Industrial monitoring systems require reliable sensor data for condition monitoring, fault diagnosis, and predictive maintenance. This study proposes an integrated virtual-sensing framework for reconstructing rotational speed from torque measurements when speed signals are unavailable. The method combines industrial data preprocessing, speed calculation, interpolation-based temporal synchronization, Exponential Moving Average (EMA) filtering, min–max normalization, and Recursive Least Squares (RLS) prediction. The framework was evaluated on the Multi-mode Fault Diagnosis Dataset of Gearbox Under Variable Working Conditions using a chronological 70/30 train–test split across four independent gearbox measurements. Its performance was compared with Linear Regression (LR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) models. The proposed EMA + RLS approach achieved the best overall results, with a mean R 2 of 0.9391 ± 0.0427, RMSE of 81.0466 ± 83.5874 RPM, and MAE of 56.1234 ± 47.0097 RPM. Analysis of the EMA smoothing parameter showed that low-to-moderate smoothing provides an effective trade-off between noise reduction and prediction accuracy. These results demonstrate that combining industrial preprocessing with adaptive recursive estimation enables accurate, transparent, and computationally efficient speed-signal reconstruction from torque data under controlled signal-unavailability conditions.
Flexible mounting interfaces in precision equipment can form lightly damped support–payload modes and amplify low-frequency vibration transmitted from the base. Suppression becomes difficult when support parameters vary and the available actuation is limited in bandwidth and force. This study presents a polytopic robust D-region H 2 control design for the dominant response-amplification band. A control-oriented two-degree-of-freedom model represents the coupled motion of the payload and flexible support channel. The H 2 performance output combines payload acceleration, support-channel relative displacement and control force. Independent ±15% variations in two equivalent masses and two stiffnesses define a 16-vertex polytope. Common-Lyapunov linear matrix inequalities minimize the worst-vertex H 2 upper bound while enforcing minimum decay-rate and damping-ratio requirements. Candidate D-region parameters are screened using LMI feasibility, augmented closed-loop stability, acceleration transmissibility and force demand under a bandwidth-limited active-channel approximation and a 200 N force-capacity criterion. In simulation, the global acceleration-transmissibility peak is reduced from 36.34 to 2.40 dB, which is 6.37 dB below that obtained using a performance-tuned linear quadratic regulator. All 16 vertices and 1000 randomly sampled plants remain stable. Experiments on an equivalent flexible-support test rig under random base excitation showed reductions of 21.6 and 11.8 dB at the two uncontrolled response peaks, while the payload-acceleration RMS decreased from 7.157 to 2.505 m/s 2 . Under half-sine excitation, the proposed controller further reduced the peak acceleration by 11.81% and shortened the 5% decay time by 64.4% relative to performance-tuned LQR. These results demonstrate target-band vibration attenuation and improved transient decay under the modeled uncertainty and tested implementation conditions.
This article investigates the trajectory tracking control of an insulator cleaning robot confronted with model inaccuracy, external disturbances, actuator faults, and prescribed performance constraints. By leveraging the universal approximation capability combined with its inherent robustness, a neural networks-based adaptive sliding mode controller is designed to handle the model inaccuracy and disturbances. In a bid to ensure the operational safety of the catenary and cantilever system, a fault-tolerant mechanism is integrated with a prescribed performance function, thereby addressing both actuator faults and control performance requirements. The proposed control scheme guarantees that the trajectory tracking error is Uniformly Ultimately Bounded (UUB) and the performance can be pre-specified even in the presence of actuator faults and disturbances. The stability of the proposed control system is rigorously proven via the Lyapunov method, and the effectiveness of the proposed approach is demonstrated through numerical simulations purely.
Power systems are prone to chaotic oscillations. Conventional sliding-mode controllers are tightly coupled with system dimensions, and the control law needs to be redesigned once the system order changes. This paper proposes a recursive sliding-mode control method independent of system dimension for chaos suppression in power systems. Sliding surfaces are constructed through a recursive structure to decouple the control law from the system dimension. Within the synchronization framework, arbitrary signals can be selected as tracking targets, so that the identical controller structure can be directly applied to power-system models of different dimensions. Based on Lyapunov theory, we prove that the closed-loop system is asymptotically stable and tracking errors converge to zero. The theoretical derivation holds for an arbitrary dimension n . Two-dimensional and four-dimensional power-system examples are adopted for simulation validation. Simulation results show that the control law can be directly applied to systems with different dimensions without modification; the target tracking signal only affects the convergence speed and does not alter closed-loop stability. The proposed recursive sliding-mode scheme provides an alternative control solution for chaos suppression in multi-dimensional and multi-scale power systems.
To address the degradation in accuracy and reliability of ultra-wideband (UWB)-based indoor localization for automated guided vehicles (AGVs) under non-line-of-sight (NLOS) conditions, this paper proposes an MCKF-enhanced Chan-Taylor fusion localization algorithm. First, the double-sided two-way ranging (DS-TWR) method is employed to obtain the distances between the tag and base stations. To mitigate NLOS-induced ranging errors, the maximum correntropy Kalman filter (MCKF) is introduced to process the measured data. Localization equations are then established based on the time difference of arrival (TDOA) principle. The Chan algorithm is used to generate the initial position estimate, which is further refined by the Taylor algorithm through iterative optimization. Experimental results show that, compared with the conventional Chan-Taylor algorithm, the proposed method improves localization accuracy by 1.95% and 56.57% in static and dynamic tests under line-of-sight (LOS) conditions, respectively. Under NLOS conditions, the accuracy is further improved by 40.22% and 32.98% in static and dynamic tests, respectively, compared with the KF+Chan-Taylor algorithm. These results demonstrate that the proposed algorithm effectively suppresses NLOS errors and enhances the accuracy and robustness of AGV indoor localization in complex environments
