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Minimum-Energy Iterative Learning Control for Multi-Agent Systems With Optimal Intermediate-Point Allocation
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DOI：10.1002/acs.70113.png)
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Abstract
En 中文
Iterative learning control applies to applications in which the same finite-duration task is repeated, with each instance termed a trial. The objective is to track a specified reference trajectory over a finite duration, termed the trial length. In some applications, such as multi-agent systems, tracking at each instant or point over the trial length is not required; only at selected points is it required, known as point-to-point iterative learning control. This article develops a new point-to-point design in which the points requiring tracking vary from trial to trial, and the solution minimizes energy, which is relevant to systems with a limited power budget. Also, an algorithm is developed to improve computational efficiency by sharing the burden among the agents forming the system. A numerical case study highlights the benefits of the new design.
Keywords:
consensus tracking
iterative learning control
minimum energy
multi-agent system
point-to-point
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