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 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.

Symmetrical valve controlled asymmetrical cylinder based on wavelet neural network

2017 ◽  
Vol 34 (7) ◽  
pp. 2154-2167 ◽  
Author(s):  
Haitao Qi ◽  
Zilong Liu ◽  
Yan Lang
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Dynamic Response ◽  
Position Control ◽  
Reference Model ◽  
Wavelet Neural Network ◽  
Content Type ◽  
Hydraulic Servo ◽  
Model Reference Adaptive

Purpose The symmetrical valve is usually used in the hydraulic servo control system to control the asymmetrical cylinder, but this system’s structure involves asymmetry, and so its dynamic characteristics are asymmetrical, which causes issues in the control system of symmetric response. The purpose of this paper is to achieve the aim of symmetric control. Design/methodology/approach In this paper, the authors proposed a method that combined wavelet neural network (WNN) and model reference adaptive control. The reference model determined the dynamic response that the system was expected to achieve, and the WNN adaptive control made the system follow the reference model to achieve the purpose of symmetric control. Findings The experimental results show that the method can achieve a more accurate symmetric control and position control compared with the solutions via the classical PID control. Originality/value The proposed combination of the WNN and the reference model can effectively compensate for the asymmetry of dynamic response of the asymmetric cylinder in forward and return directions, which can be extended to deal with other classes of applications.

Simulation of NN-Based MRAC for PMSM in AC Drive Mining Truck

2011 ◽  
Vol 383-390 ◽  
pp. 7251-7257
Author(s):  
Yong Li ◽  
Xiao Long Zhao ◽  
Fei Ma ◽  
Yu Ting Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Control Method ◽  
Permanent Magnet Synchronous Motor ◽  
Back Propagation ◽  
Adaptive Controller ◽  
Motor Speed ◽  
Ac Drive ◽  
Static Performance ◽  
Model Reference Adaptive

Permanent magnet synchronous motor (PMSM) is a multi-variables, non-linear and strong coupling system. A model reference adaptive controller (MRAC) for PMSM based on back propagation (BP) neural network (NN) is proposed to solve the shortcoming of traditional proportion integration (PI) control method, which is widely used in linear system. According to the proposed method, the simulation model is established and simulated with Simulink. The adaptive control of motor speed is achieved with the training of BP neural network. Simulation results show that the system has long response time, small overshoot and high static performance.

Integrating human experience in deep reinforcement learning for multi-UAV collision detection and avoidance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guanzheng Wang ◽  
Yinbo Xu ◽  
Zhihong Liu ◽  
Xin Xu ◽  
Xiangke Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Collision Detection ◽  
Large Scale ◽  
Control Method ◽  
Hybrid Control ◽  
Human Experience ◽  
Content Type ◽  
Aerial Vehicle ◽  
Speed Up ◽  
Multi Uav

Purpose This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training. To improve the applicability and reliability of the DRL-based approach in multi-UAV control problems. Design/methodology/approach In this paper, a fully distributed collision detection and avoidance approach for multi-UAV based on DRL is proposed. A method that integrates human experience into policy training via a human experience-based adviser is proposed. The authors propose a hybrid control method which combines the learning-based policy with traditional model-based control. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the approach. Findings A fully distributed multi-UAV collision detection and avoidance method based on DRL is realized. The reward curve shows that the training process when integrating human experience is significantly accelerated and the mean episode reward is higher than the pure DRL method. The experimental results show that the DRL method with human experience integration has a significant improvement than the pure DRL method for multi-UAV collision detection and avoidance. Moreover, the safer flight brought by the hybrid control method has also been validated. Originality/value The fully distributed architecture is suitable for large-scale unmanned aerial vehicle (UAV) swarms and real applications. The DRL method with human experience integration has significantly accelerated the training compared to the pure DRL method. The proposed hybrid control strategy makes up for the shortcomings of two-dimensional light detection and ranging and other puzzles in applications.

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

Smooth transition adaptive hybrid impedance control for connector assembly

2018 ◽  
Vol 45 (2) ◽  
pp. 287-299 ◽  
Author(s):  
Jun Wu ◽  
Fenglei Ni ◽  
Yuanfei Zhang ◽  
Shaowei Fan ◽  
Qi Zhang ◽  
...  
Keyword(s):  
Position Control ◽  
Impedance Control ◽  
Estimation Error ◽  
Adaptive Controller ◽  
Smooth Transition ◽  
Reference Trajectory ◽  
Content Type ◽  
Dynamic Uncertainty ◽  
Force Tracking ◽  
Potential Applications

Purpose This paper aims to present a smooth transition adaptive hybrid impedance control for compliant connector assembly. Design/methodology/approach The dynamics of the manipulator is firstly presented with linear property. The controller used in connector assembly is inspired by human operation habits in similar tasks. The hybrid impedance control is adopted to apply force in the assembly direction and provide compliance in rest directions. The reference trajectory is implemented with an adaptive controller. Event-based switching strategy is conducted for a smooth transition from unconstrained to constrained space. Findings The method can ensure both ideal compliance behaviour with dynamic uncertainty and a smooth transition from unconstrained to constrained space. Also, the method can ensure compliant connector assembly with a good tolerance to the target estimation error. Practical implications The method can be applied in the connector assembly by “pushing” operation. The controller devotes efforts on force tracking and smooth transition, having potential applications in contact tasks in delicate environment. Originality/value As far as the authors know, the paper is original in providing a uniform controller for improving force and position control performance in both unconstrained and constrained space with dynamic uncertainty. The proposed controller can ensure a smooth transition by only adjusting parameters.

Backstepping-based model reference adaptive controller for a multi-axial system in the presence of external disturbance and saturated input

2017 ◽  
Vol 40 (3) ◽  
pp. 776-784 ◽  
Author(s):  
Van Tu Duong ◽  
Huy Hung Nguyen ◽  
Jae Hoon Jeong ◽  
Hak Kyeong Kim ◽  
Sang Bong Kim
Keyword(s):  
Control Method ◽  
Dead Zone ◽  
External Disturbance ◽  
Adaptive Controller ◽  
Auxiliary System ◽  
Design Stage ◽  
Recursive Structure ◽  
Model Reference ◽  
Model Reference Adaptive ◽  
Axial System

This paper presents a backstepping-based model reference adaptive controller for a multi-axial system in the presence of external disturbance and saturated input. The proposed controller synthesizes the backstepping technique and the model reference adaptive control method to construct control inputs for recursive structure and uncertain modelling of the multi-axial system. To cope with the limit of saturated input, an auxiliary system is adopted. A dead-zone modification is introduced to avoid the drift phenomenon of adjusted adaptive parameters. The stability of the proposed controller is proven by Lyapunov’s theory while considering the effect of the auxiliary system and the dead-zone modification in the design stage. The effectiveness and performance of the proposed controller are evaluated by experiment on a transformer winding system.

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