An experimental comparison of two feed-forward based frictioncompensation methods is presented. The first method is based on theLuGre friction model, using identified friction model parameters, andthe second method is based on B-spline network, where the networkweights are learned from experiments. The methods are evaluated andcompared via experiments using a six axis industrial robot carryingout circular movements of different radii. The experiments show thatthe learning-based friction compensation gives an error reduction ofthe same magnitude as for the LuGre-based friction compensation.

Feedback linearization is compared to Jacobian linearization for LQ control of atwo-link industrial manipulator. A method for obtaining equivalent nominal performance forboth control designs is introduced. An experimentally verified benchmark model with industrialrelevance is used for comparing the designs. Results do not show any conclusive advantages ofFeedback linearization.

Modeling and failure prediction is an important task in manyengineering systems. For this task, the machine learning literaturepresents a large variety of models such as classification trees,random forest, artificial neural networks, fuzzy systems, amongothers. In addition, standard statistical models can be applied suchas the logistic regression, linear discriminant analysis, $k$-nearestneighbors, among others. This work evaluates advantages andlimitations of statistical and machine learning methods to predictfailures in industrial robots. The work is based on data from morethan five thousand robots in industrial use. Furthermore, a newapproach combining standard statistical and machine learning models,named \emph{hybrid gradient boosting}, is proposed. Results show thatthe a priori treatment of the database, i.e., outlier analysis,consistent database analysis and anomaly analysis have shown to becrucial to improve classification performance for statistical, machinelearning and hybrid models. Furthermore, local joint information hasbeen identified as the main driver for failure detection whereasfailure classification can be improved using additional informationfrom different joints and hybrid models.

The CDIO framework for development of engineering education is presented, including the overall ideas, the fundamental documents, and some development tools. The automatic control subject and its role in engineering education is studied using the CDIO Standards as reference. Some examples from the engineering education at Linkoping University are presented with special focus on the control education. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

This report describes the motivation, the current state and the future actions of an improvement process in engineering education at the Brazilian higher education institution called the Military Institute of Engineering. Based on the reasons for why and how to change, the CDIO framework was chosen, at the end of 2014, as the kernel of this improvement process. The activities realized, the plan for the future actions and the open questions are presented in this report.

This paper describes the motivation, the current state and the further actions of an improvement process of the engineering education at the Military Institute of Engineering (IME) in Brazil. Based on the reasons for why and how to change, the CDIO framework has been chosen as the kernel of this improvement process. The activities realized the plan of the further actions and the open questions are presented in this paper. The paper is a condensed presentation of the report (Cerqueira et. al., 2016), where a thorough background and more details can be found.

A first course in Automatic control is presented.  A main objective of the course is to put most of the emphasis on the Implement and Operate phases in the process of developing a control system for a process. The course is built around a large amount of student active learning based on three extensive laboratory exercises, where each laboratory exercise can have duration of up to two weeks. For each of the laboratory exercises there is a sequence of learning activities supporting the students’ learning: Introductory lecture, problem solving session, preparation work, help-desk session, independent work in the laboratory, and a final demonstration of the control system. In addition there is a small project where the task is to write a manual for a process operator. The laboratory tasks involve implementation of a control system in an industrial PLC (Programmable Logic Controller) and development of an operator interface.

This paper presents a data-driven approach to diagnostics of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against an available nominal batch. The method proposed considers the comparison of data in the distribution domain, which reveals information of the data amplitude. This is achieved with the use of kernel density estimates and the Kullback–Leibler distance. To decrease sensitivity to disturbances while increasing sensitivity to faults, the use of a weighting vector is suggested which is chosen based on a labeled dataset. The framework is simple to implement and can be used without process interruption, in a batch manner. The approach is demonstrated with successful experimental and simulation applications to wear diagnostics in an industrial robot gearbox and for diagnostics of gear faults in a rotating machine.

Estimation-based iterative learning control (ILC) is applied to a parallel kinematic manipulator known as the Gantry-Tau parallel robot. The system represents a control problem where measurements of the controlled variables are not available. The main idea is to use estimates of the controlled variables in the ILC algorithm, and in the paper this approach is evaluated experimentally on the Gantry-Tau robot. The experimental results show that an ILC algorithm using estimates of the tool position gives a considerable improvement of the control performance. The tool position estimate is obtained by fusing measurements of the actuator angular positions with measurements of the tool path acceleration using a complementary filter.

Fault detection algorithms (FDAs) process data to generate a test quantity. Test quantities are used to determine presence of a fault in a monitored system, despite disturbances. Because only limited knowledge of the system can be embedded in an FDA, it is important to evaluate it in scenarios relevant in practice. In this paper, simulation based approaches are proposed in an attempt to determine: i) which disturbances affect the output of an FDA the most; ii) how to compare the performance of dierent FDAs; and iii) which combinations of fault change size and disturbances variations are allowed to achieve satisfactory performance. The ideas presented are inspired by the literature of design of experiments, surrogate models, sensitivity analysis and change detection. The approaches are illustrated for the problem of wear diagnosis in manipulators where three FDAs are considered. The application study reveals that disturbances caused by variations in temperature and payload mass error affect the FDAs the most. It is also shown how the size of these disturbances delimit the capacity of an FDA to relate to wear changes. Further comparison of the FDAs reveal which performs "best" in average.