Self-triggered MPC for trajectory tracking of an Autonomous Underwater Vehicle with additive disturbance
- Resource Type
- Conference
- Authors
- Zhang, Pengyuan; Hao, Li-Ying; Wang, Runzhi
- Source
- 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON) On-Line Conference (ONCON), 2023 IEEE 2nd Industrial Electronics Society Annual. :1-6 Dec, 2023
- Subject
- Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Autonomous underwater vehicles
Additives
Trajectory tracking
Simulation
Stability criteria
Cost function
Nonlinear systems
Autonomous underwater vehicle (AUV)
model predictive control
self-triggered control
trajectory tracking
disturbances
- Language
This paper presents a novel self-triggered model predictive control (MPC) approach for handling disturbances in continuous-time nonlinear systems. The self-triggered MPC scheme involves optimizing the cost function with respect to control input and triggering time, facilitating coordinated design of triggering and control actions while incorporating tighter constraints to address disturbances. Improved system robustness is achieved by using tight constraints to measure effective control strategies tailored specifically for underwater vehicles operating in uncertain environments. Theoretical criteria are established to ensure the feasibility and closed-loop stability of the considered nonlinear systems. Simulation studies demonstrate the effectiveness of the proposed self-triggered method in enhancing control performance and reducing resource utilization, even in the presence of disturbances.