Learning time-optimal anti-swing trajectories for overhead crane systems
文献类型：会议论文
| 作者 | Long Cheng ; Yongchun Fang
|
| 出版日期 | 2016 |
| 会议日期 | JUL 06-08, 2016 |
| 会议地点 | Saint petersburg |
| 国家 | Russia |
| 英文摘要 | Considering both state and control constraints, minimum-time trajectory planning (MTTP) can be implemented in an 'offline' way for overhead crane systems [1]. In this paper, we aim to establish a real-time trajectory planning model by using machine learning approaches to approximate those results obtained by MTTP. The fusion of machine learning regression approaches into the trajectory planning module is new and the application is promising for intelligent mechatronic systems. In particular, we first reformulate the considered trajectory planning problem in a three-segment form, where the acceleration and deceleration segments are symmetric. Then, the offline MTTP is applied to generate a database of minimum-time trajectories for the acceleration stage, based on which several regression approaches including Extreme Learning Machine (ELM) and Backpropagation Neural Network (BP) are adopt to approximate MTTP results with high accuracy. More important, the resulting model only contains a set of parameters, rather than a large volume of offline data, and thus machine learning based approaches could be implemented in low-cost digital signal processing chips required by industrial applications. Comparative evaluation results are provided to show the superior performance of the selected regression approach. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/23137]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 推荐引用方式 GB/T 7714 | Long Cheng,Yongchun Fang. Learning time-optimal anti-swing trajectories for overhead crane systems[C]. 见:. Saint petersburg. JUL 06-08, 2016. |
入库方式： OAI收割
来源：自动化研究所
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