Predefined-time sliding mode control for aerial manipulator based on the robust adaptive learning rate neural network state observer
Simulations encompassing aggressive manipulator motion with payloads, abrupt external disturbance injection and removal, model mismatches, composite sensor-noise corruption, and heavy-object transportation with additional position feedback noise validate the framework’s superiority in disturbance estimation accuracy and robust anti-disturbance performance.