Sliding mode control with self-adaptive parameters of a 5-DOF hybrid robot

Area of Science:

• Robotics and Control Systems
• Manufacturing Engineering
• Mechanical Engineering

Background:

• Hybrid robots offer advantages like high stiffness and large workspaces, making them suitable for large-scale component machining (e.g., car panels).
• Challenges in dynamic modeling and external disturbances hinder precise control, impacting machining accuracy, quality, and efficiency.
• Traditional sliding mode control (SMC) is effective for nonlinear systems but suffers from chattering due to fixed approaching speeds.

Purpose of the Study:

• To develop a modified sliding mode controller (SMC) with self-adaptive parameters for a 5-degree-of-freedom hybrid robot.
• To enhance trajectory-tracking performance and overcome the limitations of traditional SMC, specifically chattering and sensitivity to parameter variations.
• To improve the overall machining accuracy and efficiency of hybrid robotic systems.

Main Methods:

• Establishment of the kinematic model for the 5-degree-of-freedom hybrid robot.
• Development of a rigid dynamic model using the principle of virtual work.
• Design of a modified SMC where the approaching speed is state-dependent, creating a self-adaptive controller.
• Simulation analysis to evaluate the controller's performance and robustness.

Main Results:

• The proposed self-adaptive SMC achieves a rapid approaching speed while simultaneously suppressing chattering.
• The controller demonstrates good robustness against external disturbances.
• Simulation results show that the modified SMC provides a more accurate and smoother trajectory compared to traditional methods.

Conclusions:

• The self-adaptive sliding mode controller effectively enhances the trajectory-tracking performance of hybrid robots.
• This approach successfully addresses the chattering issue and improves robustness in complex machining applications.
• The developed controller contributes to higher machining accuracy and efficiency in systems like those used for automotive components.