Optimal tracking control for a class of nonlinear discrete-time systems with time delays based on heuristic dynamic...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear System Dynamics

Background:

• Optimal tracking control is crucial for nonlinear discrete-time systems, especially those with time delays.
• Existing methods may struggle with the complexity and delays inherent in these systems.
• Heuristic Dynamic Programming (HDP) offers a potential framework for addressing these challenges.

Purpose of the Study:

• To propose a novel heuristic dynamic programming (HDP) iteration algorithm for optimal tracking control.
• To address nonlinear discrete-time systems specifically incorporating time delays.
• To enhance control policy and state updating mechanisms within the HDP framework.

Main Methods:

• Developed a novel HDP iteration algorithm featuring state updating, control policy iteration, and performance index iteration.
• Implemented 'backward iteration' for optimizing state updates.
• Utilized two neural networks to approximate the performance index function and compute the optimal control policy.

Main Results:

• The proposed HDP algorithm effectively handles optimal tracking control for nonlinear discrete-time systems with time delays.
• Neural networks successfully approximated the performance index and computed the optimal control policy.
• Demonstrated the algorithm's effectiveness through two illustrative examples.

Conclusions:

• The novel HDP iteration algorithm provides an effective solution for optimal tracking control in complex systems.
• The integration of backward iteration and neural networks enhances the algorithm's performance and applicability.
• The proposed method offers a viable approach for real-world control problems involving nonlinear dynamics and time delays.