Moving Horizon Estimation

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8 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

Moving Horizon Estimation for Quadrotors: An $\mathcal{L}_1$ Adaptive Optimizer Approach
Sep 25, 2026Thinh Nguyen, Minkyung Kim, Sandeep Banik +2Moving Horizon Estimation Robot State Estimation
University of Illinois Urbana-Champaign, Urbana, IL 61801 · Department of Mechanical Science and Engineering, The Grainger College of Engineering
A bioinspired internal model-based online estimator for planar pursuit
Sep 21, 2026Tengyue Liu, Xincheng Li, Sofia Morales Ferreira +2Moving Horizon Estimation Robot State Estimation
Department of Mechanical and Aerospace Engineering, University of South Florida, Tampa, FL · Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, MD
Adaptive-MHE : A Sampling-Based Adaptive MPC for Legged Loco-Manipulation via Moving Horizon Estimation
Sep 15, 2026Hossein Keshavarz, Alejandro Ramirez-Serrano, Majid KhadivRobot Dynamics Identification Legged Robotics
University of Calgary, Department of Mechanical Engineering, 2500 University Drive NW, Calgary, AB, Canada · Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany
Fault Diagnosis for Underwater Vehicles using Moving Horizon Estimation and Gaussian Processes
Sep 13, 2026Fotis Panetsos, Kostas J. KyriakopoulosMoving Horizon Estimation Fault Diagnosis
Center for AI & Robotics (CAIR) and the Electrical Eng. Program, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
IMU-Centric Moving Horizon Estimation for Lateral Dynamics Estimation Across Vehicles and Grip Conditions
Sep 9, 2026Seuffo Akouan ha Ngoune, Alessandro Toschi, Paolo Burgio +1Autonomous Driving Vehicle Dynamics
University of Modena and Reggio Emilia, Italy
Moving-Horizon Estimation and Nonlinear Model Predictive Control of Cable-Driven Soft Manipulators
Jul 27, 2026Lingxiao Xun, Haihong Li, Gang ZhengNonlinear MPC Moving Horizon Estimation
AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing
Jun 30, 2026Nam T. Nguyen, Binh Nguyen, Ahmad Amine +3Nonlinear MPC Autonomous Racing
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA. · Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA. · CTI Lab4EV, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam.
Outlier-Robust Nonlinear Moving Horizon Estimation using Adaptive Loss Functions
Apr 6, 2026Nestor Deniz, Guido Sanchez, Fernando Auat Cheein +1Moving Horizon Estimation Robust Estimation
Instituto de Investigación en Senales, Sistemas e Inteligencia Computacional, sınc(i), UNL-CONICET, Santa Fe, Argentina · Harper Adams University, Engineer Department, Edgmond, Newport TF10 8NB

Sep 25, 2026J/K move · Enter open · S save

Moving Horizon Estimation for Quadrotors: An $\mathcal{L}_1$ Adaptive Optimizer Approach

Thinh Nguyen, Minkyung Kim, Sandeep Banik, Jinrae Kim+1

University of Illinois Urbana-Champaign, Urbana, IL 61801 · Department of Mechanical Science and Engineering, The Grainger College of Engineering

Moving Horizon Estimation Robot State Estimation

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Moving Horizon Estimation (MHE) is a state estimation method based on finite-horizon optimization that can offer higher accuracy at the cost of increased computation compared to Kalman filter-based approaches. We present a linear smoothing MHE formulation as a dense Quadratic Program (QP), and a solver consisting of a continuous-time Newton's method augmented with the

\mathcal{L}_1

Adaptive Optimizer (

\mathcal{L}_1

-AO). While MHE is inherently time-varying, conventional approaches treat it as a sequence of independent, time-invariant problems and employ iterative solvers at each time step, which can be both inaccurate and computationally burdensome. In contrast, time-varying solvers track the optimal solution with fewer iterations by exploiting the temporal evolution of the problem, thereby reducing the computational load. In this research, we enhance both the performance and efficiency of MHE through a time-varying solver with an

\mathcal{L}_1

-AO augmentation that compensates for the prediction inaccuracy, which is common in practice due to noisy sensors and the lack of prior knowledge of the system. Simulation results on a quadrotor platform show that the

\mathcal{L}_1

-AO-augmented approach solves the MHE optimization problem more efficiently than the baseline time-invariant solver and achieves higher estimation accuracy under challenging conditions, compared with both the Extended Kalman Filter and the standard MHE.