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Sensor Fusion and Motion Control for Autonomous Racing Cars

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.

Rapid Control Prototyping

Hardware-in-the-Loop

Whitepapers

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

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.

Whitepapers

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

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.

Whitepapers

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

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.

Hardware-in-the-Loop

Rapid Control Prototyping

Whitepapers

AGCO Fendt

AGCO Fendt

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

Success Stories

Hardware-in-the-Loop

IAV

IAV

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

Success Stories

Hardware-in-the-Loop

Proterra

Proterra

Zero-emission battery electric bus charges at on-route bus station.

Success Stories

Hardware-in-the-Loop

Tongji University

Tongji University

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

Success Stories

Hardware-in-the-Loop

TUM Hyperloop

TUM Hyperloop

Building Hyperloop pods to revolutionize terrestrial transportation.

Success Stories

Hardware-in-the-Loop

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

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

Published Papers

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

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.

Published Papers

Hardware-in-the-Loop

Leclanché SA

Leclanché SA

Developing the next generation Li-ion battery packs for automated guided vehicles.

Hardware-in-the-Loop

Success Stories

ClearMotion

ClearMotion

A ground-breaking active suspension system to transform the driving experience.

Success Stories

Rapid Control Prototyping

Flanders Make

Flanders Make

Development of a High Precision Vehicle Drivetrain Test Bench.

Success Stories

Rapid Control Prototyping

GreenTeam

GreenTeam

Formula Student racing success achieved through powertrain innovation.

Success Stories

Rapid Control Prototyping

Mobileye

Mobileye

Driving technology towards a fully autonomous vehicle.

Success Stories

Rapid Control Prototyping

Stanford University

Stanford University

Reducing emissions with low-carbon fuels.

Success Stories

Rapid Control Prototyping

ZKW Lichtsysteme GmbH

ZKW Lichtsysteme GmbH

Developing Intelligent Lighting Systems for the Next Generation of Vehicles.

Success Stories

Rapid Control Prototyping

Incova

Incova

Designing intelligent valve-control system for a 20 ton excavator

Success Stories

Rapid Control Prototyping

Nuvera

Nuvera

Reducing commercial vehicle emissions using hydrogen fuel cells.

Success Stories

Hardware-in-the-Loop

Ponsse

Ponsse

Cutting development time for harvester control software by at least a year with model-based design.

Success Stories

Rapid Control Prototyping

Tata Motors

Tata Motors

Developing autonomous driving software including sensor perception, motion planning, and vehicle control algorithms.

Success Stories

Hardware-in-the-Loop

Volvo

Volvo

Equipment streamlines product development with a real-time, Human-in-the-Loop Simulator.

Success Stories

Hardware-in-the-Loop

Independent Generation of Sequence Elements by Motor Cortex

Independent Generation of Sequence Elements by Motor Cortex

Rapid execution of motor sequences depends on fusing movement elements into cohesive units that are executed holistically. The contribution of the primary motor and dorsal premotor cortex to this ability is determined in this paper. Also, the hypothesis that movement elements fuse makes specific predictions regarding three forms of activity, preparation, initiation, and execution is investigated.

Publication on nature.com

Published Papers

Motion Planning and Experimental Validation for an Autonomous Bicycle

Motion Planning and Experimental Validation for an Autonomous Bicycle

This paper introduces a prototype autonomous two-wheeled vehicle developed for experimental verification of motion planning and control algorithms. Finally, it presents and discusses experiments run on the actual vehicle for a particular maneuver. It emphasizes the differences between the trajectories created by different vehicle models.

Publication on ieeexplore.ieee.org

Published Papers

Rapid Control Prototyping

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