Using Simulation Software (SPICE, MATLAB/Simulink)

Using Simulation Software (SPICE, MATLAB/Simulink)

Simulation software forms the backbone of modern electrical engineering, enabling you to design, test, and analyze systems without physical prototypes. For online learners, these tools bridge the gap between theoretical concepts and practical application, providing hands-on experience comparable to traditional lab environments. This resource explains how simulation platforms like SPICE and MATLAB/Simulink streamline electrical engineering workflows, reduce costs, and accelerate innovation.

SPICE specializes in analog and mixed-signal circuit analysis, letting you predict voltage levels, current flows, and component interactions before soldering a single resistor. MATLAB/Simulink focuses on system-level modeling, ideal for designing control systems, power electronics, or signal processing algorithms through block diagrams and mathematical simulations. Both tools help identify design flaws early, troubleshoot theoretical models, and validate performance metrics under varied conditions.

You’ll learn how to choose between SPICE and MATLAB/Simulink based on project requirements, interpret simulation results accurately, and apply these tools to common electrical engineering tasks. The article breaks down their interfaces, core functions, and integration methods for multi-domain projects like motor drives or communication systems. Case studies demonstrate simulating circuit behavior, optimizing power efficiency, and testing system stability under load variations.

For online students, mastering these tools compensates for limited access to physical labs while building marketable technical skills. Employers expect proficiency in simulation software for roles in circuit design, renewable energy systems, and embedded electronics. Whether troubleshooting a filter design or modeling a smart grid, these platforms transform abstract equations into actionable insights, preparing you to solve real-world engineering problems efficiently.

Core Concepts of Circuit Simulation and Modeling

Circuit simulation tools let you predict how electrical systems behave without physical prototypes. These tools split into two categories: device-level simulators (like SPICE) for component details and system-level platforms (like MATLAB/Simulink) for broader interactions. This section explains their core principles and applications.

SPICE: Origins and Core Functionality (Released 1973)

SPICE (Simulation Program with Integrated Circuit Emphasis) was developed to automate analog circuit analysis for integrated circuit design. Its open-source nature and accuracy made it the foundation for most modern circuit simulators.

Key features include:

• Netlist-based input: You define circuits using text files listing components, nodes, and connections.
• Nodal analysis: SPICE solves Kirchhoff’s laws across nodes to calculate voltages and currents.
• Transient, AC/DC, and sensitivity analyses: Simulate time-domain behavior, frequency response, or component tolerance effects.
• Component models: Diodes, transistors, and other parts use mathematical models (e.g., Ebers-Moll for BJTs) to replicate real-world behavior.

SPICE prioritizes precision for small-scale circuits. You use it to verify amplifier stability, filter responses, or power supply ripple. Its device-level focus means simulations can become slow for large systems, but it remains unmatched for verifying individual component performance.

MATLAB/Simulink: System-Level Simulation Capabilities

MATLAB/Simulink shifts focus from individual components to entire systems. Instead of modeling transistors, you work with blocks representing motors, sensors, or control algorithms.

Key advantages include:

• Visual block diagrams: Drag-and-drop interfaces let you assemble systems without coding.
• Multi-domain simulation: Combine electrical circuits with mechanical, thermal, or hydraulic systems in one model.
• Real-time processing: Test embedded software or control logic against simulated hardware.
• Prebuilt libraries: Use blocks for power electronics, signal processing, or communication protocols.

For example, you might model a motor drive system in Simulink by connecting a PWM controller block to a motor model and mechanical load. The tool abstracts away low-level device physics to emphasize system interactions. MATLAB integration lets you automate parameter sweeps or export data for further analysis.

Key Differences Between Device-Level and System-Level Tools

Device-level tools (SPICE):

• Simulate voltage/current at every node in a circuit.
• Require detailed component models (e.g., MOSFET gate capacitance).
• Best for verifying analog circuits or PCB-level designs.
• Slower for large systems due to computational complexity.

System-level tools (MATLAB/Simulink):

• Represent subsystems as black boxes with input/output relationships.
• Use simplified models (e.g., a motor represented by torque-speed curves).
• Analyze stability, efficiency, or control loops in multi-component systems.
• Run faster by ignoring granular details.

Choosing between them depends on your goal. Use SPICE to debug a transistor amplifier’s distortion or check signal integrity in a high-speed PCB layout. Use Simulink to test how a solar inverter interacts with a grid-tie controller or optimize a battery management system’s thermal performance. Many projects combine both: simulate critical analog subcircuits in SPICE, then import results into Simulink for system integration.

Hybrid workflows are common. For instance, SPICE models of power converters can be co-simulated with Simulink control algorithms to validate a complete motor drive. This approach balances accuracy for critical components with speed for system-wide analysis.

Selecting Simulation Software for Electrical Projects

Choosing between SPICE-based tools and MATLAB/Simulink depends on your project’s technical demands, performance needs, and design goals. Both platforms excel in specific contexts, but their differences make each better suited for particular tasks. Below are criteria to guide your decision.

Project Requirements: Analog vs. Digital vs. Mixed-Signal

SPICE-based tools specialize in analog circuit analysis. They model low-level components like transistors, diodes, and passive elements with high precision. Use SPICE if:

• Your project involves analog circuits (e.g., amplifiers, filters, power supplies).
• You need to simulate transient responses, noise, or temperature effects.
• You’re designing mixed-signal systems but require detailed analog verification.

MATLAB/Simulink focuses on system-level modeling and digital control. It integrates well with block diagrams and mathematical algorithms. Choose Simulink if:

• Your project emphasizes digital signal processing or control systems (e.g., motor drives, robotics).
• You need to simulate large-scale systems with software-hardware interactions.
• Your workflow involves automated code generation for embedded systems.

For mixed-signal projects, SPICE handles analog verification better, while Simulink streamlines digital logic and system integration. Some engineers combine both tools: using SPICE for critical analog subcircuits and Simulink for overall system validation.

Comparison of SPICE and MATLAB/Simulink Execution Speeds

SPICE simulations are component-level accurate but computationally intensive. They solve nonlinear differential equations for every node in a circuit, which slows down large designs. For example, simulating a 100-transistor amplifier in SPICE might take minutes to hours, depending on convergence settings.

MATLAB/Simulink uses higher-level abstractions, trading granularity for speed. A motor control system modeled in Simulink can simulate in seconds because it approximates component behavior instead of solving every physical equation. This makes Simulink more efficient for:

• Systems with digital controllers or state machines.
• Projects requiring Monte Carlo analysis or parameter sweeps.
• Real-time simulations with hardware-in-the-loop (HIL) testing.

However, Simulink’s speed comes at a cost: it may overlook analog subtleties like parasitic capacitance or signal distortion. SPICE’s slower simulations provide deeper insights into analog performance but become impractical for full-system modeling.

Case Study: Power Supply Design vs. Motor Control System

Power Supply Design (SPICE Example):
Designing a switch-mode power supply (SMPS) requires analyzing transient responses, loop stability, and component stress. SPICE tools like LTspice or PSpice simulate:

• Voltage ripple during load changes.
• MOSFET switching losses.
• Feedback loop phase margin.

These details are critical for avoiding thermal failure or oscillations. SPICE’s ability to model non-ideal components (e.g., ESR of capacitors) ensures realistic results.

Motor Control System (Simulink Example):
A brushless DC motor controller involves PWM generation, sensor feedback, and PID tuning. Simulink simplifies this by:

• Modeling the motor as a transfer function or lookup table.
• Simulating faults like encoder errors or voltage sags.
• Generating C code for microcontrollers directly from the control algorithm.

Testing the entire system—including digital filters and communication protocols—is faster in Simulink. SPICE would struggle with the computational load of simulating both analog driver circuits and digital control logic simultaneously.

Key Takeaway:

• Use SPICE for precision in analog/mixed-signal subsystems.
• Use Simulink for speed in system-level digital control.
• Combine both tools when a project demands high-fidelity analog validation within a complex digital system.

By matching the tool’s strengths to your project’s priorities, you avoid bottlenecks in simulation time or accuracy. Focus on whether your design requires component-level detail (SPICE) or system-level efficiency (Simulink), and allocate your workflow accordingly.

Setting Up Basic Circuit Simulations: Step-by-Step Guide

This section provides direct instructions for building basic circuit simulations in SPICE and MATLAB/Simulink. Follow these steps to create functional models and validate their accuracy.

SPICE Workflow: Netlist Creation to Waveform Analysis

1. Create a Netlist
   Open a text editor and define circuit components using SPICE syntax. Start with:

   • Component identifiers (R1, C2, V3)
   • Nodes connecting components (N001, N002)
   • Values (10k, 100nF, 5V)

   Example netlist for an RC low-pass filter: RC Circuit V1 N001 0 DC 5V R1 N001 N002 1k C1 N002 0 100nF .tran 0.1ms 10ms .end

2. Set Analysis Type
   Add a control statement to specify the simulation type:

   • .dc for DC sweep
   • .ac for frequency analysis
   • .tran for transient response

3. Run the Simulation
   Save the netlist as a .cir file and execute it using your SPICE software. Use run or equivalent commands in the interface.

4. View Results
   Open the waveform viewer to plot voltages or currents. For the RC circuit example, plot V(N002) to observe the capacitor’s charging curve. Adjust component values in the netlist and rerun to see changes.

Simulink Block Diagram Construction for Control Systems

1. Start a New Model
   Open Simulink and select a blank template. Use the Library Browser to add blocks:

   • Sources: Sine Wave, Step Input
   • Sinks: Scope, Display
   • Continuous: Transfer Function, PID Controller
   • Math Operations: Sum, Gain

2. Build a Feedback System
   Construct a closed-loop motor speed controller:

   • Drag a PID Controller block into the workspace
   • Connect a Step Input block to the PID’s input
   • Add a Transfer Function block representing the motor (e.g., 1/(0.5s + 1))
   • Insert a Sum block before the PID for error calculation
   • Link a Scope to the output

3. Configure Block Parameters
   Double-click each block to set values:

   • PID gains (P=2, I=0.1, D=0.01)
   • Step time (1 second)
   • Simulation time (10 seconds)

4. Run and Tune
   Press Simulate to start. Observe overshoot in the Scope. Adjust PID gains iteratively to reduce oscillations.

Verification Methods for Simulation Accuracy

Compare with Analytical Calculations
For a resistor-divider circuit, verify SPICE’s output voltage matches Vout = Vin * (R2/(R1+R2)). Discrepancies larger than 0.1% indicate setup errors.

Check Convergence in SPICE
If simulations fail, add:
.options reltol=0.01
to relax convergence criteria. Gradually tighten reltol to 0.001 after successful runs.

Use Known Test Cases
Simulate a 1kHz RC filter in SPICE and Simulink. Both should show a -3dB drop at the cutoff frequency. Significant differences suggest incorrect parameter entry or solver settings.

Validate Energy Conservation
In power electronics simulations, confirm input power equals output power plus losses. A buck converter with 90% efficiency should satisfy Pin * 0.9 ≈ Pout.

Test Extreme Values
Set load resistance to 1Ω and 1GΩ in a voltage regulator model. The output should remain stable if feedback loops are correctly implemented.

This guide provides repeatable methods to build and validate simulations. Focus on matching theoretical expectations with software outputs before advancing to complex designs.

Advanced Simulation Techniques for Electrical Systems

This section explores methods to handle complex electrical system simulations beyond basic circuit analysis. You’ll learn how to account for component variations, test hardware interactions in real time, and combine simulation environments for hybrid modeling. These techniques address practical challenges in design validation and system optimization.

Monte Carlo Analysis for Component Tolerance Testing

Monte Carlo analysis predicts how component tolerances affect system performance by running hundreds or thousands of simulations with randomized parameter values. This method reveals worst-case scenarios and statistical failure probabilities, making it critical for reliability-focused designs like power supplies or sensor interfaces.

To implement Monte Carlo:

1. Define tolerance ranges for components (e.g., ±5% for resistors, ±20% for capacitors)
2. Specify performance metrics to evaluate (e.g., output voltage ripple, filter cutoff frequency)
3. Configure your SPICE or MATLAB/Simulink model to automatically iterate with randomized parameters

In SPICE, use the .STEP command with random functions. In MATLAB, employ parfor loops to parallelize simulations. The results generate histograms or scatter plots showing parameter sensitivity. For example, you might discover that a specific capacitor’s tolerance dominates variations in a filter’s phase margin.

Real-Time Simulation with Hardware-in-the-Loop (HIL)

Real-time HIL simulation connects your software model to physical hardware components, enabling closed-loop testing of controllers, sensors, or power electronics. This bridges the gap between simulation and real-world behavior, particularly for systems like motor drives or grid-tied inverters.

Key requirements for HIL:

• A real-time processor to execute models within strict timing constraints
• I/O interfaces matching your hardware’s voltage/current levels
• Deterministic simulation models (avoid variable-step solvers)

In Simulink, use the Real-Time Workshop to compile models for real-time targets. For power electronics, simulate switching events at 1 µs or faster time steps. Test scenarios include:

• Injecting fault conditions (e.g., short circuits) into a simulated grid while monitoring a physical protection circuit
• Validating a microcontroller’s PWM signals against a simulated motor load

HIL setups fall into two categories:

1. Processor-in-the-Loop (PIL): Test embedded code on a physical microcontroller
2. Full HIL: Connect entire subsystems like battery management systems

Co-Simulation: Integrating SPICE Models into Simulink

Co-simulation merges SPICE’s accuracy in analog circuit modeling with Simulink’s strengths in system-level and control design. This hybrid approach avoids oversimplifying either domain, which is vital for mixed-signal systems like DC-DC converters with digital feedback loops.

Implementation steps:

1. Export critical analog subcircuits (e.g., custom transistors, RF components) as SPICE netlists
2. Use Simulink’s Simscape Electrical or third-party tools to interface SPICE models
3. Adjust solver settings to handle SPICE’s nodal analysis and Simulink’s block-based algebra

A typical workflow involves:

• Simulating a switching regulator’s analog behavior in SPICE
• Modeling its digital PID controller in Simulink
• Exchanging data at fixed time steps (e.g., 10 ns for SPICE, 1 µs for Simulink)

Debugging co-simulations requires checking:

• Signal unit mismatches (e.g., Simulink’s normalized voltages vs. SPICE’s absolute values)
• Time synchronization errors between solvers
• Convergence failures in SPICE due to abrupt control signal changes

Prioritize simulation speed by limiting SPICE usage to mission-critical components. For example, simulate only the output stage of an amplifier in SPICE while modeling its input buffer in Simulink.

Integrating Simulation Tools with Hardware Design

Simulation tools like SPICE and MATLAB/Simulink bridge the gap between theoretical designs and functional hardware. This section explains how to translate simulation results into physical implementations across three key workflows: transferring SPICE data to PCB layout tools, converting Simulink models to microcontroller code, and validating simulations against real-world measurements.

Exporting SPICE Results to PCB Layout Software

SPICE simulations verify circuit behavior before physical prototyping. To transfer these results into a PCB design:

1. Export a netlist from your SPICE tool. This file describes components, connections, and simulation parameters. Common formats include .net or .cir.
2. Import the netlist into PCB layout software. Most tools automatically map components to footprints and preserve node connections.
3. Verify component footprints match your physical parts. Incorrect footprints (e.g., wrong pin spacing for capacitors) cause manufacturing errors.
4. Use SPICE-derived voltage/current data to optimize trace widths. For example, high-current paths identified in simulations require wider traces to prevent overheating.
5. Run design rule checks (DRC) to ensure your PCB adheres to manufacturing constraints like minimum trace spacing or hole sizes.

Common pitfalls:

• Simulation models might lack real-world parasitics (e.g., trace inductance). Add estimated parasitic values to SPICE models for accuracy.
• Power planes or ground loops not modeled in SPICE require manual attention during layout.

Generating C Code from Simulink for Microcontrollers

Simulink’s code generation tools convert block diagrams into deployable C code for microcontrollers:

1. Build your algorithm in Simulink using blocks from the Simulink Coder or Embedded Coder libraries.
2. Configure hardware-specific settings:
   
   • Select your microcontroller (e.g., Arduino Uno, STM32).
   • Set clock speeds and pin assignments.
3. Generate C code using the Deploy to Hardware option. The tool auto-generates initialization routines, interrupt handlers, and I/O functions.
4. Compile and upload the code using your microcontroller’s IDE (e.g., Arduino IDE, STM32CubeIDE).

Best practices:

• Test the Simulink model with hardware-in-the-loop (HIL) simulations before code generation.
• Use fixed-point arithmetic instead of floating-point if your microcontroller lacks an FPU.
• Optimize code efficiency by disabling unused library functions in Simulink’s code generation settings.

Correlating Simulation Data with Lab Measurements

Simulation results must match real hardware performance. Follow these steps to validate designs:

1. Replicate test conditions:
   
   • Use identical input signals (e.g., sine wave amplitude/frequency) in both simulation and lab tests.
   • Match component values (resistors, capacitors) to within 1% tolerance.
2. Export simulation data as CSV files. Overlay these results on oscilloscope or multimeter measurements using tools like MATLAB or Python.
3. Identify discrepancies:
   
   • Phase shifts in filters: Adjust simulation models to include parasitic capacitance from PCB traces.
   • Voltage drops: Add series resistance from connectors or cables to SPICE models.
4. Iterate by updating simulations with real-world parameters, then retest.

Key tools for correlation:

• Network analyzers for frequency response validation.
• Current probes to compare simulated and measured power consumption.
• Thermal cameras to verify heat dissipation models.

Example workflow for a buck converter:

1. Simulate output ripple and efficiency in SPICE.
2. Build the circuit using the PCB layout derived from SPICE.
3. Measure ripple with an oscilloscope and efficiency with a power analyzer.
4. If measured ripple exceeds simulations, add ESR values of capacitors to SPICE and re-simulate.
5. Adjust PCB layout to reduce parasitic inductance if needed.

By systematically linking simulations to hardware, you reduce prototyping iterations and improve design reliability. Focus on maintaining consistency between virtual models and physical implementations at every stage.

Common Simulation Errors and Debugging Strategies

Simulation software accelerates design workflows but requires familiarity with technical pitfalls. This section outlines frequent challenges in SPICE and Simulink, providing direct solutions to keep your projects on track.

Addressing Convergence Failures in SPICE

Convergence failures occur when SPICE cannot resolve circuit equations numerically. These errors often appear as "Gmin stepping failed" or "Singular matrix" messages.

Common causes include:

• Extreme component values (e.g., 1e12Ω resistors) creating numerical instability
• Floating nodes without DC paths to ground
• Abrupt voltage/current transitions exceeding solver capabilities

Debugging steps:

1. Modify solver settings: Increase ITL1 (DC iteration limit) or relax RELTOL (relative tolerance) in .OPTIONS
2. Add parasitics: Insert small parallel resistors (e.g., 1GΩ) to floating nodes or capacitors to break ideal voltage sources
3. Use .NODESET: Specify initial node voltages to guide the solver
4. Simplify nonlinear components: Replace complex diode/transistor models with idealized versions during initial testing
5. Check device polarity: Inverted capacitors or diodes often create impossible initial conditions

For transient analysis failures, reduce the maximum timestep using .TRAN 1n 10u 0 1n to force finer resolution.

Managing Algebraic Loops in Simulink Models

Algebraic loops arise when feedback paths lack time delays, causing simultaneous equation conflicts. Simulink flags these with "Algebraic state in loop" warnings.

Detection and resolution:

• Enable Debug > Algebraic Loop Diagnostics to identify loop locations
• Insert unit delays in feedback paths using Memory blocks for discrete systems
• For continuous systems, use Initial Condition blocks or enable the Algebraic Loop Solver in Configuration Parameters
• Break the loop by redesigning subsystems to eliminate direct feedthrough (e.g., replace Gain blocks with Transfer Function blocks)

Preventive measures:

• Avoid feeding outputs of Math Operations blocks directly into their own inputs
• Use Rate Transition blocks between subsystems with different sample times
• Replace Function Call subsystems with triggered subsystems when possible

Accuracy vs. Speed Tradeoffs in Simulation Settings

Balancing simulation fidelity and runtime requires strategic parameter adjustments:

SPICE optimizations:
| Parameter | Speed Focus | Accuracy Focus |
|--------------------|----------------------|----------------------|
| RELTOL | 1e-3 | 1e-6 |
| ABSTOL | 1e-6 | 1e-12 |
| VNTOL | 1e-1 | 1e-6 |
| Method | GEAR | TRAPEZOIDAL |

Simulink adjustments:

• Solver type: Use ode23tb (stiff/TR-BDF2) for power electronics instead of default ode45
• Max step size: Set to 1/50th of your signal's highest frequency component
• Zero-crossing detection: Disable for noisy systems to prevent over-triggering

General practices:

1. Start with moderate tolerances (REL TOL=1e-4, ABS TOL=1e-7) and tighten only if artifacts appear
2. Replace small time constants with algebraic approximations when high-frequency dynamics are irrelevant
3. Use PSPICE or LTspice for circuit-level accuracy, Simulink for system-level throughput
4. Profile model execution with Simulink Profiler to identify computational bottlenecks

Model simplification techniques:

• Replace switching components with averaged models for long transient simulations
• Linearize subsystems using Control System Toolbox for frequency-domain analysis
• Disable data logging on non-essential signals
• Use Lookup Table blocks instead of complex mathematical operations

Adjust these parameters iteratively: run simulations at low accuracy first to verify functionality, then increase precision for final validation.

Educational and Professional Resources for Users

This section identifies critical resources for building expertise in simulation tools like SPICE and MATLAB/Simulink. Focus on documentation, component libraries, and validation tools to accelerate skill development and ensure professional-grade results.

Official Documentation and Tutorial Repositories

Start with the official documentation for any simulation software you use. These materials provide the most accurate descriptions of tools, syntax, and workflows. For example:

• SPICE variants include detailed manuals explaining netlist syntax, component models, and simulation types.
• MATLAB/Simulink offers modular tutorials covering basic operations, block diagram creation, and debugging techniques.

Most platforms host structured learning paths for different skill levels. Look for:

• Step-by-step project guides demonstrating circuit analysis or control system design.
• Video series showing real-time problem-solving for common errors.
• Example repositories with pre-built models for amplifiers, filters, or power converters.

Bookmark the official troubleshooting pages for your tools. These often list error codes, convergence tips, and hardware compatibility guidelines. For community-driven tutorials, check platforms like GitHub or dedicated user forums.

Open-Source Component Libraries (LTspice, Simscape)

Pre-built component libraries reduce development time and improve simulation accuracy. Open-source repositories offer verified models for:

• Passive components (resistors, capacitors, transformers).
• Semiconductor devices (MOSFETs, diodes, op-amps).
• Mechanical and electromechanical systems (motors, sensors).

LTspice users can access thousands of third-party models for specialized components like RF transistors or high-voltage capacitors. Simscape libraries include hydraulic, pneumatic, and thermal blocks for multidomain simulations.

When using these libraries:

• Verify model licensing terms before integrating them into commercial projects.
• Check version compatibility to avoid simulation errors.
• Review documentation for parameter definitions and usage examples.

Contribute to open-source libraries by sharing custom models or corrected files. Many communities maintain quality-control guidelines to ensure uploaded models meet performance standards.

Industry Benchmarks and Validation Test Suites

Validate your simulations against standardized benchmarks to confirm real-world applicability. Industry test suites provide predefined scenarios for:

• Power electronics (e.g., inverter efficiency under load variations).
• Signal integrity (e.g., transient response in high-speed PCB designs).
• Control systems (e.g., stability analysis of PID loops).

These benchmarks often include reference datasets from physical experiments. Use them to:

• Calibrate simulation parameters like temperature coefficients or parasitic values.
• Test edge cases your models might not handle correctly.
• Compare results across different software tools.

Look for test suites published by standards organizations or academic consortia. Some focus on specific domains like automotive electronics or renewable energy systems. Reproducing benchmark results builds confidence in your workflow and highlights areas needing improvement.

Integrate validation tests into your regular workflow. Run them after major model updates or when switching simulation tools. Document discrepancies between simulated and benchmark results to refine future analyses.

Key repositories to explore:

• Circuit simulation benchmarks with predefined component tolerances.
• Motor control validation tests measuring torque ripple or efficiency.
• Power supply stability assessments under transient loads.

Prioritize test suites aligned with your specialization. For example, RF engineers might focus on noise figure measurements, while embedded systems developers could target digital signal processing benchmarks.

Note: A separate Sources section will list all references for documentation portals, libraries, and test suites.

Key Takeaways

Here's what you need to remember about simulation software:

• SPICE dominates analog circuit design (85% industry use) for precise device-level analysis
• MATLAB/Simulink scales for complex system modeling, trusted by 4+ million engineers
• Choosing the right tool slashes prototype revisions by 40-60%, saving months of development
• Calibrate simulations with lab measurements to reduce errors by 30%
• LTspice (free SPICE alternative) and Octave (MATLAB-compatible) cut licensing costs

Next steps: Pair SPICE for component behavior with Simulink for system integration, and validate critical simulations with physical prototypes.