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

Oluwasegun Ayokunle Somefun, K. Akingbade, F. Dahunsi

Abstract A lot of automatic feedback control and learning tasks carried out on many dynamical systems still fundamentally rely on a form of proportional–integral–derivative (PID) control law. The PID law is often viewed as a simplistic computational control algorithm. However just like all non-convex optimization problems, tuning the PID algorithm for accurate and stable closed-loop control becomes a NP-Hard Problem. This leads to a dilemma, for both users and designers, most especially in practise. It is then no wonder that tuning software is a big business in the industrial automation sector. In this review, we present and classify PID tuning methods till date into three general areas. Finally, we then present a proposal to minimize the dilemma of complexity and cost that has become associated with tuning the three main parameters of the PID control law. Hopefully, continuous attempts at the minimization of this dilemma can lead to both a money-savings investment and a significant improvement in the field of PID control design.

Takamitsu Iwata, Hajime Nakamura, Takafumi Uemura et al.

Intravascular electroencephalography (ivEEG) with microintravascular electrodes enhances neural monitoring, functional mapping, and brain–computer interfaces (BCIs), offering a minimally invasive approach to assess cortical activities; however, this approach remains unrealized. Current ivEEG methods using electrode‐attached stents are limited to recording from large vessels, such as the superior sagittal sinus (SSS), restricting access to cortical regions essential for precise BCI control, such as those for hand and mouth movements. Here, ivEEG signals from small and soft cortical veins (CV‐ivEEGs) in eight pigs using microintravascular electrodes are recorded, achieving higher resting‐state signal power and greater spatial resolution of somatosensory evoked potentials (SEPs) compared to SSS‐based ivEEG. Additionally, ivEEG recorded from deep veins clearly captures visual evoked potentials. Furthermore, comparisons between CV‐ivEEG and electrocorticography (ECoG) using epidural and subdural electrodes in two pigs demonstrate that CV‐ivEEG captures cortical SEPs comparable to ECoG. Targeted electrical stimulation via cortical vein electrodes induces specific contralateral muscle contractions in five anesthetized pigs, confirming selective motor‐region stimulation with minimal invasiveness. The findings suggest that ivEEG with microintravascular electrodes is capable of accessing diverse cortical areas and capturing localized neural activity with high signal fidelity for minimally invasive cortical mapping and BCI.

Xuehui Ma, Shiliang Zhang, Zhiyong Sun et al.

Despite the celebrated success of stochastic control approaches for uncertain systems, such approaches are limited in the ability to handle non-Gaussian uncertainties. This work presents an adaptive robust control for linear uncertain systems, whose process noise, observation noise, and system states are depicted by ellipsoid sets rather than Gaussian distributions. We design an ellipsoid-set learning method to estimate the boundaries of state sets, and incorporate the learned sets into the control law derivation to reduce conservativeness in robust control. Further, we consider the parametric uncertainties in state-space matrices. Particularly, we assign finite candidates for the uncertain parameters, and construct a bank of candidate-conditional robust control problems for each candidate. We derive the final control law by aggregating the candidate-conditional control laws. In this way, we separate the control scheme into parallel robust controls, decoupling the learning and control, which otherwise renders the control unattainable. We demonstrate the effectiveness of the proposed control in numerical simulations in the cases of linear quadratic regulation and tracking control.

Hoang Nguyen, L. Kieu, Tao Wen et al.

Recent years have seen a significant amount of transportation data collected from multiple sources including road sensors, probe, GPS, CCTV and incident reports. Similar to many other industries, transportation has entered the generation of big data. With a rich volume of traffic data, it is challenging to build reliable prediction models based on traditional shallow machine learning methods. Deep learning is a new state-of-the-art machine learning approach which has been of great interest in both academic research and industrial applications. This study reviews recent studies of deep learning for popular topics in processing traffic data including transportation network representation, traffic flow forecasting, traffic signal control, automatic vehicle detection, traffic incident processing, travel demand prediction, autonomous driving and driver behaviours. In general, the use of deep learning systems in transportation is still limited and there are potential limitations for utilising this advanced approach to improve prediction models.

Liia Buhhanevits, Hesam Ramezani, Charles de Kergariou et al.

Hygromorphic materials and composites adjust their shape and curvature in response to changes in relative humidity, similar to the pinecone scales existing in nature. This work introduces a new class of poligromorphic materials‐composites that adapt across various environments, including water, humidity, and hydrocarbons. Composed of carbon fibers (CF), polyvinyl alcohol (PVA), and maleic anhydride ethylene‐propylene (MA‐EPR) matrices, these materials are sensitive to fluctuations in fluids like water, isooctane, and toluene. Their bioinspired internal architecture mimics the asymmetric stacking of pinecone scales. MA‐EPR/CF composites show limited actuation in water but strong responsiveness in isooctane and toluene. PVA/CF materials are more sensitive to water while retaining functionality in hydrocarbons. Importantly, their actuation is reversible and stable through multiple cycles of environmental aging, and the carbon fibers provide significant load‐bearing capabilities, with stiffening effects when passing from dry to wet and immersed states.

Samad Azimi Abriz, Mansoor Fateh, Fatemeh Jafarinejad et al.

Deep learning faces challenges like limited data, vanishing gradients, high parameter counts, and long training times. This article addresses two key issues: 1) data scarcity in ophthalmology and 2) vanishing gradients in deep networks. To overcome data limitations, an image processing‐based data generation method is proposed, expanding the dataset size by 12x. This approach enhances model training and prevents overfitting. For vanishing gradients, a deep neural network is introduced with optimized weight updates in initial layers, enabling the use of more and deeper layers. The proposed methods are validated using the retinal fundus multi‐disease image database dataset, a limited and imbalanced ophthalmology dataset available on the Grand Challenge website. Results show a 10% improvement in model accuracy compared to the original dataset and a 5% improvement over the benchmark reported on the website.

S. Sangeetha, T. Aruldoss Albert Victoire, M. Premkumar et al.

This research focuses on Wireless Sensor Networks (WSNs) and proposes a three-phase approach to achieve energy-efficient routing. The approach consists of node deployment using Voronoi diagrams, clustering, and Cluster Head (CH) selection using energy-efficient game theory, and a routing strategy based on Improved Pelican Optimization (ImPe) segment routing. Random deployment of sensor nodes in WSNs can lead to coverage issues, and hence, in order to address this, Voronoi-based node deployment is employed to ensure uniform and balanced coverage of the monitoring area. An energy-efficient game theory-based approach is used for CH selection by considering the energy levels to select CHs for enhancing network longevity. The proposed routing mechanism utilizes segment routing, which provides deterministic routing paths from CHs to the sink (Base Station). Segment routing eliminates the need for route discovery and maintenance, making it energy-efficient. The ImPe algorithm that works on the characteristics of pelican search agents is employed to choose the optimal segment path for information sharing. The assessment based on delay, network lifetime, packet delivery ratio, residual energy, throughput, communication overhead, and energy utilization acquired the values of 2.57, 98.59, 98.29, 0.98, 238.51, 7.71, and 0.02 respectively.

Mengze Yu, Wei Wang, Jiaqi Yan

This paper studies distributed resilient consensus problem for a class of uncertain nonlinear multiagent systems susceptible to deception attacks. The attacks invade both sensor and actuator channels of each agent. A specific class of Nussbaum functions is adopted to manage the attack-incurred multiple unknown control directions. Additionally, a general form of these Nussbaum functions is provided, which helps to ease the degeneration of output performance caused by Nussbaum gains. Then, by introducing finite-time distributed reference systems and local-error-based dynamic gains, we propose a novel distributed adaptive backstepping-based resilient consensus control strategy. We prove that all the closed-loop signals are uniformly bounded under attacks, and output consensus errors converge in finite time to a clearly-defined residual set whose size can be reduced by tuning control parameters, which is superior to existing results. Simulation results display the effectiveness of the proposed controllers.

Ramin Esmzad, Farnaz Adib Yaghmaie, Hamidreza Modares

This paper bridges optimization and control, and presents a novel closed-loop control framework based on natural gradient descent, offering a trajectory-oriented alternative to traditional cost-function tuning. By leveraging the Fisher Information Matrix, we formulate a preconditioned gradient descent update that explicitly shapes system trajectories. We show that, in sharp contrast to traditional controllers, our approach provides flexibility to shape the system's low-level behavior. To this end, the proposed method parameterizes closed-loop dynamics in terms of stationary covariance and an unknown cost function, providing a geometric interpretation of control adjustments. We establish theoretical stability conditions. The simulation results on a rotary inverted pendulum benchmark highlight the advantages of natural gradient descent in trajectory shaping.

Zhongyun Hua, Yongyong Chen, Han Bao et al.

When used in engineering applications, most existing chaotic systems may have many disadvantages, including discontinuous chaotic parameter ranges, lack of robust chaos, and easy occurrence of chaos degradation. In this article, we propose a two-dimensional (2-D) parametric polynomial chaotic system (2D-PPCS) as a general system that can yield many 2-D chaotic maps with different exponent coefficient settings. The 2D-PPCS initializes two parametric polynomials and then applies modular chaotification to the polynomials. Setting different control parameters allows the 2D-PPCS to customize its Lyapunov exponents in order to obtain robust chaos and behaviors with desired complexity. Our theoretical analysis demonstrates the robust chaotic behavior of the 2D-PPCS. Two illustrative examples are provided and tested based on numeral experiments to verify the effectiveness of the 2D-PPCS. A chaos-based pseudorandom number generator is also developed to illustrate the applications of the 2D-PPCS. The experimental results demonstrate that these examples of the 2D-PPCS can achieve robust and desired chaos, have better performance, and generate higher randomness pseudorandom numbers than some representative 2-D chaotic maps.

Hakan Ocal

Early detection of skin cancer ensures the survival of many cases. There are still challenges in segmenting dermoscopic skin lesion images. Artifacts in the lesion images, such as various dirt, hairs, low contrast, and unclear boundaries, are challenges that affect segmentation accuracy. Convolutional neural networks have brought success in skin lesion segmentation. U‐shaped and V‐shaped deep learning‐based segmentation architectures learn boundary information in the first layers. However, this information becomes weaker in the following layers. Herein, the Edge‐aTtention module is added to the V‐Net architecture to move edge information to the last layer, and the spatial and channel squeeze‐excitation module is added to emphasize high‐level features by recalibrating the channel information to learn lesion boundaries better. The scSEETV‐Net is supported by fusing the binary cross‐entropy, which calculates the loss on a pixel‐based, and the focal Twersky loss function, which has significant success in class imbalances. The proposed architecture achieves 0.9212 Jaccard and 0.9552 Dice in the ISIC2016 dataset, 0.8273 Jaccard and 0.8949 Dice in the ISIC2017 dataset, and 0.8070 Jaccard and 0.8831 Dice in the ISIC2018 dataset. Comparative analyses show that the proposed methodology outperforms the state‐of‐the‐art techniques in the literature.

Takahito Fujimori

Linear Quadratic Regulator (LQR) is often combined with feedback linearization (FBL) for nonlinear systems that have the nonlinearity additive to the input. Conventional approaches estimate and cancel the nonlinearity based on the first principle or data-driven methods such as Gaussian Processes (GPs). However, the former needs an elaborate modeling process, and the latter provides a fixed learned model, which may be suffering when the model dynamics are changing. In this letter, we take a Deep Neural Network (DNN) using a real-time-updated dataset to approximate the unknown nonlinearity while the controller is running. Spectrally normalizing the weights in each time-step, we stably incorporate the DNN prediction to an LQR controller and compensate for the nonlinear term. Leveraging the property of the bounded Lipschitz constant of the DNN, we provide theoretical analysis and locally exponential stability of the proposed controller. Simulation results show that our controller significantly outperforms Baseline controllers in trajectory tracking cases.

Eunhyek Joa, Francesco Borrelli

We propose a Model Predictive Control (MPC) with a single-step prediction horizon to approximate the solution of infinite horizon optimal control problems with the expected sum of convex stage costs for constrained linear uncertain systems. The proposed method aims to enhance a given sub-optimal controller, leveraging data to achieve a nearly optimal solution for the infinite horizon problem. The method is built on two techniques. First, we estimate the expected values of the convex costs using a computationally tractable approximation, achieved by sampling across the space of disturbances. Second, we implement a data-driven approach to approximate the optimal value function and its corresponding domain, through systematic exploration of the system's state space. These estimates are subsequently used to calculate the terminal cost and terminal set within the proposed MPC. We prove recursive feasibility, robust constraint satisfaction, and convergence in probability to the target set. Furthermore, we prove that the estimated value function converges to the optimal value function in a local region. The effectiveness of the proposed MPC is illustrated with detailed numerical simulations and comparisons with a value iteration method and a Learning MPC that minimizes a certainty equivalent cost.

Demetrius Gulewicz, Uduak Inyang-Udoh, Trevor Bird et al.

Model predictive control has gained popularity for its ability to satisfy constraints and guarantee robustness for certain classes of systems. However, for systems whose dynamics are characterized by a high state dimension, substantial nonlinearities, and stiffness, suitable methods for online nonlinear MPC are lacking. One example of such a system is a vehicle thermal management system (TMS) with integrated thermal energy storage (TES), also referred to as a hybrid TMS. Here, hybrid refers to the ability to achieve cooling through a conventional heat exchanger or via melting of a phase change material, or both. Given increased electrification in vehicle platforms, more stringent performance specifications are being placed on TMS, in turn requiring more advanced control methods. In this paper, we present the design and real-time implementation of a nonlinear model predictive controller with 77 states on an experimental hybrid TMS testbed. We show how, in spite of high-dimension and stiff dynamics, an explicit integration method can be obtained by linearizing the dynamics at each time step within the MPC horizon. This integration method further allows the first-order gradients to be calculated with minimal additional computational cost. Through simulated and experimental results, we demonstrate the utility of the proposed solution method and the benefits of TES for mitigating highly transient heat loads achieved by actively controlling its charging and discharging behavior.

A. Jegatheesh, V. Thiyagarajan, N. Selvan et al.

Zhong-hua Li, Wen-Hua Chen, Jun Yang et al.

The quest for optimal operation in environments with unknowns and uncertainties is highly desirable but critically challenging across numerous fields. This paper develops a dual control framework for exploration and exploitation (DCEE) to solve an auto-optimization problem in such complex settings. In general, there is a fundamental conflict between tracking an unknown optimal operational condition and parameter identification. The DCEE framework stands out by eliminating the need for additional perturbation signals, a common requirement in existing adaptive control methods. Instead, it inherently incorporates an exploration mechanism, actively probing the uncertain environment to diminish belief uncertainty. An ensemble based multi-estimator approach is developed to learn the environmental parameters and in the meanwhile quantify the estimation uncertainty in real time. The control action is devised with dual effects, which not only minimizes the tracking error between the current state and the believed unknown optimal operational condition but also reduces belief uncertainty by proactively exploring the environment. Formal properties of the proposed DCEE framework like convergence are established. A numerical example is used to validate the effectiveness of the proposed DCEE. Simulation results for maximum power point tracking are provided to further demonstrate the potential of this new framework in real world applications. Note to Practitioners—In numerous engineering applications, it is highly desirable to operate a system to improve the efficiency, enhance performance or save energy. However, attaining this optimal control is a challenging task, due to the presence of unknown system and/or environment parameters. We develop a principled approach to balance between exploration and exploitation, involving active learning to estimate unknown parameters and tracking the optimal operational condition based on current estimation. This paper provides a unified framework to solve general auto-optimization control problems. The simulation results demonstrate that the proposed method outperforms existing methods in terms of efficiency and optimality for maximum power point tracking problem, and it can be readily implemented for many other engineering problems. Future research include generalizing the proposed method to nonlinear systems, as well as exploring novel applications to facilitate the widespread adoption of our method.

R. Kabilan, A. Venkatesan

SUN Jiawei, JIANG Tao, QU Shijian et al.

Stator current loop control is crucial in vector-controlled induction motors, due to its direct impact on system stability. In high-power traction systems, operating at a low switching frequency is a common practice to reduce inverter losses. This operational condition imposes limitations on the controller bandwidth and introduces significant digital delay, thereby exacerbating the cross-coupling between the d-axis and q-axis of the induction motors. This leads to increased current jitter in the dynamic process and significantly slower dynamic response, thus negatively affecting the control performance of the current loop. In order to overcome these challenges, a comprehensive analysis of the cross-coupling effect was first conducted in this study, using a mathematical model of induction motors based on the complex vector concept. Then ideas on how to overcome the delay were derived through analyzing the effect of delay on the digital vector control of asynchronous motors. At last, a complex vector controller considering the delay was designed, employing the zero-pole cancellation principle. In the discrete domain, the designed controller can eliminate the coupling terms in the controller transfer function, enabling effective current decoupling of the d-axis and q-axis. Moreover, the controller system exhibits a high dynamic response speed, as the system bandwidth can be easily adjusted to a high level, taking advantage of the typical second-order system in the transfer function of the whole control system. The results of simulation and experiment reveal the good control performance of the complex vector controller proposed in this paper. Across the entire speed range, the cross-coupling error is reduced by more than 80 percentage points, while the dynamic response speed is improved by more than 45%.

Sesha Vidhya S, Rukmani Devi S, Shanthi K. G et al.

The creation of a network of tiny sensors installed in, on or around the human body has been facilitated by advancements in wireless communications and wearable devices. Because of its potential to transform healthcare delivery, Wireless Body Area Network (WBAN) has been increasingly important in modern medical systems over the last decade. Individual nodes (sensors and actuators) embedded in a person's clothing, body, or skin form a WBAN. Both academia and industry have increased their efforts in WBAN research and development. The wearable antenna, whether on or off the human body, is a critical component of contact with particular design in WBAN networks. Ultra-wideband (UWB) technology can provide high-capacity, short-range communications with minimal energy consumption, which is appropriate for wireless body area networks. The human body's involvement in such a device creates significant challenges for both the wearable antenna's construction and the broadcast paradigm. To achieve many functionalities, multi-band and broadband antennas are better solutions. The proposed multi-band antenna is constructed from a FR4 substrate with dimensions of (24 × 25 × 1.6) mm3. The proposed design was successfully tested with different configurations and enhanced with a broad impedance bandwidth of over 100 percent, where the UWB frequency spectrum encompassed the range from 3 to 9 GHz with a reflective coefficient of −15 dB and gain of 2.5 dBi, as well as fair radiation patterns in the Federal Communications Commission range. The SAR value of the devised antenna with and without SRR being 2 W/kg, 3.5 W/kg, respectively. This solution may be a worthy contender for meeting the UWB demands as a result, could be an excellent fit for wireless body technologies.

Zhicheng Zhang, Yasumasa Fujisaki

In this paper, we explore the discrete time sparse feedback control for a linear invariant system, where the proposed optimal feedback controller enjoys input sparsity by using a dynamic linear compensator, that is, the components of feedback control signal having the smallest possible nonzero values. The resulting augmented dynamics ensures closed-loop stability, which infers sparse feedback controller from open-loop solution to closed-loop realization. In particular, we show that the implemented sparse feedback (closed-loop) control solution is equivalent to the original sparse (open-loop) control solution under a specified basis. We then extend the dynamic compensator to a feedforward tracking control problem. Finally, numerical examples demonstrate the effectiveness of proposed control approach.