Naveen Subramanyam V

Control engineer specializing in advanced motor control and power conversion systems. Proven experience designing, validating, and deploying control and estimation solutions across simulation and hardware workflows. Currently contributing to production-quality control systems and scalable validation infrastructure within the Motor Control Blockset team at MathWorks.

MPC-Based Motion and Speed Control for PMSM (Hardware):  

• Designed and implemented a real-time Model Predictive Control (MPC)-based speed controller for PMSM Field-Oriented Control (FOC) on TI C2000 hardware, developed from scratch.
• Incorporated a look-ahead preview mechanism using future reference points from a predefined trapezoidal speed profile, enabling smoother trajectory tracking compared to classical PID control.
• Validated controller performance through hardware experiments using a Teknic motor, demonstrating superior path tracking relative to PID-based control.

 Deep Learning-Based Position Estimation for PMSM (IEEE):

• Developed a Neural Network-based observer for rotor position estimation in sensorless PMSM FOC.
• Investigated the impact of training data distribution strategies (uniform, sparse, randomized operating points) on estimator robustness.
• Designed and executed a comprehensive hardware validation plan, including 100% load testing using a dynamometer setup.
• Authored and published an IEEE research paper detailing estimator design, validation methodology, and experimental results.

Automated Test Generation Framework for Simulink:

• Architected and implemented an automated test generation infrastructure for Simulink blocks from scratch, supporting configurable and scalable test creation.
• Developed build and execution workflows using Makefile and Perl, enabling systematic control over test configuration and coverage.
• Enabled long-term adoption of test-driven development (TDD) by improving test scalability, repeatability, and maintainability.

• Model Predictive Control with Multi-Objective Optimization, Formulated a start-up control problem using Model Predictive Control (MPC) and solved it as a multi-objective optimization problem using Genetic Algorithms, implemented and validated in MATLAB.
• Evolutionary Optimization using Differential Evolution, Implemented a Differential Evolution-based optimization framework and evaluated performance using ZDT-6 multi-objective benchmark functions.