Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems

被引：0
|
作者：
Chakrabarty, Ankush [1 ]
Danielson, Claus [1 ]
Wang, Yebin [1 ]
机构：
[1] Mitsubishi Elect Res Labs, Cambridge, MA 02139 USA
来源：
2020 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA) | 2020年
关键词：
Safe reinforcement learning; data-driven methods; output tracking; constrained control; convex programming; invariant sets;
D O I：
10.1109/ccta41146.2020.9206315
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We design real-time optimal tracking controllers for servomotor systems engaged in single-axis point-to-point positioning tasks. The design is challenging due to the presence of unmodeled dynamics, along with speed and acceleration constraints. As model-based optimal control design methods cannot be applied directly to this uncertain system, we propose a data-driven approximate dynamic programming approach to learn an optimal tracking controller that is constraint-enforcing. The potential of our proposed method is illustrated on a servomotor that positions the head of a laser drilling machine.
引用
收藏
页码：352 / 357
页数：6
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