News

• An adaptive pitch angle controller is developed for speed rotor regulation of wind turbine plant under full load region. 

• A fractional-order version of Model Predictive Control (MPC) is designed to stabilize the system output under normal and extreme stochastic wind circumstances. 

• The deep on-policy learning algorithm is utilized to adjust the parameters of the fractional order version of MPC. 

• To ascertain the feasibility of suggested scheme, comparative analysis with the application of MPC, GSPI controller, and PI controller are made.

Researchers have come up with a new robust and adaptive solution for ballbots, a unique kind of robot that has great mobility and possesses the ability to go in all directions.

The team led by researchers from Hanoi University of Industry, Vietnam, introduced a novel adaptive nonlinear PID (NPID) controller integrated with a radial basis function neural network (RBFNN) for ballbots, offering lightweight computation, superior stability, chattering reduction, and robustness against external disturbances. 

Researchers revealed that the initial settings of the proposed controller are selected through balancing composite motion optimization, and the adaptive control law is improved continuously during operation to handle the real-time estimation of the external force.

Ballbot is a unique kind of robot with great mobility and possesses the ability to go in all directions. Obviously, controlling such a robotic device must be tricky. Indeed, ballbot systems pose unique challenges, particularly in the form of the difficulty of maintaining balance and stability in dynamic and uncertain environments. Traditional proportional integral derivative (PID) controllers struggle with these challenges, and other advanced methods, like sliding mode control, introduce issues like chattering. Therefore, there is a need to develop a controller that combines the simplicity and adaptability of PID with the learning capabilities of the now-popular neural networks, providing a robust solution to real-world robotic mobility problems. 

Researchers from Hanoi University of Industry, Hanoi University of Science and Technology, and Shibaura Institute of Technology (Japan) have developed a new 6D pose dataset designed to improve the accuracy and adaptability of robots in industrial environments. 

A Transnational Workshop on Smart Technologies for Societal Health Jointly organized by Anna University, Chennai, India & Queen's University Belfast, UK.