scholarly journals Neural Network Control Design for an Unmanned Aerial Vehicle with a Suspended Payload

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 931 ◽  
Author(s):  
Cai Luo ◽  
Zhenpeng Du ◽  
Leijian Yu
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Sliding Mode ◽  
Network Control ◽  
Outdoor Environment ◽  
Test Results ◽  
Cluttered Environments ◽  
Aerial Vehicle ◽  
Backstepping Sliding Mode Control

Unmanned aerial vehicles (UAVs) demonstrate excellent manoeuvrability in cluttered environments, which makes them a suitable platform as a data collection and parcel delivering system. In this work, the attitude and position control challenges for a drone with a package connected by a wire is analysed. During the delivering task, it is very difficult to eliminate the external unpredictable disturbances. A robust neural network-based backstepping sliding mode control method is designed, which is capable of monitoring the drone’s flight path and desired attitude with a suspended cable attached. The convergence of the position and attitude errors together with the Lyapunov function are employed to attest to the robustness of the nonlinear transportation platform. The proposed control system is tested with a simulation and in an outdoor environment. The simulation and open field test results for the UAV transportation platform verify the controllers’ reliability.

scholarly journals Backstepping Sliding-Mode Control of Piezoelectric Single-Piston Pump-Controlled Actuator

Actuators ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 154
Author(s):  
Bin Wang ◽  
Pengda Ren ◽  
Xinhao Huang
Keyword(s):  
Sliding Mode Control ◽  
Position Control ◽  
Control Method ◽  
Sliding Mode ◽  
Piezoelectric Pump ◽  
Mode Control ◽  
Control Precision ◽  
Backstepping Sliding Mode Control ◽  
Actuator System ◽  
The Mathematical Model

A piston piezoelectric (PZT) pump has many advantages for the use of light actuators. How to deal with the contradiction between the intermittent oil supplying and position control precision is essential when designing the controller. In order to accurately control the output of the actuator, a backstepping sliding-mode control method based on the Lyapunov function is introduced, and the controller is designed on the basis of establishing the mathematical model of the system. The simulation results show that, compared with fuzzy PID and ordinary sliding-mode control, backstepping sliding-mode control has a stronger anti-jamming ability and tracking performance, and improves the control accuracy and stability of the piezoelectric pump-controlled actuator system.

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.

Research on Neural Network PID Control Algorithm for a Quadrotor

2015 ◽  
Vol 719-720 ◽  
pp. 346-351 ◽  
Author(s):  
Wei Nan Gao ◽  
Jia Lu Fan ◽  
Yan Nong Li
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Simple Structure ◽  
Position Control ◽  
Network Control ◽  
Pid Algorithm ◽  
Control Scheme ◽  
Challenging Tasks ◽  
Space Function ◽  
Aerial Vehicle

Quadrotor is a kind of popular unmanned aerial vehicle which obtains prime advantages in simple structure, vertically taking off and landing and hovering ability; hence it possesses wide application prospects in reconnaissance and rescue, geological exploration and video surveillance. However, attitude and position control of the quadrotor are challenging tasks because it is an under-actuated system with strong nonlinear, coupling and model uncertainty characteristics. In this paper, the dynamics model and the state space function of the micro-quadrotor are firstly established. Then, a cascade control scheme is proposed to decouple the control system and a multivariate RBF(Radial Basis Function) neural network control PID algorithm is proposed to realize robust control of the quadrotor. This algorithm is not only characterized by simple structure and easy implementation, but also capable of self-adaption and online learning. Simulation results show that the proposed control algorithm performs well in tracking and under disturbances and model uncertainties.

Position control for permanent magnet synchronous motor based on neural network and terminal sliding mode control

2020 ◽  
Vol 42 (9) ◽  
pp. 1632-1640
Author(s):  
Wenwu Zhu ◽  
Dongbo Chen ◽  
Haibo Du ◽  
Xiangyu Wang
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Finite Time ◽  
Position Control ◽  
Control Method ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Terminal Sliding Mode

A finite-time control strategy is proposed to solve the problem of position tracking control for a permanent magnet synchronous motor servo system. It can guarantee that the motor’s desired position can be tracked in a finite time. Firstly, for the d-axis voltage, a first-order finite-time controller is designed based on the vector control principle. Then, for the q-axis voltage, based on a radial basis function (RBF) neural network, an integral high-order terminal sliding mode controller is designed. Theoretical analysis shows that under the proposed controller, the desired position can be tracked by the motor position in a finite time. Simulation results are given to show that the proposed control method has a strong anti-disturbance ability and a fast convergence performance.

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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