Attitude Control of a Quadrotor Using Model Free Based Sliding Model Controller

2015 ◽  
Author(s):  
Haoping Wang ◽  
Xuefei Ye ◽  
Yang Tian ◽  
Nicolai Christov
Keyword(s):  
Attitude Control ◽  
Model Free

scholarly journals Single-Parameter-Tuned Attitude Control for Quadrotor with Unknown Disturbance

2020 ◽  
Vol 10 (16) ◽  
pp. 5564 ◽  
Author(s):  
Dada Hu ◽  
Zhongcai Pei ◽  
Zhiyong Tang
Keyword(s):  
Attitude Control ◽  
Disturbance Rejection ◽  
Learning Algorithm ◽  
Attitude Control System ◽  
Model Free ◽  
Covariance Matrix Adaptation ◽  
State Observers ◽  
Reward Shaping ◽  
Policy Gradient ◽  
Unknown Disturbance

In this paper, methods are presented for designing a quadrotor attitude control system with disturbance rejection ability, wherein only one parameter needs to be tuned for each axis. The core difference between quadrotor platforms are extracted as critical gain parameters (CGPs). Reinforcement learning (RL) technology is introduced in order to automatically optimize the controlling law for quadrotors with different CGPs, and the CGPs are used to extend the RL state list. A deterministic policy gradient (DPG) algorithm that is based on an actor-critic structure in a model-free style is used as the learning algorithm. Mirror sampling and reward shaping methods are designed in order to eliminate the steady-state errors of the RL controller and accelerate the training process. Active disturbance rejection control (ADRC) is applied to reject unknown external disturbances. A set of extended state observers (ESOs) is designed to estimate the total disturbance to the roll and pitch axes. The covariance matrix adaptation evolution strategy (CMA-ES) algorithm is used to automatically tune the ESO parameters and improve the final performance. The complete controller is tested on an F550 quadrotor in both simulation and real flight environments. The quadrotor can hover and move around stably and accurately in the air, even with a severe disturbance.

Simple model-free attitude control design for flexible spacecraft with prescribed performance

2018 ◽  
Vol 233 (8) ◽  
pp. 2760-2771 ◽  
Author(s):  
Chao Zhang ◽  
Guangfu Ma ◽  
Yanchao Sun ◽  
Chuanjiang Li
Keyword(s):  
Control System ◽  
Attitude Control ◽  
Control Design ◽  
Relevant Information ◽  
Tracking Accuracy ◽  
Attitude Control System ◽  
Prescribed Performance ◽  
Control Approach ◽  
Model Free ◽  
Flexible Vibrations

In this paper, a model-free attitude control approach is proposed for the spacecraft in the presence of external disturbances and flexible vibrations with both complexity and performance concerns. By utilizing prescribed performance and backstepping techniques, the controller is constructed in a simple form without requiring any relevant information of the attitude control system dynamics. Moreover, fuzzy/neural network approximations, observers, or adaptive laws are not adopted into the control design, so that the related problems introduced by these estimation structures can be avoided. Numerical simulations in different cases show that the control system can obtain quick and smooth dynamic process and expected tracking accuracy despite the influence of disturbances and flexible vibrations, which demonstrates the effectiveness of the proposed scheme. Owing to the above good features, it is suitable for practical engineering.

Model Free Adaptive Attitude Control for a Launch Vehicle

2019 ◽  
Author(s):  
Jingbang Liu ◽  
Xian Yu ◽  
Shangtai Jin ◽  
Zhongsheng Hou
Keyword(s):  
Attitude Control ◽  
Launch Vehicle ◽  
Model Free

scholarly journals The Adaptive Dynamic Programming Toolbox

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5609
Author(s):  
Xiaowei Xing ◽  
Dong Eui Chang
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Attitude Control ◽  
Adaptive Dynamic Programming ◽  
Feedback Controls ◽  
Programming Technique ◽  
Adaptive Dynamic ◽  
Optimal Feedback ◽  
Model Free ◽  
Time Control

The paper develops the adaptive dynamic programming toolbox (ADPT), which is a MATLAB-based software package and computationally solves optimal control problems for continuous-time control-affine systems. The ADPT produces approximate optimal feedback controls by employing the adaptive dynamic programming technique and solving the Hamilton–Jacobi–Bellman equation approximately. A novel implementation method is derived to optimize the memory consumption by the ADPT throughout its execution. The ADPT supports two working modes: model-based mode and model-free mode. In the former mode, the ADPT computes optimal feedback controls provided the system dynamics. In the latter mode, optimal feedback controls are generated from the measurements of system trajectories, without the requirement of knowledge of the system model. Multiple setting options are provided in the ADPT, such that various customized circumstances can be accommodated. Compared to other popular software toolboxes for optimal control, the ADPT features computational precision and time efficiency, which is illustrated with its applications to a highly non-linear satellite attitude control problem.

scholarly journals Model-Free Attitude Control of Spacecraft Based on PID-Guide TD3 Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
ZhiBin Zhang ◽  
XinHong Li ◽  
JiPing An ◽  
WanXin Man ◽  
GuoHui Zhang
Keyword(s):  
Reinforcement Learning ◽  
High Precision ◽  
Attitude Control ◽  
Response Speed ◽  
Fast Response ◽  
Fine Tuning ◽  
Training Time ◽  
Model Free ◽  
Attitude Tracking ◽  
Policy Gradient

This paper is devoted to model-free attitude control of rigid spacecraft in the presence of control torque saturation and external disturbances. Specifically, a model-free deep reinforcement learning (DRL) controller is proposed, which can learn continuously according to the feedback of the environment and realize the high-precision attitude control of spacecraft without repeatedly adjusting the controller parameters. Considering the continuity of state space and action space, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm based on actor-critic architecture is adopted. Compared with the Deep Deterministic Policy Gradient (DDPG) algorithm, TD3 has better performance. TD3 obtains the optimal policy by interacting with the environment without using any prior knowledge, so the learning process is time-consuming. Aiming at this problem, the PID-Guide TD3 algorithm is proposed, which can speed up the training speed and improve the convergence precision of the TD3 algorithm. Aiming at the problem that reinforcement learning (RL) is difficult to deploy in the actual environment, the pretraining/fine-tuning method is proposed for deployment, which can not only save training time and computing resources but also achieve good results quickly. The experimental results show that DRL controller can realize high-precision attitude stabilization and attitude tracking control, with fast response speed and small overshoot. The proposed PID-Guide TD3 algorithm has faster training speed and higher stability than the TD3 algorithm.

Model-based deep reinforcement learning with heuristic search for satellite attitude control

2019 ◽  
Vol 46 (3) ◽  
pp. 415-420
Author(s):  
Ke Xu ◽  
Fengge Wu ◽  
Junsuo Zhao
Keyword(s):  
Reinforcement Learning ◽  
Heuristic Search ◽  
Attitude Control ◽  
Local Optima ◽  
Content Type ◽  
Model Based ◽  
Model Free ◽  
Satellite Attitude ◽  
Satellite Attitude Control ◽  
Classical Control

Purpose Recently, deep reinforcement learning is developing rapidly and shows its power to solve difficult problems such as robotics and game of GO. Meanwhile, satellite attitude control systems are still using classical control technics such as proportional – integral – derivative and slide mode control as major solutions, facing problems with adaptability and automation. Design/methodology/approach In this paper, an approach based on deep reinforcement learning is proposed to increase adaptability and autonomy of satellite control system. It is a model-based algorithm which could find solutions with fewer episodes of learning than model-free algorithms. Findings Simulation experiment shows that when classical control crashed, this approach could find solution and reach the target with hundreds times of explorations and learning. Originality/value This approach is a non-gradient method using heuristic search to optimize policy to avoid local optima. Compared with classical control technics, this approach does not need prior knowledge of satellite or its orbit, has the ability to adapt different kinds of situations with data learning and has the ability to adapt different kinds of satellite and different tasks through transfer learning.

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