We study the exact linearization of configuration flat Lagrangian controlsystems with p degrees of freedom and p - 1 inputs by quasi-static feedback of classical states. First, we present a detailed analysis of the s...
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We study the exact linearization of configuration flat Lagrangian controlsystems with p degrees of freedom and p - 1 inputs by quasi-static feedback of classical states. First, we present a detailed analysis of the structure of the parameterization of the system variables by the flat output. Based on that, we systematically construct a linearizing quasi-static feedback law of the classical state such that the closed-loop system shows the behavior of decoupled integrator chains. Our approach shows that the construction of a generalized Brunovsky state can be completely circumvented. Furthermore, we present a method for determining the lengths of the integrator chains achieved by quasi-static feedback laws that allow for rest-to-rest transitions.
In the pursuit of sustainable electricity generation from offshore wind and wave energy, the combination of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has emerged as a promising solut...
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In sports, concussion prevention measures require the development of concussion evaluation criteria for improved head protective gear or early intervention through impact measurement in the field. The peak value of th...
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In this study, our main objective is to address the issue of sampled-data-based synchronization of complex networks subjected to stochastic scaling attacks using a looped-functional approach. To begin with, the design...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
With the extensive and widespread application of lithium batteries in various fields, the demand for their safety has become increasingly high. Ensuring the safety of lithium batteries is of paramount importance. In t...
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The lifetime prediction of electronic systems is of utmost significance and holds a crucial position. Sequential probability ratio (SPR), as a statistical approach, is employed in sequential analysis for making decisi...
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A promising step from linear towards nonlinear data-driven control is via the design of controllers for linear parameter-varying (LPV) systems, which are linear systems whose parameters are varying along a measurable ...
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This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were rec...
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We investigate nonlinear model predictive control (MPC) with terminal conditions in the Koopman framework using extended dynamic mode decomposition (EDMD) to generate a data-based surrogate model for prediction and op...
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