A model-free terminal sliding mode control for robots: Achieving fixed-time prescribed performance and convergence

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Traditional model-based sliding mode (SM) controllers face challenges including precise model requirements, disturbance bound knowledge, and chattering.
• Existing methods struggle with fixed-time convergence and prescribed performance for robot manipulators.

Purpose of the Study:

• To develop a robust and accurate control strategy for robot manipulators that overcomes limitations of traditional sliding mode control.
• To enhance response performance, position-tracking accuracy, and reduce overshoot and steady-state errors.
• To eliminate the need for precise system models and knowledge of disturbance upper bounds.

Main Methods:

• Incorporation of a neural network (NN) to remove the need for an accurate system model.
• Introduction of a novel fixed-time prescribed performance control (PPC) for improved response and accuracy.
• Utilization of a faster terminal sliding mode (TSM) surface and a novel fixed-time reaching control algorithm (RCA) with adaptable factors.

Main Results:

• The proposed strategy significantly improves convergence rate and tracking accuracy for robot manipulators.
• Demonstrated reduction in control signal chattering and enhanced controller robustness.
• Validated effectiveness and stability through simulations on a 3-DOF robot manipulator using Lyapunov criteria.

Conclusions:

• The novel control strategy effectively addresses limitations of traditional sliding mode controllers for robot manipulators.
• The integrated approach achieves desired performance goals, including improved accuracy and reduced chattering.
• The method offers a robust and efficient solution for advanced robot manipulator control.