Feedback Linearization Optimization

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Overview

Feedback linearization uses a system's input-output structure to transform nonlinear dynamics into a form that is easier to control or optimize. Methods include Lie-derivative and relative-degree models, augmented-Lagrangian enforcement of hard constraints in physics-informed neural networks, and Lyapunov-based robust redesign; neural residual learning can compensate for uncertain dynamics. Applications range from PDE training to quadrotor flight, robot contact, vehicle tracking, and power-converter control, with evaluations spanning simulations and hardware tests. A central challenge is maintaining stability and accurate tracking when learned models are imperfect or the decoupling transformation becomes singular.

Latest papers

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8 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian

    Sep 21, 2026Lakshmi Priya P. K., Andreas SchwungData-Driven ControlFeedback Control

    Department of Automation Technology and Learning Systems, South Westphalia University of Applied Sciences,Lübecker Ring 2, Soest, 59494, Germany

  2. Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight

    Jul 14, 2026Pei-An Hsieh, Fengjun Yang, Nikolai Matni +1Multi-Robot Formation ControlResidual Dynamics Learning

    1GRASP Laboratory, University of Pennsylvania, PA, USA

  3. Tracking Through Decoupling Singularities: A Singularity-Robust Homotopy-Continuation Extension of Feedback Linearization

    Jul 11, 2026Alex BorisevichRobotic ControlRobot Trajectory Tracking

  4. Longitudinal-Motion-Aware Lateral Control for Autonomous Vehicles: A Robust Nonlinear Control Framework

    Jul 3, 2026Sixu Li, Nitesh Kumar, Reyshwanth Ganeshan +3Autonomous DrivingRobust Control

    Texas A&M University, College Station, TX 77843, USA

  5. Enhancing Robustness in Robot-Environment Interactions through Passive Compliant Degrees of Freedom: A Hybrid Position-Force Control Approach with Feedback Linearization

    Jul 1, 2026Rahman Ardakanian, Iman Kardan, AliAkbar Akbari +1Robotic ControlRobust Control

    Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran · Center of Advanced Rehabilitation and Robotics Research, Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran · Department of Computer Engineering, Ne. C, Islamic Azad University, Neyshabur, Iran

  6. Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics

    Jun 30, 2026Amos Alwala, Gabriel da Silva Lima, Wallace Moreira BessaRobot Trajectory TrackingFeedback Control

    Smart Systems, Department of Mechanical and Materials Engineering, Faculty of Technology, University of Turku, 20520 Turku, Finland.

  7. Safe Local Navigation for Ackermann-Steered Robots in Unmapped Environments

    Jun 18, 2026Christian Schaible, Shahin SirouspourRobot NavigationSafe Robot Navigation

    Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada

  8. AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training

    May 8, 2026Binghang Lu, Runyu Zhang, Changhong Mou +2Neural PDE SolversConstrained Optimization

    Purdue University · Massachusetts Institute of Technology · Utah State University +1