Current research

[11]. TOOLING4G - Advanced Tools for Smart Manufacturingt
Role: Team Member.
Host institutions: University of Coimbra (UC); CENTIMFE - Technological Center for Moulds, Special Tools and Plastics (overall management); Aníbal H. Abrantes - Industries of Moulds and Plastics, S.A. (leader); and the project a partnership between 30 entities, including 20 companies, and 10 non-corporate R&D institutions, higher education entities, and technological interface centers.
Financing: co-financing by the “Competitiveness and Internationalization Operational Programme” (COMPETE 2020), Lisbon Regional Operational Program 20142020 (LISBOA2020), Portugal 2020 (PT2020), and by the European Union through the European Regional Development Fund (ERDF); Reference number: TOOLING4G2016/24516.

Abstract: The TOOLING4G project aims to make an important contribution to the moulds and plastics industry, enabling the partner companies to create the conditions to become competitive and to overcome global market challenges, by creating internally conditions for production flexibility. The project is structured in 7 large PPSs (Product, Process or Service), including hybrid manufacturing processes; intelligent toolssystems; efficient tools for multi-material products’ manufacturing; multi-process tools; industry digitalization; sustainable “zero defect” production chain; management and dissemination. The consortium consists of 21 mould-making and plastics companies and 10 entities of the research and innovation system, including higher education institutions and technology interface centres, that possess a set of complementary skills and human capital resources. Several innovations are expected to be developed in the project, mainly centred in materials, products and processes, and also in the endogenization of technologies and organizational paradigms in the context of the Industry 4.0 concept.
The participation of the University of Coimbra (UC) in the “PPS2 - Intelligent Tools
Systems” of TOOLING4G includes the development of a system of algorithms for communication in the mould-machine complex for real-time monitoring, prediction and control of parameters, variables, and defects in the injection process and in the mould-machine complex, in order to optimize the process and the moulded parts. In this participation, the UC is represented by the Intelligent Control research group of the “Institute of Systems and Robotics - University of Coimbra” (ISR-UC). In this context, computational intelligence and intelligent control methodologies are investigated and developed for monitoring, prediction, and control of parameters, variables, and defects.

[10]. SMITEn - Smart Meter Integrated Test Environment
Role: Team Member.
Host institutions: Institute of Systems and Robotics - University of Coimbra (ISR-UC); Critical Software, SA.
Financing: co-financing by the “Competitiveness and Internationalization Operational Programme” (COMPETE 2020), Portugal 2020 (PT2020), and by the European Union through the European Regional Development Fund (ERDF); Reference number: SMITEn2016023613.

Abstract: Smart-Grids are an integrated vision for the future of energy supply networks in response to the current challenges of environmental sustainability, reliability and quality of the European energy supply. These infrastructures integrate various heterogeneous elements that should be well interconnected and whose interoperability will become increasingly complex and critical for the security and world economy.
Smart meters are the key instrument to implement these infrastructures and should be installed in nearly 80% of the European households by 2020. However, one of the great difficulties in the development of these complex systems is the ability to test them in an environment close to that where they operate, due to the cost and time needed to make tests in a real environment, as well as to the necessary interconnection between all components of such a system.
The SMITEn project proposes R&D activities to develop an innovative solution for testing and validating smart meters, enabling a wide range of global organizations to implement and execute all the needed tests to fulfill technical requirements and validate the interoperability required by each country's government. The validation infrastructure should integrate a toolkit that should support all test scenarios. It provides real and emulated connectivity to suit different situations and enables easy replication of issues and verifiable fixes.
Proposing a reliable and flexible solution to enable and help spread smart meters all over the world, SMITEn project may provide an effective response to the transition to a low carbon economy and an important contribution to an affordable, secure and sustainable energy.
An important aspect in SMITEn are the R&D activities regarding the integration of simulators, and the design of closed loop smart metering simulators.

[9]. KhronoSim - System for Simulation and Test of Complex Systems
Role: Team Member.
Host institutions: University of Coimbra (UC); Critical Software, SA; Institute of Engineering of Porto.
Financing: co-financing by “Portugal 2020” (PT2020), in the framework of the “Competitiveness and Internationalization Operational Program” (COMPETE 2020), and by the European Union through the European Structural and Investment Funds (ESIF); Reference number: KhronoSim201617611.

Abstract: Concepts such as “Fourth Industrial Revolution (Industry 4.0)” and “Internet of the Things (IoT)” boasted into the technology speech like a blizzard, touching those who use and interest themselves of technology almost as much as those developing it. Such concepts are not surprisingly more used than understood; more are those using the concepts than those actually understanding their implications. Surprisingly enough, the increase of use of technology by the population at large, makes that security is not the least well-known aspect, though still not fully grasped however. Less well-known are the implications of complex systems working tightly coupled, with little or no human intervention, or possibility of human intervention, whatsoever. In such a scenario, testing components individually, one-by-one, is not sufficient to assert the correct functioning of the overall system. KhronoSim aims at developing a platform for testing cyber-physical systems in closed-loop. A platform that is modular, extensible and usable in multiple application domains. A platform featuring hard-real-time control, enabling the integration of simulation models to build a closed loop test environment and allowing the use of physical and virtual systems alike. The application case of the project is the simulation, control, and test of a sun-synchronous satellite.

[8]. Self-Learning Fuzzy Logic Control for Industrial Processes
Role: Post-Doctoral Researcher
Host institutions: University of Coimbra (UC); Institute of Systems and Robotics (ISR).
Financing: Foundation of Science and Technology (FCT); Grant reference - SFRH/BPD/99708/2014.

Abstract: The main objective is to research and contribute for the automatic learning of a Fuzzy Logic Controller (FLC) from data obtained from a given process while it is being manually or automatically controlled, in order to control nonlinear industrial processes. Additionally, the methodologies may also be used to understand a process for which there is little or no information available, since the FLCs are able to gather a knowledge-base about the process control. A current challenge in FLC research is to determine the most suitable fuzzy rules and membership functions of a FLC using data obtained from a given process while it is being manually or automatically controlled. Even, after the learning of the FLC, it is crucial that it can work properly over time, controlling as accurately as possible output variable of the plant, even under operating areas where the train dataset may be not sufficiently representative of the plant, and “unknown changes” of the process. To address these problems, iterative rule learning techniques will be a starting point, where for the unknown operating areaschanges the learning process may create a new fuzzy rule, modify the parameters of an existing one, or merge similar rules. Know issuesproblems with data collection, such as sampling time, missing data, and outliers will be studied due to their influence in the iterative rule learning techniques. Thus, the methodology to be developed should be efficient in terms of performance, adaptivity and robustness.

Previous research

[7]. SCIAD - Self-Learning Industrial Control Systems Through Process Data
Role: Team Member and Reseacher.
Host institutions: University of Coimbra (UC), and Acontrol;
Financing: co-financing by QREN, in the framework of the “Mais Centro - Regional Operational Program of the Centro”, and by the European Union through the European Regional Development Fund (ERDF); Reference number: SCIAD201121531.

Abstract: The objectives and scope of the project are in R&D on data-based control methodologies based auto-tuning and auto-adaptive approaches for PID and other controlers for linear and non-linear systems; R&D control methodologies based on process data for auto-design and auto-adaptation of controllers for linear and nonlinear systems; R&D on nonlinear control systems applying methods based on neural networks using processa data, and other methods; R&D on linear and non-linear multi-variable control methodologies; R&D of model predictive control (MPC) methodologies, considering knowledge driven and data driven approaches; R&D of MPC methods that are able to auto-tune, auto-adjust, auto-adapt, and learn the system model from observation of system variables; Research on the identification of process models by cognitive algorithms (neural networks, support vector regression (SVR), fuzzy systems, etc); Research of mathematical prediction methods andfor application on the optimisation of the MPC methods; R&D of mathematical optimisation, stability, and robustness of the researched control methodologies; R&D of learning methodologies for the determination of linguistic values of linguistic fuzzy variables, as well as for the determination of the control rules: R&D on evolutionary algorithms andor genetic algorithms andor hybrid andor optimization methodologies and/or unsupervised learning methodologies for learning the linguistic variables and the rules for the knowledge base of a fuzzy controller; R&D on application to simulated processes, real prototype processes, and real industrial processes.

[6]. Computational Intelligence Methodologies for Control of Industrial Processes
Role: PhD Student.
Place: University of Coimbra (UC), and Acontrol.
Financing: Foundation of Science and Technology (FCT); Grant reference - SFRH/BD/63383/2009.

Abstract (original summary proposal): This thesis is devoted to research on adaptive fuzzy controllers, predictive control, and intelligent control methodologies such as neural and neuro-fuzzy control for industrial nonlinear andor time-varying plants. Nonlinear andor time varying processes are difficult to control due to their complexity. The issues of varying parameters, presence of disturbances, non-modeled dynamics, robustness, and stability will be addressed. The developed methodologies will be validated on a main case study concerning the control of NOx and SOx emissions on a cement kiln. The process is nonlinear time-varying exhibiting the above mentioned problems. Presently, there are no automatic methodologies to control these emissions under the legal limits. We intend to research control methodologies, integrating human knowledge and/or adaptivity towards improved solutions, for the kiln and treatment systems, having large impact on the amount emission removal chemicals. These chemicals have large economic costs comparable to the maintenance costs of all the kiln electrical systems.

[5]. FAir-Control - Factory Air Pollution Control
Role: Team Member.
Host institutions: University of Coimbra (UC), and Acontrol;
Financing: Eurostars Programme of the EUREKA network, financed by “Fundação para a Ciência e a Tecnologia” (FCT), of the Ministry of Education and Science, “Agência de Inovação” (AdI), and the Seventh Framework Programme for Research and Technological Development (FP7) of the European Union; Reference number: E!6498.

Abstract: The aim of this project is to develop advanced real-time control methodologies for cost optimization of air pollutants mitigation systems. The goal is the automatic control and optimization of the feed rates of mitigation chemicals introduced into the production process (Selective Non-Catalytic Reduction), in order to control the pollutants output on the stack within the legal limits, i.e. always taking in consideration the non-violation of regulations. The main objective is to control the pollutants output on the stack within the legal limits by optimally and automatically selecting the chemicals injection rate into the cement production process, always taking in consideration the non-violation of regulations. Controlling the pollutants ration is a quite difficult problem due to the large instability of the chemicals reactions. Hence the operators work to comply to a worst case scenario, protecting themselves by injecting a quantity of chemicals much higher than needed, wasting resources and increasing the production costs. This project proposes the development of a software-based product, with embedded computational intelligence, capable of selecting the optimal chemical injection quantities for minimizing the operational cost. The base computational intelligence technology for this project is the Fuzzy Logic control. The chemical reactions generated by the chemicals introduced in the cement kiln are very complex and its effectiveness highly depends on several factors such as: temperature, pollutants concentration and dispersion of the chemicals into the gas. One other aim of this project is to develop a systems that is capable to change its strategy of injecting chemicals according to the variations in the process. To successfully achieve these objectives, it will be formed a consortium composed by 3 organizations: (1) Industrial control expert company (Acontrol), (2) Burner and kiln process expert company (Pricast), (3) University research group with expertise in advanced control and cognitive systems (UC). The impact of the project results on the process optimization, with direct reduction of factory costs, will add a high market potential to the consortium participants.

[4]. SInCACI - Intelligent Systems for Industrial Control, Acquisition and Communication
Role: Reseacher.
Host institutions: University of Coimbra (UC), and Acontrol;
Financing: “Mais Centro Operacional Program”, financed by “European Regional Development Fund” (ERDF), and “Agência de Inovação” (AdI); Reference number: SInCACI31202009.

Abstract: The objectives and scope of the project are performing R&D on intelligent control and decision methods; Development of a general-purpose Fuzzy Control System (FCS); On-line process control and monitoring; Controller specification and implementation; Visualization of the process state; Devicesprocess failure reports; Direct application to industry problems; Research computational intelligence techniques for the development of soft sensors for industrial application; R&D on industrial communication and processing modules: industrial distributed real-time communication fieldbus protocols: ControlNet, EthernetIP, DeviceNet; ProfiNet. Relevant properties to industrial fieldbuses: Real-time operation, Reliability, Deterministic, Error-proof, Easy to extend and maintain. Direct application to industry problems; Selling on market.

[3]. Project of Creation of the Research & Development Division of Acontrol
Role: Reseacher.
Host institution: University of Coimbra (UC); Principal contractor: Acontrol;
Financing: “IAPMEI - Instituto de Apoio às Pequenas e Médias Empresas e à Inovação” and “AControl - Automação e Controle, Lda”. Financed within ‘‘Projectos de Criação e Reforço de Competências Internas de I&DT’’ of ‘‘Sistema de Incentivos à Investigação e Desenvolvimento Tecnológico nas Empresas’’ (SI I&DT) of ‘‘Quadro de Referência Estratégico Nacional Portugal 2007-2013’’ (QREN); Reference number: CENTRO-07-0202-FEDER-002502.

Abstract: The University of Coimbra participates as a specialized consultant in the area of intelligent systems and algorithms.

[2]. FUZCTR - Development of Fuzzy Controllers for Modules of Manufacturing Systems
Role: Reseacher.
Host institution: “Institute of Systems and Robotics - University of Coimbra” (ISR-UC);
Financing: “AControl - Automação e Controle Industrial, Lda” company; Reference number: ACONTROLISR001/2007.

Abstract: In this project the goal is to study and implement a fuzzy control system for industrial process control applications. The first applications will be in a cement kiln plant, including, as a first step, the control of the raw mill process.

[1]. STRNET - Development of Hardware/Software for Industrial Distributed Real-Time Systems Using the ControlNet Protocol
Role: Reseacher.
Host institution: “Institute of Systems and Robotics - University of Coimbra” (ISR-UC);
Financing: “AControl - Automação e Controle Industrial, Lda” company; Reference number: ACONTROLISR002/2007.

Abstract: In this project the goal is to study and develop hardware and software of real-time system containint digital processing (microcontrollers) having the capacity of performing network communication with other modules using the ControlNet protocol, as well as the capacity of performing relevant processings in industrial automation and control environments.