A Decentralized Optimal Iterative Learning Control Approach With Efficient Computation for Collaborative Tracking - Gao, Luyuan; Zhuang, Zhihe; Tao, Hongfeng; Chen, Yiyang; Paszke, Wojciech (1975- ); Stojanovic, Vladimir
In practical industries, multiple subsystems are often required to collaborate on a particular repetitive collaborative tracking task, taking a lot of computation. Norm optimal iterative learning control (NOILC) can effectively improve the tracking accuracy for such tasks, and also provide the monotonic convergence of tracking error. However, the high-dimensional matrices and supervectors generated by the lifting technique lead to a computationally expensive problem in the lifted NOILC approach, making it difficult to apply to the collaborative tracking task with long trial length.
Keywords: iterative learning control; optimization based learning design; collaborative tracking; computational efficiency