Novel adaptive backstepping control of uncertain electrically driven haptic robot for surgical training systems

The following paper presents the design, implementation, and validation of a new adaptive control system based on the Modified Function Approximation Technique (MFAT) augmented with backstepping control for a haptic robot with unknown dynamic and actuator parameters, employed in a spinal surgical simulator. The combination of backstepping control and the MFAT policy ensures the continuous performance tracking of the haptic manipulator's trajectories using state and output feedback. Contrary to the conventional FAT, the use of basis functions in dynamic and actuator parameter approximations is completely eliminated. Besides, the proposed control scheme was improved by integrating a high order sliding mode observer to eliminate the need for velocity measurements. Consequently, offline simulation and comparative studies were carried out to validate the effectiveness of the proposed control scheme, and controlled experimental cases were conducted using the haptic manipulator (Entact W3C) for validating the system in real time.