Suboptimal Nonlinear Moving Horizon Estimation

Efficient Numerical Methods for Nonlinear MPC and Moving Horizon Estimation

This overview paper reviews numerical methods for solution of optimal control problems in real-time, as they arise in nonlinear model predictive control (NMPC) as well as in moving horizon estimation (MHE). In the first part, we review numerical optimal control solution methods, focussing exclusively on a discrete time setting. We discuss several algorithmic ”building blocks” that can be combin...

Distributed moving horizon estimation for nonlinear constrained systems

Introduction to Nonlinear Model Predictive Control and Moving Horizon Estimation

Nonlinear model predictive control and moving horizon estimation are related methods since both are based on the concept of solving an optimization problem that involves a finite time horizon and a dynamic mathematical model. This chapter provides an introduction to these methods, with emphasis on how to formulate the optimization problem. Both theoretical and practical aspects are treated, ran...

Computationally Efficient, Approximate Moving Horizon State Estimation for Nonlinear Systems

Moving horizon estimation for discrete-time nonlinear systems is addressed by using fast optimization algorithms for which stability results under general conditions are ensured. The solution of the on-line moving horizon estimation problem is obtained by using the sampling time to solve a reference problem with model-predicted measurements while waiting for the next measurement. In order to co...

Regularized Nonlinear Moving Horizon Observer

A moving horizon state observer is developed for nonlinear discrete-time systems. The new algorithm is proved to converge exponentially under a strong detectability assumption and the data being persistently exciting. However, in many practical estimation problems, such as combined state and parameter estimation, data may not be exciting for every period of time. The algorithm therefore has reg...