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Design of model predictive control via learning automata for a single UAV load transportation

机译:基于学习自动机的单无人机载运模型预测控制设计

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In recent years, autonomous aerial robots have been successfully used to perform the construction of structures composed by parts that have similar dimensions and inertial moments. However, these proposed control systems are not able to accurately control the UAVs during the handling and transporting loads with various weights and balance features. In this paper, we investigate a robust and innovative control strategy for UAV load transportation system that can deal with the load characteristics and disturbances such as ground effect and control noise. Taking into account the nonlinear and under-actuated features of the quadrotor, a Learning Automata (LA) methodology is applied to tune the Nonlinear Model Predictive Controllers (NMPCs) in the various contexts of operation. Specifically, it applies LA to select the weighting parameters of the objective function in order to minimize tracking error provided by the plant. Simulation results demonstrate the learned weighting parameters can be efficiently employed to obtain NMPC controllers for tracking optimized trajectories to deal with different load conditions.
机译:近年来,自主的空中机器人已成功用于执行由具有相似尺寸和惯性矩的零件组成的结构的构造。然而,这些提出的控制系统不能在具有各种重量和平衡特征的装卸和运输期间精确地控制无人机。在本文中,我们研究了一种用于无人机负载运输系统的鲁棒性和创新性控制策略,该策略可以处理负载特性和干扰,例如地面效应和控制噪声。考虑到四旋翼的非线性和欠驱动特性,我们采用学习自动机(LA)方法在各种操作环境下调整非线性模型预测控制器(NMPC)。具体来说,它应用LA来选择目标函数的加权参数,以最小化工厂提供的跟踪误差。仿真结果表明,所学习的加权参数可以有效地用于获得NMPC控制器,以跟踪优化轨迹以应对不同的负载条件。

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