Max Robotics — Global Service Robot Directory + US Certification

About

Farzad Pourboghrat is a leading researcher in the control and automation of robotic systems, with a career spanning foundational theory to practical embedded implementation. His most influential work addresses the fundamental challenge of controlling dynamic mobile robots under nonholonomic constraints—systems where movement is restricted, like a car that cannot slide sideways. His 2002 paper on adaptive control for such robots, which has garnered 139 citations, provides a robust framework for trajectory tracking, while his companion work on exponential stabilization (40 citations) offers a rigorous solution for ensuring system stability. Beyond mobile robotics, Pourboghrat has made significant contributions to manipulator control, including pioneering the use of neural networks for learning inverse kinematics in redundant arms—a key problem in enabling flexible, human-like motion. He has also advanced practical implementations, demonstrating how Digital Signal Processors (DSPs) can execute complex control algorithms in real time. His later work integrates computer vision with control theory, using Linear Matrix Inequalities (LMI) to solve obstacle-avoiding path planning as a convex optimization problem. With a career that bridges theoretical proofs, neural learning, and hardware realization, Pourboghrat’s research provides a comprehensive toolkit for modern robotics.