Matko Orsag PhD, Associate Professor, University of Zagreb, Faculty of Electrical Engineering and Computing

This lecture examines how classical control theory can be applied to achieve dynamic stability and environmental interaction in aerial vehicles equipped with multi-degree-of-freedom (DoF) manipulators. Key topics include the stabilization of aerial platforms using Lyapunov-based Model Reference Adaptive Control (MRAC) and the coordination of manipulator movement for precise object interaction. A unified dynamic model for aerial robots will be introduced, covering various actuation principles such as tilting propellers and centroid variation methods, including moving masses and robotic manipulators. The lecture will also discuss classical control structures through practical examples, including pick-and-place, insertion, and valve-turning tasks. Beyond object manipulation, the session will cover applications in contact-based surface inspection, demonstrating how an impedance controller can regulate end-effector forces to ensure stable surface interaction. Real-world challenges and solutions for achieving robust aerial manipulation will be discussed, providing a foundation for future advancements in the field.

Bruno Marić PhD, University of Zagreb, Faculty of Electrical Engineering and Computing

This lecture examines the role of control in enabling robots to replicate human skills, with a focus on deep micro-hole drilling in the glass container mold industry. A robotic framework is introduced that captures and reproduces expert human drilling techniques using a specialized mechanical and software setup for on-site task recording. The lecture further explores control strategies for achieving high-precision, human-like performance in robotic drilling. A single-degree-of-freedom (DOF) impedance-based drill controller is developed for an industrial position-controlled robot, facilitating safe and smooth interactions between the drill bit and the mold. By modeling the relationship between joint and tool dynamics, the proposed control system minimizes non-axial motion, outperforming standard low-level robot control and enabling the robotic system to exceed human capabilities in deep micro-hole drilling. Bridging the fields of human skill acquisition, robotic motion control, and industrial automation, this lecture highlights experimental validations and real-world applications, offering insights into the future of human-inspired robotic systems.

Mario Vašak PhD, Full Professor, University of Zagreb, Faculty of Electrical Engineering and Computing

The talk is on a data integrity cyber-attack detection framework based on minimum robust positively invariant sets. A general linear control system with a Kalman filter is considered. The set localization of the state estimator error is taken into account for developing the attack detector. An intelligent attacker algorithm is developed that has access to a subset of signals from the sensor and actuator channel of the control system. It is assumed that the attacker possesses the entire control system model to perform the most proficient attack for a certain set-up of data availability and compromisation. The attacker compromises a set of measurement data under the constraint of remaining non-discovered by the detector. The presented methodology allows assessing the effectiveness of the control system defense achievable in various data integrity attack scenarios. The developed detector and attacker algorithm were implemented on an illustrative example of a power system with two control areas and automatic generation control.

Dorijan Leko PhD Student, University of Zagreb, Faculty of Electrical Engineering and Computing

Set-theoretic methods are a powerful tool in control theory, used to ensure safety and feasibility across various control system configurations. These methods involve defining sets of feasible system states that represent safe operating conditions or constraints. By analyzing these sets and designing control strategies that adhere to these constraints, control algorithms can ensure that the system operates within designated safe regions.

This presentation outlines an approach for characterizing feasible initial states and admissible control input trajectories over a finite-time prediction horizon for a nonlinear control system. An admissible control input trajectory refers to a specific feasible initial state and defines the set of admissible inputs that can be applied throughout the prediction horizon. This enables the system to transition from the initial state to the desired state or trajectory while complying with the imposed constraints. This approach is particularly applicable in Battery Management Systems (BMSs).

Šandor Ileš PhD, Associate Professor, University of Zagreb, Faculty of Electrical Engineering and Computing

This talk provides an overview of three distinct applications of Model Predictive Control (MPC) implemented in the Laboratory for Mechatronic Systems, all focused on ensuring system stability inspired by a flexible control Lyapunov function framework. The first case study explores an MPC approach designed to minimize switching losses in a two-level synchronous generator side converter (SGSC). The second application presents an MPC solution for stabilizing a tower crane system with variable cable lengths. The final case discusses MPC-based Direct Yaw Moment Control (DYC) of an electric vehicle. Across all three applications, the talk emphasizes the stability methodologies that optimize performance, supported by both simulation and experimental results.

Marko Švec PhD Student, University of Zagreb, Faculty of Electrical Engineering and Computing

Get ready to discover a groundbreaking approach to (vehicle dynamics) control! This talk will focus on the use of the Koopman operator in model predictive control (MPC) to accelerate vehicle dynamics control algorithms. By using the Koopman operator to create a linear representation of complex nonlinear systems, a new level of control efficiency and simplicity is unlocked. This so-called Koopman-MPC framework transforms challenging nonlinear optimization problems into much simpler quadratic problems, providing a perfect blend of high performance and computational speed.

We will cover the basics of vehicle dynamics and MPC, introduce the exciting theory behind the Koopman operator, and explore state-of-the-art techniques for identifying Koopman models, including extended dynamic mode decomposition (EDMD), deep dynamic mode decomposition (Deep-DMD), and a new method called enhanced extended dynamic mode decomposition (E²DMD). The presentation will show the real-world application of this technology in vehicle control, with demonstrations including a torque vectoring algorithm powered by KMPC and validated by multiple experiments.

Ivan Cvok PhD, Powertrain Controls Lead Engineer, Rimac Technology d.o.o.

This presentation outlines the structured development of automotive control systems following the V-model, from concept to vehicle testing, with a focus on real-world industrial applications. By comparing the development process in an industrial environment to that in research, it will highlight practical challenges and discuss solutions following the V-model. Starting from high-level vehicle requirements, we will delve into the decomposition of system and software architectures, addressing key challenges and interactions during controller design. Special attention will be given to real-time implementation, operational domain considerations, active system interactions, calibration flexibility, and the use of automated code generation alongside CI/CD practices. The presentation will conclude with a comprehensive discussion of testing strategies, from unit testing and model-in-the-loop (MIL) validation to hardware-in-the-loop (HIL) and in-vehicle testing, ensuring robust system validation and performance in real-world scenarios.