Learning from adaptive neural dynamic surface control of strict-feedback systems.

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Nonlinear System Dynamics

Background:

• Achieving learning in nonstationary environments for nonlinear systems is a significant challenge in autonomous control.
• Existing methods often struggle with high-dimensional inputs and computational complexity.

Purpose of the Study:

• To develop a learning method for n th-order strict-feedback systems using adaptive dynamic surface control (DSC).
• To enable autonomous systems to learn by doing and utilize learned knowledge for improved control.
• To reduce the computational burden associated with learning in complex systems.

Main Methods:

• Proposed a stable adaptive DSC with auxiliary first-order filters to ensure signal boundedness and finite-time convergence of tracking errors.
• Utilized filter output derivatives as neural network (NN) inputs, significantly reducing NN input dimensions.
• Decomposed the system into linear time-varying perturbed subsystems and employed recursive design for NN approximation.

Main Results:

• Demonstrated finite-time convergence of tracking errors and boundedness of all closed-loop signals.
• Achieved accurate approximations of closed-loop system dynamics using radial basis function neural networks.
• Validated the ability to reuse learned knowledge for enhanced control performance, faster convergence, and reduced tracking error.

Conclusions:

• The proposed adaptive DSC learning method effectively addresses learning in nonstationary nonlinear systems.
• The method significantly reduces computational load by simplifying NN input requirements.
• The approach offers a promising direction for developing more capable and efficient autonomous control systems.