Abstract This paper addresses the issue of fixed-time neural adaptive event-triggered control for nonstrict-feedback nonlinear systems with full-state constraints, input dead-zone, and saturation. Radial basis function neural networks (RBFNNs) are used to identify the unknown nonlinearities. The paper considers both input saturation and dead-zone effects, approximating these non-smooth nonlinearities with a non-affine smooth function and then transforming them into an affine form using the mean value theorem. The approach integrates backstepping recursive design with a varying threshold event-triggered condition to create an event-triggered neural adaptive fixed-time control algorithm that employs barrier Lyapunov functions (BLFs) and RBFNNs. By applying the fixed-time stability criterion, the proposed controller ensures that the tracking error converges to a smaller region within a fixed time and that all variables in the closed-loop system remain bounded. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed method.

Hybrid methods combining Kalman filters (KFs) and Artificial Intelligence (AI) integrate state estimation with nonlinear learning in machining. This systematic review develops a taxonomy of Kalman–AI coupling patterns, examines evaluation practice, and derives deployment implications for machining applications. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement and the PRISMA extension for search reporting (PRISMA-S). Searches in Scopus and the Web of Science (WoS) Core Collection (peer-reviewed English journal/full conference papers, 2022–2026) were completed in November–December 2025, with a May 2026 Scopus/WoS update and a supplementary sensitivity search. From 7176 original and 1325 update records, 4419 database records were screened; with supplementary screening, 21 studies (2022–2026) were included. Four coupling patterns were identified: (A) Kalman-based preprocessing and feature conditioning; (B) learned system or observation components within Kalman loops; (C) Kalman-updated neural or polynomial models; and (D) cascaded or ensemble observer–learning schemes. Patterns A (9/21) and B (7/21) dominated. Reported comparisons usually favoured hybrids. Applications covered milling, turning, drilling, spindle thermal behaviour, and robotic machining, targeting tool wear and remaining useful life (RUL), force/deformation, fault detection, thermal error, and path correction. Evidence remains fragmented because datasets are narrow, tightly matched baselines and controlled ablations isolating the Kalman and AI contributions are limited, explicit uncertainty quantification is uncommon, and shared benchmarks are scarce. Shared datasets, controlled ablation protocols, physics-informed hybrids, and shop-floor demonstrators are priorities.

This paper presents a robust nonlinear control strategy for a laser scanner’s actuator operating under stochastic mechanical disturbances. External perturbations are represented as bounded stochastic torque inputs, capturing the aggregate effect of structural imbalance, impacts, or system degradation. Experimental observations of vibration-induced angular fluctuations motivate this modeling choice, highlighting the need for control strategies tolerant to bounded stochastic disturbances resulting from unpredictable mechanical faults. A smooth hyperbolic-based control law is proposed to achieve robust velocity tracking under these uncertain conditions. Global asymptotic stability is formally established through Lyapunov analysis, and simulation results confirm that the proposed method effectively maintains convergence and bounded control effort in the presence of estimated disturbance torque. The formulation is suitable for fault-tolerant control of precision actuators where physical irregularities are difficult to isolate or diagnose in real time.

In this paper, the focus is on ice clamping of workpieces using feedback-controlled Peltier cells, which represents a novel nonlinear control application. As only the hot-side temperature is accessible for measurements, an Unscented Kalman filter (UKF) estimates the temperatures on both the hot and cold sides of the Peltier element and a lumped disturbance heat flow acting on the cold side. These estimates are employed in linearized discrete-time model predictive control (MPC), which is adapted in each time step based on Taylor linearizations around desired trajectories, also considering the predicted future linearization errors, and tracks a given cold-side temperature profile despite interfering heat inflow from the machining process. Here, the nonlinear system model is exploited to calculate favorable desired values that correspond to low currents, minimizing the overall energy consumption and avoiding another possible operating point with high currents. The achieved tracking precision and estimation accuracy is pointed out in simulation results for a typical ice clamping scenario subject to disturbances.

To achieve successful autonomous navigation, a vision system that provides continuous and precise three-dimensional data of the system’s surroundings is always needed. An indispensable step in the acquisition of reliable data is the calibration of the system, preferably with a time-efficient and low-complexity approach. In this paper, a robust and efficient calibration method is proposed for the information fusion of a stereo vision system and a Technical Vision System. The proposed methodology achieves an error of xe= 6.9401 mm, ye= 8.0997 mm and ze= 15.1822 mm in 3.0202 seconds of processing time, as proven through experimental results.

As the drive force depends in a nonlinear manner on the currents, an accurate tracking control of permanent magnet linear motors is challenging. In this paper, a cascaded control is proposed and combined with a recursive estimator. In the inner loop of the cascaded structure, an inversion-based control design is employed in combination with an eigenvalue assignment. The outer loop involves a backstepping tracking control of the armature position, where a nonlinear error dynamics is assigned. Given nonlinear friction and other disturbances, a lumped disturbance force is estimated by a dual Kalman filter – in addition to the state variables. This combination achieves a high robustness of the overall control structure. The control performance is investigated in detailed simulations, where also measurement noise and external disturbances are included.

This study presents a robust control strategy to mitigate the effects of friction and mechanical vibrations in a 1-DOF electromechanical actuator for laser scanning systems. Using hyperbolic tangent functions ensures bounded control signals, preventing actuator saturation. Real-time vibration measurements with MPU6050 modules were conducted to characterize the system’s mechanical behavior. The stability of the system is ana-lyzed using the direct Lyapunov method and Barbalat’s lemma, ensuring global asymptotic stability. Simulations demonstrated the control strategy’s effectiveness in improving stability and accuracy. Future work will focus on implementing this control in a physical prototype for validation.

This paper proposes an estimator-based tracking control for a thermoelectric cooling system with Peltier cells. It is intended for use in a novel manufacturing system leveraging ice clamping. Starting from physical principles and conservation equations, a 4th-order state space model is developed and exploited within an input-output linearization, where the internal dynamics can be shown to be asymptotically stable. A backstepping-based tracking control is designed to accurately track the desired cold side temperature even in the presence of disturbances. Unknown states and disturbances are observed using an Unscented Kalman Filter, which outperforms previous results with alternative estimators. Conclusive simulation results are discussed and demonstrate the performance of the combined control and estimation structure.

non-contact 3D spatial coordinate measurement systems, based on optical laser scanning as technical vision systems (TVS) for signal processing, are essential methodologies for obtaining topographies in high-risk environments where human exploration is limited. However, these systems have limitations in resolution, particularly when addressing features such as surface texture and small curvatures at edges. Therefore, in this work, we propose the implementation of speckle pattern projection as a complementary innovative solution. Supported by the digital image correlation (DIC) methodology and the use of multivariate methods such as principal component analysis (PCA), we obtain results from different wall surfaces in a pipe prototype. Additionally, we analyze the behavior of the signal received by the 3D scanner sensor, which provides complementary information about the study surface. This demonstrates that the combination of speckle pattern projection and three-dimensional laser scanning is an additional tool for advanced detection of substance material during pipeline monitoring.

At an earlier stage of European funding for projects on technology-enhanced learning, the main thrust was to develop e-learning technologies and on projects that sought to promote the take-up of platforms and services. This contribution is prepared by students after attending lectures of a multidisciplinary course in the context of a complementary studies frame. The students of this course summarized, through a case study, concepts and methods in a straightforward, but structured way. Thanks to the help of a software tool based on Python, an original and open learning platform is realized for students and lecturers and it represents a part of this contribution. Concerning the specific lecture, cargo loads and transportation are important logistical topics in many industries. They dictate the profit of the final product, expenditure, time consumption and labor force utilization. The optimal transportation of any type of cargo is crucial for businesses. More in general, it is possible to say that the proposed problem can be generalized and applied in other main economic problems in which optimization problems are involved. In this work we focused on the optimization of fluid transportation under specified conditions, or constraints, in other words. The aim of the project is to determine the optimal parameters of the system to control the transportation in an optimal way. This material which includes an open software to test the developed concepts through the lecture can be used by students and lecturers. An open link is accessible to the users.

This work presents improvements to a patented 3D laser scanning system with a friction-compensated laser positioning mechanism. By employing a dynamic friction model, the system accurately identifies angular positioning errors through a combination of manual and automatic measurements with different control inputs. The study conducts experiments involving multiple friction cycles and SEM micrograph analysis to compare the microscopic irregularities of steel surfaces before and after friction-based positioning. Parameters of the friction system are derived or estimated from processed input-output test data. A nonlinear control is proposed to compensate for friction effects, resulting in improved positioning accuracy. Experimental implementation using an STM32 board demonstrates a remarkable reduction in the uncertainty of 3D coordinates measured by applying friction-compensated laser positioning. The findings highlight the effectiveness of the proposed friction compensation method, making the 3D laser scanning system more precise and reliable for diverse applications.

Laser Scanners used for Structural Health Monitoring applications such as Pipelines Structural Inspections normally needs Point Clouds from a large quantity of individual measurements that should be adjusted or post-processed to decrease overall point-cloud errors depending on scanner's characteristics. The posterior adjustment is commonly addressed by different mathematical methods or computational algorithms. According to application requirements methods such as machine learning, signal filtering, or RANSAC algorithms are used. This paper shows the application of an adapted/modify RANSAC algorithm especially suited for the pipeline inspection task. Aiming to increase the percentage of useful data per capture.

In this paper, a least squares method is used to estimate parameter values in the Lotka-Volterra model.The data used are graphics processing units (GPU) shipment worldwide by three key competitors, namely Nvidia, Intel, AMD.The goal is to quantify the parameter values of a model with minimal error in order to qualitatively solve the problem and fit the raw data as closely as possible.Based on the real measurements, the predator between the competitors is recognized through the identification procedure comparing the sign of the coefficients with the original Lotka-Volterra model structure.

Stereo vision systems are well know depth estimation methods with a large number of applications such as automatic inspection, autonomous navigation, process control, etc. The functioning principle of these systems is the triangulation between the real-world surface point and its respective projections on the image planes of each camera. One of the key points in order to obtain accurate measurements on stereo vision systems are the calibration of extrinsic and intrinsic parameters. This is why the work of this paper focuses on a camera calibration method to correct the error generated by the lens distortion. The proposed method divides the image in quadrants and generates an equation for each quadrant to correct the error generated by the lens distortion. The performed experiment demonstrated an accuracy improvement using the calibration method compared to the measures taken without a calibration method.

The present work is dedicated to improvement of the energetic efficiency of the mobile robot performing its task of cross-terrain inspections. It was improved by optimization of charge/discharge dynamics in a pair "solar panel-battery". The irradiance intensity was enhanced by additional system design improvement, in order to extend the time of mission performance. The validity of proposed approach was proved in a computational experiment.

Friction is one of the significant factors to degrade the transient accuracy of positioning systems. In this respect, the present paper proposes implementing a nonlinear control to perform friction compensation to improve the positioning accuracy for a controlled DC motor of a laser positioning system with friction, which is assumed to be appropriately described using a dynamic friction model. The global asymptotic positioning stability proof was performed using Lyapunov’s direct method.

This paper proposes an adaptive Sliding Mode Control (SMC) strategy using a Model Predictive Control (MPC) for a two rotational joints robot to be used in a tracking problem. The considered tracking problem is the sensorless orientation of a photovoltaic panel with respect to the solar position. The estimated velocity of the sun is obtained by drawing the solar Azimuth and Elevation angle from the Application Programming Interface (API) of a German Metereological Service (meteomatics), providing real time data. The measured data are processed by an Kalman Filter (KF) to estimate the position, velocity and acceleration of the angles of the joints of the robot. The estimated and reference angles and their derivatives are used in the SMC law. Exemplary tracking results are presented at the end.

This paper deals with a Peltier cell used in an innovative manufacturing system with a cascaded Kalman filter (KF) structure to estimate states and parameters to be used in a closed loop control law. While disturbances, states, and parameters of the Peltier cell are estimated online using a cascaded Kalman filter structure, a sliding mode control (SMC) strategy is proposed to maintain the cold-side temperature even in the presence of disturbances. One theorem is given that allows the design of a constructive control strategy. Simulation results are shown and with them the effectiveness of the method.

This paper deals with a possible approach to controlling marine fish stocks using the prey-predator model described by the Lotka-Volterra equations.The control strategy is conceived using the sliding mode control (SMC) approach which, based on the Lyapunov theorem, offers the possibility to track desired functions, thus guaranteeing the stability of the controlled system.This approach can be used for sustainable management of marine fish stocks: through the developed algorithm, the appropriate number of active fishermen and the suitable period for fishing can be determined.Computer simulations validate the proposed approach.

In the present paper, the almost periodicity of the first principal components is studied, with the aim of being able to use less information in order to obtain acceptable reconstructions of compressed images. The results of this study show that by working with the periodic principal components of images under analysis, it is possible to obtain an additional reduction to that obtained by using the original principal components. Specifically, it is shown that if the principal components that are considered periodic are replaced by their period plus a trend, it can be said that the reconstruction achieved using these periodic principal components is very close to the reconstruction achieved using the original principal components.

Frequency measurement is one of the most important tasks in modern electrical metrology. In particular, for highly sensitive sensors with a frequency output, the resolution and sensitivity depends of the frequency measurement method used. The principle of rational approximations is a technique for frequency measurement, and its use has been proposed for application in sensors. In this work, a device which implements the principle of rational approximations is presented, and its characteristics are analyzed. Particularly, the system is used to measure the signal generated by a quartz crystal microbalance, and the relationship between the measured frequency, temperature and measurement time are evaluated.

This paper proposes an Artificial Neural Network (ANN) to accurately predict the real angles obtained by a Triangulation Vision System. The performance of the ANN is compared with the K-Nearest Neighbors algorithm from previous publications. For the experimentation it was necessary to create a database to train and prove both methods in different coordinates on a determinate area through the dynamic triangulation method. Afterwards, the root mean square error is calculated to obtain the accuracy of each algorithm. Finally, several laser scanning measurements were taken at different distances to analyze the measurement dispersion of both algorithms.

The paper addresses a non-traditional problem of pattern recognition, when information about pattern is represented in the form of a random signal taken from the output of a corresponding physical sensor. It is supposed that there exist two types of signal to recognize, namely, specified in the statistical sense signals and totally unknown signals. Such the conditions are called conditions of increased a priory uncertainty. Developing a technique to recognize specified signals in conditions of increased a priory uncertainty is the objective of this paper. Methods for selection and recognition of a statistically defined random signal are proposed for the cases when signal description is done by various probabilistic models. Additional consideration is given to peculiarities of employing these methods for solving applied problems of pattern recognition in radar, medical diagnostics and speaker identification.

Robotic group communication in a densely cluttered terrain is one of the main aid to optimize group navigation and come through this sector efficiently. This paper describes the basic set of tasks need to be solved for distributed robotic group behavior. The transferring of data obtained from technical vision system (TVS), which uses the principles of dynamic triangulation, is given. Combination of mentioned methods dovetailing with the presented path planning method can improvement robotic motion planning and navigation in unknown cluttered terrain.

The paper deals with a heuristical approach to compensate friction hysteresis in an aggregate actuator which is proposed to be applied in a camless engine system and which consists of piezoelectric, mechanic and hydraulic components. The hydraulic displacement amplifier shows a conspicuous hysteresis effect that appears to be a dead time that depends on the speed of the engine. In general, with high engine speeds, the dead time effect is reduced. The compensation of the hysteresis effect is not model-based since the real time application must run within a very short time. The presented approach consists of an adaptive pre-action on the desired servo piston trajectory which is generated by a feedforward action fed by phase-variable Gaussian curves as desired valve trajectories. Simulations show the effectiveness of the proposed compensating techniques.

Previous research have shown the advantages of a novel Technical Vision System (TVS), developed at the Autonomous University of Baja California (UABC), which uses triangulation to determine spatial coordinates on any object under observation. Present paper proposes a new application for the TVS use on drones with remote laser scanner in agriculture to determine a vegetation index of scanned crops. Thereby the needed algorithm to position the laser ray in the TVS field-of-view are proposed, implemented using microcontroller and tested using various experimental factors. Experimentation results shows the advantages and disadvantages of every algorithm comparing the positioning errors after control.

In agricultural tasks, monitoring of large fields is required. In the last years automatic/autonomous monitoring has been researched; where unmanned aerial vehicles (UAV)-commonly known as drones-are used. For these systems, constrains related with autonomy during flight arise. In order to control properly the UAVs during flight, they require to measure physical variables, in a fast and accurate way. Also, the weight of instruments must be reduced for improving autonomy. In general, aerial vehicles obtain parameters like position, velocity and acceleration using inertial navigation systems. Regarding to this concern, in this work application of a novel technique for measurements onboard UAVs-particularly inertial measurement unit or IMU-is proposed. There are accelerometers inside the IMU. These accelerometers have a frequency domain output. The speed and position are calculated by the INS from acceleration. The acceleration is obtained from frequency measurements of the accelerometers output. For this reason an accurate and fast frequency measurement method is required. In this work, for this particular application, frequency measurement using principle of rational approximations is proposed. This technique allows to measure frequency in short time and with high accuracy, using a few electronic components. Due this properties, it perfectly fits requirements for UAVs.

The ubiquity of complex components and variables is well-known in all fields of engineering. The didactic importance of complex numbers and Euler's formula is emphasized by this fact. Students express the topic to be difficult to comprehend and “imaginary”. So to present this topic in an appropriate and student-centered way is of great challenge for each lecturer. In this paper the Euler's Formula is also applied for explaining and for understanding the aliasing effect when it occurs in the reconstruction of sampled signals. The fundamental target of this contribution is to overcome barriers in student's understanding and, in doing so, to improve the quality of engineering education. Guided by this objective a design-based research was carried out. Theory-based new didactic approaches have been developed in order to increase student's conceptual understanding of complex arithmetic. In addition, new theoretical insights into the comprehension problems are gained. The research design, the intervention developed and the empirical findings will be presented.

This paper presents an adaptive Kalman filter used as an observer in combination with a scaled least squares (LS) technique to improve a textile based sensor fusion. The focus of the application is to detect and monitor workplace particulate pollution. To control the sensor system around a reference current, a robust proportional-integral (PI) controller is used. In context of temperature variation, the sensor parameters resistance R and inductance L change in a linear way which is based on the linear range of the sensor characteristic. The adaption is performed with the help of an output-error (OE) model. The identification technique is based on the recursive least squares (RLS) method, which is used to estimate the parameters of the textile based model using input-output scaling factors. Through this proposed technique, a broader sampling rate and an input signal with low frequency can be used to identify the nano parameters characterizing the linear model. The simulation results emphasize that the proposed algorithm is effective and robust.

In this paper a model of a hybrid actuator is proposed. It consists of a piezo-mechanical structure (including a hydraulic transmission ratio) and a hydraulic aggregate. Moreover, a cascade control strategy based on Model Predictive Control (MPC) is proposed to track periodic valve trajectory signals. The proposed cascade control structure consists of an internal and an external controller. The secondary, internal controller is needed to accomplish a Soft Landing. The MPCs are combined with two feed forward control actions, one for each part of the model. Simulation results carried out the suitability of the control approach.

This paper deals with setting up the parameters of a PI controller to be applied in a system for the regeneration of potable water. Because of the presence of a saturation, the system to be controlled is a nonlinear one and its nonlinearity consists of the positiveness of its output. Moreover, an anti-windup control structure is considered to manage saturation effects. In order to analyse the stability and the dynamic performance of the controlled system a Lyapunov approach is proposed. Through this approach the condition of the three parameters which characterise the controller (PI and anti-windup scheme) are calculated to obtain a compromise between a stability and fast dynamics. Simulations using real parameters of the system are shown.

Single-input and single-output (SISO) controlled autoregressive moving average system by using a scalar factor input-output data is considered. Through data scaling, a simple identification technique is obtained. Using input-output scaling factors a data Recursive Least Squares (RLS) method for estimating the parameters of a linear model and contemporary sinusoidal disturbance detection is deduced. For estimating parameters of a model in nano range a very high frequency input signal with a very small sampling rate is needed. The main contribution of this work consists of the use of a scaled Recursive Least Square with a forgetting factor. Using this proposed technique, a low input signal frequency and a wider sampling rate can be used to identify the parameters. In the meantime, the scaling technique reduces the effect of the external disturbance so that RLS can be applied to identify the disturbance without considering a model of it. The proposed technique is quite general and can be applied to any kind of linear systems. The simulation results indicate that the proposed algorithm is effective.

The ubiquity of Euler's Formula is well-known in the field of engineering. The general application of Euler's Formula to all fields of engineering challenges each lecturer to present this topic in an appropriate way. The didactic importance of this formula is emphasized by the fact of its use at any level of the scientific research. In fact, Euler's Formula is often presented as definition tout court and strong critics arise from the students' side. The fundamental target of this contribution is to overcome barriers in students understanding and, in doing so, to improve the quality of engineering education. Based on constructivist learning theories this contribution presents a new lecture scheme just using a basic mathematical background already known by students after the second semester of standard mathematical courses. Moreover, the paper reinforces the application aspects of this formula to illustrate the practical meaning and the importance of Euler's Formula in different fields of engineering. The research-based concept and the implementation of the new lecture structure are to be presented as well as the results of the evaluation and the impact on teaching and learning mechanisms in Engineering Education.

The presented paper is a follow up work and improvement of a previously published research of a 3D Vision System for mobile robot navigation application. Sensor fusion and redundancy are integrated to the system in order to increase the overall robustness as well as the accuracy of the system measurements by finding the energetic center of the laser signal used by the vision system. New experimental results are presented to demonstrate the increase of system accuracy.

A hybrid actuator composed by a piezo and a hydraulic part and with a Switching Model Predictive Control (SMPC) structure for camless engine motor applications are described in this paper. The Preisach dynamic model with a hysteresis effect is considered. Hysteresis phenomena are often present in technical systems and processes and this represents a challenging issue for their control. The approach presented in this paper is quite general and can be applied also in many other systems and processes. Simulations with real data are shown.

In permanent magnetic machines the nonlinearity due to the quadratic terms of the current makes difficulties in the control system. In order to cancel the nonlinearity, a strategy based on a pre-compensation action, which is conceived through two input partition matrices is presented. After canceling the nonlinearity, a Model Predictive Control is used to obtain a positioning of the actuator. Simulation results are reported to validate the proposed technique.

This paper deals with a hybrid actuator composed by a piezo and a hydraulic part and with a cascade PID-PID control structure for camless engine motor applications. The idea is to use the advantages of both, the high precision of the piezo and the force of the hydraulic part. In fact, piezoelectric actuators (PEAs) are commonly used for precision positionings, despite PEAs present nonlinearities, such as hysteresis, saturations, and creep. In the control problem such nonlinearities must be taken into account. In this paper the Preisach dynamic model with the above mentioned nonlinearities is considered together with cascade PID-PID controllers combined with a feed-forward action. In particular, the hysteresis effect is considered and a model with a switching function is used also for the controller design. Simulations with real data are shown.

The paper proposes a hybrid Kalman filter integrating a robust and optimal algorithm for the use as an observer in model-varying predictive control (MVPC) of a nonlinear system. Moreover, a position MPC is derived in detail. Even though the proposed approach is quite general, a real case coming from automotive application is studied using computer simulation to demonstrate the effectiveness of the proposed technique. Simulations and results with real data are also discussed.

The paper presents a novel technique to control the current of an electromagnetic linear actuator fed by a multilevel IGBT voltage inverter with dynamic energy storage. In order to provide short response time, high precision and low switching frequency at the same time we combine current hysteresis regulation with model predictive control (MPC). The proposed MPC technique works in very short receding horizon and includes also wavelet algorithm to optimize the MPC operation. Through simulations with real actuator data the proposed technique shows very promising results.

Robotic grasps exhibiting visco-elastic contact interactions with the manipulated object are considered. Control of internal forces is investigated. The presence of nonnegligible compliance at contacts, implies that the object dynamics cannot be neglected when attempting to control internal forces without affecting the object position. A dynamic internal force control is proposed. It is decoupled with respect to the rigid-body object motions.