Applications of MATLAB in Engineering

Abstract

MATLAB has evolved from a numerical computing environment into a comprehensive engineering platform with rich toolboxes, modeling languages (Simulink, Stateflow), and code-generation capabilities. This paper surveys core applications of MATLAB across major engineering disciplines—electrical, electronics & communication, mechanical, civil, and computer engineering—emphasizing signal processing, control systems, power systems, image processing, communication systems, optimization, and machine learning. We present compact case studies, reproducible code snippets, and a discussion on performance, validation, and deployment (including embedded code generation). Limitations and best practices are articulated to guide students and practitioners in selecting MATLAB for research and industrial workflows.

Introduction

MATLAB (MATrix LABoratory) is a high-level programming environment focused on matrix-based computation, visualization, and algorithm development. It is widely used in academia, research, and industry for rapid prototyping, model-based design (via Simulink), and hardware integration.

Objectives of the Paper

• Highlight cross-disciplinary applications of MATLAB.

• Provide reproducible code snippets for typical workflows.

• Discuss validation, deployment, and best practices.

• Deliver a structured survey and reproducibility checklist for MATLAB users.

MATLAB Ecosystem

1. Core Language: Array-oriented, includes toolboxes for signal processing, ML, image processing, control, and more.

2. Simulink: A graphical interface for simulating dynamic systems; supports physical modeling (Simscape), state machines (Stateflow), and code generation.

3. Integration: Interfaces with Python, ROS, FPGAs, and supports cloud and embedded deployment.

Key Applications by Discipline

A. Electrical & Electronics Engineering

• Circuit Analysis: Solves systems using Node/Mesh methods.

• Transient & AC Analysis: Simulink + Simscape Electrical used for realistic simulations.

• Signal Processing: FFTs, filters, and spectral analysis.

• Communications: End-to-end simulations for systems like 5G and SDRs.

• Control Systems: Transfer function/state-space modeling, PID design, root locus.

• Power Systems: Load flow, fault studies, renewable integration.

• Hardware Support: Arduino, Raspberry Pi, and DAQ hardware integration.

• Code Generation: MATLAB/Simulink Coder enables deployment to C/C++, DSPs, FPGAs.

B. Mechanical Engineering

• Modal analysis, control system design, HIL testing, CFD integration, digital twins.

C. Civil & Structural Engineering

• Structural dynamics, traffic modeling, reservoir optimization.

D. Computer & Data Science

• ML/DL: Classification, clustering, regression.

• Optimization: Design-centric programming.

• Computer Vision: Preprocessing, segmentation, metrics.

Case Studies with Reproducible Code

Includes real MATLAB snippets for:

• Signal Denoising

• PID Controller Tuning

• Power Flow Modeling

• Edge Detection

• OFDM Simulation

• Optimization in Design

• Fault Classification via SVM

Each snippet illustrates a portable, domain-relevant example that can be adapted with real-world data.

Validation & Reproducibility

• Version control for MATLAB/toolboxes.

• Fixed RNG for reproducibility.

• Data handling using datastore, timetable.

• Unit testing with matlab.unittest.

• Performance profiling and vectorization for scalability.

Deployment & Interoperability

• Embedded Coder: Generates C for microcontrollers/SoCs.

• GPU/FPGA Acceleration: With GPU arrays and HDL Coder.

• Interfacing: Supports Python, ROS, and Simulink Real-Time for HIL testing.

Essential MATLAB Commands (by Category)

Includes fundamental commands for:

• Variables & Arrays

• Matrix Algebra

• Plotting & Visualization

• Signal Processing

• Control Systems

• Programming Logic

These commands form the basis of everyday engineering and research workflows.

Strengths, Limitations, and Use Cases

Strengths

• Rich, well-documented domain-specific toolboxes.

• Fast development via Simulink.

• Strong visualization and GUI capabilities.

Limitations

• Costly licenses.

• Slower execution vs. optimized C/C++ (can be mitigated).

• Proprietary ecosystem limits portability.

When to Use MATLAB

• Ideal for rapid prototyping, domain-specific simulations, and code generation.

• Use with Python/C++ in hybrid workflows when open-source flexibility is needed.

Conclusion

MATLAB remains a cornerstone for engineering computation, simulation, and deployment. Its strength lies in end-to-end workflows—from algorithm design to real-time testing and code generation. With disciplined validation and reproducible practices, MATLAB accelerates research translation to industry-grade solutions across multiple engineering domains.

References

[1] MathWorks. MATLAB Documentation and Simulink Documentation. [2] S. K. Mitra, Digital Signal Processing: A Computer-Based Approach. [3] K. Ogata, Modern Control Engineering. [4] R. C. Gonzalez and R. E. Woods, Digital Image Processing. [5] S. Boyd and L. Vandenberghe, Convex Optimization. [6] Kundur, P., Power System Stability and Control. [7] Proakis, J. G., and Salehi, M., Digital Communications. [8] MathWorks. (2023). Simscape Electrical Documentation. https://www.mathworks.com/help/physmod/sps/index.html [9] Chapman, S. J. (2022). MATLAB Programming for Engineers. Cengage Learning. [10] Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-Time Signal Processing. Prentice Hall. (Concepts often implemented in MATLAB). [11] Glover, J. D., Sarma, M. S., & Overbye, T. J. (2012). Power System Analysis & Design. Cengage Learning. [12] Palm, W. J. (2020). Introduction to MATLAB for Engineers (4th ed.). McGraw-Hill Education. [13] Attaway, S. (2022). MATLAB: A Practical Introduction to Programming and Problem Solving (6th ed.). [14] Krein, P. T. (2018). Elements of Power Electronics (2nd ed.). Oxford University Press. [15] Often utilizes MATLAB/Simulink and Simscape for power electronics simulation. [16] Haykin, S., & Van Veen, B. (2018). Signals and Systems (3rd ed.). Wiley. [17] Kuo, B. C., & Golnaraghi, F. (2013). Automatic Control Systems (10th ed.). Wiley. [18] Lyshevski, S. E. (2003). Engineering and Scientific Computations Using MATLAB. Wiley. [19] Singh, M., & Panigrahi, B. K. (2011). MATLAB/Simulink-Based Transient Analysis of Electrical Machines and Drives. International Journal of Electrical Engineering Education, 48(1), 99-113. [20] Garcia, C., et al. (2020). A MATLAB-Based Framework for the Design and Analysis of Hybrid Renewable Energy Systems. Energies, 13(15), 3891.

Copyright

Copyright © 2025 Seraz Ahmad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.