Data-Driven Optimal Tracking Control for Nonlinear Systems: New Research from Northeastern University

Strategic Insights -

Researchers from Northeastern University have made new findings in the field of information technology, detailing a data-driven constrained optimal tracking control scheme for nonlinear systems subject to input and performance constraints. This research aims to make system states pursue desired trajectories while minimizing cost and strictly limiting tracking errors. The study's financial supporters include the National Key R&D Program of China, Liaoning Revitalization Talents Program, and Nature Science Foundation of Liaoning Province of China. The research has been peer-reviewed and published in the journal Neural Networks.

Key Takeaways:

  • A data-driven constrained optimal tracking control scheme is designed to make system states pursue desired trajectories while minimizing cost and strictly limiting tracking errors.
  • A finite-time performance function is deployed to ensure that errors converge to steady-state regions within a user-defined time.
  • A nonquadratic cost function is employed to construct a modified Hamilton-Jacobi-Bellman equation, ensuring input limitations are satisfied.
  • An adaptive dynamic programming algorithm, implemented with neural networks (NNs) in an actor-critic structure, is employed to learn the optimal control policy without relying on any prior information about the system dynamics.
  • The weights of the actor-critic NNs are tuned using the least-squares method based on the collected dataset.
  • The research concludes that the designed algorithm is effective and beneficial, as demonstrated through simulations on Chua's circuit.
  • The researchers from Northeastern University, led by Huaguang Zhang, also include Lulu Zhang, Xiaohui Yue, and Tianbiao Wang in this research.

Statistics:

  • The research is supported by the National Key R&D Program of China, Liaoning Revitalization Talents Program, and Nature Science Foundation of Liaoning Province of China.
  • The research was published in the journal Neural Networks in 2025.
  • The journal Neural Networks is based in Oxford, England, and is published by Elsevier.
  • Huaguang Zhang and his team conducted the research at Northeastern University, College of Information Science and Engineering, in Shenyang, People's Republic of China.

Sources:

  • Data-driven Optimal Tracking Control for Nonlinear Systems With Performance Constraints Via Adaptive Dynamic Programming. Neural Networks, 2025;191.
  • NewsRx. Researchers from Northeastern University Report New Studies and Findings in the Area of Information Technology (Data-driven Optimal Tracking Control for Nonlinear Systems With Performance Constraints Via Adaptive Dynamic Programming). Information Technology Newsweekly. November 4, 2025; p 758.