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MATLAB Programming and Debugging

MATLAB programming involves scripts, functions, arrays, matrices, indexing and data structures. More advanced work includes vectorisation, object-oriented programming, code profiling and optimisation. Debugging often comes down to finding errors in array dimensions, indexing, data types or the logic of a calculation.

Numerical Methods and Scientific Computing

Common subjects include solving linear and nonlinear equations, numerical integration, differentiation, interpolation and optimisation. Differential equations are another major area, with MATLAB solvers such as ode45 and ode15s used for different types of problems.

Other topics include matrix decompositions, eigenvalues, least-squares methods, convergence, numerical stability and error analysis.

Data Analysis and Signal Processing

Working with experimental data, sensor readings and recorded signals. MATLAB is used for filtering, interpolation, regression and curve fitting, as well as statistical tests and data visualisation.

Signal processing includes FFT, digital filter design, spectral analysis and spectrograms. Typical work involves examining vibration measurements, analysing electrical signals, removing noise and identifying frequency components.

Modelling and Simulation with MATLAB and Simulink

Electric motors, power converters, suspension systems, hydraulic drives and thermal processes are among the systems modelled in MATLAB. The work can involve deriving equations of motion, calculating losses, studying transient responses or checking how a change in one parameter affects the whole system.

In Simulink, the focus is often on the control part: PID tuning, feedback loops, state-space models, transfer functions and signal routing. A model may need to be rebuilt when its block structure no longer reflects the actual system.

Simscape covers the physical side of modelling, including electrical circuits, mechanical assemblies and electromechanical systems. Other relevant topics include parameter identification, stability analysis and comparison of simulated results with experimental measurements.

MATLAB Toolboxes

The subject of a project often determines which toolbox is needed:

• Control System Toolbox: system analysis, stability and controller design.
• Signal Processing Toolbox: digital filtering and spectral analysis.
• Image Processing Toolbox: image alignment, object counting, boundary detection, region analysis and measurements from images.
• Statistics and Machine Learning Toolbox: hypothesis testing, regression models, clustering and predictive modelling.
• Deep Learning Toolbox: neural network development.
• Optimization Toolbox: numerical and constrained optimisation.
• Robotics System Toolbox: robot modelling and motion planning.

MATLAB in Research, Teaching and Engineering

MATLAB appears in research papers, laboratory work and engineering projects, but the tasks can be quite different:

• Research: processing measurement results, numerical experiments, parameter estimation and comparison of theoretical and experimental data.
• Engineering: motor and converter calculations, controller tuning, analysis of test results and design verification.
• Teaching: MATLAB lecture examples, practical assignments, lab work and Simulink exercises.