电光与控制, 2019, 26 (9): 84, 网络出版: 2020-12-20
任务空间内旋翼飞行机器人目标物作业控制
Object Operation Control for a Rotary-Wing Flight Robot in Task Space
旋翼飞行机器人 运动型模型 动力学模型 目标物作业控制 线性自抗扰控制算法 rotar-wing flight robort kinematical model dynamical modek object operation control linear active disturbance rejection control algori
摘要
旋翼飞行机器人是在多旋翼飞行器上加装机械臂的新型空中机器人系统。针对其与外界环境的交互作业, 提出了一种抗干扰任务空间目标物作业控制策略。首先, 在广义坐标系下推导出旋翼飞行机器人的运动学模型与动力学模型, 明确了系统的输入输出关系; 其次, 将整个系统分为位置、姿态和机械臂3个控制环, 并采用线性自抗扰控制算法设计了抗干扰控制器, 在控制算法中, 将线性扩张观测器和PD控制律分别用来估计与补偿集总干扰; 进而, 分析了所设计控制算法的稳定性以及对控制器参数进行了整定; 最后, 搭建了实验系统对目标物抓取作业控制进行了实验验证。实验结果表明, 所提控制算法抗干扰能力强、响应速度快, 能够有效保持旋翼飞行机器人作业时的位姿稳定。
Abstract
Rotary-wing flight robot is a novel aerial robot system containing a rotorcraft and a manipulator. To implement the operation when interacting with the environment, an object operation control strategy with the ability of disturbance rejection in task space is proposed. Firstly, the kinematical model and dynamical model of the rotary-wing flight robot are established in the generalized coordinates. And the relation between inputs and outputs is determined. Then, the robot system is divided into three control loops of position, attitude and manipulator loop.Linear Active Disturbance Rejection Control (ADRC) algorithm is employed to design the controller for each control loop. In the control structure, the linear extended state observer and PD control law are used to estimate and compensate the lumped disturbance. Moreover, the stability analysis and parameter tuning are conducted for the control algorithm. Finally, the object operational control experiment is executed on the rotary-wing flight robot system. All the results show that the proposed control algorithm has strong ability of disturbance rejection and fast response speed, which can keep the stability of position/attitude of the rotary-wing flight robot in operation.
周川, 丁力. 任务空间内旋翼飞行机器人目标物作业控制[J]. 电光与控制, 2019, 26(9): 84. ZHOU Chuan, DING Li. Object Operation Control for a Rotary-Wing Flight Robot in Task Space[J]. Electronics Optics & Control, 2019, 26(9): 84.