Hasil untuk "Control engineering systems. Automatic machinery (General)"

Jan Olucak, Torbjørn Cunis

Many problems in nonlinear systems analysis and control design, such as local region-of-attraction estimation, inner-approximations of reachable sets or control design under state and control constraints can be formulated as nonconvex sum-of-squares programs. Yet tractable and efficient solution methods are still lacking, limiting their application in control engineering. To address this gap, we propose a filter line-search algorithm that solves a sequence of quadratic subproblems. Numerical benchmarks demonstrate that the algorithm can significantly reduce the number of iterations, resulting in a substantial decrease in computation time compared to established methods for nonconvex sum-of-squares programs. An open-source implementation of the algorithm along with the numerical benchmarks is provided

Paolo Castaldi, Mohammad Narimani, Seyyed Ali Emami et al.

Chang Xu, Naoki Hayashi, Masahiro Inuiguchi

This study addresses the challenge of data scarcity in training reinforcement learning (RL) controllers for power system economic dispatch problems (EDP) by integrating Generative Adversarial Network (GAN)-generated synthetic data and transfer learning (TL). Traditional data collection for power systems may face limitations like privacy concerns hindering the performance of deep neural network-based controllers. To overcome this, a GAN-based framework is proposed to generate synthetic load demand data, preserving characteristics of real datasets. A TL technique is then employed to fine-tune a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent, pretrained in a synthetic environment, into a target environment with real-world data. Experiments evaluate three GAN-generated datasets, including scenarios with mode collapse, and compare results against regression-based data generation methods. Key findings demonstrate that even low-quality synthetic data, when combined with TL, significantly enhances RL performance. For instance, a mode-collapsed GAN model reduced test operation cost by 54.7% and power unbalance by 89.9% compared to a baseline TD3 agent. This work highlights the potential of synthetic data augmentation and TL in data-scarce power system applications, offering a viable pathway to improve controller performance without additional real-world data collection.

CHEN Xiulin, WANG Sanhu, ZOU Yangju

This paper addresses the issues of floating high voltage at light loads and insufficient cross-regulation in open-loop high-isolation multi-output power supplies. It initially introduces methods of separate winding arrangements and core projection coincidence to reduce distributed capacitance and leakage inductance in transformers. Subsequent analysis reveals the impact of distributed capacitors on converter gain based on the fundamental harmonic wave analysis method. Additionally, a series compensation method is utilized to mitigate this impact of leakage inductance on convertor gain. This combination leads to the development of a CLLC resonant converter with an asymmetric structure. Finally, the paper presents the development of a 4-output prototype with a total output power of 420 W. Testing results indicate that both the primary and secondary sides of transformer meet an AC18 kV isolation requirement. The voltage accuracy of power output across the full load range is controllable within ± 10%. In fault mode, the faulty branch actively shuts off the output, while normal functioning is maintained in the other branches, verifying the correctness of the proposed design scheme.

Lei You, Minghua Luo, Xinglin Zhu et al.

The belt conveyor is susceptible to longitudinal tearing, which poses a serious threat to the safety of coal mines. Traditional methods for detecting longitudinal tears have limitations such as poor image quality, limited applicability, and high hardware costs. An improved encoder-decoder network was proposed to solve the longitudinal tear detection problem. This method utilizes a line structured light system for image acquisition. The input images are downscaled using a sorting algorithm to extract the information of pixels with high grayscale values as the input feature map for the neural network. The reduced-dimensional encoder-decoder network then semantically segments the input feature map, and the resulting pixel segmentation is mapped to the location of the longitudinal tear. Finally, the position and length of the tear are calculated by back-projecting the semantic segmentation result to the world coordinate system. Experimental results demonstrate that this method effectively reduces hardware resource consumption and improves detection speed. The DICE and MIOU scores for the improved network are 97.69% and 95.47%, respectively, while the recall and precision for improved detection are 96.60% and 95.67%, respectively. Therefore, this method can successfully monitor longitudinal tear failures and ensure the safety of transportation.

Maarten Vlaswinkel, Duarte Antunes, Frank Willems

Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge in the number of corresponding tunable parameters in actuator set points and control settings. Automated calibration is therefore essential to keep development time and costs at acceptable levels. In this work, an innovative self-learning calibration method is presented based on in-cylinder pressure curve shaping. This method combines Principal Component Decomposition with constrained Bayesian Optimization. To realize maximal thermal engine efficiency, the optimization problem aims at minimizing the difference between the actual in-cylinder pressure curve and an Idealized Thermodynamic Cycle. By continuously updating a Gaussian Process Regression model of the pressure's Principal Components weights using measurements of the actual operating conditions, the mean in-cylinder pressure curve as well as its uncertainty bounds are learned. This information drives the optimization of calibration parameters, which are automatically adapted while dealing with the risks and uncertainties associated with operational safety and combustion stability. This data-driven method does not require prior knowledge of the system. The proposed method is successfully demonstrated in simulation using a Reactivity Controlled Compression Ignition engine model. The difference between the Gross Indicated Efficiency of the optimal solution found and the true optimum is 0.017%. For this complex engine, the optimal solution was found after 64.4s, which is relatively fast compared to conventional calibration methods.

Shirly Sudhakaran, R Maheswari, V Kanchana Devi

RFID (radio frequency identification) is a progressively adopted technology in today’s automated world. Wireless technologies have enabled contactless payments, tracking, identifying, and many more features in a system that can be introduced to build a smart environment. This work overviews the usage of the IoT (Internet of Things) platform for tracking passengers and enabling online payments through wireless sensors and RFID technology in Chennai Suburban Railways. The tracking system consists of an RFID reader that can locate and track passive as well as mobile objects attached with passive RFID tags. The proposed system incorporates the installation of RFID readers at every entrance and exit of the railway station, and every passenger carries their own RFID tags. This not only enables online payments for passengers but also helps the government in tracking the crowd for demand monitoring. The new methodology creates a digital workspace and enforces lawful safety regulations both for the administration and the consumers. A prototype of the proposed system is implemented in real-time to understand the workings of the system. Data collection is done through RFID tags that act as transit cards and an analysis for consumer demand is done using the DBSCAN (Density-Based Spatial Clustering of Application with Noise) algorithm with a Randomized KD-tree for the analysis of spatial and temporal patterns. A new algorithm, the iDBSCAN (improved Density-Based Spatial Clustering of Application with Noise) algorithm is proposed for faster performance on the datasets.

Alessio Dallabona, Patrik Schermann, Mogens Blanke et al.

The application of nonlinear control schemes to electro-hydraulic actuators often requires several alterations in the design of the controllers during their implementation. This is to overcome challenges that frequently arise in such control algorithms owing to model nonlinearities. Moreover, advanced control solutions for this type of systems often introduce input algebraic loops that pose significant design and tuning difficulties. Conventional methods to avoid such loops introduce chatter, which considerably degrade tracking performance and has oil degradation and wear as side effects. This study presents a nonlinear control architecture for hydraulic actuators that comprises low-complexity modules that facilitate robust high performance in tracking and avoids the drawbacks of chatter. The salient feature is a dynamic input-mapping inversion module that avoids algebraic loops in the control input and is followed by dedicated position control. The stability of the closed-loop system is analyzed using arguments from Lyapunov theory for cascaded non-autonomous nonlinear systems. The effectiveness of the proposed solution is evaluated on a high-fidelity simulator of a wind turbine pitch system, and validated on a full-scale laboratory setup that includes a hydraulic pitch system and blade bearing. Appropriate quantitative metrics are used to evaluate the closed-loop system performance in comparison to a state-of-the-art nonlinear design.

Zixing Wang, Yongkang Huo, Fulvio Forni

We consider the design of a new class of passive iFIR controllers given by the parallel action of an integrator and a finite impulse response filter. iFIRs are more expressive than PID controllers but retain their features and simplicity. The paper provides a model-free data-driven design for passive iFIR controllers based on virtual reference feedback tuning. Passivity is enforced through constrained optimization (three different formulations are discussed). The proposed design does not rely on large datasets or accurate plant models.

Ziqi Yang, Lihua Xie

This letter presents a nonlinear disturbance observer-parameterized control barrier function (DOp-CBF) designed for a robust safety control system under external disturbances. This framework emphasizes that the safety bounds are relevant to the disturbances, acknowledging the critical impact of disturbances on system safety. This work incorporates a disturbance observer (DO) as an adaptive mechanism of the safety bounds design. Instead of considering the worst-case scenario, the safety bounds are dynamically adjusted using DO. The forward invariance of the proposed method regardless of the observer error is ensured, and the corresponding optimal control formulation is presented. The performance of the proposed method is demonstrated through simulations of a cruise control problem under varying road grades. The influence of road grade on the safe distance between vehicles is analyzed and managed using a DO. The results demonstrate the advantages of this approach in maintaining safety and improving system performance under disturbances.

Yerong Sun, Kechuan Yi

So as to study the influence of speed factors on the stability of tractor automatic navigation system, combined with neural network control theory, the author proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using back propagation neural network to establish two-wheel tractor-path dynamics model and straight-line path tracking deviation model, the overall system simulation was carried out by using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed: when the tractor was tracked with the automatic control of linear path under the condition of the variable speed, the maximum deviation of the lateral position deviation was 12.7cm, and the average absolute deviation was kept within 4.88cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the condition of constant speed and variable speed, using the joint sliding mode control method designed by the author, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations.

Jiyuan Sun, Pin Li, Yan Zheng et al.

The single axis linear displacement measurement system of CMM is composed of grating ruler, servo motor and linear motion mechanism. Although the measuring accuracy of grating ruler is high, the accuracy of servo motor and linear motion mechanism is low. Therefore, the complex structure limits the measurement accuracy of the linear displacement measurement system. This paper introduces a novel linear displacement measurement system named magnetic levitation ruler. According to the working principle of grating ruler and the characteristics of magnetic levitation technology, the magnetic circuit design and structural design of magnetic levitation ruler are completed in this paper. The mover core of the magnetic levitation ruler is in the stable working magnetic field provided by the stator yoke. The horizontal control coil wound on the mover core can obtain more stable ampere force to improve the control accuracy of the mover core displacement. Therefore, the mover core can be moved in step mode, and the length of each step is fixed. Each step is the minimum scale of the magnetic levitation ruler. Therefore, the mover core can implement displacement measurement while moving in a linear motion. This paper analyzes the working principle of levitation, horizontal motion, and displacement measurement of magnetic levitation ruler, and determines the structural materials and parameters of magnetic levitation ruler with the help of finite element analysis software. The simulation results show that the levitation force of the magnetic levitation ruler is proportional to the current passing through the levitation coils, and the thrust of the horizontal control coil is less disturbed by the magnetic field. Compared with the linear displacement measurement system with rotational servo motor or permanent magnet synchronous linear motor as the core, the magnetic levitation ruler has stable magnetic field, strong controllability, high integration, and is easier to achieve high-precision control.

Wenxin Guo, Weiwei Qin, Chen Hu et al.

Abstract This paper investigates the adaptive robust control problem based on reinforcement learning for an affine nonlinear system with unknown time‐varying uncertainty. Inspired by the ability to estimate uncertainty of neural network, a novel policy iteration algorithm is proposed which alternates between the value evaluation, uncertainty estimation, and policy update steps until the adaptive robust control law is obtained. Especially during the step of uncertainty estimation, the unknown time‐varying uncertainty is approximated by a radial basis function neural network and introduce it into the reinforcement learning framework. By designing an appropriate utility function, the algorithm improves both convergence rate and final approximate error comparing with existing reinforcement learning algorithm. The Lyapunov stability theorem provides theoretical demonstrations of the stability and convergence. Furthermore, the uniformly ultimately bounded stability of the affine nonlinear system is demonstrated with unknown time‐varying uncertainty. Finally, the performance of the proposed algorithm is demonstrated through a torsion pendulum system.

Islam M Elbaz, MA Sohaly, H El-Metwally

In this paper, we study a stochastic SIS epidemic model with distributed delays. The positiveness of the solutions is established. We obtain sufficient conditions for the extinction of the disease through the study of stochastic stability of the disease-free equilibrium and stability of the same equilibrium in the mean. Compared to many works on the deterministic and stochastic SIS models and their stability, the distributed delays involved in the model offer new conditions with much more boundedness on the rate of losing immunity. The disease is extinct for small and large enough values of the intensity of noise and regardless of the initial history functions and the magnitude of the basic reproductive number R 0 .

Luca Ballotta, Vijay Gupta

In this paper, we investigate the architecture of an optimal controller that maximizes the convergence speed of a consensus protocol with single-integrator dynamics. Under the assumption that communication delays increase with the number of hops from which information is allowed to reach each agent, we address the optimal control design under delayed feedback and show that the optimal controller features, in general, a sparsely connected architecture.

Giannis Delimpaltadakis, W. P. M. H. Heemels

In this paper, we study the relationship between systems controlled via Control Barrier Function (CBF) approaches and a class of discontinuous dynamical systems, called Projected Dynamical Systems (PDSs). In particular, under appropriate assumptions, we show that the vector field of CBF-controlled systems is a Krasovskii-like perturbation of the set-valued map of a differential inclusion, that abstracts PDSs. This result provides a novel perspective to analyze and design CBF-based controllers. Specifically, we show how, in certain cases, it can be employed for designing CBF-based controllers that, while imposing safety, preserve asymptotic stability and do not introduce undesired equilibria or limit cycles. Finally, we briefly discuss about how it enables continuous implementations of certain projection-based controllers, that are gaining increasing popularity.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

YAN Meng, FU Liang, ZHA Guotao et al.

Micro perforation plate (MPP) sound liner is a very effective method to control pipe noise in high-speed airflow environment based on the principle of Helmholtz resonator. It generally has single layer, double-layer or even multi-layer structure. At present, the acoustic design theory of MPP sound liner is mature and reliable in the absence of air flow, but the design process is complicated in the presence of air flow, especially in the environment of high-speed air flow, and there are many influencing factors, so it has not formed a mature and unified theoretical basis for design. In this paper, an acoustic impedance model of double-layer MPP sound liner with two resonant frequencies is derived which is based on the acoustic impedance model of single layer MPP sound liner by the transfer matrix method (TMM). The model is used to calculate acoustic impedance and acoustic absorption of a known double-layer MPP sound liner at 130 dB sound intensity level and 60 m/s velocity. At the same time, the flow tube method is used for measurement. The results show that the theoretical results are close to the measured results. The maximum error of sound absorption coefficient is only 0.05, which can meet the requirements of engineering application.