operational-space disturbance estimation

To address degraded tracking performance of mechanical systems—such as exoskeletons—under unknown dynamic disturbances, this paper proposes a novel disturbance observer framework that jointly optimizes estimation speed and uncertainty quantification. We theoretically reveal an inherent trade-off between estimation responsiveness and uncertainty in disturbance reconstruction, and accordingly design two observers: the Interacting Multiple Model Extended Kalman Filter (IMM-EKF) and the Multi-Kernel Correntropy Extended Kalman Filter (MKCE-EKF). The IMM-EKF achieves adaptive model-set switching to accommodate time-varying interaction forces, while the MKCE-EKF employs an information-entropy-driven covariance adaptation mechanism to enhance robustness against non-Gaussian uncertainties. Experimental validation on a lower-limb exoskeleton demonstrates significant improvements: hip joint tracking errors are reduced by 36.3% and 16.2%, and knee joint errors by 46.3% and 24.4%, respectively—both metrics outperforming conventional EKF-based methods.

This study addresses the simulation-to-reality mismatch in robot control caused by physical disturbances despite accurate models, proposing a sampling-based disturbance observer (DOB). This method overcomes the limitation of classical DOBs that rely on explicit dynamics models by leveraging state rollout and cost query interfaces, thereby extending disturbance compensation to black-box simulators and learned world models. Furthermore, it innovatively decouples the state and cost disturbance channels for independent estimation and compensation. Experimental results demonstrate that the proposed approach effectively bridges the Sim-to-Real gap across diverse simulated and real-world robotic tasks, yielding significant improvements in control performance.

This work addresses the challenge of achieving high-precision task-space tracking while ensuring rigorous safety guarantees for redundant manipulators operating in dynamically uncertain environments with human interaction. The authors propose a robust operational-space control framework that integrates an extended state observer (ESO) with sliding-window conformal prediction. This approach enables online estimation of disturbance bounds without requiring full-state measurements and, for the first time, incorporates distribution-free conformal prediction into disturbance estimation. By coupling this with robust control barrier functions (CBFs), the method provides probabilistic safety assurances while significantly reducing the conservatism inherent in conventional approaches. Experimental validation on a Franka Emika Panda 7-DOF manipulator demonstrates millimeter-level tracking accuracy under real-time 1 kHz control and effective resilience against diverse external disturbances.

State estimation for nonlinear systems with heterogeneous sensor fusion remains challenging due to the difficulty of designing robust, computationally efficient observers. Method: This paper proposes a lightweight observer design framework based on trajectory optimization. It formulates observer parameter tuning as a numerical optimization problem—minimizing the discrepancy between predicted and pre-recorded measurement trajectories—integrating classical observer theory with moving-horizon estimation principles. The framework supports modular, plug-and-play integration of heterogeneous sensors (e.g., IMU, UWB). Contribution/Results: Unlike conventional manual or heuristic tuning, our approach significantly reduces engineering complexity and computational overhead. In real-world rover localization experiments fusing IMU and UWB range measurements, it achieves positioning accuracy comparable to extended Kalman filtering while reducing attitude estimation error by 32%. Its core innovation lies in the first systematic reformulation of nonlinear observer design as a data-driven trajectory optimization task—rigorously grounded in control theory yet highly practical for deployment.

This work addresses the limited robustness of learning-based control under strong disturbances and out-of-distribution scenarios, where overreliance on learned models often leads to performance degradation. To overcome this, the authors propose Neural-ESO, a dual-path architecture that combines a neural network-based feedforward predictor for rapid disturbance estimation with an extended state observer (ESO) that corrects prediction errors, thereby reducing dependence on the learning component. By innovatively integrating neural networks with ESO and incorporating Lipschitz continuity constraints, the method establishes, for the first time, uniform ultimate boundedness of the closed-loop error dynamics, offering theoretically guaranteed robustness. Evaluated on a quadrotor landing task under strong ground effect, Neural-ESO consistently outperforms state-of-the-art baselines across training, deployment, and transfer phases, achieving a superior balance between accuracy and reliability.

This work proposes a unified space situational awareness framework to address the challenges posed by the highly nonlinear, non-Keplerian dynamics in cislunar space, which degrade state estimation accuracy, alongside complexities in remote sensing, sensor placement, and task scheduling. The framework uniquely integrates cost-optimized observer architecture design, mutual information–driven task scheduling, and high-frequency orbit-attitude joint estimation via an error-state multiplicative unscented Kalman filter. Sensor configurations are optimized using the Tree of Parzen Estimators, substantially reducing system cost. Simulations demonstrate that the proposed approach maintains robust orbit estimation performance while significantly decreasing the number of required sensors, further revealing scalable trade-offs among resource allocation, scheduling strategy, and overall system performance.

This paper addresses the challenge of achieving high-precision, robust motion control for aerial manipulators (multirotor base + robotic arm) under strong dynamic coupling. To this end, we propose a prescribed-performance control framework based on a variable-gain extended state observer (ESO). Our key innovations include: (i) a novel variable-gain ESO that accurately estimates fast time-varying coupled dynamics in real time; (ii) a prescribed error trajectory constraint mechanism that rigorously guarantees tracking errors remain within a user-defined performance envelope at all times; and (iii) an integrated nonlinear feedback error shaping and coupling compensation strategy. Experimental validation on physical hardware demonstrates effectiveness across highly dynamic tasks—including aerial swinging, bartending, and cart-pulling—achieving millimeter-level tracking accuracy even under demanding conditions (end-effector velocity: 1.02 m/s; acceleration: 5.10 m/s²). The approach significantly enhances system robustness and adaptability to varying operational conditions.

This study addresses the challenge of sustained spacecraft attitude stabilization under unseen actuator faults—specifically gain errors, sign reversals, and constant biases—by introducing a novel evaluation paradigm centered on a “stability gate” metric that prioritizes long-term stability over instantaneous recovery. The proposed approach integrates recurrent neural networks for online estimation, an analytical control law, Nussbaum-gain adaptation, a disturbance observer, and multi-source sensor fusion into a structured estimation-and-control architecture. Validated on the Basilisk six-degree-of-freedom platform, the framework achieves success rates of 97.8% and 94.4% under sign-reversal and gain-fault scenarios, respectively. Notably, the inclusion of the disturbance observer elevates the success rate for constant-bias faults from 0% to 59.4%, substantially outperforming baseline methods such as end-to-end reinforcement learning and conventional PID control.

This work addresses the degradation in control accuracy caused by unknown, time-varying, unstructured disturbances in modern robotic systems by proposing a general disturbance estimation framework based on meta-learning and state-feedback calibration. The approach extracts features from observations within a finite-time window to construct a unified meta-representation that requires no prior structural assumptions, and integrates an online state-feedback mechanism to calibrate learning residuals, enabling accurate estimation of diverse rapidly varying disturbances. Theoretical analysis establishes the synchronous convergence of online learning error and disturbance estimation error. Experimental validation on a quadrotor platform demonstrates the method’s effectiveness and robustness in handling unstructured disturbances and distribution shifts.

本文针对软气动执行器的非线性和不确定性问题，提出基于非最小坐标离散弹性杆模型的实时动态模型控制框架，结合准静态前馈逆模型、任务空间PI控制器和动态观测器，实现高精度控制。