scholarly journals A fast moving horizon estimation algorithm based on nonlinear programming sensitivity

2008 ◽  
Vol 18 (9) ◽  
pp. 876-884 ◽  
Author(s):  
Victor M. Zavala ◽  
Carl D. Laird ◽  
Lorenz T. Biegler
Keyword(s):  
Nonlinear Programming ◽  
Fast Moving ◽  
Estimation Algorithm ◽  
Moving Horizon Estimation

A fast-moving horizon estimation method based on the symplectic pseudospectral algorithm

2021 ◽  
pp. 014233122199269
Author(s):  
Xinwei Wang ◽  
Jie Liu ◽  
Haijun Peng ◽  
Xudong Zhao
Keyword(s):  
Fast Moving ◽  
Optimization Problem ◽  
Estimation Method ◽  
Sampling Period ◽  
Estimation Algorithm ◽  
Continuous Systems ◽  
Moving Horizon Estimation ◽  
The Core ◽  
Sampling Instant ◽  
Nonlinear Optimal Control Problem

In this paper, a fast-moving horizon state estimation algorithm for nonlinear continuous systems with measurement noises and model disturbances is developed. The optimization problem required to be solved at each sampling instant is formulated into a backward nonlinear optimal control problem over the finite past. Once prior knowledge of the observed system is available, constraints can be further imposed. The highly efficient and accurate symplectic pseudospectral algorithm is taken as the core solver, which leads to the symplectic pseudospectral moving horizon estimation (SP-MHE) method. The developed SP-MHE is first evaluated by numerical simulations for a hovercraft. Then the developed method is extended to parameter estimation and applied to a chaotic system with an unknown parameter. Simulation results show that the SP-MHE can generate accurate estimations even under large sampling periods or large noise where regular filters fail. In addition, the SP-MHE exhibits excellent online efficiency, suggesting it can be used for scenarios where the sampling period is relatively small.

MEMS gyroscope/TAM-integrated attitude estimation based on moving horizon estimation

2016 ◽  
Vol 231 (8) ◽  
pp. 1451-1459 ◽  
Author(s):  
Jingli Huang ◽  
Guorong Zhao ◽  
Xiangyu Zhang
Keyword(s):  
Mechanical System ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Micro Electro Mechanical System ◽  
Attitude Estimation ◽  
Moving Horizon Estimation ◽  
Estimation Model ◽  
External Interference ◽  
Estimation System ◽  
The Military

To improve the accuracy of the attitude sensor micro electro mechanical system gyroscope in low cost satellite, a nonlinear moving horizon estimation algorithm based on micro-electro mechanical system gyroscope/three-axis magnetometer is proposed in this paper. First, a quaternion micro-electro mechanical system gyroscope/three-axis magnetometer-integrated attitude estimation model is established so as to improve the accuracy of micro-electro mechanical system gyroscope. Thanks to the concealment and autonomy, these two low cost sensors have great potential in the military area. Second, taking advantage of optimal problem in coping with constraints, a real time moving horizon estimation algorithm with equality constraint is designed to deal with the disability of solving quaternion normalization analytically in the frame work of Kalman. In this algorithm, Gauss–Newton iterative method is used to obtain the optimal state estimation in the “window”. Meanwhile, strong tracking filter of arrival cost is designed outside of the “window” to enhance system robustness for that three-axis magnetometer is vulnerable to external interference. Third, the proposed MHE is applied in the micro-electro mechanical system gyroscope/three-axis magnetometer attitude estimation system. The simulation results show that the method has higher accuracy and robustness.

scholarly journals Enhancement of the Moving Horizon Estimation Performance Based on an Adaptive Estimation Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Steve Alan Talla Ouambo ◽  
Alexandre Teplaira Boum ◽  
Adolphe Moukengue Imano ◽  
Jean-Pierre Corriou
Keyword(s):  
Dynamical Systems ◽  
Least Squares ◽  
Adaptive Estimation ◽  
Dead Zone ◽  
Estimation Algorithm ◽  
Research Topic ◽  
Moving Horizon Estimation ◽  
Forgetting Factor ◽  
Constrained Dynamical Systems ◽  
Variable Forgetting Factor

Although moving horizon estimation (MHE) is a very efficient technique for estimating parameters and states of constrained dynamical systems, however, the approximation of the arrival cost remains a major challenge and therefore a popular research topic. The importance of the arrival cost is such that it allows information from past measurements to be introduced into current estimates. In this paper, using an adaptive estimation algorithm, we approximate and update the parameters of the arrival cost of the moving horizon estimator. The proposed method is based on the least-squares algorithm but includes a variable forgetting factor which is based on the constant information principle and a dead zone which ensures robustness. We show by this method that a fairly good approximation of the arrival cost guarantees the convergence and stability of estimates. Some simulations are made to show and demonstrate the effectiveness of the proposed method and to compare it with the classical MHE.

scholarly journals An Evaluation of the Moving Horizon Estimation Algorithm for Online Estimation of Battery State of Charge and State of Health

2014 ◽  
Author(s):  
Bibin Pattel ◽  
Hoseinali Borhan ◽  
Sohel Anwar
Keyword(s):  
Initial Conditions ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Of Charge ◽  
Convergence Time ◽  
Moving Horizon Estimation ◽  
State Of Health ◽  
Estimation Problems ◽  
Comparison Of The Results

Moving Horizon Estimation (MHE) has emerged as a powerful technique for tackling the estimation problems of the state of dynamic systems in the presence of constraints, nonlinearities and disturbances. In this paper, the Moving Horizon Estimation approach is applied in estimating the State of Charge (SoC) and State of Health (SoH) of a battery and the results are compared against those for the traditional estimation method of Extended Kalman Filter (EKF). The comparison of the results show that MHE provides improvement in performance over EKF in terms of different state initial conditions, convergence time, and process and sensor noise variations.

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