Higher-Order Iterative Learning Control with Optimal Control Gains Based on Evolutionary Algorithm for Nonlinear System

For the nonlinear discrete-time system, higher-order iterative learning control (HOILC) with optimal control gains based on evolutionary algorithm (EA) is developed in this paper. Since the updating actions are constituted by the tracking information from several previous iterations, the suitably designed HOILC schemes with appropriate control gains usually achieve fast convergence speed. To optimize the control gains in HOILC approach, EA is introduced. The encoding strategy, population initialization, and fitness function in EA are designed according to the HOILC characteristics. With the global optimization of EA, the optimal control gains of HOILC are selected adaptively so that the number of convergence iteration is reduced in ILC process. It is shown in simulation that the sum absolute error, total square error, and maximum absolute error of tracking in the proposed HOILC based on EA are convergent faster than those in conventional HOILC.

Static/dynamic input-based event-triggered control for a network resource constraint environment

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced

Static/dynamic input-based event-triggered control for a network resource constraint environment

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced

Sojourn Times in A Queueing System with Breakdowns and General Retrial Times

This paper centers on a discrete-time retrial queue where the server experiences breakdowns and repairs when arriving customers may opt to follow a discipline of a last-come, first-served (LCFS)-type or to join the orbit. We focused on the extensive analysis of the system, and we obtained the stationary distributions of the number of customers in the orbit and in the system by applying the generation function (GF). We provide the stochastic decomposition law and the application bounds for the proximity between the steady-state distributions for the queueing system under consideration and its corresponding standard system. We developed recursive formulae aimed at the calculation of the steady-state of the orbit and the system. We proved that our discrete-time system approximates M/G/1 with breakdowns and repairs. We analyzed the busy period of an auxiliary system, the objective of which was to study the customer’s delay. The stationary distribution of a customer’s sojourn in the orbit and in the system was the object of a thorough and complete study. Finally, we provide numerical examples that outline the effect of the parameters on several performance characteristics and a conclusions section resuming the main research contributions of the paper.