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Control of quadrotor trajectory tracking with sliding mode control optimized by neural networks
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering ( IF 1.6 ) Pub Date : 2020-07-09 , DOI: 10.1177/0959651820932716
Somayeh Raiesdana 1
Affiliation  

Quadrotor or unmanned helicopter is a mobile robot which often flies in unknown environment to perform special missions. In navigational tasks, the robot is intended to fly autonomously toward a target position by following an optimum trajectory. For a successful navigation, controlled attitude, minimum position and velocity error and obstacles collision avoidance are often considered during trajectory tracking procedure. By considering environmental variabilities and due to the existence of noises, uncertainties and unpredictable factors, it is indispensable to steer and control moving robots using sophisticated autonomous algorithms. In this work, a nonlinear model of four-rotor helicopter is simulated. An optimized terminal sliding mode control is then designed to control trajectory tracking. In order to improve the time indices for sliding mode controller, this controller is modified with neural networks. The idea is to optimize the controller parameters through a network learning process which is based on the control process error. The proposed method is evaluated with simulated and real-world indoor navigation tasks. Trajectories that are tracked by quadrotor are generated by a simultaneous localization and mapping algorithm and refined with an optimization technique. A well-known simultaneous localization and mapping technique (a camera-based extended Kalman filter-simultaneous localization and mapping) is employed to generate maps, and a path planning algorithm (particle swarm optimization) is utilized to optimize a collision-free flight path using the probability-based maps generated by simultaneous localization and mapping. Simulations and experiment are done in unknown but structured indoor environments containing a number of obstacles. The steady state error, the reaching and settle time and the chattering effect are all quantified and assessed. The controlled experimental flight robustness and sensitivity are further verified for noises occurred on vision and data acquisition system. Results indicate suitable performance for the proposed neural network-sliding mode controller. Less error and more stability were achieved comparative to the conventional sliding mode controllers.

中文翻译:

神经网络优化滑模控制的四旋翼飞行器轨迹跟踪控制

Quadrotor或无人直升机是一种经常在未知环境中飞行执行特殊任务的移动机器人。在导航任务中,机器人旨在通过遵循最佳轨迹自主飞向目标位置。为了成功导航,在轨迹跟踪过程中经常考虑受控姿态、最小位置和速度误差以及障碍物避碰。通过考虑环境变化以及由于噪声、不确定性和不可预测因素的存在,使用复杂的自主算法来引导和控制移动机器人是必不可少的。在这项工作中,模拟了四旋翼直升机的非线性模型。然后设计优化的终端滑模控制来控制轨迹跟踪。为了提高滑模控制器的时间指标,该控制器用神经网络进行了修改。其思想是通过基于控制过程误差的网络学习过程来优化控制器参数。所提出的方法通过模拟和真实世界的室内导航任务进行评估。四旋翼飞行器跟踪的轨迹由同步定位和映射算法生成,并通过优化技术进行细化。采用众所周知的同时定位和映射技术(基于相机的扩展卡尔曼滤波器-同时定位和映射)生成地图,并利用路径规划算法(粒子群优化)优化无碰撞飞行路径,使用通过同时定位和映射生成的基于概率的地图。模拟和实验是在包含许多障碍物的未知但结构化的室内环境中进行的。稳态误差、到达和稳定时间以及颤振效应都被量化和评估。对于视觉和数据采集系统上出现的噪声,进一步验证了受控实验飞行的鲁棒性和灵敏度。结果表明所提出的神经网络滑动模式控制器具有合适的性能。与传统的滑模控制器相比,实现了更少的误差和更高的稳定性。对于视觉和数据采集系统上出现的噪声,进一步验证了受控实验飞行的鲁棒性和灵敏度。结果表明所提出的神经网络滑动模式控制器具有合适的性能。与传统的滑模控制器相比,实现了更少的误差和更高的稳定性。对于视觉和数据采集系统上出现的噪声,进一步验证了受控实验飞行的鲁棒性和灵敏度。结果表明所提出的神经网络滑动模式控制器具有合适的性能。与传统的滑模控制器相比,实现了更少的误差和更高的稳定性。
更新日期:2020-07-09
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