Lyapunov Analysis of Two Imbalanced Power System Areas

The transient stability is the capability of the system to preserve synchronism while being affected by large disturbances. It is a nonlinear problem that requires a simultaneous solution for many differential equations. Therefore, a thorough analysis is needed to resolve it. In this paper, we present the transient stability for multimachine under different fault cases and to analyze using the Lyapunov function. It serves as an analytical tool to determine the necessary condition to be stable. The system is stable as long as it is contained in the region of attraction. Meanwhile, the swing equation and reduced admittance matrix are used to model the system in three conditions, pre-fault, during the fault, and post-fault. The numerical simulations are conducted to verify that the synchronism can be preserved despite under faults on the transmission lines by achieving the critical clearing time.

Global Finite-Time Stabilization Problem of Affine Nonlinear Systems with Unknown Functions

In this paper, the global finite-time stabilization (FTS) of nonlinear systems with unknown functions (UFs) is studied. Firstly, in order to deal with UFs, a Lemma is proposed to avoid the Assumptions of UFs. Secondly, based on this Lemma, the control algorithm designed by using backstepping has no partial derivative of virtual controllers, so it avoids the “differential explosion” problem of backstepping. Thirdly, by using Lyapunov analysis method, backstepping and FTS method, a global FTS control algorithm of nonlinear systems with UFs is proposed. Finally, the feasibility of developed control approach is illustrated by the simulation results of a manipulator.

Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics

This paper contributes towards the development of motion tracking algorithms for time-critical applications, proposing an infrastructure for dynamically solving the inverse kinematics of highly articulate systems such as humans. The method presented is model-based, it makes use of velocity correction and differential kinematics integration in order to compute the system configuration. The convergence of the model towards the measurements is proved using Lyapunov analysis. An experimental scenario, where the motion of a human subject is tracked in static and dynamic configurations, is used to validate the inverse kinematics method performance on human and humanoid models. Moreover, the method is tested on a human-humanoid retargeting scenario, verifying the usability of the computed solution in real-time robotics applications. Our approach is evaluated both in terms of accuracy and computational load, and compared to iterative optimization algorithms.

In Search of Chaos and Complexity of a Cognitive Language-Learning System

In this article, we investigate the long-term dynamics of a known cognitive-based language-learning system under the variation of a system parameter. Stability of the equilibrium points is studied. Period root to chaos is investigated by bifurcation analysis. A Lyapunov analysis is performed to verify the complex dynamics in the system. Existence of chaos is confirmed by 0-1 test. A noise-induced cognitive phenomenon is proposed under the effect of power noise. Chaotic and nonchaotic dynamics are explored in the noise-induced system. Furthermore, disorder as well as complexity, are investigated for both the systems using the concept of weighted recurrence. The whole analysis can be effective to understand the dynamical features and nonlinear structure of the cognitive language-learning model.