Application of Whale Optimization Algorithm for tuning of a PID controller for a drilling machine

International audienceThe aim of this work is to implement the recently developed metaheuristic algorithm known as the Whale Optimization Algorithm to tune a PID controller of a high-performance drilling machine. The algorithm is evaluated by setting the Integral Absolute Error as the objective function. The simulation results are then compared with the widely used conventional tuning technique namely Ziegler-Nichols (Z-N) along with another commonly used evolutionary computation technique, the Particle Swarm Optimization (PSO). The results obtained in this work indicates that this novel algorithm can give satisfactory results while tuning the PID controller. Index Terms-Meta-heuristic algorithm, Whale optimization algorithm, PID controller

Ajuste óptimo y automático de un sistema de control en cascada. Aplicación al seguimiento de trayectorias en servosistemas con fricción y zona muerta.

En la actualidad, el control en cascada continúa siendo una de las estrategias más utilizada en la industria de procesos y manufacturera. Los nuevos requisitos de precisión y robustez en los sistemas de control de posición y trayectoria para la fabricación de componentes en la micro escala, obligan a rediseñar los métodos de ajuste para hacer frente a no linealidades duras como la fricción y la zona muerta. Este trabajo presenta el diseño y aplicación de un método de ajuste automático basado en simulación y optimización para el ajuste de los parámetros de sistema de control cascada del lazo de control de posición y de velocidad de un servomecanismo en presencia de fricción, elasticidad y holgura. La optimización basada en el método de Nelder-Mead utiliza como función de coste u objetivo, la minimización del máximo error de posición durante la inversión en presencia de no linealidades. El estudio realizado en simulación, los experimentos en tiempo real en el control de trayectoria y los estudios de viabilidad demuestran la validez de la estrategia propuesta con una mejora de más de 10% en la reducción del máximo error de posición, y sienta las bases a la implementación a nivel industrial de método propuesto

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Monitorización inteligente en tiempo real del acabado superficial de micro-piezas basado en el modelado híbrido incremental

Este trabajo propone la aplicación de una estrategia de modelado híbrido incremental (HIM) para la estimación en tiempo real de la rugosidad superficial en procesos de micromecanizado. Esta estrategia comprende fundamentalmente dos pasos. En primer lugar, se obtiene un modelo híbrido incremental representativo del proceso de micromecanizado. El resultado final de este modelo es una función de dos entradas (avance por diente cuadrático y vibración media cuadrática (rms) en el eje Z) y una salida (rugosidad superficial). En segundo lugar, se evalúa el modelo híbrido incremental en tiempo real para obtener la rugosidad superficial. El modelo se corrobora experimentalmente mediante su integración en un sistema embebido de monitorización en tiempo real del acabo superficial. La evaluación del prototipo demuestra una tasa de éxito en la estimación de la rugosidad superficial del 83%. Estos resultados son la base para el desarrollo de sistemas embebidos en la monitorización del acabo superficial de micro-piezas en tiempo real y el posterior desarrollo de una herramienta a nivel industria

Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Design of PSO-based PID Controller for Time Delay Systems

In recent years, with the development of digital computers/semiconductor devices and the establishment of various control theories, control technology has grown remarkably. However, for controllers on the industrial sites, economic feasibility, performance, reliability, and easiness of maintenance need to be essentially considered, and thus, PID controllers, which are based on a classical control technique, are still widely used, accounting for more than about 90%. A PID controller basically has a simple structure that consists of three parameters, and has relatively superior control characteristics even in a nonlinear system and a system where an accurate mathematical model cannot be obtained. Also, an engineer on the sites can easily tune controller parameters, and the establishment and maintenance of a system are convenient. However, the performance of a PID controller, which is widely used on general industrial sites, varies significantly depending on the degree of optimization of parameter tuning. On the sites, the parameters of a controller are mostly tuned based on the experience of an engineer, rather than an analytical method for a system, and it is inevitably vulnerable to the changes and uncertainties of a system. In this situation, the development of a PID controller parameter tuning technique that has superior performance and can be applied to various types of systems is required, rather than a controller design based on the experience of individuals. A number of tuning rules have been suggested until recently, and the most representative PID tuning method is the Ziegler-Nichols tuning method, which is widely used on the sites. Also, there are the Cohen-Coon method, the IMC method, and the Lopez ITAE method, which tune parameters by simplifying a hight-order system into a First-Order Plus Time Delay(FOPTD) system with a time delay. Recently, controller design methods that introduce behavioral patterns found in the natural world into an optimization technique have been studied. The representative method includes a Genetic Algorithm (GA), which has been implemented using natural selection and evolutionary mechanism. Also, a Particle Swarm Optimization (PSO) algorithm, which has recently been suggested, simulates social behavior patterns found in the communities of insects, birds, and fish. This introduces a concept, where a number of individuals find an optimal solution in a search area based on the information on each individual and the entire community, into an optimization search algorithm. Despite the relative simplicity of the algorithm, many studies have been performed based on its superior control performance. In this study, a PSO-based PID controller that optimally tunes the parameters of a controller using a PSO algorithm that is based on the social behavior patterns of organisms was proposed. To appropriately tune the three kinds of gains of the proposed PID controller (proportional gain, integral gain, and derivative gain), IAE was used as the objective function so that the sum of the absolute values of errors, which are the difference between the input and the output, could be minimized. Also, to strengthen the global search of particles in the early stage of search and to strengthen the local search in the convergence stage, the inertial load was linearly decreased as the generation number increased. To examine the validity of the proposed tuning method, simulations were performed by applying the proposed method to three kinds of systems (first-order, second-order, and fifth-order systems with a time delay)and the superiority of the proposed PSO-based PID controller was demonstrated by comparing its response characteristics with those of the Z-N tuning method, the Cohen-Coon method, the IMC method, and the Lopez ITAE method, which have frequently been used.제 1 장 서론 1.1 연구 배경 및 동향 1.2 연구 내용과 구성 제 2 장 PSO 알고리즘 2.1 PSO 알고리즘의 특징 2.2 PSO 알고리즘의 구조 2.2.1 개체 및 집단 2.2.2 Pbest 및 Gbest 2.2.3 초기집단의 생성 및 적합도 평가 2.2.4 속도 업데이트 2.2.5 위치 업데이트 2.2.6 종료 조건 2.2.7 PSO 알고리즘의 학습과정 제 3 장 PSO-PID 제어기 설계 3.1 PID 제어 3.2 PID 제어기 동조규칙 3.2.1 Ziegler-Nichols 동조법 3.2.2 Cohen-Coon 동조법 3.2.3 IMC 동조법 3.2.4 Lopez ITAE 동조법 3.3 PSO-PID 제어기 설계 3.3.1 PSO 기반 PID 제어기 3.3.2 PSO 기반 PID 제어기 동조 과정 제 4 장 모의 실험 및 검토 4.1 시스템 I 4.2 시스템 II 4.3 시스템 III 제 5 장 결 론 참고문