descent Method ◽  
Nonlinear Simulation ◽  
Control Approach ◽  
Active Noise ◽  
Nonlinear Noise

The nonlinear active noise control (ANC) is studied. The nonlinear ANC system is approximated by an equivalent model composed of a simple linear sub-model plus a nonlinear sub-model. Feedforward neural networks are selected to approximate the nonlinear sub-model. An adaptive active nonlinear noise control approach using a neural network enhancement is derived, and a simplified neural network control approach is proposed. The feedforward compensation and output error feedback technology are utilized in the controller designing. The on-line learning algorithm based on the error gradient descent method is proposed, and local stability of closed loop system is proved based on the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise control method based on neural network compensation is very effective to the nonlinear noise control, and the convergence of the NNEH control is superior to that of the NN control.

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