Robust Stabilization of Linear Time-Delay Systems under Denial-of-Service Attacks.

This research examines new methods for stabilizing linear time-delay systems that are subject to denial-of-service (DoS) attacks. The study takes into account the different effects that a DoS attack can have on the system, specifically delay-independent and -dependent behaviour. The traditional proportional-integral-derivative (PID) acts on the error signal, which is the difference between the reference input and the measured output. The approach in this paper uses what we call the PID state feedback strategy, where the controller acts on the state signal. Our proposed strategy uses the Lyapunov-Krasovskii functional (LKF) to develop new linear matrix inequalities (LMIs). The study considers two scenarios where the time delay is either a continuous bounded function or a differentiable and time-varying function that falls within certain bounds. In both cases, new LMIs are derived to find the PID-like state feedback gains that will ensure robust stabilization. The findings are illustrated with numerical examples.

Incorporating management opinion in green supplier selection model using quality function deployment and interactive fuzzy programming.

The need for environmental protection and involvement of ecological aspects in the business operations is forcing the organizations to re-examine their action plans and rebuild their supply chain activities. Many organizations are incorporating environmental rules and regulations in their everyday matters by focusing on green supplier selection. The proposed research paper develops a multi-objective interactive fuzzy programming model for the selection of suppliers. This model works on a business quartet of green appraisal score, cost, quality, and time. The model uses an environmental scale for different green parameters and all the suppliers are scored based on this scale. In this research model, Quality Function Deployment (QFD) methodology is integrated with the multi-objective interactive fuzzy programming. QFD technique is utilized to compute the weights of several green factors used for the selection of suppliers. The model uses a Fuzzy linguistic scale and a triangular membership function to link expert opinions along with their experience to solve the problem. Finally, the model is validated on a numerical case study of the textile industry for green supplier selection which achieves a 100% satisfaction for cost and time, 75% satisfaction for green appraisal score, and 93.95% for the quality. The proposed model assists the decision-makers in selecting green suppliers to improve the overall sustainability of their organizations.

Neuro-adaptive output feedback control of the continuous polymerization reactor subjected to parametric uncertainties and external disturbances.

This paper proposes an adaptive neural network based output feedback backstepping fast terminal sliding mode control (NN-BFTSMC) for continuous polymerization reactor with external disturbances and parametric uncertainties. Firstly, neural networks (NN) are employed to approximate the uncertain nonlinear functions. Next, the average molecular weight and the reactor temperature tracking controllers are designed based on the finite-time NN-BFTSMC. In addition, the NN-BFSMC effectively estimates and compensates the upper bounds of the external disturbances. Moreover, the chattering effect is eliminated without losing the robustness accuracy. The finite-time stability of the closed-loop system is proved by Lyapunov theory. At last, numerical simulations and comparative studies are introduced to illustrate the superior performance of the proposed method.