Optimization of sliding mode control with PID surface for robot manipulator by Evolutionary Algorithms

In this paper, a sliding mode controller (SMC) with PID surface is designed for the trajectory tracking control of a robot manipulator using different optimization algorithms such as, Antlion Optimization Algorithm (ALO) Sine Cosine Algorithm (SCA) Grey Wolf Optimizer (GWO) and Whale Optimizer Algorithm (WOA). The aim of this work is to introduce a novel SMC-PID-ALO to control nonlinear systems, especially the position of two of the joints of a 2DOF robot manipulator. The basic idea is to determinate four optimal parameters (Kp, Ki, Kd and lamda) ensuring the best performance of a robot manipulator system, minimizing the integral time absolute error criterion (ITAE) and the integral time square error criterion (ISTE). The robot manipulator is modeled in Simulink and the control is implemented using the MATLAB environment. The obtained simulation results prove the robustness of ALO in comparison with other algorithms

Méthodologies de diagnostic des systèmes dynamiques: Théories et exemples

La problématique de surveillance couvre un domaine très large et a fait l'objet de nombreux travaux de recherche. Deux communautés scientifiques différentes, à savoir (Systèmes Continus : SC et Systèmes à Evénements Discrets : SED), ont contribué au développement des méthodes de détection et localisation de défauts dont le but est de garantir une sécurité optimale des installations industrielles. Cependant, ces méthodologies présentent certaines limitations puisque l'abstraction considérée ne tient compte que d'un aspect dynamique. Cela est particulièrement vrai lorsque les systèmes considérés sont à dynamique hybride. Dans beaucoup d'applications, plusieurs outils, avec différents niveaux d'abstraction, doivent être conjointement utilisés pour améliorer les performances des approches de surveillance. Ce livre est consacré aux techniques de surveillance à base de modèle utilisant le concept des indicateurs de résidus. L'objectif est de présenter de nouveaux outils et méthodes de surveillance pour les systèmes complexes. Outre les notions relatives à l'aspect hybride. De nouvelles définitions et théories sont proposées pour la mise en œuvre des procédures efficaces de diagnostic

Methodology for monitoring and diagnosing faults of hybrid dynamic systems: a case study on a desalination plant

The imperative of quality and productivity has increased the complexity of technological processes, posing the problem of reliability. Today, fault diagnosis remains a very important task because of its essential role in improving reliability, but also in minimizing the harmful consequences that can be catastrophic for the safety of equipment and people. Indeed, an effective diagnosis not only improves reliability, but also reduces maintenance costs. Systems in which dynamic behaviour evolves as a function of the interaction between continuous dynamics and discrete dynamics, present in the system, are called hybrid systems. The goal is to develop monitoring and diagnostic procedures to the highest level of control to ensure safety, reliability and availability objectives. This article presents an approach to the diagnosis of hybrid systems using hybrid automata and neural-fuzzy system. The use of the neural-fuzzy system allows modeling the continuous behaviour of the system. On the other hand, the hybrid automata gives a perfect estimate of the discrete events and make it possible to execute a fault detection algorithm mainly consists of classifying the appeared defects. On the implementation plan, the results were applied in a water desalination plant

A Stochastic Epidemiological SIRD-V Model With LSM-EKF Algorithm for Forecasting and Monitoring the Spread of COVID-19 Pandemic: Real Data

Predictive algorithms for the coronavirus epidemic are indispensable tools for monitoring the dynamic spread of COVID-19 and for implementing intervention and preparedness measures to mitigate the outbreak. Many of the existing mathematical models used for epidemic analysis are deterministic in nature, which may not fully capture the complex dynamics of disease transmission. In this paper, we introduce a novel stochastic predictive algorithm known as the LSM-EKF-SIRD-V algorithm. This algorithm combines a SIRD-V model, which accounts for susceptible, infected, recovered, deceased, and vaccinated cases, with the Least Square Method (LSM) and an Extended Kalman Filter (EKF). It provides daily dynamic predictions of the system’s parameters and is employed to analyze the COVID-19 disease profile in Algeria from January 29, 2021, to October 2, 2022. The primary goal of this approach is to create a decision-support system that empowers governments and health authorities with future pandemic statistics. This information enables them to adapt and optimize hospitalization resources, allowing for more effective intervention and preparedness measures to control the spread of the pandemic. Simulation results demonstrate the effectiveness of the proposed algorithm in accurately predicting the future dynamics of coronavirus spread based on historical and current case data

Diagnosis of hybrid systems through bond graph, observers and timed automata

Despite technological advances and progress in industrial systems, the fault diagnosis of a system remains a very important task. In fact an effective diagnosis contributes not only to improved reliability but also to a decrease in maintenance costs. This paper presents an approach to a diagnosis of hybrid systems thanks to the use of Bond Graphs, Observer and Timed Automata. Dynamic models (in normal and failing mode) are generated by an observer based methods as well as through state equations generated by the Bond Graphs model. The procedure of fault localization through a method based on the observer does not allow locating faults with the same signature of failure. Thus the diagnosis technique for the localization of these defects will be based on the time analysis using Timed Automata. The proposed approach is then validated by simulation tests in a two tanks hydraulic system

Methodology to knowledge discovery for fault diagnosis of hybrid dynamical systems: demonstration on two tanks system

The work carried out in this article concerns on the implementation off a diagnostic procedure for hybrid dynamic systems (HDS) whose objective is to guarantee the proper functioning of industrial installations. In this context, the main contributions of this work are summarized into three parts: The first part is oriented to the modeling approach dedicated to HDS. The aim is to find an adequate model combining both aspects (continuous and discrete dynamics). The use of Neuro-fuzzy networks makes it possible to build a model of the system and to follow all the modes without it being necessary to identify or discern them. The second part concerns the synthesis of a fault diagnostic technique based on a fuzzy inference system. A Neuro-Fuzzy network based is used for residual generation, while for the residual evaluation, a fuzzy reasoning model is used which can mainly introduce heuristic information into the analysis scheme and takes the appropriate decision regarding the actual behaviour of the process. The proposed approach is successfully applied to monitoring faults of a non-linear three-tank system and the results confirm the effectiveness of this approach

A neural-fuzzy approach for fault diagnosis of hybrid dynamical systems: demonstration on three-tank system

This work is part of the diagnostic field of hybrid dynamic systems (HDS) whose objective is to ensure proper operation of industrial facilities. The study is initially oriented to the modelling approach dedicated to hybrid dynamical systems (HDS). The objective is to look for an adequate model encompassing both aspects (continuous and event). Then, fault diagnosis technique is synthesised using artificial intelligence (AI) techniques. The idea is to introduce a hybrid version combining neural networks and fuzzy logic for residual generation and evaluation. The proposed approach is then validated on three tank system. The modelling and diagnosis approaches are developed using MATLAB/Simulink environment