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

Sana Motallebi, Mohammad Javad Yazdanpanah, Abdol-Hossein Vahabie

Freezing of gait (FoG) is a common and disabling symptom of Parkinson's disease (PD), characterized by impaired motor control due to altered oscillatory activity and disrupted synchronization within the cortico-subcortical basal ganglia-thalamus network. Current therapeutic strategies, including deep brain stimulation, often fail to fully alleviate FoG, highlighting the need for innovative approaches to better understand and manage this complex symptom. This study investigates the steady-state dynamics underlying FoG by employing equilibrium point analysis of the physiologically connected network in both healthy and PD configurations. An artificial neural network (ANN) controller, adapted from existing designs with modifications, is used to restore stability in the PD network dynamics. Using a Kuramoto model, we examined the synchronization behaviour of interconnected nuclei constrained by excitation-inhibition communication, focussing on phase balance dynamics through stability analysis of equilibrium points. The simulations indicate that the adaptive ANN controller can modulate these phase dynamics by targeting either the subthalamic nucleus (STN) and substantia nigra pars reticulata (SNr) or the STN and globus pallidus pars interna (GPi), leading to improved control of substantia nigra pars compacta (SNc) synchronization within the network. This research enhances the theoretical understanding of the mechanisms underlying the functional architecture, specifically the excitatory-inhibitory network involved in FoG. It also demonstrates that a modified neural control approach, which targets the minimal number of nuclei and adapts internally without reliance on external sensory feedback, indicates the potential to modulate network dynamics in a theoretical setting.

Armin Gießler, Felix Strehle, Jochen Illerhaus et al.

In this paper, we propose a novel dynamic state-feedback controller for polytopic linear parameter-varying (LPV) systems with constant input matrix. The controller employs a projected gradient flow method to continuously improve its control law and, under established conditions, converges to the optimal feedback gain of the corresponding linear quadratic regulator (LQR) problem associated with constant parameter trajectories. We derive conditions for quadratic stability, which can be verified via convex optimization, to ensure exponential stability of the LPV system even under arbitrarily fast parameter variations. Additionally, we provide sufficient conditions to guarantee the boundedness of the trajectories of the dynamic controller for any parameter trajectory and the convergence of its feedback gains to the optimal LQR gains for constant parameter trajectories. Furthermore, we show that the closed-loop system is asymptotically stable for constant parameter trajectories under these conditions. Simulation results demonstrate that the controller maintains stability and improves transient performance.

Abdelhamid Djari, Noureddine Bouarroudj, Yehya Houam et al.

Hao Chen, Abhishek Gupta, Yin Sun et al.

This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula>, and fast <inline-formula><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula>-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for average-run-length (<inline-formula><tex-math notation="LaTeX">$ {\mathtt {ARL}}$</tex-math></inline-formula>) and upper bounds for average-detection-delay (<inline-formula><tex-math notation="LaTeX">$ {\mathtt {ADD}}$</tex-math></inline-formula>) in terms of the threshold parameter. We show that the MMD-CUSUM test enjoys the same level of performance as the i.i.d. case under fast <inline-formula><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula>-mixing processes. The MMD-CUSUM test also achieves strong performance under exponentially <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>/<inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula>-mixing processes, which are significantly more relaxed than existing results. The MMD-CUSUM test statistic adapts to different settings without modifications, rendering it a completely data-driven, dependence-agnostic change point detection scheme. Numerical simulations are provided at the end to evaluate our findings.

Fang Luo, Yin Zhang, Lunhui Xu et al.

The ongoing COVID-19 pandemic remains a significant threat, emphasizing the critical importance of mask-wearing to reduce infection risks. However, existing methods for mask detection encounter challenges such as identifying small targets and achieving high accuracy. In this paper, we present an enhanced YOLOv7 model tailored for mask-wearing detection. we employing a Generative Adversarial Network (GAN) to augment the original dataset, introducing the Convolutional Block Attention Module (CBAM) mechanism into the YOLOv7 model to enhance its small target detection capabilities, and replacing the model’s activation function with Parametric Rectified Linear Unit (FReLU) to improve overall performance. Experimental validation on a dataset showcases an average precision of 97.8% and a real-time inference speed of 64 frames per second (fps), meeting the real-time mask-wearing detection requirements effectively.

Francesco Parino, Lorenzo Zino, Alessandro Rizzo

Behavioral factors play a crucial role in the emergence, spread, and containment of human diseases, significantly influencing the effectiveness of intervention measures. However, the integration of such factors into epidemic models is still limited, hindering the possibility of understanding how to optimally design interventions to mitigate epidemic outbreaks in real life. This paper aims to fill in this gap. In particular, we propose a parsimonious model that couples an epidemic compartmental model with a population game that captures the behavioral response, obtaining a nonlinear system of ordinary differential equations. Grounded on prevalence-elastic behavior&#x2014;the empirically proven assumption that the disease prevalence affects the adherence to self-protective behavior&#x2014;we consider a nontrivial negative feedback between contagions and adoption of self-protective behavior. We characterize the asymptotic behavior of the system, establishing conditions under which the disease is quickly eradicated or a global convergence to an endemic equilibrium is attained. In addition, we elucidate how the behavioral response affects the endemic equilibrium. Then, we formulate and solve an optimal control problem to plan cost-effective interventions for the model, accounting for their healthcare and social-economical implications. Numerical simulations on a case study calibrated on sexually transmitted diseases demonstrate and validate our findings.

Zhiyu Yang, Yunlong Wan, Li Du et al.

Advancements in autonomous vehicles and deep learning have notably improved vehicle trajectory prediction accuracy. However, extracting interaction features in complex driving scenarios, such as vehicle-to-vehicle interactions and lane constraints, presents challenges. Deep learning-based methods struggle to achieve optimal predictive performance under limited computational resources. This study introduces a global attention mechanism to enhance feature extraction from driving scene encodings, focusing the decoder on interactive behaviours and boosting long-term prediction performance. An adaptive scheduled sampling model is employed, using actual driving scenarios probabilistically for training, addressing slow learning of actual driving behaviours and lack of initial feature correction. This method increases attention to actual interactions, reducing reliance on natural scenes and improving model generalizability. On the NGSIM dataset, sampling attention encoder-decoder (SAED) achieves a 1–5 s average displacement error (ADE) of 1.34 m, with 4 s and 5 s final displacement errors (FDEs) of 1.64 and 2.06 m, respectively. Compared to methods based on long short-term memory (LSTM), SAED reduces the model's storage space by 24.68% under the same network layer count. That demonstrates its effectiveness in extracting interactive behaviours in complex scenarios and enhances the accuracy of long-term predictions.

G. Geetha, P. Geethanjali

In recent times, there has been a notable growth in research investigations into the fault diagnosis of electrical machines. The effective detection of permanent magnet synchronous motor bearing faults is a significant challenge; however, it is crucial for ensuring safety and cost-effectiveness in industries. The data referring to faults needs to be studied under distinct operating conditions, with effective features. Consequently, a meticulous choice of features is required before fault identification. The study aims to find the fewest and most reliable features in the dataset from Paderborn University so that a simple and accurate way can be found to diagnose faults using current signals. The selection of optimal features is initially performed using three algorithms: the equilibrium optimizer, the emperor penguin optimizer (EPO), and the butterfly optimization method. The k nearest neighbour (kNN) and random forest classifiers are used for classification. The results are performed based on the metrics of sensitivity, specificity, accuracy, precision, F1-score, and Matthew's Correlation Coefficient. The results indicate that machine learning models employing different feature selection techniques exhibit superior performance across different feature dimensions. Specifically, the model utilizing the kNN classifier and features selected through the EPO method achieved the maximum accuracy of 100%. The model's efficacy was also compared to the similar work presented in the literature. The efficacy of the optimal features is experimentally confirmed by analyzing current data from squirrel cage induction motors and has shown a high accuracy of 95.2%.

Bing Dai, Guizhi Meng

In this paper, to achieve attitude tracking and disturbance rejection of micro quodrotor in the presence of uncertain external disturbances, the output regulation technique is utilized. First of all, the model that established regarding micro quadrotor is transformed to a stabilization problem of the second-order augmented system by employing the internal model. Second, an adaptive internal model is designed via exosystem information. The nonlinear unknown functions of the system are approximated by fuzzy logic systems, and the fuzzy adaptive laws are designed to tackle the uncertainty parameters. Then the new adaptive fuzzy state controllers and adaptive control laws are proposed by combining the backstepping technique and the adaptive control method. Unlike the traditional controller design method, the proposed controllers and adaptive control laws are designed respectively for roll angle, pitch angle, yaw angle and the height of the four-channel helicopter. Moreover, the new algorithm can perform well in attitude tracking and disturbance rejection of the closed-loop systems. Finally simulation results have demonstrated the effectiveness of the proposed approach.

Jing Wang, Siyuan Wang, Shan Lu et al.

Abstract Aiming at the reconnaissance task of unmanned vehicle formation under the malicious attack, a security state estimation method based on attack signal reconstruction is proposed. First the reconstruction of attack signal is transformed into a sparse error correction problem by stacking the measurement information of adjacent vehicles, and is solved by orthogonal matching pursuit (OMP) algorithm. Then the attack compensation based particle filter is designed to estimate the target state for each vehicle. An information fusion strategy is designed to obtain the final reconnaissance result based on agent centrality and the number of attacks on unmanned vehicles. Finally, simulations are provided to illustrate the effectiveness of the proposed method.

Satoshi Tsuruhara, Kazuhisa Ito

Adaptive FRIT (A-FRIT) with exponential forgetting (EF) has been proposed for time-varying systems to improve the data dependence of FRIT, which is a direct data-driven tuning method. However, the EF-based method is not a reliable controller because it can cause significant degradation of the control performance and instability unless the persistent excitation (PE) condition is satisfied. To solve this problem, we propose a new A-FRIT method based on directional forgetting (DF) and exponential resetting that can forget old data without instability regardless of the PE condition. To confirm the effectiveness of the proposed method, we applied it to artificial muscle control with strong asymmetric hysteresis characteristics and evaluated its robust performance against load changes during the experiment. The experimental results show that the proposed method based on DF achieves high control performance and is robust against changes in the characteristics and/or target trajectory. The proposed method is also practical because it does not require system identification, model structure, or prior experimentation.

Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger et al.

We present a model predictive control (MPC) formulation to directly optimize economic criteria for linear constrained systems subject to disturbances and uncertain model parameters. The proposed formulation combines a certainty equivalent economic MPC with a simple least-squares parameter adaptation. For the resulting adaptive economic MPC scheme, we derive strong asymptotic and transient performance guarantees. We provide a numerical example involving building temperature control and demonstrate performance benefits of online parameter adaptation.

Siyuan Liu, Xiang Yin, Dimos V. Dimarogonas et al.

This paper investigates an important class of information-flow security property called opacity for stochastic control systems. Opacity captures whether a system's secret behavior (a subset of the system's behavior that is considered to be critical) can be kept from outside observers. Existing works on opacity for control systems only provide a binary characterization of the system's security level by determining whether the system is opaque or not. In this work, we introduce a quantifiable measure of opacity that considers the likelihood of satisfying opacity for stochastic control systems modeled as general Markov decision processes (gMDPs). We also propose verification methods tailored to the new notions of opacity for finite gMDPs by using value iteration techniques. Then, a new notion called approximate opacity-preserving stochastic simulation relation is proposed, which captures the distance between two systems' behaviors in terms of preserving opacity. Based on this new system relation, we show that one can verify opacity for stochastic control systems using their abstractions (modeled as finite gMDPs). We also discuss how to construct such abstractions for a class of gMDPs under certain stability conditions.

Rasheed Kibria, Farimah Farahmandi, M. Tehranipoor

Numerous security vulnerability assessment techniques urge precise and fast finite state machines (FSMs) extraction from the design under evaluation. Sequential logic locking, watermark insertion, fault-injection assessment of a system-on-a-chip (SoC) control flow, information leakage assessment, and reverse engineering at gate-level abstraction, to name a few, require precise FSM extraction from the synthesized netlist of the design. Unfortunately, no reliable solutions are currently available for fast and accurate extraction of FSMs from the highly unstructured gate-level netlist for effective security evaluation. The major challenge in developing such a solution is the precise recognition of FSM state flip-flops (FFs) in a netlist having a massive collection of FFs. In this article, we propose finite state machine extractor ultra (FSMx-Ultra), a framework for extracting FSMs from extremely unstructured gate-level netlists. FSMx-Ultra utilizes state-of-the-art graph theory concepts and algorithms to distinguish FSM state registers from other registers and then constructs gate-level state transition graphs (STGs) for each identified FSM state register using automatic test pattern generation (ATPG) techniques. The results of our experiments on 14 open-source benchmark designs illustrate that FSMx-Ultra can recover all FSMs quickly and precisely from synthesized gate-level netlists of diverse complexity and size utilizing various state encoding schemes.

Sujun Zhang, Xingsheng Gu

A discrete whale optimization algorithm (DWOA) is presented to solve the no-wait flow shop scheduling problem (NWFSSP) with the optimization objective makespan. An effective combination of nearest neighbor (NN) and standard deviation heuristics (SDH) is used to acquire initial solutions of the population. After that, three crossover operators, the two-point crossover (TPX), multiple-point crossover (MPX) and job-based crossover (JBX) operators, are designed to mimics the humpback whales hunting process. Moreover, the dynamic transform mechanism of search process is designed to better balance the exploration and exploitation ability of DWOA. In order to further improve the optimization effect of DWOA, the parallel neighborhood search (PNS) and the serial neighborhood search (SNS) are selected to execute the local search and promoting global search scheme, respectively. Finally, to test the performance of DWOA, extensive experiments are conducted on benchmarks designed by Reeves and Taillard, which contain parameters tuning, effectiveness of the improvement strategies in the DWOA and comparison with several existing algorithms. From the experimental results, it is validated that the effectiveness of the improved mechanisms and the performance of the DWOA which can find better makespan values compared with other algorithms for solving NWFSSP.

P. Dinesh Kumar, K. Valarmathi

Nodes are deployed randomly in the network area of the WSN. data transmission from source to destination via intermediate nodes should be done in a secure fashion. Due to the large size of packet loss and energy consumption of sensor nodes, a secure and energy-efficient path must be required. The main objective of this research is to provide secure data transmission among node-to-node for efficient delivery of data packets to the destination. The system uses a novel hybrid firefly and BAT algorithm for path selection, an innovative trust value generation, and optimal neighborhood selection using fuzzy logic. The research employs Elliptic Curve Cryptography (ECC) combined with Diffie-Hellman exchange for key generation and key exchange. Path selection is done by fuzzy logic and optimization of selection has been carried out by hybrid BAT and Firefly algorithms. Key generation includes a time-based randomness factor that increases the complexity of cryptanalysis, thereby providing the most security. The performance of the simulation is analyzed and depicted in terms of delay, throughput, energy, and processing time. The research has been carried out using a network simulator with nodes deployed randomly in the network area with mobility as the primary concern that requires dynamic path selection.

Seth Siriya, Jingge Zhu, Dragan Nešić et al.

We consider the problem of adaptive stabilization for discrete-time, multi-dimensional linear systems with bounded control input constraints and unbounded stochastic disturbances, where the parameters of the true system are unknown. To address this challenge, we propose a certainty-equivalent control scheme which combines online parameter estimation with saturated linear control. We establish the existence of a high probability stability bound on the closed-loop system, under additional assumptions on the system and noise processes. Finally, numerical examples are presented to illustrate our results.

Kaito Ito, Kenji Kashima

This letter investigates dynamical optimal transport of underactuated linear systems over an infinite time horizon. In our previous work, we proposed to integrate model predictive control and the celebrated Sinkhorn algorithm to perform efficient dynamical transport of agents. However, the proposed method requires the invertibility of input matrices, which severely limits its applicability. To resolve this issue, we extend the method to (possibly underactuated) controllable linear systems. In addition, we ensure the convergence properties of the method for general controllable linear systems. The effectiveness of the proposed method is demonstrated by a numerical example.

Serdar Ekinci, Davut Izci, M. Kayri

Zuowen Liao, W. Gong, Xuesong Yan et al.

Nonlinear equations system (NES) arises commonly in science and engineering. Repulsion techniques are considered to be the effective methods to locate different roots of NES. In general, the repulsive radius needs to be given by the user before the run. However, its optimal parameter setting is difficult and problem-dependent. To alleviate this drawback, in this paper, we first propose a dynamic repulsion technique, and then a general framework based on the dynamic repulsion technique and evolutionary algorithms (EAs) is presented to effectively solve NES. The major advantages of our framework are: 1) the repulsive radius is controlled dynamically during the evolutionary process; 2) multiple roots of NES can be simultaneously located in a single run; 3) the diversity of the population is preserved due to the population reinitialization; and 4) different repulsion techniques and different EAs can be readily integrated into this framework. To extensively evaluate the performance of our framework, we choose 42 problems with diverse features as the test suite. In addition, some representative differential evolution and particle swarm optimization variants are incorporated into the framework. Our method is also compared with other state-of-the-art methods. Experimental results indicate that the dynamic repulsion technique can improve the performance of the original repulsion technique with static repulsive radius. Moreover, the proposed method is able to yield better results compared with other methods.