Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

Key words:  Constrained input system  Hamilton-Jacobi-Isaacs  H-infinity control  Finite-horizon zero-sum games  Neural network control

摘要:

Abstract:

In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game. The game value function is approximated by a neural network with time-varying weights. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain. The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line. The effectiveness of the method is shown on the Rotational. Translational Actuator benchmark nonlinear control problem.