Active queue control aims to improve the overall communication network throughput, while providing lower delay and small packet loss rate. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. In this paper, two artificial neural networks (ANN)-based control schemes are proposed for adaptive queue control in...

This paper presents the design of an indirect adaptive feedback linearization (FBL)based control using dynamic neural networks (DNN) for full-car nonlinear electrohydraulic suspensions. Particle swarm optimization (PSO) algorithm is used in training the DNN to learn the dynamics of the system. A multi-loop, PSO-optimized proportional-integral-derivative (PID) control is implemented for the feed...

Temperature Measurement is one of the major controlling parameters in all process industries. A closed loop system is formed by connecting a controller with that process with the combination of various transducers leading to stability. This paper deals with comparison of various PID control tuning techniques and Particle Swarm Optimization (PSO) technique. A single input single output (SISO) Re...

Modern power system has complex composition structure and high stability operation requirements. While the emergence of various new energy sources uncertainty external disturbances bring a great challenge to Automatic Generation Control (AGC) system. In order improve robustness AGC facilitate practical engineering application, this paper proposes novel multistage Proportional Integral Derivativ...

H∞ control theory is usually associated with highorder controllers. In this paper, simple tuning rules (extending the well-known SIMC) for the Proportional-Integral (PI) algorithm are derived analytically based on H∞ Weighted Sensitivity. The presented approach deals with stable, integrating and unstable plants in a unified way, avoiding any notion of coprime factorization. The final tuning inv...