Optimal adaptive barrier-function super-twisting nonlinear global sliding mode scheme for trajectory tracking of parallel robots.

Compared to serial robots, parallel robots have potential superiorities in rigidity, accuracy, and ability to carry heavy loads. On the other hand, the existence of complex dynamics and uncertainties makes the accurate control of parallel robots challenging. This work proposes an optimal adaptive barrier-function-based super-twisting sliding mode control scheme based on genetic algorithms and global nonlinear sliding surface for the trajectory tracking control of parallel robots with highly-complex dynamics in the presence of uncertainties and external disturbances.

H∞-based control of multi-agent systems: Time-delayed signals, unknown leader states and switching graph topologies.

The paper investigates a leader-following scheme for nonlinear multi-agent systems (MASs). The network of agents involves time-delay, unknown leader's states, external perturbations, and switching graph topologies. Two distributed protocols including a consensus protocol and an observer are utilized to reconstruct the unavailable states of the leader in a network of agents.

Complex dynamical behaviors of a novel exponential hyper-chaotic system and its application in fast synchronization and color image encryption.

This paper proposes a novel exponential hyper-chaotic system with complex dynamic behaviors. It also analyzes the chaotic attractor, bifurcation diagram, equilibrium points, Poincare map, Kaplan-Yorke dimension, and Lyapunov exponent behaviors. A fast terminal sliding mode control scheme is then designed to ensure the fast synchronization and stability of the new exponential hyper-chaotic system.

A composite feedback approach to stabilize nonholonomic systems with time varying time delays and nonlinear disturbances.

In this work, we propose a robust stabilizer for nonholonomic systems with time varying time delays and nonlinear disturbances. The proposed approach implements a composite nonlinear feedback structure in which a linear controller is designed to yield a fast response and a nonlinear feedback control law is considered to increase the system's damping ratio. This structure results in the simultaneous improvement of the steady-state accuracy and transient performance of time-delay nonholonomic systems.

A frequency-domain approach to improve ANNs generalization quality via proper initialization.

The ability to train a network without memorizing the input/output data, thereby allowing a good predictive performance when applied to unseen data, is paramount in ANN applications. In this paper, we propose a frequency-domain approach to evaluate the network initialization in terms of quality of training, i.e.