Vaishali Patne
Papers
1
Total Citations
2
H-Index
1
About
Vaishali Patne is a researcher at the forefront of embedded control systems, specializing in the intersection of nonlinear model predictive control (NMPC), FPGA-based hardware acceleration, and deep learning. Her most-cited work, "FPGA Implementation of Low Complexity Nonlinear Model Predictive Control Using Deep Learning Approach" (2022), tackles a critical bottleneck in real-time control: the computational burden of solving complex online optimization problems on resource-limited hardware. By leveraging deep learning to reduce complexity and implementing the solution on FPGA, Patne demonstrates a practical path to deploying advanced control algorithms in embedded applications where traditional methods falter. This contribution is particularly impactful for industries requiring fast, reliable control in constrained environments, such as robotics and autonomous systems. With 2 citations already, her work is gaining traction among researchers seeking to bridge the gap between theoretical control methods and real-world hardware constraints. Patne’s research is a testament to her ability to innovate at the hardware-software interface, making her a promising voice in the future of efficient, real-time control systems.
Research Focus
Key Achievements
Top Papers
- 1