arXiv subjects

Jinfeng Chen

Publications and source records attributed to Jinfeng Chen.

8 recordsLinked to original sources

arXiv2026

Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control

A learning-enabled disturbance-rejection framework based on a Neural Extended State Observer (Neural-ESO) is presented in this letter. Unlike existing learning-based control methods that largely rely on the learned model once deployed, Neural-ESO adopts a dual-pathway architecture: a predictive pathway uses a neural network to provide a feedforward disturbance estimate that accelerates convergence, while a corrective pathway employs a conventional ESO to compensate prediction errors and prevent over-reliance on the neural component. Using Lyapunov theory and a small-gain analysis, we show that enforcing a Lipschitz bound on the learning component guarantees uniform ultimate boundedness of the closed-loop error dynamics. The proposed framework is validated on a quadrotor landing task subject to strong ground-effect disturbances across normal and out-of-distribution scenarios, demonstrating accuracy-robustness trade-off and greater operational reliability during training, deployment, and transfer compared with state-of-the-art baselines.

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arXiv2026

Resilient Control Lyapunov Function-based Quadratic Program for Quadrotors Under Cyberattacks

Ensuring the operational safety of quadrotors under partial actuator failures, lumped external disturbances, and malicious cyberattacks is a critical challenge due to the system's underactuated and highly nonlinear nature. Building on the existing result of a fault-tolerant control approach for a quadrotor experiencing a complete loss of two opposing rotors \cite{chen2024quadrotor}, this letter further addresses the additional challenge of malicious cyberattacks, which could be unknown and unbounded. While the baseline control law, rooted in proportional-derivative (PD) feedback and observer-based decoupling, effectively handles mismatched disturbances, it remains vulnerable to maliciously injected cyberattacks on the pseudo-control channels. To address this, a Resilient Control Lyapunov Function-based Quadratic Program (RCLF-QP) is developed, where a resilient compensational term with real-time online adaptation is designed in the conventional CLF to compensate for the maliciously injected unknown and unbounded attacks. Compared with the PD feedback control, the proposed QP-based constrained optimization control framework provides a systematic and extensible framework that allows new control objectives and constraints to be seamlessly integrated without altering the underlying stability guarantees. The overall proposed controller integrates a model-based extended state observer with the proposed RCLF-QP mechanism to mitigate both lumped disturbances caused by aerodynamics and strong wind, and adversarial cyberattacks injected by malicious adversaries. Simulations in a high-fidelity environment demonstrate that the proposed RCLF-QP control architecture prevents trajectory divergence and system instability in scenarios where the baseline controller fails in maintaining the stability of Quadrotors under malicious attacks.

eess.SY↗

arXiv2025

Unified Disturbance Aware Safe Kinematic Control for Closed-Architecture Robots

In commercial robotic systems, it is common to encounter a closed inner-loop torque controller that is not user-modifiable. However, the outer-loop controller, which sends kinematic commands such as position or velocity for the inner-loop controller to track, is typically exposed to users. In this work, we focus on the development of an easily integrated add-on at the outer-loop layer by combining disturbance rejection control and robust control barrier function for high-performance tracking and safe control of the whole dynamic system of an industrial manipulator. This is particularly beneficial when 1) the inner-loop controller is imperfect, unmodifiable, and uncertain; and 2) the dynamic model exhibits significant uncertainty. Stability analysis, formal safety guarantee proof, and hardware experiments with a PUMA robotic manipulator are presented. Our solution demonstrates superior performance in terms of simplicity of implementation, robustness, tracking precision, and safety compared to the state of the art. A demonstration video is available at https://youtu.be/e0palGVU_50.

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arXiv2025

A Model-Based Extended State Observer for Discrete-Time Linear Multivariable Systems

In the absence of a detailed and accurate plant model, extended state observer (ESO) has been clearly shown as an effective tool in both control applications and theoretical analysis. Less clear is how to further enhance such a tool when the model is available, however inaccurate, together with additional measurement information. To this end, a model-based ESO (MB-ESO) and its generalized form (GMB-ESO) for discrete-time linear multivariable systems are developed in this paper by explicitly incorporating the additional state-space model and measurement information, which is otherwise bypassed by the conventional ESO. In addition, GMB-ESO is shown to accommodate systems with non-diagonal disturbance gain matrices. Necessary and sufficient conditions for the existence of the MB-ESO and GMB-ESO are established in terms of a well-defined disturbance vector relative degree and the absence of invariant zeros in the disturbance-to-measurement path. Under noise-free measurements and infinite observer bandwidth, exact disturbance reconstruction can be achieved within a finite number of steps, while under measurement noise, \(\varepsilon\)-accurate reconstruction can be guaranteed with finite observer bandwidth. Finally, the disturbance recontruction error of the MB-ESO is shown, in both theoretical analysis and simulation, to decrease monotonically with time, when the disturbance gain matrix is diagonal, and an explicit bound on the disturbance estimation error is derived.

eess.SY↗

arXiv2024

Disturbance Rejection-Guarded Learning for Vibration Suppression of Two-Inertia Systems

Model uncertainty presents significant challenges in vibration suppression of multi-inertia systems, as these systems often rely on inaccurate nominal mathematical models due to system identification errors or unmodeled dynamics. An observer, such as an extended state observer (ESO), can estimate the discrepancy between the inaccurate nominal model and the true model, thus improving control performance via disturbance rejection. The conventional observer design is memoryless in the sense that once its estimated disturbance is obtained and sent to the controller, the datum is discarded. In this research, we propose a seamless integration of ESO and machine learning. On one hand, the machine learning model attempts to model the disturbance. With the assistance of prior information about the disturbance, the observer is expected to achieve faster convergence in disturbance estimation. On the other hand, machine learning benefits from an additional assurance layer provided by the ESO, as any imperfections in the machine learning model can be compensated for by the ESO. We validated the effectiveness of this novel learning-for-control paradigm through simulation and physical tests on two-inertial motion control systems used for vibration studies.

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arXiv2023

Robust Control Barrier Functions for Safe Control Under Uncertainty Using Extended State Observer and Output Measurement

Control barrier functions-based quadratic programming (CBF-QP) is gaining popularity as an effective controller synthesis tool for safe control. However, the provable safety is established on an accurate dynamic model and access to all states. To address such a limitation, this paper proposes a novel design combining an extended state observer (ESO) with a CBF for safe control of a system with model uncertainty and external disturbances only using output measurement. Our approach provides a less conservative estimation error bound than other disturbance observer-based CBFs. Moreover, only output measurements are needed to estimate the disturbances instead of access to the full state. The bounds of state estimation error and disturbance estimation error are obtained in a unified manner and then used for robust safe control under uncertainty. We validate our approach's efficacy in simulations of an adaptive cruise control system and a Segway self-balancing scooter.

eess.SY↗

arXiv2023

A General Model-Based Extended State Observer with Built-In Zero Dynamics

A general model-based extended state observer (GMB-ESO) is proposed for single-input single-output linear time-invariant systems with a given state space model, where the total disturbance, a lump sum of model uncertainties and external disturbances, is defined as an extended state in the same manner as in the original formulation of ESO. The conditions for the existence of such an observer, however, are shown for the first time as 1) the original plant is observable; and 2) there is no invariant zero between the plant output and the total disturbance. Then, the finite-step convergence and error characteristics of GMB-ESO are shown by exploiting its inherent connection to the well-known unknown input observer (UIO). Furthermore, it is shown that, with the relative degree of the plant greater than one and the observer eigenvalues all placed at the origin, GMB-ESO produces the identical disturbance estimation as that of UIO. Finally, an improved GMB-ESO with built-in zero dynamics is proposed for those plants with zero dynamics, which is a problem that has not been addressed in all existing ESO designs.

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arXiv2022

A Binary Characterization Method for Shape Convexity and Applications

Convexity prior is one of the main cue for human vision and shape completion with important applications in image processing, computer vision. This paper focuses on characterization methods for convex objects and applications in image processing. We present a new method for convex objects representations using binary functions, that is, the convexity of a region is equivalent to a simple quadratic inequality constraint on its indicator function. Models are proposed firstly by incorporating this result for image segmentation with convexity prior and convex hull computation of a given set with and without noises. Then, these models are summarized to a general optimization problem on binary function(s) with the quadratic inequality. Numerical algorithm is proposed based on linearization technique, where the linearized problem is solved by a proximal alternating direction method of multipliers with guaranteed convergent. Numerical experiments demonstrate the efficiency and effectiveness of the proposed methods for image segmentation and convex hull computation in accuracy and computing time.

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