Pavel Pakshin

About

Pavel Pakshin is a leading researcher in iterative learning control (ILC) and multi-agent systems, with a focus on enhancing precision and adaptability in networked robotic environments. His work addresses critical challenges in modern smart manufacturing, where robots must perform high-precision repetitive tasks under random perturbations, changing reference trajectories, and actuator nonlinearities. Pakshin’s major contributions include developing networked ILC algorithms that leverage information from previous repetitions to improve control accuracy, even when agents are subject to external disturbances and measurement noise. His 2020 paper on ILC for multi-agent systems under random perturbations has garnered 19 citations, reflecting its impact on the field. He has also advanced higher-order ILC algorithms and designs for discrete systems with actuator nonlinearities, as seen in his 2024 and 2025 works. Notably, his research on weak stability of nonlinear repetitive processes (2016) provides foundational insights into 2D systems modeling. Pakshin’s work is instrumental in enabling robots to adapt to dynamic tasks and network conditions, making him a key figure in the evolution of smart manufacturing and autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Iterative Learning Control of a Multiagent System under Random Perturbations

   19 citations · 2020
2. 2
   Iterative Learning Control of Multi-Agent Systems under Changing Reference Trajectory*

   7 citations · 2022
3. 3
   Higher-Order Iterative Learning Control Algorithms for Linear Systems

   3 citations · 2024
4. 4
   Iterative Learning Control Design for a Class of Discrete Systems With Actuator Nonlinearity

   3 citations · 2025
5. 5
   Iterative Learning Control of Stochastic Multi-Agent Systems with Variable Reference Trajectory and Topology

   2 citations · 2023
6. 6
   Weak stability of nonlinear repetitive processes

   2 citations · 2016

Key Collaborators

Contact & Links