Policy iteration approximate dynamic programming using Volterra series based actor

There is an extensive literature on value function approximation for approximate dynamic programming (ADP). Multilayer perceptrons (MLPs) and radial basis functions (RBFs), among others, are typical approximators for value functions in ADP. Similar approaches have been taken for policy approximation. In this paper, we propose a new Volterra series based structure for actor approximation in ADP. The Volterra approx-imator is linear in parameters with global optima attainable. Given the proposed approximator structures, we further develop a policy iteration framework under which a gradient descent training algorithm for obtaining the optimal Volterra kernels can be obtained. Associated with this ADP design, we provide a sufficient condition based on actor approximation error to guarantee convergence of the value function iterations. A finite bound of the final convergent value function is also given. Finally, by using a simulation example we illustrate the effectiveness of the proposed Volterra actor for optimal control of a nonlinear system.