Sun Chao, Feng Zhi, Hu Guoqiang
IEEE Trans Cybern. 2022 Jul;52(7):5984-5998. doi: 10.1109/TCYB.2021.3055206. Epub 2022 Jul 4.
We investigate a distributed time-varying formation control problem for an uncertain Euler-Lagrange system. A time-varying optimization-based approach is proposed. Based on this approach, the robots can achieve the expected formation configuration and meanwhile optimize a global objective function using only neighboring and local information. We consider the time-varying optimization where the objective functions can change in real time. In this case, the consensus-based formation tracking control issues and formation containment tracking control issues in the literature can be solved by the proposed approach. By a penalty-based method, the robots' states asymptotically converge to the estimated optimal solution to an equivalent time-varying optimization problem, whose optimal solution can achieve simultaneous formation and optimization. Furthermore, we consider two more general scenarios: 1) the local objective functions can have non-neighbor's information and 2) the optimization problems can have inequality constraints.
我们研究了一个不确定欧拉-拉格朗日系统的分布式时变编队控制问题。提出了一种基于时变优化的方法。基于此方法，机器人能够实现预期的编队构型，同时仅使用邻域和局部信息来优化一个全局目标函数。我们考虑目标函数可以实时变化的时变优化。在这种情况下，文献中的基于一致性的编队跟踪控制问题和编队包容跟踪控制问题都可以通过所提出的方法来解决。通过一种基于惩罚的方法，机器人的状态渐近收敛到一个等效时变优化问题的估计最优解，该最优解能够实现同时编队和优化。此外，我们还考虑了另外两种更一般的情况：1）局部目标函数可以包含非邻域的信息；2）优化问题可以有不等式约束。
