具有有界噪声的机器人系统在线调谐模型预测控制

K. Belda, L. Pavelková
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引用次数: 3

摘要

研究了一种用于机器人运动控制的离散预测控制设计。该设计考虑了时变状态空间机器人模型。假设使用的机器人状态必须由测量的机器人输出来估计。这些输出表示包括有界噪声在内的受控量。考虑到这种安排,本文引入了一种新的基于线性规划的状态和噪声参数估计方法,并将其纳入控制设计中。估计状态用于更新机器人模型中的状态相关元素和控制设计本身。估计的噪声参数被用于控制参数的高级调谐,即惩罚矩阵。最后,以一个多输入多输出机械臂作为机器人系统的具体代表,对所提出的理论成果进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online tuned model predictive control for robotic systems with bounded noise
This paper deals with a discrete predictive control design for motion control of robotic systems. The design considers time-varying state-space robot model. It is assumed that used robot state has to be estimated from measured robot outputs. These outputs represent controlled quantities including a bounded noise. Considering this arrangement, the paper introduces a novel solution to the state and noise parameter estimations based on linear programming that is incorporated in the control design. Estimated states are utilised for updating state-dependent elements in the robot model and for control design itself. Estimated noise parameters are employed in advanced tuning of control parameters, namely penalisation matrices. The proposed theoretical outcomes are demonstrated on one multi-input multi-output robot-manipulator as a specific representative of robotic systems.
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