Optimization-based adaptive trajectory tracking controller design of self-balanced vehicle with asymptotic prescribed...

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence in Engineering

Background:

• Self-balanced vehicles (SBVs) face challenges with nonlinear external disturbances and unmodeled dynamics, impacting trajectory tracking accuracy and stability.
• Existing control methods may struggle to guarantee both transient and steady-state performance under such complex conditions.

Purpose of the Study:

• To propose a novel adaptive trajectory tracking controller for SBVs that ensures asymptotic prescribed performance.
• To enhance motion stability and robustness against nonlinear disturbances and unmodeled dynamics.

Main Methods:

• A kinematic-based velocity planner was developed to stabilize the SBV's velocity signal.
• A prescribed performance function (PPF) was designed to define desired transient-state and steady-state performances (TSP).
• An optimization-based predictive control (OPC) strategy was integrated with a modified radial basis function neural network (RBFNN) approximator to compensate for uncertainties.

Main Results:

• The proposed controller demonstrated accurate trajectory tracking for the SBV.
• The modified RBFNN effectively compensated for unmodeled dynamics and nonlinear external disturbances.
• Lyapunov theorem was used to prove the overall system stability.

Conclusions:

• The novel adaptive controller based on asymptotic prescribed performance significantly improves SBV trajectory tracking.
• The method offers enhanced robustness against external disturbances and unmodeled dynamics, validated through simulations.