Adaptive control is essential for the successful operation of robots in unknown environments. Existing literature involves the use of mathematical assumptions and regression techniques for estimation. This BTP develops an ...

We use different optimizers in everyday Machine Learning and Deep Learning applications. The task for any machine learning algorithm is to 𝑚𝑖𝑛 𝑓 (𝑥), where 𝑓 is the objective function and 𝑥 is the input parameter. ...

Conventionally, optimal control solutions demand offline a priori knowledge of the system dynamics. Adaptive optimal control methods have been advantageous in allowing for dynamic controllers to approximate optimal control ...

Federated learning (FL) is a popular framework for training a model in a distributed setting, where the data is spread over a network of devices. In traditional FL, the devices communicate with a central server to share ...

This report presents the development of a Simulink-Controlled Multi-Robot Simulation system integrated with Gazebo and ROS, tailored for robotics education and research. The core objective of the project is to provide a ...