Iterative learning formation control method for discrete multi-agent system under random test length

Abstract

The invention belongs to the field of formation tasks of a multi-unmanned aerial vehicle or multi-robot system, and particularly relates to an iterative learning formation control method for a discrete multi-agent system under a random test length. The method comprises the steps of converting a state error problem of multi-agent coordination tracking into a stability control problem of a trackingerror within a certain time period; designing a distributed P-type iterative learning controller; analyzing convergence of the designed distributed P-type iterative learning controller under initial state accurate resetting and initial state random transformation by using a lambda norm, and solving a gain matrix; and performing simulation verification on the convergence analysis proof under the two conditions. According to the method, the correction state error related to the given formation pattern is defined, the condition that the length of each iteration test is consistent is relaxed, andcompared with an existing result of a single intelligent agent, the method is more convenient to calculate a gain matrix.

Description

technical field [0001] The invention belongs to the field of formation tasks of multi-unmanned aerial vehicles or multi-robot systems, and in particular relates to a discrete multi-agent system iterative learning formation control method under a random test length. Background technique [0002] In the industrial field, when a group of vehicles or mobile robots collaborate to repeatedly transport very large and heavy objects in a given form, they must maintain the required formation. The above-mentioned problem of maintaining formation formation throughout the movement process can currently be solved by applying an iterative learning control (ILC) method. [0003] In a recent study, Z. Chun et al. (See "Adaptive learning tracking for robot manipulators with varying trial lengths," J. Franklin Inst. Eng. Appl. Math., vol.356, no.12, pp.5993–6014 , 2019) considered adaptive learning control of robotic manipulator systems, where the operation length varies randomly with the nu...

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Problems solved by technology

[0004] D.Shen et al. (see “Iterative learning control for discrete nonlinear systems with randomly iteration varying lengths,” Syst.Contr.Lett., vol.96, pp.81–87, 2016) and L.Wang et al. (see “Sampled -data iterative learning control for continuous-time nonlinear systems with iteration-varying lengths,” Int.J.Robust Nonlin.Contr., doi:10.1002 / rnc.4066, 2018) for systems with randomly varying trial lengths, dealing with random processes probability problem, but it is very difficult to solve the gain matrix

In addition, in the existing multi-agent formation learning problem research (see the applicant's "An iterative learning approach to formation control of multi-agent systems,'Syst.Contr.Lett.,vol.61,no.1,pp .148–154, 2012; and “Robust formation control of discrete-time multi-agent systems by iterative learning approach,” Int. J. Syst. Sci., vol.46, no.4, pp.625–633, 2015. ), without considering the fact that each agent such as a mobile robot cannot guarantee the same length of learning trial for each iteration

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