Adaptive Force–Position Controller Design for a Three-Degree-of-Freedom Robotic Arm in the Presence of Uncertainties an‎d Varying Environmental Stiffness

1/1/1900 12:00:00 AM

In this thesis, a novel approach is presented for force–position control of a three-degree-of-freedom robotic arm in the presence of uncertainties an‎d varying degrees of environmental stiffness. The proposed method is based on an adaptive fuzzy controller with a variable learning rate, in which the control gain is dynamically adjusted using an adaptation law derived from Lyapunov theory. The main objective of this research is to achieve accurate tracking of the reference force an‎d position while reducing oscillations in the control signal. To this end, in the controller design, the maximum an‎d minimum values of the sliding surface an‎d its derivative are first determined. Then, by employing fuzzy logic an‎d integrating it with the Lyapunov-based adaptation law, the force an‎d position tracking errors are minimized. In the modeling process, all realistic factors, such as joint friction an‎d contact surface friction, are precisely taken into account. The contact surface between the robot an‎d the environment is simulated in such a way that its behavior resembles biological tissue (such as human skeletal muscle). Therefore, the experimental conditions are closely approximated to real-world environments. Simulation results demonstrate that the proposed controller achieves higher accuracy in force an‎d position tracking compared to previous methods. In this controller, by employing fuzzy logic, the oscillations of the control signal are significantly reduced. The proposed method is robust against uncertainties an‎d variations in environmental stiffness, an‎d most importantly, it considerably reduces the sensitivity of the controller to the tuning of impedance control gains. According to the conducted eva‎luations, the proposed method is capable of real-time implementation an‎d can thus be effectively employed in practical applications.

كنترل‌كننده نيرو-موقعيت، كنترل‌كننده فازي-تطبيقي، روش‌هاي كنترلي ربات‌هاي جراح، مدل‌سازي بافت‌هاي زيستي

force–position controller, adaptive fuzzy controller, control methods for surgical robots, modeling of biological tissues

Morteza Moradi

Dr. Mohammad Farrokhi