Adaptive Prescribed Performance Trajectory Tracking for Uncertain Autonomous Underwater Vehicles With Input Saturation
Authors: Athanasios K. Gkesoulis, Christos Vlachos, George K. Fourlas, Charalampos P. Bechlioulis, George C. Karras
Organizations: The authors are with the Robotics Institute, Athena Research Center, Marousi, Greece · The authors are with the Division of Systems and Automatic Control, Department of Electrical and Computer Engineering, University of Patras, Patras, Greece · The authors are with the Department of Informatics and Telecommunications, School of Sciences, University of Thessaly, Lamia, Greece
Accurate trajectory tracking is essential for autonomous underwater vehicles operating with uncertain hydrodynamics and limited thruster authority. This paper develops a low-complexity adaptive prescribed performance controller for uncertain underwater vehicle dynamics with coupled and saturated inputs. The proposed method adapts the performance functions directly through the mismatch between the requested and saturated control inputs, enlarging the performance funnels only when the available actuation cannot meet the nominal specifications. A coupled invariant-set analysis establishes boundedness of all closed-loop signals, prescribed bounds on the filtered tracking errors, and explicit feasibility conditions. Comparative simulations on a BlueROV2 underwater vehicle show that the adaptive design maintains prescribed performance during a figure-eight maneuver, whereas conventional prescribed performance control loses feasibility under input limits. A real-time pool experiment further demonstrates stable trajectory tracking and the practical implementation of the proposed method on an underwater robotic platform.
Figures & tables
Fig. 1: BlueROV2 experimental platform operating above the mapped ArUco fiducials in the test tank.
Fig. 2: Closed-loop adaptive prescribed performance architecture under input saturation. The mismatch between the requested and applied inputs adapts the performance functions, which recover their nominal values when saturation is inactive.
Fig. 3: Comparative BlueROV2 simulation under the common input limits ∣τi∣≤4 . (a) APPC filtered errors and adaptive performance functions. (b) PPC filtered errors and fixed performance functions. (c) APPC requested and applied inputs. (d) PPC requested and applied inputs. APPC maintains prescribed performance, whereas PPC violates the yaw bound at t=14.459 s and the simulation is subsequently stopped.
Fig. 4: Experimental planar trajectory tracking over the reported interval.
Fig. 5: Experimental closed-loop signals over the complete reported interval. (a)–(c) Filtered errors and adaptive performance functions for surge, sway, and yaw, respectively. (d)–(f) Corresponding requested and applied input commands under ∣τi∣≤4 .
Robot trajectory tracking develops methods that make robots follow desired paths or motions accurately despite disturbances, uncertainty, and physical constraints.
Centre for Systems and Control, Indian Institute of Technology Bombay, Mumbai · Institute for Control, Optimization, and Networks, Purdue University, West Lafayette, USA