This paper proposes a new variable gain robust state observer for a class of uncertain nonlinear systems. The variable gain robust state observer proposed in this paper consists of fixed observer gain matrices and non...
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ISBN:
(数字)9798350380040
ISBN:
(纸本)9798350380057
This paper proposes a new variable gain robust state observer for a class of uncertain nonlinear systems. The variable gain robust state observer proposed in this paper consists of fixed observer gain matrices and nonlinear modification functions which are determined by appropriate updating rules. It is shown that sufficient conditions for the existence of the proposed variable gain robust state observer can be reduced to solvability of Linear Matrix Inequalities (LMIs). Finally, we give a simple numerical example.
Frustrated magnetic systems with anisotropic exchange interactions have been recognized as key platforms for discovering exotic quantum states and quasiparticles. In this study, we report the pressure evolution of mag...
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This article introduces a new transcription, change point localization, and mesh refinement scheme for direct optimization-based solutions and for uniform approximation of optimal control trajectories associated with ...
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An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constant...
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This system aims to provide comprehensive care to individuals with paralysis, including symptom monitoring, medical treatment, and rehabilitation. The IoT-based system comprises interconnected devices, such as sensors...
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Quantum information spreading and scrambling in many-body systems attract interest these days. Tripartite mutual information (TMI) based on operator-based entanglement entropy (EE) is an efficient tool for measuring t...
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Quantum information spreading and scrambling in many-body systems attract interest these days. Tripartite mutual information (TMI) based on operator-based entanglement entropy (EE) is an efficient tool for measuring them. In this paper, we study random spin chains that exhibit phase transitions accompanying nontrivial changes in topological properties. In their phase diagrams, there are two types of many-body localized (MBL) states and one thermalized regime intervening these two MBL states. Quench dynamics of the EE and TMI display interesting behaviors, providing an essential perspective concerning the encoding of quantum information. In particular, one of the models is self-dual, but information spreading measured by the TMI does not respect this self-duality. We investigate this phenomenon from the viewpoint of spatial structure of the stabilizers. In general, we find that knowledge of the phase diagram corresponding to the qubit system is useful for understanding the nature of quantum information spreading in that system. The connection between the present paper and random circuit of projective measurements and also topological Majorana quantum memory is remarked upon.
Due to high power and flexibility, Markov regenerative process (MRGP) is widely used for modeling and evaluating system dependability. However, owing to the renewal nature of MRGP, the analysis of MRGP is challenging....
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Phase reduction is a powerful technique in the study of nonlinear oscillatory systems. Under certain assumptions, it allows us to describe each multidimensional oscillator by a single phase variable, giving rise to si...
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In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is *** the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering ...
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In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is *** the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.
The cooperative output regulation problem has been extensively studied on the basis of the distributed observer ***,the majority of the existing research assumes that the dynamics is known *** remove this condition,th...
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The cooperative output regulation problem has been extensively studied on the basis of the distributed observer ***,the majority of the existing research assumes that the dynamics is known *** remove this condition,the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is ***,a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external ***,the unknown regulator equations are solved using the iterative recurrent neural network ***,the state-based data-driven distributed control law is synthesized to solve the *** optimal gains are derived by solving convex optimization problems using input and state ***,a numerical example is presented to verify the feasibility of the proposed framework.
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