Generalized Potential Function-Based Cooperative Current-Sharing Control for High-Power Parallel Charging Systems
Author(s) -
Hongtao Liao,
Jun Peng,
Yanhui Zhou,
Zhiwu Huang,
Feng Zhou
Publication year - 2017
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2017.p0387
Subject(s) - computer science , control theory (sociology) , lyapunov function , function (biology) , decentralised system , power control , stability (learning theory) , power sharing , control lyapunov function , current (fluid) , control (management) , mathematical optimization , power (physics) , lyapunov redesign , mathematics , artificial intelligence , engineering , physics , quantum mechanics , evolutionary biology , electrical engineering , biology , lyapunov exponent , nonlinear system , machine learning , chaotic
In this paper, a new decentralized gradient-based cooperative control method is proposed to achieve current sharing for parallel chargers in energy storage-type light rail vehicle systems. By employing a generalized artificial potential function to characterize the interaction rule for subchargers, the current-sharing control problem is converted into an optimization problem. Based on the gradient of the potential function, a decentralized gradient cooperative control law is derived. A general saturation function is introduced in the proposed control to guarantee the boundedness of the control output. The stability of the closed-loop system under the proposed decentralized gradient control is proven with the aid of a Lyapunov function. Simulation results are provided to verify the feasibility and validity of the proposed distributed current-sharing control method.
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