Vol 53, No 6 (2019)

With the big demand on water supply during the last century due to population growth, the approbation of new technology to assure water quality at lower cost is essential. This paper presents a drinking water distribution system (DWDS) based on a nonlinear fuzzy modeling technique. The approach uses a multi-input multi-output (MIMO) Takagi–Sugeno (T–S) fuzzy model, which is relevant for constructing a large class of nonlinear processes. The proposed framework is validated on a real drinking water distribution system, the MIMO fuzzy T–S model was implemented, in the context of nonlinear predictive control to regulate the water quality (the chlorine concentration in drinking water). The objective is to keep the system outputs within upper and lower limits from the requirement of health regulations.

In this paper, a novel concept of robust Nonsingular Fast Terminal Sliding Mode controller (NFTSMC) is adopted for the trajectory tracking problem of a non-linear system. The developed controller is based on NFTSM controller and \({{{\text{H}}}_{\infty }}\) approach. The use of the NFTSM controller offers a fast convergence rate, avoids singularities, but still suffers from chattering. In order to overcome this limitation, a new term in the control law is inspired by the technique of \({{{\text{H}}}_{\infty }}{\text{;}}\) it interferes by managing uncertainties and external disturbances without knowing their upper bound. Stability analysis of the closed-loop system is accomplished using the Lyapunov criterion. Several simulation results are given to show the effectiveness of the proposed approach.

In the paper presented a novel technique of intelligent constructing exact statistical prediction and tolerance limits on future random quantities for prognostics and health management of complex systems under parametric uncertainty is proposed. The aim of this technique is to develop and publish original scientific contributions and industrial applications dealing with the topics covered by Prognostics and Health Management (PHM) of complex systems. PHM is a set of means, approaches, methods and tools that allows monitoring and tracking the health state of a system in order to detect, diagnose and predict its failures. This information is then exploited to take appropriate decisions to increase the system’s availability, reliability and security while reducing its maintenance costs. The proposed technique allows one to construct developments and results in the areas of condition monitoring, fault detection, fault diagnostics, fault prognostics and decision support.

Brightness and color are two basic visual features of the human visual system. In the retina, color-sensitive cells are sensitive to brightness and color information and exhibit salient direction selectivity. However, evidence from neurobiology indicates that apart from color features, the rod and cone cells of the retina are also sensitive to high or low luminance, respectively termed the light and dark adaptation mechanisms. Classical single-opponent and color-opponent contour detection model frameworks include the computational processes of single red (R), green (G), blue (B) and yellow (Y) channels and opponent R–GB–Y channels, respectively. Thus, to combine luminance cues and traditional color cues to improve boundary detection in natural scenes, we propose the use of a dark and a light channel to simulate light and dark adaptation mechanisms. The results of the proposed model considering three datasets (BSDS300, BSDS500, NYUD) demonstrate an improvement compared with current bio-inspired contour detection models.