Adaptive Control of Nonlinear Systems

Últimos artículos

• Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept

  Christian Wittke, Stephan Myschik, Oliver Niggemann · 16 de julio de 2026

  We investigate conditional invertible neural networks (cINNs) as probabilistic inverse-dynamics models for multirotor control. For a planar X8 coaxial multicopter, we learn $p(u \mid s_t, c_t)$ from an incremental nonlinear dynamic inversion (INDI) teacher using rational-quadratic spline coupling an…

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

  Amos Alwala, Gabriel da Silva Lima, Wallace Moreira Bessa · 1 de julio de 2026

  The control of agile quadrotors in dynamic and uncertain environments remains an open area of investigation to this day, particularly when the complete system dynamics are partially known or highly nonlinear. This work introduces a novel machine learning-based feedback-linearization control framewor…

• What Actually Works for Spacecraft Fault-Tolerant Control: An Honest Settled-Gate Benchmark of Learned and Classical Methods

  Alireza Shojaei · 25 de junio de 2026

  Recent learned fault-tolerant-control (FTC) work reports high success on spacecraft actuator faults, but often in simulation, on narrow fault sets, and with transient metrics that a trajectory need only touch once. We ask what recovers spacecraft pointing when success means holding it on faults neve…

• Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control

  Ali Kafili Gavgani, Amin Talaeizadeh, Aria Alasty, Hossein Nejat Pishkenari · 9 de junio de 2026

  Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under-actuation. Tilt-rotor configurations overcome this limitation by enabling full actuation. This paper investigates neural-network-based control strate…

• A Heuristic Approach for Performance Tuning in RL-based Quadrotor Control via Reward Design and Termination Conditions

  Fausto Mauricio Lagos Suarez, Akshit Saradagi, Vidya Sumathy, George Nikolakopoulos · 20 de mayo de 2026

  Reinforcement learning (RL)-based quadrotor control policies have achieved impressive performance in tasks such as fast navigation in cluttered environments and drone racing, where the focus is on speed and agility. However, in several applications, such as infrastructure inspection, it is critical …

• Temporal Attention for Adaptive Control of Euler-Lagrange Systems with Unobservable Memory

  Giansalvo Cirrincione, Adriano Fagiolini · 11 de mayo de 2026

  Adaptive control of Euler-Lagrange systems is challenging when friction is governed by a finite-horizon internal state that is not directly observable from joint measurements. In this setting, the measured closed-loop state is no longer Markovian, and standard certainty-equivalence adaptive laws may…

• Verifiable Error Bounds for Physics-Informed Neural KKL Observers

  Hannah Berin-Costain, Harry Wang, Kirsten Morris, Jun Liu · 24 de marzo de 2026

  This paper proposes a computable state-estimation error bound for learning-based Kazantzis--Kravaris/Luenberger (KKL) observers. Recent work learns the KKL transformation map with a physics-informed neural network (PINN) and a corresponding left-inverse map with a conventional neural network. Howeve…

• Intelligent Control of Differential Drive Robots Subject to Unmodeled Dynamics with EKF-based State Estimation

  Amos Alwala, Yuchen Hu, Gabriel da Silva Lima, Wallace Moreira Bessa · 17 de marzo de 2026

  Reliable control and state estimation of differential drive robots (DDR) operating in dynamic and uncertain environments remains a challenge, particularly when system dynamics are partially unknown and sensor measurements are prone to degradation. This work introduces a unified control and state est…

• A Learning-based Control Methodology for Transitioning VTOL UAVs

  Zexin Lin, Yebin Zhong, Hanwen Wan, Jiu Cheng, Zhenglong Sun, Xiaoqiang Ji · 4 de diciembre de 2025

  Transition control poses a critical challenge in Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL UAV) development due to the tilting rotor mechanism, which shifts the center of gravity and thrust direction during transitions. Current control methods' decoupled control of altitude and pos…

• Deep reinforcement learning-based spacecraft attitude control with pointing keep-out constraint

  Juntang Yang, Mohamed Khalil Ben-Larbi · 19 de noviembre de 2025

  This paper implements deep reinforcement learning (DRL) for spacecraft reorientation control with a single pointing keep-out zone. The Soft Actor-Critic (SAC) algorithm is adopted to handle continuous state and action space. A new state representation is designed to explicitly include a compact repr…

• Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft

  Yifei Li, Erik-Jan van Kampen · 18 de noviembre de 2025

  The symmetry of dynamical systems can be exploited for state-transition prediction and to facilitate control policy optimization. This paper leverages system symmetry to develop sample-efficient offline reinforcement learning (RL) approaches. Under the symmetry assumption for a Markov Decision Proce…