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.

Sep 25, 2026

Moving Horizon Estimation for Quadrotors: An L1\mathcal{L}_1 Adaptive Optimizer Approach

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 L1\mathcal{L}_1 Adaptive Optimizer (L1\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 L1\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 L1\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.

Thinh Nguyen, Minkyung Kim, Sandeep Banik +2

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

Moving Horizon EstimationRobot State Estimation
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Sep 21, 2026

A bioinspired internal model-based online estimator for planar pursuit

Bioinspired feedback controls for pursuit, tracking, and collective motion are often expressed in terms of the relative configuration between interacting agents. In practice, however, onboard sensors may not directly provide all quantities required for feedback control, necessitating estimation of unobserved quantities. This paper develops a bioinspired internal model-based estimator for reconstructing those quantities from partial sensory observations and known self-motion. State reconstruction is posed as an optimization problem that treats the relative kinematics as constraints and minimizes the disagreement between the internal model outputs and measurements from onboard sensors. Pontryagin's Maximum Principle is used to derive the necessary optimality conditions. A forward-backward algorithm is used to provide a numerical solution and a moving horizon formulation is employed for online implementation. The estimator is evaluated numerically against classical state estimators. Real-time implementation of the proposed framework on robotic hardware is demonstrated through two pursuit strategies.

Tengyue Liu, Xincheng Li, Sofia Morales Ferreira +2

Department of Mechanical and Aerospace Engineering, University of South Florida, Tampa, FL · Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, MD

Moving Horizon EstimationRobot State Estimation
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Sep 15, 2026

Adaptive-MHE : A Sampling-Based Adaptive MPC for Legged Loco-Manipulation via Moving Horizon Estimation

Legged robots have demonstrated a remarkable ability to traverse various terrains, yet generating effective loco-manipulation behaviors remains challenging. A key difficulty is that object and terrain parameters are typically unknown to the robot, and mismatches between these parameters and their simulated counterparts introduce a sim-to-real gap that degrades control performance. Classical system identification (Sys-ID) methods often assume differentiable dynamics, an assumption that does not hold for contact-rich legged systems. Sampling-based Sys-ID avoids this restriction by directly matching simulated and recorded state trajectories through massively parallel rollouts, but existing approaches are typically applied offline and do not adapt as environmental conditions change. We present Adaptive-MHE an online sampling-based Sys-ID framework, based on moving horizon estimation (MHE), that estimates the physical parameters of objects and terrain in the environment (e.g., mass, friction) and couples this estimate with a sampling-based model predictive controller, enabling adaptive loco-manipulation in changing and uncertain environments. In simulation and hardware experiments, our framework consistently outperforms baselines and matches the performance of a controller with access to ground-truth parameters.

Hossein Keshavarz, Alejandro Ramirez-Serrano, Majid Khadiv

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

Robot Dynamics IdentificationLegged RoboticsModel Predictive ControlMoving Horizon EstimationSystem IdentificationRobot State Estimation
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Sep 13, 2026

Fault Diagnosis for Underwater Vehicles using Moving Horizon Estimation and Gaussian Processes

This work proposes a model-based fault detection and diagnosis framework for underwater vehicles subject to actuator faults that explicitly accounts for the presence of unmodeled dynamics. To this end, a Moving Horizon Estimator (MHE) is developed to estimate the lumped disturbance, capturing both unmodeled and fault effects. Gaussian Processes (GPs) are employed to approximate the unmodeled dynamics, providing predictions of the corresponding mean and uncertainty across diverse operating conditions. During online operation, the residual between the MHE lumped disturbance estimate and the GP prediction is evaluated using a Generalized Likelihood Ratio Test. By incorporating GP-based predictions within the diagnostic framework, robustness to unmodeled dynamics is achieved, enabling effective fault detection and isolation as well as accurate quantitative estimation of fault magnitude. The proposed methodology is experimentally validated in a laboratory water tank, demonstrating reliable diagnostic performance under both open-loop and closed-loop control.

Fotis Panetsos, Kostas J. Kyriakopoulos

Center for AI & Robotics (CAIR) and the Electrical Eng. Program, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

Moving Horizon EstimationFault DiagnosisFault DetectionFault LocalizationUnderwater Robotics
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Sep 9, 2026

IMU-Centric Moving Horizon Estimation for Lateral Dynamics Estimation Across Vehicles and Grip Conditions

Accurate estimation of lateral vehicle dynamics near the adhesion limit is important for stability control and high-performance driving, but lateral velocity is rarely measured directly because sensors such as optical sensors are costly. This paper presents an inertial measurement unit (IMU)-centric Moving Horizon Estimation framework that reconstructs lateral velocity using standard onboard signals, without relying on exteroceptive odometry or detailed tire-parameter tuning. Experimental validation on human-driven sports cars and an autonomous open-wheel race car across tracks, maneuvers, and conditions demonstrates accurate and robust lateral velocity and lateral acceleration estimates. The proposed framework is available at https://github.com/Aseuffo/IMU-Centric-MHE

Seuffo Akouan ha Ngoune, Alessandro Toschi, Paolo Burgio +1

University of Modena and Reggio Emilia, Italy

Autonomous DrivingVehicle DynamicsMoving Horizon EstimationRobot State Estimation
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Jul 27, 2026

Moving-Horizon Estimation and Nonlinear Model Predictive Control of Cable-Driven Soft Manipulators

Precise control of soft manipulators remains challenging due to the difficulty of developing accurate yet computationally tractable models for model-based estimation and control. Reduced Cosserat-rod models provide a physics-based and control-oriented description of soft-robot dynamics, offering an explicit alternative to purely data-driven input-output representations. In this paper, we propose a moving-horizon estimation (MHE) and nonlinear model predictive control (NMPC) framework for cable-driven soft manipulators based on reduced Cosserat dynamics. A smooth cable-length-driven modeling formulation is developed by approximating the complementarity relationship between cable tension and cable slackness, enabling cable-length control without direct tension sensing. Based on this formulation, an MHE method is introduced to estimate the reduced state and reconstruct the manipulator configuration from end-effector pose measurements and cable-length information. An NMPC controller is then formulated to achieve task-space control under cable-length and cable-rate constraints. The proposed framework is validated through numerical simulations and experiments. Simulation results demonstrate the effectiveness of the estimator and controller for pose and strain-related regulation on a multi-cable soft manipulator. Experimental results on a four-cable prototype further show that the proposed MHE-NMPC scheme can be implemented in real time and enables accurate end-effector position tracking through cable-length control.

Lingxiao Xun, Haihong Li, Gang Zheng

Nonlinear MPCMoving Horizon EstimationContinuum Robot ControlRobot State EstimationSoft Robotics
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Jun 30, 2026

AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing

This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentiable MPCC with online parameter estimation to handle varying road-surface conditions. For online parameter estimation, we leverage a parameterized Pacejka Magic Formula together with a regularized moving-horizon estimation scheme with exponentially decaying weights to capture road interactions and update parameters in real time. Furthermore, we propose a differentiable MPCC (Diff-MPCC) framework that enables optimal adjustment of objective weights based on predefined long-horizon performance costs. To implement Diff-MPCC for online objective weight adaptation, we propose a Pacejka-informed machine learning model that is trained in a supervised manner using data generated by Diff-MPCC to tune the objective weights. Simulation results demonstrate that AD-MPCC reliably ensures safety and achieves faster lap times compared to baseline controllers in both single-surface and multiple-surface scenarios.

Nam T. Nguyen, Binh Nguyen, Ahmad Amine +3

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.

Nonlinear MPCAutonomous RacingModel Predictive ControlVehicle DynamicsMoving Horizon EstimationAdaptive Control
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Apr 6, 2026

Outlier-Robust Nonlinear Moving Horizon Estimation using Adaptive Loss Functions

In this work, we propose an adaptive robust loss function framework for MHE, integrating an adaptive robust loss function to reduce the impact of outliers with a regularization term that avoids naive solutions. The proposed approach prioritizes the fitting of uncontaminated data and downweights the contaminated ones. A tuning parameter is incorporated into the framework to control the shape of the loss function for adjusting the estimator's robustness to outliers. The simulation results demonstrate that adaptation occurs in just a few iterations, whereas the traditional behaviour L2\mathrm{L_2} predominates when the measurements are free of outliers.

Nestor Deniz, Guido Sanchez, Fernando Auat Cheein +1

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

Moving Horizon EstimationRobust EstimationRobust Loss FunctionsAdaptive Loss Weighting
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