Intelligent control: Intelligent controllers play an essential role in the design of more precise and energy-efficient smart systems, resulting in more safety, productivity, and sustainability. Combining modern nonlinear control techniques (sliding modes, adaptive methods and feedback linearization) with soft computing and machine learning algorithms (artificial neural networks, fuzzy logic, reinforcement learning and Gaussian process regression), it is possible to design control strategies capable of dealing with the uncertainties and external disturbances inherent to the unstructured world in which we live.

Autonomous systems: By interacting more closely with humans, smart systems will increasingly face unexpected situations that could not have been foreseen by their designers. In this way, autonomous systems must be able to make quick decisions and still respect the moral and ethical issues of our society. This exciting research area is at the fore front of technological development and also offers the possibility of dealing with a multidisciplinary scientific topic, ranging from biologically inspired questions to important philosophical aspects. This research line is carried out in cooperation with TIERS research group at the University of Turku.

Smart manufacturing: This research delves into the integration of advanced technologies to create interconnected and self-regulating industrial systems. By leveraging real-time data analytics and predictive modeling, these smart systems can anticipate and adapt to dynamic manufacturing environments, minimizing downtime, reducing waste, and enhancing overall productivity. Additionally, our research in Smart Industry explores human-robot collaboration and human-machine interfaces to ensure seamless interaction between workers and automated systems, fostering safer and more efficient production environments. The ultimate goal of this research line is to transform traditional industry into agile, adaptable, and sustainable smart factories of the future. This research line is carried out in cooperation with DMS research group at the University of Turku.


Digital Waters Flagship (2024 – 2027)

Funding: Research Council of Finland

In this project, we work on the development of autonomous multi-robot platforms for a new environmental observation and monitoring network.

Water is definitely a crucial resource and a matter of global concern. In this context, the Digital Waters (DIWA) Flagship is proposed to become a world-leading water research cluster and innovation ecosystem that generates high-impact research results, contributing to the development of policies and innovations for the next industrial revolutions in the water sector. DIWA aims to provide a strong value chain in Finnish water know-how, yielding a unique ecosystem to develop smart and sustainable water solutions. The DIWA Flagship is led by Professor Björn Klöve, (University of Oulu) and Professor Petteri Alho (University of Turku). The Faculty of Technology at the University of Turku is represented in the consortium by Professor Tomi Westerlund (Theme Leader), Professor Jukka Heikkonen (Task Leader) and the Head of the Smart Systems group, Associate Professor Wallace Moreira Bessa (Task Leader). Our aim in Smart Systems is the surface-to-air autonomy, ensuring robot operation and collaboration in any weather conditions, with a focus on intelligent control and machine learning algorithms.

DIWA Doctoral Education Pilot (2024 – 2027)

Funding: Finnish Ministry of Education and Culture

This pilot project is closely linked to the DIWA Flagship and benefits from the DIWA ecosystem which includes 13 research infrastructures, 1 EU water partnership (Water4all), 9 large EU and 37 international research organizations, over 100 industrial partners, 9 public bodies, and 12 committees and NGOs.

Digital Waters Doctoral Education Pilot pioneers the transition towards digital representation of real-world water systems, known as Digital Twin technology. From hydrological storages to ecosystem responses, our aim is to train tools to support analysis, planning, and governance in water resources. DIWA Pilot is led by Professor Björn Klöve from the University of Oulu. At the University of Turku, several researchers are actively involved in this endeavor, including Professor Petteri Alho (Coordinator at UTU), Professor Tomi Westerlund, Professor Jukka Heikkonen, Professor Juha Plosila, Associate Professor Wallace Moreira Bessa (Smart Systems) and others.

Intelligent Work Machines (2024 – 2027)

Funding: Finnish Ministry of Education and Culture

This doctoral education pilot connects academic research excellence with a relevant industrial research and development challenges and accelerate industrial renewal in the machine industry.

Intelligent Work Machines, including those used in mining, forestry, agriculture, and logistics, play a crucial role in the Finnish industry. Machines and vehicles alone contribute over 25% (15 billion €/yr) to Finnish exports, solidifying their importance. Therefore, we strive to educate upcoming professionals up to the doctoral level, equipping them with the necessary multidisciplinary engineering knowledge for developing intelligent machinery. Intelligent Work Machines Pilot is led by Prof. Matti Vilkko from Tampere University. At the University of Turku, activities are coordinated by Associate Professor Wallace Moreira Bessa (Smart Systems).

ProDigy (2023 – 2024)

Funding: Satakunta Regional Council

The aim of the ProDigy project is to identify the reasons for low productivity in Satakunta. Through data-based solutions, it aims to increase productivity by focusing on the key development objectives in the marine industry using data models and solutions such as digital twins.

The project bolsters the smart specialization of the Satakunta region, enhancing productivity growth and competitiveness through the implementation of AI and data analytics within industrial value networks, thus facilitating the green transition in industry. At the micro level, the project focuses on fostering co-creation between industry and research, utilizing research infrastructures, and promoting testing and experimentation. This involves constructing ship data models via digital twins and leveraging these solutions across various sectors, including equipment manufacturing. The utilization of digital twins has shown significant potential in reducing prototyping costs by up to 30-50% and decreasing product design lead times by 20-30%, particularly beneficial for new product generations and overall product lifecycle management. Additionally, the project will bolster coordinated R&D activities, aligning with TSE Pori unit’s RDI concepts and mechanical engineering, thus contributing to the sustainable competitiveness of industry in Satakunta.

CaNeLis (2022 – 2025)

Funding: Business Finland

Our goal in this project is to develop a multisensor data fusion approach for the intelligent control of welding processes.

Weld properties can be measured using many different types of sensors, and each sensor type has its strengths and weaknesses for estimating attributes such as weld shape and quality. In fact, due to its inherent limitations, a single sensor type alone cannot provide a trustworthy estimate of weld properties. However, data fusion methods can be employed to combine the imperfect information (raw data) from different sensors so that the strengths of one type can compensate for the weaknesses of others. The adoption of a suitable method for sensor data fusion allows more valid information to be captured from the raw data, which can lead to a significant improvement in the overall accuracy and reliability of the weld. In this regard, data-driven machine learning methods can handle this task by means of the temporal and spatial correlation between the raw sensor data related to different physical quantities.