Control Systems Engineering (LM‑25) at University of Padua

Control Systems Engineering (LM‑25) at the University of Padua (Università degli Studi di Padova) lets you study in Italy in English inside one of the most respected public Italian universities. The degree sits among the most applied English-taught programs in Italy, combining rigorous theory, modern computation, and hands‑on labs. With the DSU grant, tuition reductions, and other scholarships for international students in Italy, the income‑based model that powers tuition-free universities Italy can make this programme remarkably affordable.

Why choose Control Systems Engineering to study in Italy in English

You will learn how to model, analyse, and control complex dynamical systems. These systems power robotics, autonomous vehicles, energy grids, aerospace, manufacturing, biomedical devices, and smart buildings. Studying in English means you read and write research, standards, and documentation in the language used worldwide. At the same time, you benefit from the transparency and cost structure typical of public Italian universities.

Key advantages:

• A two‑year, 120 ECTS programme that balances foundations and applications.
• A curriculum that spans classical, robust, optimal, nonlinear, and model predictive control.
• Strong coverage of embedded, cyber‑physical, and networked control systems.
• Direct exposure to identification, optimisation, and real‑time implementation.
• Clear pathways to affordability through the DSU grant and scholarships for international students in Italy.

What you will learn: the core pillars

Modelling of dynamical systems
You translate real plants into state‑space models, transfer functions, or differential equations. You work with linear and nonlinear dynamics, delays, uncertainties, and disturbances. You learn how modelling assumptions affect stability, performance, and robustness.

Classical and modern control
You review PID tuning and frequency‑domain techniques, then move to state‑space methods, pole placement, observers, Kalman filtering, and LQR/LQG (linear quadratic regulator/gaussian). You build intuition about controllability, observability, and separation principles.

Robust and optimal control
You design controllers that guarantee performance despite model errors and noise. Topics include H∞ control, μ‑synthesis, and convex optimisation via LMIs (linear matrix inequalities). You learn how to formalise trade‑offs using norms and performance metrics.

Model predictive control (MPC)
You implement MPC for constrained systems, both linear and nonlinear. You learn how to set horizons, choose cost functions, and solve optimisation problems in real time. You also study economic MPC for energy and process industries.

Nonlinear control
You use Lyapunov theory, feedback linearisation, sliding modes, backstepping, and passivity‑based control. You see how these tools stabilise robots, aircraft, and chemical reactors that linear controllers cannot handle well.

System identification and estimation
You learn how to build models from data. You cover least squares, prediction error methods, subspace identification, and Bayesian identification. You also study state estimation under uncertainty with Kalman filters and particle filters.

Embedded and real‑time control
You implement controllers on microcontrollers, FPGAs, or real‑time OSs. You handle discretisation, sampling, scheduling, jitter, and quantisation. You learn how to validate timing and safety constraints.

Networked, distributed, and cyber‑physical control
You design control loops that run over imperfect networks. You manage delays, packet loss, and limited bandwidth. You also explore consensus control, event‑triggered control, and security for cyber‑physical systems.

Optimisation for control
You study convex optimisation, quadratic programming, second‑order cone programming, and mixed‑integer formulations. You apply these tools to MPC, estimation, and trajectory planning.

Safety, verification, and formal methods
You see how reachability, barrier certificates, and model checking can prove safety properties. You learn how to combine data‑driven methods with formal guarantees.

Digital tools and platforms you will actually use

• MATLAB/Simulink, Python (NumPy, SciPy, CasADi), Julia, Modelica.
• Optimisation solvers like Ipopt, CPLEX, Gurobi, OSQP.
• Control toolboxes for linear, robust, and MPC design.
• ROS/ROS2 for robotics, Gazebo/Isaac Sim for simulation.
• Real‑time targets (dSPACE, Speedgoat, or similar), microcontrollers, FPGAs.
• Git for version control; CI/CD for reproducible experiments.
• Automatic code generation from high‑level models to embedded targets.
• Jupyter or similar notebooks for transparent analysis and reporting.

Example elective directions

• Autonomous systems and robotics: perception‑aware control, SLAM‑in‑the‑loop, safe RL.
• Aerospace and automotive control: flight/drive‑by‑wire, guidance, and navigation.
• Energy and power systems: grid‑forming inverters, demand response, microgrids.
• Process and chemical control: advanced MPC, soft sensors, fault detection.
• Biomedical and neuroengineering control: closed‑loop drug delivery, prosthesis control.
• Learning‑based control: reinforcement learning with stability certificates, adaptive MPC.
• Human‑in‑the‑loop and haptics: passivity, transparency, and stability with time delays.
• Cybersecurity for control: resilient and secure feedback architectures, anomaly detection.

From classroom to thesis: what you might build

Your final thesis (often 30 ECTS) is where you prove your engineering value. Sample projects:

• A constrained MPC controller for an autonomous vehicle, deployed on embedded hardware.
• A robust flight controller with gain scheduling validated in hardware‑in‑the‑loop.
• A distributed control scheme for microgrids with explicit stability and performance guarantees.
• A nonlinear observer for soft robots, implemented with real‑time sensing.
• A formal verification framework for safety‑critical control loops in a cyber‑physical system.
• A reinforcement learning controller wrapped with Lyapunov‑based safety certificates.
• An event‑triggered control system that reduces bandwidth while keeping stability margins.

Careers: where LM‑25 can take you

Robotics and autonomous systems
Control engineer, autonomy engineer, motion planning specialist, or systems integrator for ground, aerial, or underwater robots.

Automotive, rail, and aerospace
Control and estimation engineer, flight control engineer, ADAS/AD (advanced driver assistance/autonomous driving) algorithm engineer, embedded software engineer.

Energy, sustainable systems, and smart grids
MPC engineer for energy management, grid stability analyst, microgrid control specialist, optimisation engineer for renewables and storage.

Process, chemical, and pharmaceutical industries
Advanced process control engineer, optimisation specialist, fault detection and diagnosis engineer, digital twin developer.

Medical and bioengineering
Control engineer for medical devices, neuroprosthetics, rehabilitation robots, or closed‑loop insulin delivery.

Industrial automation and robotics
PLC/embedded control developer, machine vision‑in‑the‑loop engineer, digital factory control architect.

Research and PhD
Nonlinear and robust control, learning‑based control, distributed optimisation, cyber‑physical systems, formal methods, human‑robot interaction, and more.

Skills employers will see on your CV

• Modelling clarity: you state assumptions and quantify uncertainty.
• Stability and robustness literacy: you use Lyapunov, H∞, and MPC guarantees.
• Real‑time implementation: you respect sampling, scheduling, and memory limits.
• Optimisation fluency: you select the right solver and prove convergence or feasibility.
• Software discipline: version control, testing, documentation, and reproducibility.
• Safety mindset: you design with certification, verification, and fail‑safe logic.
• Communication: you translate maths into design specs for multidisciplinary teams.

Admissions: who should apply

You are a strong candidate if you hold a bachelor’s in:

• Automation/control, electrical, mechanical, aerospace, or energy engineering
• Computer engineering with a solid signals and systems background
• Applied mathematics or physics with strong control prerequisites

Expect to show:

• English at CEFR B2 or higher
• Good command of linear algebra, calculus, differential equations, and probability
• Prior exposure to signals and systems, basic control, and programming
• (Sometimes) a pre‑evaluation or interview to align your background with the curriculum

Funding your degree: how public Italian universities make it affordable

Because Padua is one of the leading public Italian universities, tuition depends on family income. Many students pay very low or even zero fees after evaluation. This is why tuition-free universities Italy is a practical path, not just a headline. You can further reduce costs through:

• DSU grant (Diritto allo Studio Universitario): may cover accommodation, meals, and study materials; awarded by income and merit.
• Scholarships for international students in Italy: national and university calls with stipends and fee waivers.
• Merit‑based reductions: complete credits with strong grades, and your second‑year fee can drop.
• Part‑time work: non‑EU students can usually work up to 20 hours per week. Typical roles include research assistant, control/MPC intern, embedded developer, or optimisation engineer.

Ethics, transparency, and trustworthy control

Control engineers build systems that affect safety, energy use, privacy, and fairness. This programme trains you to:

• Report uncertainties and model limits clearly.
• Respect safety standards and certification paths for critical systems.
• Design controllers that are explainable, auditable, and resilient to faults or attacks.
• Use open, reproducible practices while protecting sensitive industrial data.
• Quantify environmental and social impacts where relevant, aligning with corporate and public goals.

Continuous professional development after graduation

To stay ahead, consider micro‑credentials in:

• Safe and robust reinforcement learning for control
• Distributed MPC, consensus, and optimisation on graphs
• Formal methods and control barrier functions for autonomy
• Energy systems control, demand response, and grid‑forming inverters
• Cyber‑physical system security and anomaly detection
• Real‑time optimisation, fast MPC, and embedded solvers
• Digital twins for predictive maintenance and closed‑loop optimisation
• Human‑robot interaction and haptic control with passivity guarantees

Final perspective

Control Systems Engineering (LM‑25) at the University of Padua (Università degli Studi di Padova) gives you the mathematical depth, optimisation skill, and implementation discipline to design systems that really work. As one of the mature English-taught programs in Italy delivered by a top public Italian university, it blends academic rigour with affordability through tuition-free universities Italy mechanisms, the DSU grant, and scholarships for international students in Italy. If you want to study in Italy in English and graduate able to stabilise, optimise, and automate complex systems, this programme is a precise investment in your future.

Ready for this programme?
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