Adaptive Neural-Network Boundary Control for a Flexible Manipulator With Input Constraints and Model Uncertainties

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Flexible manipulators present control challenges due to their inherent dynamics.
• Input constraints, model uncertainties, and external disturbances degrade manipulator performance.
• Existing control methods often struggle with these complex, real-world conditions.

Purpose of the Study:

• To develop an adaptive neural-network (NN) boundary control scheme for flexible manipulators.
• To address unknown input saturations, dead zones, and model uncertainties.
• To guarantee the uniform ultimate boundedness of tracking errors.

Main Methods:

• Utilized radial basis function neural networks (NNs) to model uncertainties and input nonlinearities.
• Employed a backstepping approach to design adaptive NN boundary controllers.
• Developed update laws for the neural network parameters.

Main Results:

• The proposed adaptive NN boundary control scheme effectively handles input constraints and model uncertainties.
• The control laws ensure the uniform ultimate boundedness of deflection and angle tracking errors.
• Numerical simulations validate the effectiveness of the developed control technique.

Conclusions:

• The adaptive NN boundary control scheme provides robust and stable control for flexible manipulators.
• This method enhances tracking accuracy in the presence of significant uncertainties and disturbances.
• The approach offers a promising solution for advanced robotic control applications.