一种基于相对模型的分布式预测控制算法研究

Distributed Receding Horizon Control Algorithm on Relative States

针对分布式系统中高精度的相对状态比绝对状态更易获取的特点,提出了一种基于相对状态模型的分布式预测控制方法。该方法的本质是各子系统通过预测其与相邻子系统的相对状态轨迹来求解分布式最优控制问题。与常规基于绝对状态的分布式预测控制方法相比,该方法的特点在于通过相对状态模型的引进改进算法的实时性,并通过减少测量噪声源提高控制精度。该方法适用于一般非线性分布系统,且在满足一定条件下能够保证整个系统的渐近收敛性。最后,在Matlab仿真环境中将该方法应用于多机器人编队控制中,并验证的该方法的可行性。

In most applications of multiple distributed control problems, higher precise relative states are easier to be obtained compared to absolute states. Based on this, a new relative states based distributed receding horizon formation control method was designed. The proposed distributed control algorithm was implemented by online solving a nonlinear optimal control problem through predicting the relative states trajectory. Compared to the traditional distributed receding horizon control algorithm, the most attracting advantages of the proposed algorithm are 1） the reduced computational burden and 2） the control precision improvement due to introducing less measurement errors. Also, under some conditions, the convergence can be ensured for most of multiple distributed systems with nonlinear models. At last, the algorithm is used in multiple robot formation control system in Matlab simulation environment to verify the method.