Jul 2026· 2026 IEEE 27th China Conference on System Simulation Technology and its Applications (CCSSTA)· pp. 1216-1221· 0 citations· 25 references
Abstract
While the fixed-time gradient-based multivariable extremum seeking (FxTGMES) algorithm guarantees convergence within a bounded time independent of initial conditions, its nonlinear power terms often induce excessively large initial control gains. This inherently leads to pronounced transient overshoot and degrades the overall dynamic response. To address this issue, an improved fixed-time gradient-based multivariable extremum seeking (IFxTGMES) algorithm is proposed by integrating a dynamic compensation network. Specifically, a low-pass filter is employed to attenuate the high-gain-induced transient overshoot, while a proportional-derivative (PD) feedback loop is introduced to provide phase lead compensation, effectively neutralizing the inherent phase lag caused by the filter. Simulation results verify that the proposed IFxTGMES algorithm successfully suppresses the initial transient overshoot and enhances dynamic response quality, while strictly preserving the fixed-time convergence property.
Traditional proportional-integral-derivative (PID) tuning methods often struggle in nonlinear systems with limited measurement availability. This study investigates PID controller tuning within a Riccati-based static output-feedback (SOF) optimization framework. Two SOF-based approaches—a gradient-based method and a mo...
This work presents a delayed safe Newton-based extremum seeking (SANES) framework for minimizing an unknown static map subject to an unknown safety constraint. The objective and safety measurements are assumed to be affected by the same constant time delay. To compensate for delayed measurements, a model-free predictor...
The power-voltage characteristics of photovoltaic systems become nonconvex under partial shading conditions, leading to significant efficiency degradation due to varying solar irradiance and temperature. To address this challenge, an improved finite-time fractional-order super-twisting fast terminal sliding mode contro...
Jian-Xiang Yang, Yi-Ran Fan, Ze-Kai Ying et al.· ISA transactions· 0 citations
In addressing the challenge of achieving precise tracking control for uncertain nonlinear systems under complex disturbances, this paper proposes a Robust Data-induced Learning Control (R-DiLC) framework. The R-DiLC introduces a dual-timescale adaptation mechanism that effectively compensates for both iteration-invaria...
Chang-Xin Lu, De-Yuan Meng, Jing-Yao Zhang· ISA transactions· 0 citations
This paper investigates the predefined-time adaptive neural tracking control problem for a class of nonlinear pure feedback systems with full state constraints, and proves that all closed loop signals are predefined-time-bounded and the tracking error meets the preset performance specifications.
Yang Li, Ya-Qi Yu, Quan-Min Zhu et al.· Mathematics· 0 citations
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