Sampling-Based Event-Triggered Control for Neutral-Type Complex-Valued Neural Networks with Partly Unknown Markov Jump and Time-Varying Delay

This work is devoted to studying the stochastic stabilization of a class of neutral-type complex-valued neural networks (CVNNs) with partly unknown Markov jump. Firstly, in order to reduce the conservation of our stability conditions, two integral inequalities are generalized to the complex-valued domain. Secondly, a state-feedback controller is designed to investigate the stability of the neutral-type CVNNs with H ∞ performance, making the stability problem a further extension, and then, the stabilization of the CVNNs with H ∞ performance is investigated through a sampling-based event-triggered (SBET) control for the first time that the transmission event is not triggered except when it violates the event-triggered condition. Finally, two examples are given to illustrate the validity and correctness of our obtained theorems.

Stabilizing multiple equilibria and cycles with noisy prediction-based control

<p style='text-indent:20px;'>Pulse stabilization of cycles with Prediction-Based Control including noise and stochastic stabilization of maps with multiple equilibrium points is analyzed for continuous but, generally, non-smooth maps. Sufficient conditions of global stabilization are obtained. Introduction of noise can relax restrictions on the control intensity. We estimate how the control can be decreased with noise and verify it numerically.</p>

Dynamic Design of Optimal Stochastic Stabilization System of Cutting Power on a Band Saw Machine

The article is devoted to the development of methodological foundations for constructing an optimal system of stochastic stabilization of cutting power based on the results of structural identification of models of the dynamics of the system '' woodworking machine-cutting process '' and uncontrolled disturbance. In order to solve the problem of structural identification of the '' woodworking machine-cutting process ' system and the disturbance acting in the process of wood-cutting, the article proposes a special technology, the use of which made it possible to determine the transfer function of the '' woodworking machine-cutting process '' and estimate the spectral density of the disturbance acting during the processing. It has been established that when the physical and mechanical properties of wood and the state of the cutting tool change, the structure of the transfer function and spectral density does not change, but only the parameters change.As a result of solving the synthesis problem, the structure and parameters of the optimal controller are determined, which ensures the specified quality of the processed surface with minimal energy consumption. To assess the quality of control, it is proposed to use a quadratic criterion, which is the sum of two weighted variances of the stator current deviation of the main motion motor (characterizes energy costs) and the variance of the feed drive speed control signal.Studies of the robust stability of the optimal system with the obtained controller under the influence of unstructured disturbances made it possible to determine the class and estimate the maximum norms of unstructured disturbances at which the system maintains stability and a given control quality. The use of the proposed approach to the construction of an optimal system of stochastic stabilization of cutting power makes it possible to achieve a reduction in energy costs by 12% for a given quality of the processed surface by increasing the stabilization accuracy by two orders of magnitude.

Stochastic Stabilization of Malware Propagation in Wireless Sensor Network via Aperiodically Intermittent White Noise

In this paper, we propose a novel heterogeneous model to describe the propagation dynamics of malware (viruses, worms, Trojan horses, etc.) in wireless sensor networks. Our model takes into consideration different battery-level sensor nodes contrary to existing models. In order to control the spread of malware, we design an aperiodically intermittent controller driven by white noise, which has striking advantages of lower cost and more flexible control strategy. We give a distinct condition on stability in probability one using graph-theoretical Lyapunov function and stochastic analysis method. Our results show that the nonlinear malware propagation system can be stabilized by intermittent stochastic perturbation under the intermittent time related to stochastic perturbation intensity. Our theoretical results can be applied to understand the observed mechanisms of malware and design interventions to control the spread of malware. Numerical simulations illustrate our analytical results clearly.