Knowledge Center

Sensor Fusion and Motion Control for Autonomous Racing Cars

Learn how a research team from the Technical University of Munich uses Speedgoat real-time solutions, MATLAB^® and Simulink^® to develop and test an autonomous driving software stack, capable of operating a racing vehicle close to its physical limits. As an integral part of the framework, sensor fusion and motion control algorithms are optimized and validated for safe and accurate real-time operation, using Rapid Control Prototyping and Hardware-in-the-Loop simulation.

FPGA-based rapid control prototyping of permanent magnet synchronous motor servo drives

Due to tight time constraints and unknown disturbances, the position control problem in permanent magnet synchronous machine (PMSM) drives remains exceedingly challenging. Download this technical article to learn more about experimental validation of a cascade control structure for position control in PMSM drives.

Speed Up Digital Control Development for Motors, Power Converters, and Battery Systems with Simulink

Digital control design for power electronics using Simulink^® makes it easy to try new ideas, test them, and go to hardware without coding. You can use system-level models for desktop simulation, real-time simulation, and production code generation, speeding up designing and testing your power electronics control systems.

10 Ways to Speed Design for Digitally Controlled Power Converters with Simulink

This whitepaper highlights ways to accelerate digital control development for power converters with system-level simulation, how to validate control code on the processor without damaging electrical system hardware and developing real-time simulations of your electrical system.

Aalto University

Students' mission to get Finland's first satellite into orbit.

AGCO Fendt

Automated testing of tractor controllers using Hardware-in-the-Loop test benches.

Cranfield University

Novel technique improves speed and accuracy of micrometer scale precision CNC machine by 40%.

IAV

Decreasing Plant Downtimes Through Test Automation of PLC Control Functions with a Digital Twin.

Lehigh University

Achieving breakthroughs in the field of real-time hybrid simulation of tall buildings.

Schindler Elevator Corporation

Validating Schindler’s next generation elevator controller family with hardware-in-the-loop simulation.

Scientific Aviation Association

Find out how students at the Scientific Aviation Association are using a Baseline real-time target machine to accelerate their hybrid powertrain testing and certification process.

Tongji University

Accelerating the development of a driver-adaptive ADAS control strategy using hardware-in-the-loop simulation.

TUM Hyperloop

Building Hyperloop pods to revolutionize terrestrial transportation.

University of Alabama

Critical infrastructure monitoring and control using real-time hybrid simulation.

Implementing a PLCnext-Based Turbine Control System in Simulink - Sokratel

Find out how Sokratel uses continuous integration (CI/CD) frameworks to continuously test their turbine control systems using Speedgoat test systems

Verification of Avionics Systems Using Simulink Test and Simulink Real-Time - GE Aerospace

Find out how GE Aerospace uses an integrated requirements-based testing approach for controller certification using Speedgoat test systems

Accelerating Safe Railway Application Development Using Model-Based Design - Alstom

Find out how Alstom employs requirements-based testing to develop safety-critical train controls using Speedgoat test systems

Managing the Complexity of FPGA-Based Rapid Control Prototyping - Siemens Healthineers

Find out how Siemens Healthineers rapidly prototype controls to accelerate development of their X-ray imaging devices using Speedgoat test systems equipped with ultra-fast FPGAs

Hiroshima University

Testing with a digital twin allows adjusting control parameters or evaluating new control designs seamlessly

Designing a Generic, Software-Defined Multimode Radar Simulator For FPGAs Using Simulink HDL Coder and Speedgoat Real-Time Hardware

This publication focuses on the implementation and testing of a fully-parameterized radar signal processing prototype. A Speedgoat Performance machine with two Simulink-Programmable FPGA I/O modules IO342 are used for the implementation of a radar signal processing design containing several common waveforms and tunable parameters and a radar scene generator for delay, doppler, and amplitude measurement.This setup helped increase the simulation fidelity while reducing the time to test.

Design, Simulation and Hardware-in-the-Loop (HIL) Testing of an Electric Scooter Powertrain

This publication focuses on an algorithm to control a brushless DC motor. A Speedgoat performance machine runs a digital twin of the motor on both the CPU and the FPGA-based I/O module IO334 and is connected via the analog channels to the controller, an MCU by Texas Instruments. With this HIL setup, the performance of the control algorithm was tested. 

Certification Process for a Hybrid Electric Aircraft

The scientific aviation association (FVA) is developing the FVA 30, a hybrid electric motor glider, to research alternative propulsion systems. This article focuses on the certification process of the FVA 30 power train, using a Speedgoat target computer.

Battery Management System Integration into an Electronic Control Module for a Hybrid Electric Aircraft

Th­is article focuses on BMS integration into the electronic control module (ECM) of the FVA 30 hybrid electric motor glider using a Speedgoat real-time target machine. The challenge is to design an ECM for reliable data processing, allowing pilots to monitor and control the drivetrain.

Assessment of State-of-Charge Estimation Method for Lithium-Ion Batteries

In this paper, a numerical model of lithium-ion batteries is developed and deployed to a Speedgoat Baseline target machine. The estimation method for the state-of-charge (SOC), based on a nonlinear autoregressive with exogenous input (NARX) and artificial neural networks (ANNs) that are correctly trained with multiple datasets, is designed, and experimentally validated by hardware-in-the-loop simulation.

Publication on mdpi.com

Hardware-in-the-Loop Testing (HIL) of State-of-Charge (SoC) Estimation for Li-Ion Batteries

This study presents the design and validation of an SoC estimation method for lithium-ion batteries in hybrid-electric vehicles (HEV). The battery model is deployed on a Speedgoat Baseline machine connected to a Raspberry Pi emulating the ECU based on an artificial neural network for HIL testing. The algorithm can estimate the SoC of the battery with 2% accuracy during real-time testing.