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2011 | Buch

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Modelling and Control for Intelligent Industrial Systems

Adaptive Algorithms in Robotics and Industrial Engineering

verfasst von: Gerasimos G. Rigatos

Verlag: Springer Berlin Heidelberg

Buchreihe : Intelligent Systems Reference Library

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Incorporating intelligence in industrial systems can help to increase productivity, cut-off production costs, and to improve working conditions and safety in industrial environments. This need has resulted in the rapid development of modeling and control methods for industrial systems and robots, of fault detection and isolation methods for the prevention of critical situations in industrial work-cells and production plants, of optimization methods aiming at a more profitable functioning of industrial installations and robotic devices and of machine intelligence methods aiming at reducing human intervention in industrial systems operation.

To this end, the book analyzes and extends some main directions of research in modeling and control for industrial systems. These are: (i) industrial robots, (ii) mobile robots and autonomous vehicles, (iii) adaptive and robust control of electromechanical systems, (iv) filtering and stochastic estimation for multisensor fusion and sensorless control of industrial systems (iv) fault detection and isolation in robotic and industrial systems, (v) optimization in industrial automation and robotic systems design, and (vi) machine intelligence for robots autonomy. The book will be a useful companion to engineers and researchers since it covers a wide spectrum of problems in the area of industrial systems. Moreover, the book is addressed to undergraduate and post-graduate students, as an upper-level course supplement of automatic control and robotics courses.

Inhaltsverzeichnis

Frontmatter

Industrial Robots in Contact-Free Operation

Abstract

A study of industrial robotic systems is provided, for the case of contact-free operation. This part of the book includes the dynamic and kinematic analysis of rigid-link robotic manipulators, and expands towards more specialized topics, such as dynamic and kinematic analysis of flexible-link robots, and control of rigid-link and flexible-link robots in contact-free operation.

Gerasimos G. Rigatos

Industrial Robots in Compliance Tasks

Abstract

An analysis of industrial robot control is given, for the case of compliance tasks. First, rigid-link robotic models are considered and the impedance control and hybrid position-force control methods are analyzed. Next, force control methods are generalized in the case of flexible-link robots performing compliance tasks.

Gerasimos G. Rigatos

Mobile Robots and Autonomous Vehicles

Abstract

An analysis of the kinematic model of automatic ground vehicles is given and nonlinear control for this type of vehicles is presented. Moreover, the kinematic and dynamic model of unmanned surface vessels is studied and nonlinear control for the dynamic ship positioning problem is in turn formulated.

Gerasimos G. Rigatos

Adaptive Control Methods for Industrial Systems

Abstract

A method for the design of stable adaptive control schemes for a class of industrial systems is first studied. The considered adaptive controllers can be based either on feedback of the complete state vector or on feedback of the system’s output. In the latter case the objective is to succeed simultaneous estimation of the system’s state vector and identification of the unknown system dynamics. Lyapunov analysis provides necessary and sufficient conditions in the controller’s design that assure the stability of the control loop. Examples of adaptive control applications to industrial systems are presented.

Gerasimos G. Rigatos

Robust Control Methods for Industrial Systems

Abstract

Robust control approaches for industrial systems are studied. Such methods are based on sliding-mode control theory where the controller’s design is performed in the time domain and Kharitonov’s stability theory where the controller’s design is performed in the frequency domain. Applications of robust control to industrial systems are given.

Gerasimos G. Rigatos

Filtering and Estimation Methods for Industrial Systems

Abstract

Filtering and stochastic estimation methods are proposed for the control of linear and nonlinear dynamical systems. Starting from the theory of linear state observers the chapter proceeds to the standard Kalman filter and its generalization to the nonlinear case which is the Extended Kalman Filter. Additionally, Sigma-Point Kalman Filters are proposed as an improved nonlinear state estimation approach. Finally, to circumvent the restrictive assumption of Gaussian noise used in Kalman filter and its variants, the Particle Filter is proposed. Applications of filtering and estimation methods to industrial systems control with a reduced number of sensors are presented.

Gerasimos G. Rigatos

Sensor Fusion-Based Control for Industrial Systems

Abstract

Sensor fusion with the use of filtering methods is studied and state estimation of nonlinear systems based on the fusion of measurements from distributed sources is proposed for the implementation of stochastic control loops for industrial systems. Extended Kalman and Particle Filtering are first proposed for estimating, through multi-sensor fusion, the state vector of an industrial robotic manipulator and the state vector of a mobile robot. Moreover, sensor fusion with the use of Kalman and Particle Filtering is proposed for the reconstruction from output measurements the state vector of a ship which performs dynamic positioning.

Gerasimos G. Rigatos

Distributed Filtering and Estimation for Industrial Systems

Abstract

Distributed Filtering and estimation methods for industrial systems are studied. Such methods are particularly useful in case that measurements about the industrial system are collected and processed by n different monitoring stations. The overall concept is that at each monitoring station a filter is used to track the state of the system by fusing measurements which are provided by various sensors, while by fusing the state estimates from the distributed local filters an aggregate state estimate for the industrial system is obtained. In particular, the chapter proposes first the Extended Information Filter (EIF) and the Unscented Information Filter (UIF) as possible approaches for fusing the state estimates provided by the local monitoring stations, under the assumption of Gaussian noises. The EIF and UIF estimated state vector can, in turn, be used by nonlinear controllers which can make the system’s state track desirable setpoints. Moreover, the Distributed Particle Filter (DPF) is proposed for fusing the state estimates provided by the local monitoring stations (local filters). The motivation for using DPF is that it is well-suited to accommodate non-Gaussian measurements. The DPF estimated state vector can again be used by a nonlinear controller to make system converge to desirable setpoints. The performance of the Extended Information Filter, of the Unscented Information Filter and of the Distributed Particle Filter is evaluated through simulation experiments in the case of a 2-UAV (unmanned aerial vehicle) model monitored and remotely navigated by two local stations.

Gerasimos G. Rigatos

Fault Detection and Isolation for Industrial Systems

Abstract

The chapter analyzes a fault detection and isolation approach for efficient condition monitoring of industrial systems. As shown, two main issues in statistical methods for fault diagnosis are residuals generation and fault threshold selection. For residuals generation an accurate model of the system in the fault-free condition is needed. Such models can be obtained through nonlinear identification techniques or through nonlinear state estimation and filtering methods. On the other hand the fault threshold should enable both diagnosis of incipient faults and minimization of the false alarms rate.

Gerasimos G. Rigatos

Application of Fault Diagnosis to Industrial Systems

Abstract

Applications of statistical methods for fault diagnosis are presented. First, the problem of early diagnosis of cascading events in the electric power grid is considered. Residuals are generated with the use of a nonlinear model of the distributed electric power system and the fault threshold is determined with the use of the generalized likelihood ratio assuming that the residuals follow a Gaussian distribution. Next, the problem of fault detection and isolation in electric motors is analyzed. It is proposed to use nonlinear filters for the generation of residuals and to derive a fault threshold from the generalized likelihood ratio without prior knowledge of the residuals statistical distribution.

Gerasimos G. Rigatos

Optimization Methods for Motion Planning of Multi-robot Systems

Abstract

Optimization through nonlinear programming techniques, such as gradient algorithms, can be an efficient approach for solving various problems in the design of intelligent robots, e.g. motion planning for multi-robot systems. A distributed gradient algorithm is proposed for enabling coordinated convergence of an ensemble of mobile robots towards a goal state, and at the same time for assuring avoidance of collisions between the robots as well as avoidance of collisions with obstacles in the motion plane. The stability of the multi-robot system is proved with Lyapunov’s theory and particularly with LaSalle’s theorem. Motion planning with the use of distributed gradient is compared to motion planning based on particle swarm optimization.

Gerasimos G. Rigatos

Optimization Methods for Target Tracking by Multi-robot Systems

Abstract

The chapter studies the two-fold optimization problem of distributed motion planning and distributed filtering for multi-robot systems. Tracking of a target by a multi-robot system is pursued assuming that the target’s state vector is not directly measurable and has to be estimated by distributed filtering based on the target’s cartesian coordinates and bearing measurements obtained by the individual mobile robots. The robots have to converge in a synchronized manner towards the target, while avoiding collisions between them and avoiding collisions with obstacles in the motion plane. To solve the overall problem, the following steps are followed: (i) distributed filtering, so as to obtain an accurate estimation of the target’s state vector. This estimate provides the desirable state vector to be tracked by each one of the mobile robots, (ii) motion planning and control that enables convergence of the vehicles to the goal position and also maintains the cohesion of the vehicles swarm. The efficiency of the proposed distributed filtering and distributed motion planning scheme is tested through simulation experiments.

Gerasimos G. Rigatos

Optimization Methods for Industrial Automation

Abstract

Evolutionary algorithms are powerful optimization methods which in several cases can go beyond nonlinear programming optimization techniques and can be successfully applied to complex optimization problems. In this chapter, a genetic algorithm with a new crossover operator is developed to solve the warehouse replenishment problem. The automated warehouse management is a multi-objective optimization problem since it requires to satisfy goals and performance indexes that are usually conflicting with each other. The decisions taken must ensure optimized usage of resources, cost reduction and better customer service. It is shown that the proposed genetic algorithm produces Pareto-optimal permutations of the stored products.

Gerasimos G. Rigatos

Machine Learning Methods for Industrial Systems Control

Abstract

Machine learning methods are of particular interest in the design of intelligent industrial systems since they can provide efficient control despite model uncertainties and imprecisions. The chapter proposes neural networks with Gauss-Hermite polynomial basis functions for the control of flexible-link manipulators. This neural model employs basis functions which are localized both in space and frequency thus allowing better approximation of the multi-frequency characteristics of vibrating structures. Gauss-Hermite basis functions have also some interesting properties: (i) they remain almost unchanged by the Fourier transform, which means that the weights of the associated neural network demonstrate the energy which is distributed to the various eigenmodes of the vibrating structure, (ii) unlike wavelet basis functions the Gauss-Hermite basis functions have a clear physical meaning since they represent the solutions of differential equations of stochastic oscillators and each neuron can be regarded as the frequency filter of the respective vibration eigenfrequency.

Gerasimos G. Rigatos

Machine Learning Methods for Industrial Systems Fault Diagnosis

Abstract

Machine learning methods can be of particular interest for fault diagnosis of systems that exhibit event-driven dynamics. For this type of systems fault diagnosis based on automata and finite state machine models has to be performed. In this chapter the application of fuzzy automata for fault diagnosis is analyzed. The output of the monitored system is partitioned into linear segments which in turn are assigned to pattern classes (templates) with the use of membership functions. A sequence of templates is generated and becomes input to fuzzy automata which have transitions that correspond to the templates of the properly functioning system. If the automata reach their final states, i.e. the input sequence is accepted by the automata with a membership degree that exceeds a certain threshold, then normal operation is deduced, otherwise, a failure is diagnosed. Fault diagnosis of a DC motor is used as a case study.

Gerasimos G. Rigatos

Applications of Machine Vision to Industrial Systems

Abstract

Applications of vision-based industrial robotic systems are rapidly expanding due to the increase in computer processing power and low prices in machine vision hardware. Visual servoing over a network of synchronized cameras is an example where the significance of machine vision and distributed filtering for industrial robotic systems can be seen. A robotic manipulator is considered and a cameras network consisting of multiple vision nodes is assumed to provide the visual information to be used in the control loop. A derivative-free implementation of the Extended Information Filter is used to produce the aggregate state vector of the robot by processing local state estimates coming from the distributed vision nodes. The performance of the considered vision-based control scheme is evaluated through simulation experiments.

Gerasimos G. Rigatos

Backmatter

Titel: Modelling and Control for Intelligent Industrial Systems
verfasst von: Gerasimos G. Rigatos
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-642-17875-7
Print ISBN: 978-3-642-17874-0
DOI: https://doi.org/10.1007/978-3-642-17875-7