Tube-MPC-based trajectory tracking control for robotic manipulators

Area of Science:

• Robotics
• Control Systems Engineering
• Applied Mathematics

Background:

• Robotic manipulators require precise trajectory tracking for complex tasks.
• Input constraints and external disturbances pose significant challenges to control system performance.
• Existing control strategies often struggle with balancing robustness and efficiency.

Purpose of the Study:

• To develop an advanced Tube Model Predictive Control (Tube-MPC) strategy for robotic manipulators.
• To enhance trajectory tracking performance under input constraints and external disturbances.
• To improve the robustness and convergence speed of robotic manipulator control systems.

Main Methods:

• Utilizing Model Predictive Control (MPC) for nominal control input computation under constraints.
• Implementing a nonlinear disturbance observer (NDO) for online estimation of lumped disturbances.
• Integrating a fixed-time sliding mode control (SMC) term for enhanced error dynamics and convergence.
• Employing Lyapunov-based analysis to guarantee closed-loop stability and practical fixed-time convergence.

Main Results:

• The proposed Tube-MPC strategy significantly reduces joint-tracking Root Mean Square Error (RMSE) by 45.8%.
• Convergence time is shortened by approximately 38.6% compared to conventional Tube-MPC.
• The approach demonstrates superior robustness against time-varying torque disturbances.

Conclusions:

• The developed Tube-MPC strategy effectively addresses trajectory tracking control for robotic manipulators.
• The combination of NDO and fixed-time SMC enhances system performance and robustness.
• This method offers a promising solution for real-world robotic applications demanding high precision and reliability.