scholarly journals Modeling and control of a hexacopter with a passive manipulator for aerial manipulation

2021 ◽  
Author(s):  
Pin Guo ◽  
Kun Xu ◽  
Huichao Deng ◽  
Haoyuan Liu ◽  
Xilun Ding
Keyword(s):  
External Disturbance ◽  
Outdoor Environment ◽  
Aerial Manipulation ◽  
Modeling And Control ◽  
Whole Process ◽  
Aerial Robot ◽  
Trajectory Linearization ◽  
The Stability ◽  
Model Reference Adaptive ◽  
Trajectory Linearization Control

AbstractIn this paper, a multi-propeller aerial robot with a passive manipulator for aerial manipulation is presented. In order to deal with the collision, external disturbance, changing inertia, and underactuated characteristic during the aerial manipulation, an adaptive trajectory linearization control (ATLC) scheme is presented to stabilize the multi-propeller aerial robot during the whole process. The ATLC controller is developed based on trajectory linearization control (TLC) method and model reference adaptive control (MRAC) method. The stability of the proposed system is analyzed by common Lyapunov function. Numerical simulations are carried out to compare the ATLC with TLC controller facing collision, external disturbance and changing inertia during an aerial manipulation. Experimental results prove that the developed robot can achieve aerial manipulation in the outdoor environment.

scholarly journals Backstepping Control of an Unmanned Helicopter Subjected to External Disturbance and Model Uncertainty

2021 ◽  
Vol 11 (12) ◽  
pp. 5331
Author(s):  
Wenlong Zhao ◽  
Zhijun Meng ◽  
Kaipeng Wang ◽  
Haoyu Zhang
Keyword(s):  
External Disturbance ◽  
Flight Test ◽  
Lyapunov Theory ◽  
Outdoor Environment ◽  
Wind Gust ◽  
External Disturbances ◽  
Attitude Tracking ◽  
Control Scheme ◽  
Highly Nonlinear ◽  
The Stability

A helicopter is a highly nonlinear system. Its mathematical model is difficult to establish accurately, especially the complicated flapping dynamics. In addition, the forces and moments exerted on the fuselage are very vulnerable to external disturbances like wind gust when flying in the outdoor environment. This paper proposes a composite control scheme which consists of a nonlinear backstepping controller and an extended state observer (ESO) to handle the above problems. The stability of the closed-loop system can be guaranteed based on Lyapunov theory. The external disturbances and model nonlinearities are treated as a lumped disturbance. Meanwhile, the ESO is employed to compensate the influence by estimating the lumped disturbance in real-time. Numerical simulation results are presented to demonstrate that the algorithm can achieve accurate and agile attitude tracking under the external wind gust disturbances even with model uncertainties. When coming to the flight test, a block dropping device was designed to generate a quantifiable and replicable disturbance, and the experimental results indicate that the algorithm introduced above can reject the external disturbance rapidly and track the given attitude command precisely.

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.

Simulation Studies on Model Reference Adaptive Controller for Permanent Magnet Synchronous Motor Drive

2011 ◽  
Vol 268-270 ◽  
pp. 509-512 ◽  
Author(s):  
Zhi Yong Qu ◽  
Zheng Mao Ye
Keyword(s):  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Adaptive Controller ◽  
Fast Response ◽  
Synchronous Motor ◽  
System Stability ◽  
Motor Drive ◽  
Simulation Studies ◽  
The Stability ◽  
Model Reference Adaptive

Permanent magnet synchronous motor systems are usually used in industry. This kind of systems is nonlinear in nature and generally difficult to control. The ordinary linear constant gain controller will cause overshoot or even loss of system stability. Application of adaptive controller to a permanent magnet synchronous motor system is investigated in this paper. The dynamic model of the system is given and the stability is also analyzed using Popov's criterion. The steady state error can be eliminated using adaptive controller combined with an integration term. Simulation results show the performance of adaptive controller with fast response and less overshoot.

scholarly journals Lyapunov Based Estimation of Flight Stability Boundary under Icing Conditions

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Binbin Pei ◽  
Haojun Xu ◽  
Yuan Xue
Keyword(s):  
Equilibrium Point ◽  
External Disturbance ◽  
Stability Boundary ◽  
Wind Tunnel Test ◽  
Test Results ◽  
Theoretical Support ◽  
Effect Model ◽  
Short Period ◽  
The Stability ◽  
Period Model

Current fight boundary of the envelope protection in icing conditions is usually defined by the critical values of state parameters; however, such method does not take the interrelationship of each parameter and the effect of the external disturbance into consideration. This paper proposes constructing the stability boundary of the aircraft in icing conditions through analyzing the region of attraction (ROA) around the equilibrium point. Nonlinear icing effect model is proposed according to existing wind tunnel test results. On this basis, the iced polynomial short period model can be deduced further to obtain the stability boundary under icing conditions using ROA analysis. Simulation results for a series of icing severity demonstrate that, regardless of the icing severity, the boundary of the calculated ROA can be treated as an estimation of the stability boundary around an equilibrium point. The proposed methodology is believed to be a promising way for ROA analysis and stability boundary construction of the aircraft in icing conditions, and it will provide theoretical support for multiple boundary protection of icing tolerant flight.

Adaptive Integrated Control for Omnidirectional Mobile Manipulators Based on Neural-Network

2011 ◽  
pp. 310-325
Author(s):  
Xiang-min Tan ◽  
Dongbin Zhao ◽  
Jianqiang Yi ◽  
Dong Xu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Integrated Control ◽  
External Disturbance ◽  
Torque Control ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Modeling And Control ◽  
Control Approach ◽  
Proposed Model

An omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. However, modeling and control of such systems are very challenging because of their complicated mechanism. In this paper, an unified dynamic model is developed by Lagrange Formalism. In terms of the proposed model, an adaptive integrated tracking controller, based on the computed torque control (CTC) method and the radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.

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