8 citations · 33 across the 17 of their papers we have counts for
6 papers · 1 filter
Combining Deep Reinforcement Learning with a Jerk-Bounded Trajectory Generator for Kinematically Constrained Motion Planning
Seyed Adel Alizadeh Kolagar, Mehdi Heydari Shahna, Jouni Mattila
Deep reinforcement learning (DRL) is emerging as a promising method for adaptive robotic motion and complex task automation, effectively addressing the limitations of traditional c…
Robust Model-Free Control Framework with Safety Constraints for a Fully Electric Linear Actuator System
Mehdi Heydari Shahna, Pauli Mustalahti, Jouni Mattila
This paper introduces a novel model-free control strategy for a complex multi-stage gearbox electromechanical linear actuator (EMLA) system, driven by a permanent magnet synchronou…
System-Level Efficient Performance of EMLA-Driven Heavy-Duty Manipulators via Bilevel Optimization Framework with a Leader--Follower Scenario
Mohammad Bahari, Alvaro Paz, Mehdi Heydari Shahna +1
The global push for sustainability and energy efficiency is driving significant advancements across various industries, including the development of electrified solutions for heavy…
Model-Free Generic Robust Control for Servo-Driven Actuation Mechanisms with Layered Insight into Energy Conversions
Mehdi Heydari Shahna, Jouni Mattila
To advance theoretical solutions and address limitations in modeling complex servo-driven actuation systems experiencing high non-linearity and load disturbances, this paper aims t…
Robust Sensor-Limited Control with Safe Input-Output Constraints for Hydraulic In-Wheel Motor Drive Mobility Systems
Mehdi Heydari Shahna, Pauli Mustalahti, Jouni Mattila
In-wheel drive (IWD) systems enhance the responsiveness, traction, and maintenance efficiency of vehicles by enabling each wheel to operate independently. This paper proposes a nov…
Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks
Mehdi Heydari Shahna, Seyed Adel Alizadeh Kolagar, Jouni Mattila
In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability, which may pose challenges in ensuring stability…