scholarly journals Research on RBV Control Strategy of Large Angle Maneuver

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Jiangtao Xu ◽  
Na Luo ◽  
Yu Fu ◽  
Litong Wang ◽  
Xiande Wu
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Control Strategy ◽  
External Disturbance ◽  
Large Angle ◽  
Robust Adaptive Control ◽  
Dynamic Inversion ◽  
Sideslip Angle ◽  
Nonlinear Simulation ◽  
Robust Adaptive

Considering the hypersonic aerospace vehicle, with high dynamic, strong varying parameters, strong nonlinear, strong coupling, and the complicated flight environment, conventional flight control methods based on linear system may become invalid. To the high precision and reliable control problem of this vehicle, nonlinear flight control strategy based on neural network robust adaptive dynamic inversion is proposed. Firstly, considering the nonlinear characteristics of aerodynamic coefficients varying with Mach numbers, attack angle, and sideslip angle, the complete nonlinear 6-DOF model of RBV is established. Secondly, based on the time-scale separation, using the nonlinear dynamic inversion control strategy achieves the pseudolinear decoupling of RBV. And then, using the neural network with single hidden layer approximates the dynamic inversion error for system model uncertainty. Next, the external disturbance and network approximating error are suppressed by robust adaptive control. Finally, using Lyapunov’s theory proves that all error signals of closed loop system are uniformly bounded finally under this control strategy. Nonlinear simulation verifies the feasibility and validity of this control strategy to the RBV control system.

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.

RBF NEURAL NETWORK ROBUST ADAPTIVE CONTROL FOR WIND GENERATOR SYSTEM

Mechanika ◽  
2011 ◽  
Vol 17 (5) ◽  
Author(s):  
Y. Zuo ◽  
Y. N. Wang ◽  
Y. Zhang ◽  
Z. L. Shen ◽  
Z. S. Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Rbf Neural Network ◽  
Robust Adaptive Control ◽  
Generator System ◽  
Wind Generator ◽  
Robust Adaptive

scholarly journals Adaptive NN Control for Multisteering Plane Aircraft with Dead Zone or Backlash Input Nonlinearity

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiang-fei Meng ◽  
Ying Wang ◽  
Mao-long Lv
Keyword(s):  
Neural Network ◽  
Dead Zone ◽  
External Disturbance ◽  
Tracking Error ◽  
Network Control ◽  
Control Allocation ◽  
Input Nonlinearity ◽  
Adaptive Neural Network Control ◽  
Robust Adaptive ◽  
Backlash Nonlinearity

Considering that many factors such as actuator input dead zone, backlash, and external disturbance could affect the exactness of trajectory tracking, therewith a robust adaptive neural network control scheme on the basis of control allocation is proposed for the sake of tracking control of multisteering plane aircraft with actuator input dead zone or backlash nonlinearity. First of all, an actuator input dead zone or backlash nonlinearity control assignment model is established and the control allocation equation is derived. Secondly, the system nonlinear uncertainty is compensated by means of radial basis function neural network, and a robust term is introduced to achieve robustness against external disturbance and system errors. Finally, by utilizing Lyapunov stability theorem, it has been proved that all the signals in the closed-loop system are bounded, and the tracking error converges to a small residual set asymptotically. Simulation results on ICE101 multisteering plane aircraft demonstrate the outstanding tracking performance and strong robustness as well as effectiveness of the proposed approach, which can effectively overcome the adverse influence of dead zone, backlash nonlinearity, and external disturbance on the system.

High-accuracy robust adaptive motion control of a torque-controlled motor servo system with friction compensation based on neural network

2018 ◽  
Vol 233 (7) ◽  
pp. 2318-2328 ◽  
Author(s):  
Jian Hu ◽  
Yuangang Wang ◽  
Lei Liu ◽  
Zhiwei Xie
Keyword(s):  
Neural Network ◽  
Motion Control ◽  
Servo System ◽  
High Accuracy ◽  
Robust Adaptive Control ◽  
Lyapunov Theory ◽  
Parametric Estimation ◽  
Friction Compensation ◽  
Nonlinear Friction ◽  
Robust Adaptive

In this paper, a high-accuracy motion control of a torque-controlled motor servo system with nonlinear friction compensation is presented. Friction always exists in the servo system and reduces its tracking accuracy. Thus, it is necessary to compensate for the friction effect. In this paper, a novel controller that combines robust adaptive control with friction compensation based on neural network observer is proposed. An improved LuGre friction model is applied into the friction compensation as it is known as a good model to express the nonlinear friction. A single hidden-layer network is utilized to observe the immeasurable friction state. Then, the robust adaptive controller is used to handle the parametric uncertainty, the parametric estimation error, friction compensation error, and other uncertainties. Lyapunov theory is utilized to analyze the stability of the closed-loop system. The experimental results demonstrate the effectiveness of the proposed algorithm.

scholarly journals Adaptive Observer-Based Fault Detection and Fault-Tolerant Control of Quadrotors under Rotor Failure Conditions

2020 ◽  
Vol 10 (10) ◽  
pp. 3503 ◽  
Author(s):  
Yu-Hsuan Lien ◽  
Chao-Chung Peng ◽  
Yi-Hsuan Chen
Keyword(s):  
Fault Detection ◽  
Flight Control ◽  
Control Strategy ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
External Disturbance ◽  
Fault Tolerant Control ◽  
Adaptive Observer ◽  
Fault Estimation ◽  
Failure Conditions

This paper aims to propose a strategy for the flight control of quad-rotors under single rotor failure conditions. The proposed control strategy consists of two stages—fault detection (FD) and fault tolerant control (FTC). A dual observer-based strategy for FD and fault estimation is developed. With the combination of the results from both observers, the decision making in whether a fault actually happened or the observed anomaly was caused by an external disturbance could be distinguished. Following the FD result, a control strategy for normal flight, as well as the abnormal one, is presented. The FTC considers a real-time coordinate transformation scheme to manipulate the target angles for the quad-rotor to follow a prescribed trajectory. When a rotor fault happens, it is going to be detected by the dual observers and then the FTC is activated to stabilize the system such that the trajectory following task can still be fulfilled. Furthermore, in order to achieve robust flight in the presence of external wind perturbation, the sliding mode control (SMC) theory is further integrated. Simulations illustrate the effectiveness and feasibility of the proposed method.

FAT-Based Robust Adaptive Control of Electrically Driven Robots in Interaction with Environment

Robotica ◽  
2018 ◽  
Vol 37 (5) ◽  
pp. 779-800 ◽  
Author(s):  
Alireza Izadbakhsh ◽  
Payam Kheirkhahan ◽  
Saeed Khorashadizadeh
Keyword(s):  
Fourier Series ◽  
Series Expansion ◽  
External Disturbance ◽  
Approximation Error ◽  
Robust Adaptive Control ◽  
Fourier Series Expansion ◽  
Orthogonal Functions ◽  
Actuator Dynamics ◽  
Approximation Techniques ◽  
Robust Adaptive

SummaryThis paper presents a robust adaptive impedance controller for robot manipulators using function approximation techniques (FAT). Recently, FAT-based robust impedance controllers have been presented using Fourier series expansion for uncertainty estimation. In fact, sinusoidal functions can approximate nonlinear functions with arbitrary small approximation error based on the orthogonal functions theorem. The novelty of this paper in comparison with previous related works is that the number of required regressor matrices in this paper has been reduced. This superiority becomes more dominant when the manipulator degrees of freedom (DOFs) are increased. First, the desired signals for motor currents are calculated, and then the desired voltages are obtained. In the proposed approach, only a simple model of the actuator and manipulator dynamics is used in the controller design and all the rest dynamics are treated as external disturbance. The external disturbances can then be approximated by Fourier series expansion. The adaptation laws for Fourier series coefficients are derived from a Lyapunov-based stability analysis. Simulation results on a 2-DOF planar robot manipulator including the actuator dynamics indicate the efficiency of proposed method.

Switching Control With Learning for a Class of Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 448-450
Author(s):  
X. Zhang ◽  
S. S. Nair
Keyword(s):  
Adaptive Learning ◽  
Control Strategy ◽  
Controller Design ◽  
Switching Control ◽  
Robust Adaptive Control ◽  
Control Effort ◽  
On Line ◽  
Robust Adaptive ◽  
Adaptive Control Strategy ◽  
Stable Learning

Analytical details are developed for a robust adaptive control strategy that combines switching control and on-line adaptive learning, for a class of nonlinear systems. The condition for stable learning is derived, guidelines for design parameter selection are provided, and the tradeoff between performance and chattering control effort is examined. The results of the study are summarized in the form of a constructive procedure for controller design for the class of systems.

Sign in / Sign up

Export Citation Format

Share Document