Abstract:SUMMARYA generalized g-level H 1 sub-optimal input decoupling (SOID) filtering problem is formulated. It is a generalization of g-level H 1 SOID filtering problem when, besides an input with unknown statistical properties but with a finite RMS norm, there exists an additional input to the given plant or system. The additional input is a linear combination of sinusoidal signals each of which has an unknown amplitude and phase but known frequency. The analysis, design, and performance limitations of generalized … Show more
“…Remark 2: Assumption 1 implies that the triple (A i , B i ,C i ) is right-invertible, the pair (A i , B i ) stabilizable, and the pair (A i ,C i ) detectable (see, e.g., [23,Ch. 3]).…”
In this paper we study the problem of achieving regulated output synchronization in a network of minimumphase SISO agents. Our problem formulation is characterized by the combination of three different challenges: the network is heterogeneous (meaning that the agents are governed by nonidentical models); the agents are non-introspective (meaning that they do not have access to information about their own state or output); and the agents are not allowed to exchange internal controller states via the network. To handle these challenges, we present an observer-based control methodology that combines elements of low-gain and high-gain design techniques.
“…Remark 2: Assumption 1 implies that the triple (A i , B i ,C i ) is right-invertible, the pair (A i , B i ) stabilizable, and the pair (A i ,C i ) detectable (see, e.g., [23,Ch. 3]).…”
In this paper we study the problem of achieving regulated output synchronization in a network of minimumphase SISO agents. Our problem formulation is characterized by the combination of three different challenges: the network is heterogeneous (meaning that the agents are governed by nonidentical models); the agents are non-introspective (meaning that they do not have access to information about their own state or output); and the agents are not allowed to exchange internal controller states via the network. To handle these challenges, we present an observer-based control methodology that combines elements of low-gain and high-gain design techniques.
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