Passivity based nonlinear model predictive control (PNMPC) of multi-robot systems for space applications.

Area of Science:

• Robotics
• Control Systems
• Aerospace Engineering

Background:

• Autonomous multi-robot systems are increasingly vital for space applications like structure assembly and servicing.
• Current Model Predictive Control (MPC) methods for space robotics often rely on linear models, which are inadequate for highly non-linear systems.
• Existing Nonlinear MPC (NMPC) applications lack guaranteed closed-loop stability for unconstrained non-linear systems.

Purpose of the Study:

• To develop a novel Nonlinear Model Predictive Control (NMPC) approach for multi-robot space systems that guarantees closed-loop stability.
• To enhance the performance and robustness of autonomous space robot operations.
• To integrate passivity concepts into NMPC for improved control of non-linear space robotic systems.

Main Methods:

• Introduction of a novel passive nonlinear model predictive control (PNMPC) scheme.
• Utilization of a passivity-based state constraint and a terminal storage function.
• Application of PNMPC to multi-robot systems in space, addressing non-linear dynamics and constraints.

Main Results:

• The proposed PNMPC scheme ensures closed-loop stability for multi-robot space systems.
• The approach demonstrates superior performance compared to existing methods.
• Passivity-based concepts are effectively combined with NMPC for robust control.

Conclusions:

• PNMPC offers a stable and high-performance alternative to traditional control methods for non-linear multi-robot space systems.
• The integration of passivity guarantees stability, while NMPC ensures optimal performance.
• This research advances the control strategies for autonomous operations in space.