Multi-stage trajectory tracking of robot manipulators under stochastic environments.

Area of Science:

• Robotics
• Control Systems Engineering
• Stochastic Systems

Background:

• Robot manipulators often face challenges in precise trajectory tracking due to complex dynamics and stochastic environments.
• Achieving accurate end-effector positioning from initial to final states is critical for task completion.

Purpose of the Study:

• To investigate multi-stage trajectory tracking for robot manipulators driven by direct current (DC) motors in stochastic environments.
• To develop a robust control strategy for achieving accurate end-effector path following.

Main Methods:

• Utilized inverse kinematics and task space partitioning for multi-stage trajectory planning.
• Employed adaptive backstepping techniques to design controllers for stochastic Lagrangian subsystems.
• Developed a multi-stage switched controller incorporating a state-dependent switching signal.

Main Results:

• Demonstrated that all signals within the closed-loop error switched system remain bounded in probability.
• Showcased that the mean-square tracking error can be minimized through parameter tuning.
• Validated the proposed control method's effectiveness via simulation results.

Conclusions:

• The proposed multi-stage switched control strategy effectively addresses trajectory tracking challenges in stochastic robotic systems.
• The adaptive backstepping approach ensures robust control and precise end-effector positioning.
• Simulation results confirm the theoretical findings and the practical applicability of the method.