Deep Neural Network (DNN)-based controllers have emerged as a tool to compensate for
unstructured uncertainties in nonlinear dynamical systems. A recent breakthrough in the …

The charging current variation at various level of battery operating conditions and state of
charge (SOC), often requires precise and accurate control in EV charging to achieve desired …

In the presence of unmodelled dynamics and uncertainties with no a priori constant bounds,
conventional robust adaptation strategies for switched systems cannot allow the control …

A continuous adaptive controller is developed for nonlinear dynamical systems with linearly
parameterizable uncertainty involving time-varying uncertain parameters. Through a unique …

Recent advancements in adaptive control have equipped deep neural network (DNN)-
based controllers with Lyapunov-based adaptation laws that work across a range of DNN …

As a complex non‐linear system, the chaotic oscillation of power system seriously threatens
the safe and stable operation of power grids. In this paper, a finite time adaptive …

Deep Neural Network (DNN)-based controllers have emerged as a tool to compensate for
unstructured uncertainties in nonlinear dynamical systems. A recent breakthrough in the …

Adaptive control, which is a well established field in nonlinear control, is used to handle
structured uncertainties in the dynamics by using an online estimator for the unknown …

Chaotic oscillation in the power system often poses a great threat to the stability of the power
grid, and the dynamic behaviors of fourth-order power system with power disturbance, which …

随着电力行业的飞速发展, 电力系统中出现极具危害的混沌振荡可能性增大,
维持电力系统稳定的重要性日益突出. 通过建立含功率扰动项的四阶电力系统模型, 对Lyapunov …