Data-Driven H Control for Nonlinear Distributed Parameter Systems. Academic Article uri icon


  • The data-driven H control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H control policy from real system data rather than the mathematical model. First, Karhunen-Love decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

published proceedings

  • IEEE Trans Neural Netw Learn Syst

author list (cited authors)

  • Luo, B., Huang, T., Wu, H., & Yang, X.

citation count

  • 84

complete list of authors

  • Luo, Biao||Huang, Tingwen||Wu, Huai-Ning||Yang, Xiong

publication date

  • November 2015