Abstract

This chapter presents an online adaptive learning algorithm to solve the infinitehorizon optimal control problem for non-linear systems. This include simultaneous tuning of both actor and critic NNs (i.e. both neural networks are tuned at the same time) and no need for knowledge of the drift term f(x) in the dynamics. This algorithm is based on integral reinforcement learning (IRL) and solves the Hamilton-Jacobi-Bellman (HJB) equation online in real time by measuring data along the system trajectories, without knowing f(x). In the linear quadratic case x = Ax + Bu it solves the algebraic Riccati equation (ARE) online without knowing the system matrix.

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