基于无穷范数的视觉伺服自适应控制方法

针对基于视觉的机器人伺服过程中, 控制系统的精度和稳定性容易受到环境噪声、 设备性能等因素影响, 导致收敛指标不理想的问题, 提出一种基于无穷范数优化任务序列( INOTS: Infinite Norm Optimization Task Sequence)的自适应增益算法, 通过将实时全局任务细化为不同子任务, 根据不同时刻子任务的特点自动配置增益。 仿真结果表明, 相比于固定增益及传统自适应增益调节算法, 该增益算法可有效提高系统响应速度,增强控制系统的抗干扰能力, 提高系统稳定性。

Vision Servo Adaptive Control Method Based on Infinite Norm

Aiming at the problem that the accuracy and stability of the control system are easily affected byenvironmental noise, equipment performance and other factors in the process of robot vision servo, an adaptive gain algorithm based on infinite norm optimization task sequence ( INOTS: Infinite Norm Optimization Task Sequence) is proposed. By dividing the real-time global task into different subtasks, the gain is automatically configured according to the characteristics of the subtask at different times. The simulation results indicate that compared to the fixed gain and the traditional adaptive gain adjustment algorithm, the proposed gain algorithm can improve the response speed of the system effectively, enhance the anti-interference ability of the control system, and improve the anti-interference ability of the control system.