The final output of modern processes is significantly influenced by the selection of the set points of the process variables, as they fundamentally impact the product quality characteristics and the process performance metrics. This paper proposes a decision support system structure based on models built using fuzzy cognitive maps, an optimization problem that employs these models as constraints, and an observer that augments the optimization problem and allows to implement a run-to-run control approach, enabling the system to correct the suggested set-point values in case of a mismatch between the desired process output and the actual one. An application example related to an industrial process is included to illustrate the proposed approach.

Decision support systems (DSSs) are a convenient tool to aid plant operators in the selection of process set points. Inputs to these systems for variables that are not easily measured online often come from assessments made by experts, with an associated degree of uncertainty. The application of fuzzy sets and systems as part of DSSs provides a systematic approach to addressing the uncertainty in its variables. This paper builds on prior work on DSSs utilising fuzzy cognitive maps and introduces a non-singleton fuzzification stage which directly addresses uncertainty in system inputs. The motivation of the proposed system is grounded in the real world challenges of producing high-quality olive oil and the paper provides promising application and analysis results as part of the Virgin Olive Oil Production Process.

The selection of the set points of the process variables plays a fundamental role in the final output of modern processes, as the quality characteristics of the final product and the performance metrics of the process are heavily influenced by these decisions. This paper presents a decision support system to determine these set points based on three components: a model of the system of interest based on the Fuzzy Cognitive Maps methodology, an optimization problem that provides the most appropriate actions to perform in each particular situation, and an observer that augments the optimization problem and allows to include feedback to the system, practically implementing a run-to-run control approach. A simple application example is included to illustrate the proposed approach.

Web Laboratories (WebLabs) are essential resources for engineering education. Within this type of laboratory, a new Sharable Content Object Reference Model (SCORM) package design model is shown in this work. The proposed model is based on a set of resources that form a complete educational structure; taking into account the sequencing and navigation among the elements that compose it. In addition, it can be uploaded to a Learning Management System (LMS), offering to the students an effective tool to work with web labs. The package is structured in 4 sections or pages: Introduction, Theory, Virtual/Remote Laboratory (VRL) and Assessment; where the last performs an automated verification of the work done by the learner. In this way, one of the advantage of this methodology is that the instant results of the assessments, performed by the SCORM package, is stored in the inner LMS. Some WebLabs performing this methodology are being developed in the University of Jaén. In this work, an example is developed: "Modelling of Dynamic System: DC Motor". This WebLab is used in the Industrial Automation course that is included in several Industrial Engineering degrees.

This paper outlines a new procedure for building educational Web Laboratories (WebLabs). These WebLabs are based on JAVA and are offered to learners from Learning Management Systems (LMSs). The Virtual-Remote Laboratories (VRL) are JAVA Applets embedded in Sharable Content Object Reference Model (SCORM) packages that are able to interact with the LMS where they are hosted. This interaction is based on the exchange of information defined by the Run-Time Environment (RTE) and the Run-Time Navigation (RTN) SCORM data models. Both models are managed by the LMS where the SCORM content has been loaded. There are three types of data: read-only, write-only and read/write. In addition, a new JAVA package called scormRTE is presented; it has been developed to facilitate the VRL-LMS information exchange. This package enables a JAVA applet to manipulate directly all the elements of RTN and RTE data models. For the validation, a VRL, created with Easy Java Simulations (EJS), has been developed using this proposal. This VRL has been embedded into a SCORM package in order to build a virtual WebLab about dynamic system modeling. In particular, it is the modelling and identification of a car's suspension dynamics. This WebLab is used in an Industrial Automation course that is offered in several Engineering Grades of the University of Jaén. It is available in the institutional LMS.

Nowadays, complex dynamic systems, such as robotics, demand new and highly sophisticated software developments. But, basically, robotics arm applications need to get information, through sensors, from the variant and unknown environment, this is available to controllers which also have information about the robot's state (position, velocity, acceleration, torque, etcetera) and taking into account a reference, they generate the next state for the robot. Therefore, every robotic platform should require a set of sensors to take information from the environment, controllers and robots (with the low level controller). In order to cope with devices, which part of them exhibit vendor-dependent characteristics, a standard interface is proposed to allow the designers to abstract the vendor specific drivers' knowledge and to guide them in this work. This paper also explores the advantages of model-based techniques to design robotic-arm applications. In particular the Unified Modelling Language has been used for modelling the functionality in the robotic's manipulator applications, from where components of the OROCOS (Open RObot COntrol Software) are automatically generated. The proposition, characterized by its openness, productivity, portability and interoperability, is based on the construction of a complex robotic manipulator platform that integrates a Staubli industrial robot and a MEKA anthropomorphic robotic arm. Both can be used, in an easy way, in advanced experiments as it is shown in the results.

The olive paste preparation is one of the most important task within the olive oil elaboration process. It greatly determines the quality of the obtained oil, and imposes an upper bound on the achievable extraction yield. Currently, the malaxing state control of the olive paste is performed by the master miller, who observes periodically the paste inside the thermo mixer and acts on variables manually. In this paper, the implementation of a new methodology to control the malaxing state of the olive paste is presented. The methodology is based on a software sensor that obtains some olive paste features from the acquired images inside the thermo mixer, such as granularity, presence of floating oil and viscosity. This software sensor is formed by two camera devices, and it is coded by image processing algorithms from textural and color information. Concretely, the grey level co-ocurrence matrix and the segmentation method based on threshold, were employed. Also, a fuzzy controller was designed to act automatically on the thermo mixer variables. This controller is based on the master miller expert knowledge, and it allows to adjust the malaxing time needed to prepare the olive paste to the minimum value. This fact supposes a significative improvement in the energy consumption of the plant.

The virgin olive oil elaboration process (VOOEP) is an industrial process whose control relies heavily in the knowledge of experts. In this paper, the basis of a fuzzy expert system for the determination of the set points of the VOOEP which uses inputs from the expert operator is presented, and a system is constructed and validated based on the knowledge of expert operators of the process. The expert system is built based on the decomposition of the original problem into three sub-problems, deriving a sub-system for each of them. Besides, a notion of maximum attainable quality estimate and a paste preparation variable are introduced.

This paper presents a visual servo strategy for an anthropomorphic manipulator with eye-to-hand configuration. As novelty, a time-of-flight (ToF) camera, together with a model-based predictive control (MPC), have been used to execute the manipulator tasks in an unstructured environment. The ToF camera provides 3-D data of a target object and the MPC cost function achieves convergence of robot motion. With this configuration, there is no requirement to estimate a 3-D feature point position from a 2-D image and the camera model. A procedure for the target segmentation on the depth data, which is based on a previous training, is also presented. Experimental results, obtained in a robotic platform, show that the proposed approach is well suited for visual servoing purposes.

Web-based laboratories have been used as valuable resources to support teaching for many years. The methodology and operation of laboratories over the Internet have changed considerably since the early implementations. In fact, one of the last steps taken related to this issue has been the integration of laboratories and Learning Management Systems (LMS). In this paper we present methods that facilitate the development of virtual and/or remote labs software to interact with Learning Management Systems using Shareable Content Object Reference Model (SCORM), the most important standard for e-learning content. A virtual lab model is developed to show how the methods are used in order to integrate the virtual lab software with the LMS via SCORM.

Many machine vision applications require manipulation of the acquired image to prepare it for processing. One type of pre-processing operation consists in identifying the areas of interest—or regions of interest (ROIs)—for the image analysis. But in occasions, the image areas to be analyzed may vary from one part to another. This is the case of the automated inspection of headlamp lenses that, due to the part positioning mechanism, the image areas to be processed do not always stand in the same image site. This paper presents an automatic procedure to adjust the region of interest in the images acquired for the inspection of a commercial model of headlamp lens. This proposal is included in a machine vision prototype for the automated inspection of such lenses. The results herein presented are achieved with this prototype.

A vehicle headlamp is a high-sophisticated device basically composed of three elements: housing, lighting system and lens. Nowadays, they are assembled with no dimensional control, obtaining a final product with high physical deviations. Since the car manufacturer is requiring more and more precision, the number of faulty headlamps is increasing, and thus the cost to the headlamp producer. To solve this problem, a new automatic assembly machine was built. It integrated different technologies such as sensor systems, linear actuators and computational capacities. This work presents the conception, design and development of a fuzzy control algorithm for an automatic machine capable of correcting the dimensional tolerances of vehicle headlamps during its assembly process. Particulary, this paper describes the problem of modeling and control an industrial system, with a high complex dynamics and unknown model, by a rule-based model constructed from measured data. To validate the proposed control system, different experimental tests were carried out in both the lab and the assembly line in the factory. The obtained results show how the prototype, together with the proposed fuzzy software, solve the problem of assembling vehicle headlamps obtaining a final product with minimum dimensional errors.

In this work, the conception, design and development of a prototype for the automatic assembly and dimensional correction of vehicle headlamps is presented. Firstly, we analyze different measurement systems that can be used to determine the position of the elements that compounds the headlamp; secondly, we show the main conclusions obtained from the application of different technologies of actuators used to control the position of the components during the assembly. Finally, it is described the experimental results which validate the proposed system . They were carried out in both the lab and the assembly line in the factory. The new prototype solve the problem of assembling vehicle headlamps obtaining a final product with minimum dimensional errors.

This paper presents a novel lighting system to reveal surface defects on transparent parts with non-planar surface. Thanks to this system, the defect segmentation is straightforward and fast to compute and so a real-time inspection of these parts is possible. To aid in the conception of the imaging conditions, the lighting system is completely described and also a comparative study with others commercial lighting systems is done. Then, a new adaptive threshold selection algorithm for defect segmentation is proposed. Finally, the system performance is assessed using a commercial model of headlamp lens.

Force feedback is necessary for accurate force control in robotic manipulators, and thus far, wrist force/torque (F/T) sensors have been used. But an important problem arises when only these types of sensors are used. In a dynamic situation where the manipulator moves in either free or constrained space, the interaction forces and moments at the contact point and also the noncontact ones are measured by the mentioned sensor. In this paper, an estimator based on a sensor fusion strategy integrating the measurements of three different sensors (a wrist F/T sensor, an inertial sensor, and joint sensors) was developed to determine the contact force and torque exerted by the manipulator to its environment. The resulting observer helps to overcome some difficulties of uncertain world models and unknown environments since it reduces the high-frequency and low-frequency spectral contents, i.e., the low-frequency component due to inertia of a heavy tool mass and the high-frequency component due to impacts. The new improvement was experimentally validated in a force/position impedance control loop applied to a StÄubli RX60 industrial robotic platform.

In this work, we present implementation and experiment of the theory of dynamic force sensing for robotic manipulators. In the robot manipulation context, end-effector contact forces may be difficult to measure due to tool interference, yet indirect measurement such as from wrist-mounted force sensors provide force measurement contaminated by inertial forces of the tool distal to the force sensor. In order to extract the contact force exerted by the robot, it is necessary to separate the contact forces from inertial forces. We propose a complete formulation and implementation of a new control strategy based on multi-sensor fusion with three different sensors - that is, encoders mounted at each joint of the robot with six degrees of freedom, a wrist force sensor and accelerometers - whose goal is to obtain a suitable contact force estimator. These new observers contribute to overcome many of the difficulties of uncertain world models and unknown environments, which limit the domain of application of contemporary robots used without external sensory feedback; in addition, the final observer can be applied to any kind of real robot system. An impedance control scheme was proposed to verify the improvement. The experiments were carried out on an industrial Staubli RX60 manipulator with open control system architecture

In this paper, we propose a method for self-calibration of a robotic manipulator force observer, which fuses information from force sensors and accelerometers in order to estimate the contact force exerted by a manipulator to its environment, by means of active motion. In robotic operation, during contact transition accelerometers and force sensors play a very important role and serve to overcome many of the difficulties of uncertain world models and unknown environments, limiting the domain of application of current robots used without external sensory provided. The calibration procedure helps to improve the performance as well as enhanced stability and robustness for the transition phase. A variety of accelerometers were used to validate the procedure. A dynamic model of the robot-grinding tool using the new sensors was obtained by system identification. An impedance control scheme was proposed to verify the improvement. The experiments were carried out on an ABB industrial robot with open control system architecture.

In this work, we present implementation and experiment of the theory of dynamic force sensing for robotic manipulators, which uses a sensor fusion technique in order to extract the contact force exerted by the end-effector of the manipulator from those measured by a wrist force sensor, which are corrupted by the inertial forces on the end-effector. We propose a new control strategy based on multisensor fusion with three different sensors— that is, encoders mounted at each joint of the robot with six degrees of freedom, a wrist force sensor and an accelerometer— whose goal is to obtain a suitable contact force estimator for the three Cartesian axes. This new observer contributes to overcome many of the difficulties of uncertain world models and unknown environments, which limit the domain of application of currents robots used without external sensory feedback. An impedance control scheme was proposed to verify the improvement. The experiments were carried out on an ABB industrial robot with open control system architecture.

In this paper, robotic sensor fusion of acceleration and force measurement is considered. We discuss the problem of using accelerometers close to the end-effectors of robotic manipulators and how it may improve the force control performance. We introduce a new model-based observer approach to sensor fusion of information from various different sensors. During contact transition, accelerometers and force sensors play a very important role and it can overcome many of the difficulties of uncertain models and unknown environments, which limit the domain of application of currents robots used without external sensory feedback. A model of the robot-grinding tool using the new sensors was obtained by system identification. An impedance control scheme was proposed to verify the improvement The experiments were carried out on an ABB industrial robot with open control system architecture.

This paper presents a way of implementing a model-based predictive controller (MBPC) for mobile robot navigation using genetic algorithms to perform online nonlinear optimization. Obstacle avoidance is considered by the controller. Constraints on the maximum attainable speeds are also considered by the algorithm. Experimental results obtained when applying the controller to a TRC LABMATE mobile platform are given in the paper.