Analysis and Design of circuits. Basic concepts of circuits. Kirchhoff’s Laws. Circuits with sources and resistances. Operational amplifiers. Dynamic elements and circuits of first and second order in transient state. Measurement devices. Alternative current circuits’ analysis using phasors (complex numbers). Three-phase circuits and power systems. Introduction to electrical installations, regulations and safety. Electrical machines. Electromagnetics and electromechanics. Transformers. Operational principles of rotating electrical machines. Structure, models and applications of the main types of electrical machines: Synchronous machines. Induction motors. Direct current machines. Laboratory exercises with respect to the above topics.
Introduction to mechatronic systems. Mathematical representation and response calculation for basic mechatronic systems. Basic notions of the Laplace and Fourier transforms. Mechatronic system synthesis using subsystems (components). Principles and specifications of mechatronic design. Diodes and applications. Bipolar transistors, field transistors, and applications. Op Amps and analog signal processing. General principles of sensors and actuators, step motors. Logical circuits and applications. Microprocessors and digital / analog communication with subsystems. A/D and D/A signal conversion and basic digital signal processing. Laboratory work. Use of MATLAB/SIMULINK, Electronics Workbench, LabView.
Introduction to dynamical systems and automatic control. Mathematical representation, types and notions of dynamical systems. State space representation of of dynamical systems. Modeling of physical systems. Response calculation in the time domain. Response calculation in the frequency domain. Elementary systems and their characteristics. Multivariable systems. Introduction to automatic control systems: specifications and basic topologies. Stability of closed loop systems and the Routh criterion. Steady state response specifications. Transient response specifications. PID controllers and Lead-Lag compensators and their properties. Control system topology selection. Design principles for automatic control systems. Design using the Root Locus method and the frequency domain. Brief introduction into state feedback control systems. Laboratory work. Use of MATLAB/SIMULINK.
Introduction to stochastic signals and systems. Fundamental notions of stochastic signals in the time domain. Non-parametric estimation of stochastic signals in the time domain. Fundamental notions of stochastic signals in the frequency domain. Non-parametric estimation of stochastic signals in the frequency domain. Theory of stationary and linear stochastic signals and systems. Theory and properties of parametric ARMA models. ARMA models originating from the sampling of continuous time models. Introduction to non-stationary and seasonal stochastic signals. Theory of optimal prediction. Identification, estimation, and validation of stochastic parametric models. Introductory remarks on vector stochastic signals . Use of MATLAB and R.
Introduction to industrial robots. Kinematic problem. Transformations in space. Kinematic equations. Solutions of the inverse kinematic problem. Speeds and static forces. Trajectory calculation in Cartesian space. Interpolation in joint space. Position control of a robot with many degrees of freedom. Control systems referred to the Cartesian coordinate system. Compliance control. Force sensors. Force control algorithms. Natural and artificial restrictions. Hybrid position/force control. Programming languages and industrial robots. Vision based control. Robot motion design. Applications to industrial robots. Laboratory exercises with respect to the above topics and synthetic project.
Introduction to rotating machinery diagnostics and prognostics. Basic principles and problems. Sensors and data acquisition devices. Measurements of vibration signals. Types of signals and basic signal processing techniques in time and frequency domain. Digital signal filtering. Introduction to time-frequency signal processing. Time domain, frequency domain and time-frequency rotating machinery diagnostics. Introduction to rotating machinery prognostics. Applications and laboratory training using Labview, Matlab and Ansys. Synthetic project on the condition monitoring of a rotating machine based on experimental data.
Theory and practice for analog and digital multivariate systems and systems of automatic control with emphasis on applications in mechanical engineering. Digital scalar systems: Sampling of continuous systems. Discretization and digital dynamical models. Zeta transform and difference equations. Discrete transfer functions. Frequency response and Bode diagrams. Nyquist plot. Direct and indirect design of digital automatic control systems. Multivariate analog and digital systems in state space: Analysis of multivariate systems in state space. State space block diagrams. State transition matrix and response determination. Relations among state space equations and transfer functions. Controllability and observability. Canonical forms. Stability. Design of automatic control systems based on state feedback. State observers. Tutorial with automatic control systems and synthetic project in Matlab and Simulink.
Introduction to theory and practice of dynamic systems and automatic control with emphasis on modelling, simulation, analysis and automatic control of systems in mechanical engineering and aeronautics. Transfer functions and block diagrams. Transfer functions of aircraft longitudinal and lateral dynamics. Stability. Sensors, control surfaces, actuators and servo-mechanisms. System response determination in time and Laplace domains. Design of fundamental automatic control systems based on root locus and Ziegler-Nichols technique. Characteristics of systems response. Frequency response and Bode diagrams. Control systems characteristics. Aircraft pitch and roll automatic control. Autopilots. Systems for flight altitude and velocity automatic control. Systems for aircraft stability augmentation. Systems representation in state space. Aircraft state feedback control. Introduction to state observers.
Introduction to the Dynamic Identification and Structural Healthy Monitoring based on measurements of vibration signals. General principles & specifications of Dynamic Identification and Structural Health Monitoring problems. Non-parametric and parametric deterministic and stochastic models for the representation of the structural dynamics in time and frequency domain. Methods for the identification of deterministic models. Methods for the identification of stochastic models. Non-parametric methods for Structural Health Monitoring based on measurements of vibration signals. Parametric methods for Structural Health Monitoring based on measurements of vibration signals. Structural Healthy Monitoring under varying operating conditions. Advanced topics. Experimental applications. Use of MATLAB and R.
Basic ideas on stochastic signals and systems. Statistical moments, stationarity, spectral representation and ergodicity of stochastic processes. Types of stochastic processes. Introduction to minimum variance based control. Rational transfer function models: ARX, linear regression, ARMAX, pseudolinear regression, ARARX, ARARMAX, output error, Box-Jenkins. State space models. Vector systems, uniqueness and identifiability, canonical and pseudo-canonical types. Prediction theory. The Kalman Filter. Estimation and estimators statistical properties: Ordinary Least Squares, Weighted Least Squares, Generalized Least Squares, Prediction Error Methods, and Maximum Likelihood. Recursive estimation methods. Experimental design. Practical exercise based on MATLAB and examples of applications from stochastic dynamic analysis and automatic control of vibrating structures such as aircraft, vehicles, bridges, buildings and other engineering structures.