I am currently assistant at the Institute of Engineering and Technology, Nicolaus Copernicus University, Poland. I received B.Sc. in Automatics and Robotics in 2016, M.Sc. in Informatics in 2017 at Nicolaus Copernicus University, and I worked as an electronic devices constructor for over 2 years. I am currently pursuing the Ph.D. degree in Automatics and Robotics at Warsaw University of Technology.
Automatic adaptation of control structure to current operating point to provide superior dynamic and robustness of the system
Maximization of profit, minimization of energy, and satisfaction of complex limitations in real-world engineering problems
Autonomous collision-free operation in an unknown dynamic environment with particular emphasis on energy-efficiency
In order to provide optimal system response, optimization algorithm is used as adjustment mechanism of controller coefficients. The proposed Adaptive Procedure for Optimization Algorithms (APOA) and the novel desired-response adaptive system (DRAS) allow to apply most of optimization algorithms to adaptation process. The developed adaptive approach is implemented in PMSM drive to adapt state feedback speed controller during moment of inertia variations.
The application of the Artificial Bee Colony algorithm to automatic tuning of state feedback speed controller (SFC) for two-mass system (TMS) is proposed in this paper. The objective function is described and discussed in details. The obtained coefficients of the controller are examined on the laboratory stand, also with variable moment of inertia values, to indicate robustness of the designed controller.
In this paper, the energy-efficient local path planning algorithm is proposed. Future movement prediction has been introduced to Artificial Potential Field algorithm to allow autonomous ground vehicle to bypass obstacles in advance. A novel method for local minimum avoidance based on virtual obstacles called top-quarks is introduced. Such a combination allows to reduce traveled route length, improve its smoothness, and bypass local minima. Moreover, it allows to reduce the used electric power by 21.4 %
The scheduling for palletizing taks using single robotic arm that handle three production lines is not a trivial problem. To solve such the constrained multi-objective optimization problem, the Artificial Bee Colony algorithm supported by Deb’s rules has been applied. It was shown that the proposed approach significantly increases the production rate and satisfies the particular requirements, i.e., minimum energy per palletized item ratio, equality of containers’ filling.
