Robust Controller Design of Networked Control Systems with Nonlinear Uncertainties

We address robust stabilization problem for networked control systems with nonlinear uncertainties and packet losses by modelling such systems as a class of uncertain switched systems. Based on theories on switched Lyapunov functions, we derive the robustly stabilizing conditions for state feedback stabilization and design packet-loss dependent controllers by solving some matrix inequalities. A numerical example and some simulations are worked out to demonstrate the effectiveness of the proposed design method.

Nonlinear pedagogy : implications for teaching games for understanding (TGfU)

Nonlinear Dynamics, provides a framework for understanding how teaching and learning processes function in Teaching Games for Understanding (TGfU). In Nonlinear Pedagogy, emergent movement behaviors in learners arise as a consequence of intrinsic self-adjusted processes shaped by interacting constraints in the learning environment. In a TGfU setting, representative, conditioned games provide ideal opportunities for pedagogists to manipulate key constraints so that self-adjusted processes by players lead to emergent behaviors as they explore functional movement solutions. The implication is that, during skill learning, functional movement variability is necessary as players explore different motor patterns for effective skill execution in the context of the game. Learning progressions in TGfU take into account learners’ development through learning stages and have important implications for organisation of practices, instructions and feedback. A practical application of Nonlinear Pedagogy in a national sports institute is shared to exemplify its relevance for TGfU practitioners.

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Numerical method and analytical technique of the modified anomalous subdiffusion equation with a nonlinear source term

In this paper, we consider a modified anomalous subdiffusion equation with a nonlinear source term for describing processes that become less anomalous as time progresses by the inclusion of a second fractional time derivative acting on the diffusion term. A new implicit difference method is constructed. The stability and convergence are discussed using a new energy method. Finally, some numerical examples are given. The numerical results demonstrate the effectiveness of theoretical analysis