简介：在这份报纸，quasi-Newton-type优化了控制(ILC)算法为分离线性时间不变的系统的一个类被调查的反复的学习。建议学习算法是由一个quasi-Newton-type矩阵更新学习获得矩阵而不是植物的倒置。借助于数学归纳方法，建议算法的单调集中被分析，它证明追踪的错误单调地在重复的一个有限数字以后收敛到零。与存在相比优化了ILC算法，由于伪的superlinear集中--牛顿方法，建议学习法律与更快的会聚的率操作并且对柔韧系统模型有病条件，并且因此拥有大量应用。数字模拟表明有效性和有效性。

简介：Backlash-likehysteresisisoneofthenonsmoothandmulti-valuednonlinearitiesusuallyexistinginmechanicalsystems.Thetraditionalidentificationmethodisquitedifficulttobeusedtomodelthesystemsinvolvedwithsuchcomplexnonlinearities.Inthispaper,anonsmoothrecursiveidentificationalgorithmforthesystemswithbacklash-likehysteresisisproposed.Inthismethod,theconceptofClarkesubgradientisintroducedtoapproximatethegradientsatnonsmoothpointsandtheso-calledbundlemethodisusedtoobtaintheoptimizationsearchdirectioninnonsmoothcases.Then,arecursivealgorithmbasedontheideaofbundlemethodisdevelopedforparameterestimation.Afterthat,theconvergenceanalysisofthealgorithmisinvestigated.Finally,simulationresultstovalidatetheproposedmethodonasimulatedmechanicaltransmissionsystemarepresented.