An Iterative Optimal Control and Estimation Design for Nonlinear Stochastic System

Abstract

This paper presents an iterative linear-quadratic-Gaussian method for locally-optimal control and estimation of nonlinear stochastic systems. The new method constructs an affine feedback control law obtained by minimizing a novel quadratic approximation to the optimal cost-to-go function. It also constructs a non-adaptive filter optimized with respect to the current control law. The control law and filter are iteratively improved until convergence. The performance of the algorithm is illustrated on a complex biomechanical control problem involving a stochastic model of the human arm