2010
DOI: 10.1002/rnc.1539
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Global output regulation for output feedback systems with an uncertain exosystem and its application

Abstract: SUMMARYThis paper presents the solvability conditions for the global robust output regulation problem for a class of output feedback systems with an uncertain exosystem by using output feedback control. An adaptive control technique is used to handle the unknown parameter vector in the exosystem. It is shown that this unknown parameter vector can be exactly estimated asymptotically if a controller containing a minimal internal model is employed. The effectiveness of our approach has been illustrated by an asym… Show more

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Cited by 31 publications
(31 citation statements)
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“…Remark One of the distinguished features of our problem considered here is that we allow the whole nonlinear multiagent system to contain unknown parameters, especially, the leader system is also uncertain since σ is unknown for feedback, different from other references . More importantly, both the exogenous signal v ( t ) and uncertain parameters w , σ in the multiagent systems can be arbitrarily large, ie, the initial condition v (0) can be arbitrarily large and double-struckW,double-struckS can be any subsets of Rnw, Rnσ and unknown for feedback, as opposed to that in the work of Dong and Huang, requiring v1ptfalse(0false)V0, false(w,σfalse)double-struckW×false{σfalse} with V0,double-struckW being known compact sets, which is also different from the works of Su and Huang and Xu and Huang requiring v1ptfalse(0false)V0, false(w,σfalse)double-struckW×double-struckS with V0,double-struckW,double-struckS being known.…”
Section: Problem Formulation and Preliminariesmentioning
confidence: 99%
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“…Remark One of the distinguished features of our problem considered here is that we allow the whole nonlinear multiagent system to contain unknown parameters, especially, the leader system is also uncertain since σ is unknown for feedback, different from other references . More importantly, both the exogenous signal v ( t ) and uncertain parameters w , σ in the multiagent systems can be arbitrarily large, ie, the initial condition v (0) can be arbitrarily large and double-struckW,double-struckS can be any subsets of Rnw, Rnσ and unknown for feedback, as opposed to that in the work of Dong and Huang, requiring v1ptfalse(0false)V0, false(w,σfalse)double-struckW×false{σfalse} with V0,double-struckW being known compact sets, which is also different from the works of Su and Huang and Xu and Huang requiring v1ptfalse(0false)V0, false(w,σfalse)double-struckW×double-struckS with V0,double-struckW,double-struckS being known.…”
Section: Problem Formulation and Preliminariesmentioning
confidence: 99%
“…Extending the results from the aforementioned works to the case with relative degree r ≥ 2 additionally involves an adaptive recursive design procedure, the specific challenge of which lies in satisfying neighbor's rule in multiagent system and thus making the control strategy distributed, implying that both the parameter update laws and the stabilizing controller for each augmented system with both static and dynamic uncertainties should be distributed. Extending the recursive procedure used in the work of Dong and Huang for the cooperative output regulation problem of and certain leader system is nontrivial after taking into account of the effect of arbitrarily large uncertain parameters both in and , as opposed to the case that parameters belong to a known compact set as in other references . Detailed technical differences are presented in Remark .…”
Section: Introductionmentioning
confidence: 99%
“…However, due to the use of the input driven filter, the order of the controller in [20] is significantly higher than the order of the controller here.…”
Section: An Examplementioning
confidence: 92%
“…It should be noted that the same problem has been studied in [20] using output feedback control. However, due to the use of the input driven filter, the order of the controller in [20] is significantly higher than the order of the controller here.…”
Section: An Examplementioning
confidence: 95%
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