Increasing stability and transparency in bilateral haptic-teleoperation robotic systems with artificial intelligent control methods

In today's technology, the ability of robots to perceive environmental conditions, make decisions and act in line with these decisions has an important place in every aspect of human life. Haptic-Teleoperation systems are a technology that allows human-robot interaction, which is used to obtain information about unknown environments and to collect necessary data. In this thesis, it is aimed to compensate the important problems such as time delay, dead zone, dynamic uncertainty and data loss that affect the stability and transparency performance of the systems with Artificial-Intelligence, Adaptive and Sliding-Mode control algorithms, and at the same time, it is aimed to minimize the effects of these problems on the systems. A Robust-Adaptive control algorithm has been designed and applied to the system in a way that it will be a system with more or less dead zone to compensate for the dead zone problem. Time delay and dynamic uncertainty problems are compensated as a result of hybrid use of Modified-Wave Variables and Nonlinear-Distortion-Observer methods with the control algorithms mentioned above. Finally, the communication channel is modeled with an artificial neural network and data loss is minimized. The optimum values of the control parameters affecting the performance of the designed and applied control methods on the system were found by PSO and GWO optimization algorithms. In addition, the user's performance has been increased by providing visual feedback to the user with the designed visual interfaces. In this thesis, three different robotic systems were used. In line with the graphical and numerical data obtained as a result of simulation and laboratory-based real-time experimental studies, the performance criteria of the proposed control algorithms with each other and related studies in the literature were compared and examined.