This work is set to design pose-free visual feedback control laws. Our contribution is linked to improving 3D pose-free visual servoing strategy using 2D predictions of visual features’ trajectories. Given a set rigid body characterized by a set of 3D point features, our proposed visual servoing strategy enhanced with predictive elements is based on computing the following sample time positions of the point features given the known camera velocity from the previous time moment. In this way, the architecture decides to discard the point feature before exiting the field of view. Thus, any unnecessary computations are removed, and the camera’s motion is ensured to be more natural. The simulation results emphasize the potential of the new visual control architecture.
Smart homes use a variety of technology that automate tasks and increase convenience in our daily lives. Functions performed by the devices can be improving security access, controlling lighting and adjusting temperature. Given the importance of convenience and cost efficiency in such settings, as well as the large number of devices involved, it is necessary to monitor power usage in smart homes. Furthermore, rising energy consumption increases the carbon footprint, exacerbates climate threats and puts a strain on energy supplies. As a result, monitoring energy use becomes critical for guaranteeing sustainability and efficiency. In this paper, we analyzed the energy consumption and usage in a home with multiple smart devices that are connected to a smart meter and we tried to offer optimization. This research focuses on optimizing energy usage while balancing user convenience, incorporating factors such as air pressure, dew point, and wind speed. To evaluate the optimization process, a hybrid approach using Linear Regression, Random Forest Regression and Gradient Boosting was developed. The study aimed to optimize energy consumption by predicting net energy usage. By utilizing predictions and optimized control strategies, smart home appliances can be managed proactively and efficiently.
Logical dependencies, extracted from co-changes from the versioning system, have multiple applications across numerous fields, including fault detection, software reconstruction, key class identification, among others. This paper will focus on the influence of code co-changes on software clustering for architectural reconstruction. Specifically, we will analyze their impact on the clustering solution of Apache Ant in order to assess whether co-changes usage enhances the quality of the obtained solution.
This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance the predictability and efficiency of wastewater treatment processes. The study aims to develop advanced predictive models that can simulate the dynamics of wastewater treatment more accurately and adjust operational strategies dynamically. By integrating LSTM networks, the research enables continuous prediction of Effluent Quality Index (EQI) variables under stochastic and deterministic scenarios, thereby improving the accuracy and efficiency of predicting pollutant levels. The research uses an LSTM model to learn from a comprehensive dataset derived from historical simulations of BSM2, where key parameters such as the oxygen transfer coefficient (K L a) are systematically varied to measure their impact on effluent quality. The LSTM's capability to handle complex, nonlinear data and its adaptability to time series forecasting significantly enhances model performance, offering a robust tool for real-time decision-making and process optimization in wastewater treatment facilities. This approach not only improves the accuracy and efficiency of predicting pollutant levels but also supports environmental compliance and operational sustainability, making it a valuable tool for environmental engineers and professionals in the field of wastewater treatment.
This paper presents a controller based on input-output linearisation (IOL) for a vapor compression cycle leveraging a moving-boundary model of the evaporator. After a presentation of the test rig and adaptations of a previously published high-order simulation model, the control-oriented moving-boundary model is derived. Special emphasis is laid on steady-state solutions of the governing partial differential equations. The corresponding ansatz functions enable a high model accuracy while keeping the model order low. The IOL based on the moving-boundary model is compared by simulations with a previously published IOL based on a lumped-parameter approach. Here, the IOL based on the moving-boundary model shows a high performance in a much larger operation range. These results can be confirmed through the comparative evaluation with experimental data. Finally, we show the general applicability of the controller with an implementation on a dedicated test rig at the Chair of Mechatronics, University of Rostock.
This work presents the estimation and control stages of an induction motor. Starting from classical field orientation control scheme, in our approach the unknown state of the system is estimated based on the reduced-order observer, and for control of output variable (the speed rotor) was used two controllers: a robust adaptive controller, and a multiple linear predictive controller, obtained by using linear approximation of the nonlinear model of the considered system. The validity of the proposed estimation and control scheme is illustrated by performed several tests in a simulation environment.
Efficient operation and maintenance of wastewater treatment plants (WWTPs) are essential for safeguarding public health and the environment. The emergence of mechanical faults within complex systems can lead to disruptions, increased operational costs, and environmental risks. As the world moves towards a digitally connected and sustainable future, the development of Deep Learning (DL) tools for fault detection and isolation (FDI) in wastewater treatment processes is expected to become paramount. Therefore, in this study, we developed two neural models, a Feedforward Neural Network (FFNN) and a Long Short-Term Memory (LSTM), to address the detection of mechanical faults such as bias, stuck, spikes, and precision degradation of the Dissolved Oxygen (DO) sensor. The classification results showed remarkable accuracy performances during testing: for Dataset 1, FFNN achieved 96.56%, while LSTM reached 99.36%; and for Dataset 2, FFNN achieved 99.36%, and LSTM reached 99.57%.
Addressing control performance of the Permanent Magnet Synchronous Motor (PMSM) with sensorless control systems which use Field Oriented Control (FOC), this paper proposes a control structure which uses a Super Twisting-Terminal Sliding Mode Control (ST-SMC) type control. Moreover, concerning the speed estimation for the PMSM rotor, the performance of a Sliding Mode Observer (SMO) type observer is improved by using a Phase Locked Loop (PLL) control technique. The control performance of the sensorless control system is improved by optimally tuning the parameters of the ST-SMC controller using an Ant Colony Optimisation (ACO) algorithm. The control structures, observer and controller synthesis and proof of their convergence are also presented. Several computer simulations achieved in the Matlab/Simulink software environment demonstrate the superiority of the proposed sensorless PMSM control system compared to the classical version. The performance criteria used for comparisons are classical indicators such as settling and rise time, speed signal ripple, stationary error, and Total Harmonic Distortion (THD).
This work proposes a four-stage robust adaptive cascaded control architecture for handling the flight of quadrotors with unknown uncertainties via the dynamic inversion. The main targets of the control architecture are the accurate tracking of the reference trajectory and the robustness in terms of parametric uncertainties. The dynamics of quadrotors is innovatively brought to a strict feedback form and then, a robust control scheme is designed by considering four controllers (for position, velocity, attitude, and angular rate), adaptive control laws (to suppress the uncertainties), and fixed-time command filters. The stability of the closed-loop system is proved via the Lyapunov theory, and then, the proposed control architecture is software validated by complex numerical simulations.
Cardiovascular diseases are the cause of increased mortality in all countries of the world, which explains the deepening of their study and the search for additional means of analyzing cardiac data. The article presents mathematically based methods for the processing and analysis of cardio data that are recorded during the normal life of people. The results of linear (in the time and frequency domain) and non-linear procedures (Poincare method) as well as graphical methods (Power Spectral Density) for determining heart rate variability in healthy and heart diseased individuals are shown. A statistical analysis was applied to determine the significance of the obtained results when comparing the indicators of the two studied data sets.
As people tend to integrate technology more and more in their lives, one of the most evident aspects which has a great impact on their learning methods are the E-learning platforms which provide interactive means to develop new skills in problem-solving topics. The development of an E-learning platform that simplifies the way in which users engage with the information while simultaneously enhancing their level of comprehension is, without a question, the fastest and most effective strategy. Trend, in matter of learning techniques, has also brought new challenges and opportunities to enhance assimilation of information. Thus, during the process of learning, focus has to be actively maintained through a series of stimuli. Algorithm visualization blended with Artificial Intelligence is the solution that the paper intends to present as a reaction to the actual inclination.
The paper focuses on the design of a control system for an operator with Parkinson's diseases driving an electric vehicle. The fractional order model of a person with Parkinson’s disease is discussed. The fractional model is extended to the dynamics of an electric car operated by a person with disabilities. The associated mathematical models are analyzed, insisting on the influence of dead times on driving performance. The effects generated by dead times on the visual perception of motion parameters as well as dead times on the generation of a control decision are discussed, times determined by the incapacity of the operator affected by Parkinson's disease. Methods for approximating the dead time dynamics are proposed and the stability of the human-machine system is investigated, as a hierarchical system, using vector Lyapunov techniques. The theoretical results are verified by numerical simulation.
Emulsification processes show a plethora of use cases in different industries. The complexity and intransparency of many emulsion systems make it hard to apply classic control approaches. The operation of these systems therefore often diverts towards a manual open-loop control. While population balance models (PBM) have been explored for multiple decades, they are rarely used in practice for closed-loop control due to the high computational effort. For this purpose a data-driven modeling approach specifically tailored to the control of complex emulsification devices is introduced. A new simplified description scheme of particle size distributions in combination with Gaussian process regression on a reasonably sized dataset can predict the system change. It additionally gives a useful measure of uncertainty for the predicted change, which is propagated onto the discrete distribution description. The concept is proven with leave-one-out cross-validation, before showing its potential in a model predictive control (MPC) simulation.
This paper presents the development and implementation of an IoT-based system for monitoring and controlling temperature in a bioreactor. The system comprises an ESP8266 microcontroller, an OLED display, a solid-state relay (SSR), a temperature sensor, and a peltier module. The entire system operates an air or water pump which delivers warm or cool into the bioreactor by using a bypass. This work emphasizes the electronic design aspects and the implementation of IoT capabilities. By utilizing the microcontroller's PWM signal to modulate the SSR output, the system achieves precise temperature regulation. The IoT functionality enables real-time data acquisition and transmission to a cloud-based platform, facilitating remote monitoring and control of the bioreactor operation.
This paper investigates the data-driven discovery of equations of motion for twin-tailed fighter aircraft, with a specific focus on addressing challenges posed by buffeting-induced vibrations in the F-15 model. Employing the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, our research delves into the complexities of F-15 twin-tail dynamics. The utilized nonlinear model takes into account critical factors such as aerodynamic damping, cubic nonlinearities and inter-tail coupling. While the SINDy algorithm demonstrates promise in capturing the original dynamics, its sensitivity to wind disturbances prompts the application of the SINDYc algorithm. This enhanced approach accurately predicts system dynamics and provides valuable insights into effective control inputs. In this study we follow a step-by-step process, initially excluding wind disturbances to focus on intrinsic dynamics, followed by their introduction to assess adaptability. Coefficients derived from the system identification algorithm closely align with the original synthetic data we generated using an existing model and the observed negligible error underscores the success of our data-driven methodology. This research contributes to advancing data-driven dynamics in aerospace engineering, providing an insight of how data-driven approaches could be utilized in the field.
This paper explores the utilization of machine learning techniques to develop an approximate input-output linearizable neural network model aimed at improving disturbance attenuation. The incorporation of a learning mechanism allows for the synthesis of control laws that effectively address disturbance attenuation by imposing a specific parameterization and an L-2 gain constraint during the learning process. The constraint is subsequently relaxed into a set of diagonally dominant (DD) matrix constraints. This relaxation leads to a series of linear constraints that can be seamlessly incorporated into the loss criterion. Therefore, a log-sum-exp (LSE) function-a smoothed version of the max function- of these linearized constraints is added to the loss criterion which results in an unconstrained problem amenable to training via back-propagation. The proposed methodology is applied to two variants of nonlinear perturbed pendulum systems. The results emphasize the effectiveness of the model, both on its own and as a framework for developing control laws for disturbance attenuation.
Due to their importance, traction control and antilock braking systems have become standard equipment in modern vehicles. However, accurate models of tire dynamics are often difficult to obtain and usually include nonlinearities, making their use in control systems challenging. This paper describes a traction control system based on model predictive control and Koopman operator theory, which aims to approximate nonlinear systems with linear ones through a state space transformation. A linear model predictive controller based on the Koopman predictor is compared to a standard nonlinear model predictive controller. Experiments in a high-fidelity vehicle dynamics simulation environment show a comparable reference tracking performance of the two controllers, with a reduced execution time for the proposed Koopman operator-based algorithm, both on a standard PC and embedded hardware.
The Artificial Pancreas Problem (APP) offers a potential framework for Control Engineering studies, specifically in the field of continuous monitoring and actuation to control glucose levels. The models that give a satisfactory level of accuracy are nonlinear by nature however, the standard approach in linear control is to find a linear representation of the model. The current paper proposes a comparison between standard linearization and linearization via the Koopman Operator for an input-affine nonlinear model from insulin intake to glucose level. Each model also has an additive disturbance component. To account for it, the current paper proposes a method of modeling the disturbance based on Gauss Processes. For a meaningful comparison between the considered linear matrix inequality-based controllers (LMI) and linear-quadratic regulators (LQR), the paper introduces the term Glucose Absolute Error (GAE) as an error index adapted for the Insulin-Glucose system.
Model predictive control has emerged as a prominent technique in control engineering due to its ability to handle constraints on both control signals and system states. This capability makes model predictive control a powerful tool, particularly for complex systems with operational limitations. However, a major challenge associated with model predictive control is the "curse of dimensionality" arising from the constrained optimization problem solved at each time step. This problem becomes computationally expensive as the system dimension increases. This study proposes an accelerated model predictive control algorithm that addresses the curse of dimensionality. We achieve this by solving an equivalent suboptimal model predictive control problem within a reduced-dimensional subspace. The subspace is efficiently calculated using singular value decomposition of the Hessian matrix associated with the quadratic cost function. An adaptation law dynamically determines the subspace size, balancing accuracy and computational efficiency of the model predictive control controller.
The paper aims at bringing enhancements for the control of thermo-energetic processes by proposing anticipative action in order to reduce the perturbation effects in the process. Our contribution is organized in three connected sections. After a short introduction, in the first section, the dynamic model of the transfer energy between agent and product is estimated based on thermal balance equations, to explore the static and dynamic evolution of the process and to design the control system algorithms. In the second section, the nominal digital system is designed based on dynamic model and the imposed performances for the close loop system are validated through simulation. In the final section, the cascade and feedforward structures are designed respectively. The performances based on anticipative action, guarantee the invariance of the system, being validated in a simulation environment. The simulation results confirm the effectiveness of this research and the possibility of transferring these results towards the industrial heat processes. Finally, the conclusions and perspectives are given.
