Search Results (607)

For an induction motor operating as a symmetric three-phase system, the performance of indirect field-oriented vector control relies heavily on the accuracy of the rotor time constant. Any inaccuracies result in severe torque errors and compromise dynamic performance because of the coupling between the flux and torque controls. Although conventional IFOC methods are intended to compensate for the rotor time constant error, they rely on induction machine parameters such as the mutual and leakage inductances. This paper proposes an online method for tuning the rotor time constant independent of other parameters. First, an active power model of three-phase symmetric induction motor is selected to estimate the stator resistance based on a model reference adaptive system, which requires only the rotor time constant. Additionally, high-frequency current injection and torque ripple estimation without phase delay or amplitude decay are introduced to compensate for the rotor time constant. When a high-frequency current is injected, the rotor time constant and stator resistance can be simultaneously tuned without depending on other parameters. A high-frequency current is injected only when a rotor time constant error is detected from the estimated stator resistance. This behavior is enabled by the correlation between the stator resistance and the rotor time constant. Simulation results using MATLAB/Simulink regarding the symmetric three-phase induction motor validate the proposed method. Full article

Based on a fractional-order equivalent circuit model, this paper proposes a multi-timescale collaborative State of Energy (SOE) and State of Health (SOH) estimation method (FOASTFREKF-EKF) for lithium batteries to mitigate the influence of model inaccuracies and battery aging on SOE estimation. Initially, a fractional-order equivalent circuit model is built, and its parameters are identified offline using the Starfish Optimization Algorithm (SFOA) to establish a high-fidelity battery model. An H∞ filter is then integrated to improve the algorithm’s resilience to external disturbances. Furthermore, an adaptive noise covariance adjustment mechanism is employed to reduce the effect of operational noise, and a time-varying attenuation factor is introduced to improve the algorithm’s tracking and convergence capabilities during abrupt system-state changes. A joint estimator is subsequently constructed, which uses an Extended Kalman Filter (EKF) for the online determination of battery parameters and SOH assessment. This approach minimizes the effect of varying model parameters on SOE accuracy while reducing computational load through multi-timescale methods. Experimental validation under diverse operating conditions shows that the proposed algorithm achieves root mean square errors (RMSE) of less than 0.21% for SOE and 0.31% for SOH. These findings demonstrate that the method provides high accuracy and reliability under complex operating conditions. Full article

In this paper, we proposed a novel method for grinding trajectory planning on large-curved forgings to improve grinding performance and grinding efficiency. Our method consists of four main steps. Firstly, we conducted simulations and analyses on the contact state and contact pressure between the grinding tool and curved workpieces, and explored different grinding methods. Based on the Preston equation, a material removal model was established to analyze the grinding force. Secondly, we proposed an adaptive impedance control method based on grinding force analysis, which can control the contact force indirectly by adjusting the end position of the robot. To address the inability of impedance control to adjust impedance parameters in real time, a control strategy involving online estimation of environmental position and stiffness is adopted. Based on the Lyapunov asymptotic stability principle, an adaptive impedance control model is established, and the effectiveness of the adaptive algorithm is verified through simulation. Thirdly, Position correction is realized through gravity compensation of the grinding force and discretization of the impedance control model. Subsequently, a dynamic trajectory adjustment strategy is proposed, which integrates position correction for the current grinding point and position compensation for the next grinding point, to achieve the force control objective in the grinding process. Finally, a constant force grinding experiment was conducted on large-curvature blades using a robotic automatic grinding system. The grinding system effectively removed the knife marks on the blade surface, resulting in a surface roughness of 0.5146 μm and a grinding efficiency of approximately 0.89 cm²/s. The simulation and experimental results indicate that the smoothness and grinding efficiency of the blades are superior to the enterprise’s existing grinding technology, verifying the feasibility and effectiveness of our proposed method. Full article

This paper proposes a finite-time adaptive observer with online disturbance learning for time-varying disturbed systems. By integrating parameter-dependent Lyapunov functions and slack matrix techniques, the method eliminates conservative static disturbance bounds required in prior work while guaranteeing fixed-time convergence. The proposed approach features a non-diagonal gain structure that provides superior noise rejection capabilities, demonstrating 41% better performance under measurement noise compared to conventional methods. A power systems case study demonstrates significantly improved performance, including 62% faster convergence and 63% lower steady-state error. These results are validated through LMI-based synthesis and adaptive disturbance estimation. Implementation analysis confirms the method’s feasibility for real-time systems with practical computational requirements. Full article

Ball joints are components of the vehicle axle, and their friction characteristics must be considered when evaluating vibration behavior and ride comfort in driving simulator-based simulations. To model the three-dimensional friction behavior of ball joints, real-time capability and intuitive parameterization using data from standardized component test benches are essential. These requirements favor phenomenological modeling approaches. This paper applies a spherical, three-dimensional friction model based on the LuGre model, compares it with alternative approaches, and introduces a universal parameter estimation framework using machine learning. Furthermore, the kinematic operating ranges of ball joints are derived from vehicle measurements, and component-level measurements are conducted accordingly. The collected measurement data are used to estimate model parameters through gradient-based optimization for all considered models. The results of the model fitting are presented, and the model characteristics are discussed in the context of their suitability for online simulation in a driving simulator environment. We demonstrate that the proposed parameter estimation framework is capable of learning all the applied models. Moreover, the three-dimensional LuGre-based approach proves to be well suited for capturing the dynamic friction behavior of ball joints in real-time applications. Full article

Cognitive diagnosis models (CDMs) assess the proficiency of examinees in specific skills. Online education has increased the amount of data that is available on the response behaviour of examinees. Traditional CDMs determine the state of skills by modelling information on item response results and ignoring vital response time information. In this study, a CDM, named RT-CDM, which models the condition dependence between response time and response accuracy on the speed-accuracy exchange criterion, is proposed. The model’s continuous latent trait function and response time function, used for more precise cognitive analyses, makes it a tractable, interpretable skill diagnosis model. The Markov chain Monte Carlo algorithm is used to estimate the parameters of the RT-CDM. We evaluate RT-CDM through controlled simulations and three real datasets—PISA 2015 computer-based mathematics, EdNet-KT1, and MATH—against multiple baselines, including classical CDMs (e.g., DINA/IRT), RT-extended IRT and joint models (e.g., 4P-IRT, JRT-DINA), and neural CDMs (e.g., NCD, ICD, MFNCD). Across datasets, RT-CDM consistently achieves superior predictive performance, demonstrates stable parameter recovery in simulations, and delivers stronger diagnostic interpretability by leveraging RT alongside RA. Full article

The accurate estimation of parameters in dynamical systems stands for an open key research issue in modeling, control, and fault diagnosis. The presence of noise in input and output signals poses a serious challenge for accurate real-time dynamical system parameter estimation. This paper proposes a new robust algebraic parameter estimation methodology for integer-order dynamical systems that explicitly incorporates the signal filtering dynamics within the estimator structure and enhances noise attenuation through fractional differentiation in frequency domain. The introduced estimation methodology is valid for Liouville-type fractional derivatives and can be applied to estimate online the parameters of differentially flat, oscillating or vibrating systems of multiple degrees of freedom. The parametric estimation can be thus implemented for a wide class of oscillating or vibrating, nth-order dynamical systems under noise influence in measurement and control signals. Positive values are considered for the inertia, stiffness, and viscous damping parameters of vibrating systems. Parameter identification can be also used for development of actuators and control technology. In this sense, validation of the algebraic parameter estimation is performed to identify parameters of a differentially flat, permanent-magnet direct-current motor actuator. Parameter estimation for both open-loop and closed-loop control scenarios using experimental data is examined. Experimental results demonstrate that the new parameter estimation methodology combining signal filtering dynamics and fractional calculus outperforms other conventional methods under presence of significant noise in measurements. Full article

In this research article, a fast and efficient hybrid Maximum Power Point Tracking (MPPT) control technique is proposed for photovoltaic (PV) systems. The method combines two phases—offline and online—to estimate the appropriate duty cycle for operating the converter at the maximum power point (MPP). In the offline phase, temperature and irradiance inputs are used to compute the real-time reference peak power voltage through an Adaptive Neuro-Fuzzy Inference System (ANFIS). This estimated reference is then utilized in the online phase, where the Robust Backstepping Super-Twisting (RBST) controller treats it as a set-point to generate the control signal and continuously adjust the converter’s duty cycle, driving the PV system to operate near the MPP. The proposed RBST control scheme offers a fast transient response, reduced rise and settling times, low tracking error, enhanced voltage stability, and quick adaptation to changing environmental conditions. The technique is tested in MATLAB/Simulink under three different scenarios: continuous variation in meteorological parameters, sudden step changes, and partial shading. To demonstrate the superiority of the RBST method, its performance is compared with classical backstepping and integral backstepping controllers. The results show that the RBST-based MPPT controller achieves the minimum rise time of $0.018$$\mathrm{s}$, the lowest squared error of $0.3015$$\mathrm{V}$, the minimum steady-state error of $0.29$%, and the highest efficiency of $99.16$%. Full article

To overcome the limitations of traditional integer-order models, which fail to accurately capture the dynamic behavior of lithium-ion batteries, and to improve the insufficient accuracy of state of charge (SOC) and state of health (SOH) collaborative estimation, this study proposes a cooperative estimation framework based on a fractional-order model. First, a fractional-order second-order RC equivalent circuit model is established, and the whale optimization algorithm is applied for offline parameter identification to improve model accuracy. Second, a strong tracking strategy is introduced into the improved unscented Kalman filter to address the convergence speed issue under inaccurate initial SOC conditions. Meanwhile, the extended Kalman filter is employed for SOH estimation and online parameter identification. Furthermore, a multi-time-scale collaborative estimation algorithm is proposed to enhance overall estimation accuracy. Experimental results under three dynamic operating conditions driving cycles demonstrate that the proposed method effectively solves the SOC/SOH collaborative estimation problem, achieving a mean SOC estimation error of 0.45% and maintaining the SOH estimation error within 0.25%. Full article

The remote prognostic, diagnosis, and maintenance of electrolytic capacitors are research topics of interest due to their presence in numerous electronic devices and their increased susceptibility to degradation over time. The authors’ focus in this article is on the proposal of a new diagram for monitoring the parameters of the capacitors that compose the filter bank of a DC–DC buck converter by connecting them in parallel. Each capacitor is modeled by an equivalent series R–C circuit composed of an equivalent capacitance and an equivalent series resistance (ESR). The method used allows successive investigation of the three capacitors that compose the bank by triggering discharge/charge sequences, acquiring the voltages at the capacitor terminals, and estimating the time constants of each capacitor using a parameter observer. During the estimation of the parameters of a capacitor, the converter uses the other two capacitors maintained in operation. The monitoring cycle of all capacitors of the bank lasts less than $40 \mathrm{m}\mathrm{s}$, not significantly affecting the operation of the converter. The study undertaken is correlated with the thermal map of the board on which the converter is made. The dispersion of the measured values of the equivalent capacitances is below $0.25\%$, and of the ESR below $2.6\%$. The major advantage of the method is that the monitoring is performed online and in real time. Full article

This article solves the anomalies’ operational detection in the network traffic problem for cyber police units by developing an adaptive neural network platform combining a variational autoencoder with continuous stochastic dynamics of the latent space (integration according to the Euler–Maruyama scheme), a continuous–discrete Kalman filter for latent state estimation, and Hotelling’s T² statistical criterion for deviation detection. This paper implements an online learning mechanism (“on the fly”) via the Euler Euclidean gradient step. Verification includes variational autoencoder training and validation, ROC/PR and confusion matrix analysis, latent representation projections (PCA), and latency measurements during streaming processing. The model’s stable convergence and anomalies’ precise detection with the metrics precision is ≈0.83, recall is ≈0.83, the F1-score is ≈0.83, and the end-to-end delay of 1.5–6.5 ms under 100–1000 sessions/s load was demonstrated experimentally. The computational estimate for typical model parameters is ≈5152 operations for a forward pass and ≈38,944 operations, taking into account batch updating. At the same time, the main bottleneck, the O(m³) term in the Kalman step, was identified. The obtained results’ practical significance lies in the possibility of the developed adaptive neural network platform integrating into cyber police units (integration with Kafka, Spark, or Flink; exporting incidents to SIEM or SOAR; monitoring via Prometheus or Grafana) and in proposing applied optimisation paths for embedded and high-load systems. Full article

A simple Lagrangian travelling slice model has been successfully used to predict the relations between the process parameters and the strand temperatures in the continuous casting of steel. The present paper aims to include a simple macrosegregation, grain structure and mechanical stress and deformation model on top of the thermal slice framework. The basis of all the mentioned models is the slice heat-conduction model that considers the complex heat extraction mechanisms in the mould, with the sprays, rolls, and through radiation. Its main advantage is the fast calculation time, which is suitable for the online control of the caster. The macroscopic thermal and species transfer models are based on the continuum mixture theory. The macrosegregation model is based on the lever rule microsegregation model. The thermal conductivity and species diffusivity of the liquid phase are artificially enhanced to consider the convection of the melt. The grain structure model is based on cellular automata and phase-field concepts. The calculated thermal field is used to estimate the thermal contraction of the solid shell, which, in combination with the metallostatic pressure, drives the elastic-viscoplastic solid-mechanics models. The solution procedure of all the models is based on the meshless radial basis function generated finite difference method on the macroscopic scale and the meshless point automata concept on the grain structure scale. Simulation results point out the areas susceptible to hot tearing. Full article

With the growing global demand for clean energy, fuel cells have been adopted as key components in renewable energy systems. Their high efficiency and environmentally friendly operation make them attractive. However, during maximum power point tracking (MPPT), traditional proportional–integral–derivative (PID) controllers often fail to maintain optimal power output. Dynamic load changes and complex operating conditions exacerbate this issue. As a result, system response is slowed, and tracking accuracy is reduced. To address these problems, an online identification method based on recursive least squares (RLS) is employed. A cubic power–current model is identified in real time. Polynomial fitting and the golden section search are then applied to estimate the current at the maximum power point. Following model-based estimation, adaptive particle swarm optimization (APSO) is utilized to tune the PID controller parameters. Precise regulation is thus achieved. The use of RLS enables real-time model identification. The golden section search improves the efficiency of current estimation. APSO enhances global optimization, while PID provides fast dynamic response. By integrating these methods, both tracking accuracy and system responsiveness are significantly improved in fuel cell MPPT applications. Simulation results demonstrate that the proposed strategy enhances maximum power output by up to 12.40% compared to conventional P&O, fuzzy logic control, GWO-PID, and PSO-PID methods, as well as maintaining a consistent improvement of 1.50% to 1.90% even when compared to other optimization algorithms. Full article

Plate heat exchangers are used in heat substations for domestic hot water preparation and building heating in municipal central heating systems. Water from the municipal water supply is heated by hot water from a district heating network. This paper presents a numerical method for simultaneously determining heat transfer correlations on the cold and hot water sides based on flow-thermal measurements of the plate heat exchanger. The unknown parameters in the functions approximating the Nusselt numbers, which depend on the Reynolds and Prandtl numbers, are determined using the least-squares method, so the sum of the squares of the differences in the calculated and measured temperatures at the heat exchanger outlet reaches a minimum. One or two correlations were sought for a plate heat exchanger, and the total number of parameters sought is between three and six. The limits of the 95% confidence intervals for all estimated parameters were also determined. Correlations for Nusselt numbers determined experimentally for a clean plate heat exchanger can be used in the online monitoring of the degree of fouling of plate heat exchangers installed in the substations of a large urban district heating network. Full article

When complex computations cannot be performed on board a mobile robot, sensory data must be transmitted to a remote station to be processed, and the resulting actions must be sent back to the robot to execute, forming a repeating cycle. This involves stochastic round-trip times in the case of non-deterministic network communications and/or non-hard real-time software. Since robots need to react within strict time constraints, modeling these round-trip times becomes essential for many tasks. Modern approaches for modeling sequences of data are mostly based on time-series forecasting techniques, which impose a computational cost that may be prohibitive for real-time operation, do not consider all the delay sources existing in the sw/hw system, or do not work fully online, i.e., within the time of the current round-trip. Marginal probabilistic models, on the other hand, often have a lower cost, since they discard temporal dependencies between successive measurements of round-trip times, a suitable approximation when regime changes are properly handled given the typically stationary nature of these round-trip times. In this paper we focus on the hypothesis tests needed for marginal modeling of the round-trip times in remotely operated robotic systems with the presence of abrupt changes in regimes. We analyze in depth three common models, namely Log-logistic, Log-normal, and Exponential, and propose some modifications of parameter estimators for them and new thresholds for well-known goodness-of-fit tests, which are aimed at the particularities of our setting. We then evaluate our proposal on a dataset gathered from a variety of networked robot scenarios, both real and simulated; through >2100 h of high-performance computer processing, we assess the statistical robustness and practical suitability of these methods for these kinds of robotic applications. Full article