Dayaram Sonawane
Papers
1
Total Citations
2
H-Index
1
About
Dayaram Sonawane is a researcher at the forefront of embedded control systems, specializing in the intersection of nonlinear model predictive control (NMPC), field-programmable gate array (FPGA) implementation, and deep learning. His most cited work addresses a critical bottleneck in real-time control: the computational complexity of solving online optimization problems on resource-limited hardware. By pioneering an FPGA-based, low-complexity NMPC framework that leverages deep learning, Sonawane has demonstrated a path to deploying advanced control algorithms in embedded systems where traditional methods falter. His 2022 paper on this topic has garnered 2 citations, reflecting its growing relevance to researchers in control theory and hardware acceleration. This work is notable for its practical approach to bridging the gap between sophisticated control theory and real-world hardware constraints, offering a scalable solution for applications in robotics, automotive systems, and industrial automation. Sonawane’s contributions are particularly valuable for students and engineers seeking to implement intelligent, real-time control on compact, low-power devices.
Research Focus
Key Achievements
Top Papers
- 1