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2 pages, 166 KiB

Open AccessEditorial

Special Issue on Advanced Technology of Intelligent Control and Simulation Evaluation

by Yangming Guo, Jiang Long, Qingdong Li and Zun Liu

Appl. Sci. 2023, 13(19), 10793; https://doi.org/10.3390/app131910793 - 28 Sep 2023

Viewed by 534

Abstract

Control and simulation evaluation have experienced a rapid development during the last few decades [...] Full article

(This article belongs to the Special Issue Advanced Technology of Intelligent Control and Simulation Evaluation)

Research

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37 pages, 12595 KiB

Open AccessArticle

A Systematic Parameter Analysis of Cloud Simulation Tools in Cloud Computing Environments

by Muhammad Asim Shahid, Muhammad Mansoor Alam and Mazliham Mohd Su’ud

Appl. Sci. 2023, 13(15), 8785; https://doi.org/10.3390/app13158785 - 29 Jul 2023

Cited by 3 | Viewed by 2812

Abstract

To provide various applications in various domains, a large-scale cloud data center is required. Cloud computing enables access to nearly infinite computing resources on demand. As cloud computing grows in popularity, researchers in this field must conduct real-world experiments. Configuring and running these [...] Read more.

To provide various applications in various domains, a large-scale cloud data center is required. Cloud computing enables access to nearly infinite computing resources on demand. As cloud computing grows in popularity, researchers in this field must conduct real-world experiments. Configuring and running these tests in an actual cloud environment is costly. Modeling and simulation methods, on the other hand, are acceptable solutions for emulating environments in cloud computing. This research paper reviewed several simulation tools specifically for cloud computing in the literature and presented the most effective simulation methods in this research domain, as well as an analysis of a variety of cloud simulation tools. Cloud computing tools such as CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports were evaluated. Furthermore, a parametric evaluation of cloud simulation tools is presented based on the identified parameters. Several 5-parameter tests were performed to demonstrate the capabilities of the cloud simulator. These results show the value of our proposed simulation system. CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports are used to evaluate host processing elements, virtual machine processing elements, cloudlet processing elements, userbase average, minimum, and maximum, and cloudlet ID Start Time, Finish Time, Average Start, and Average Finish for each simulator. The outcomes compare these five simulator metrics. After reading this paper, the reader will be able to compare popular simulators in terms of supported models, architecture, and high-level features. We performed a comparative analysis of several cloud simulators based on various parameters. The goal is to provide insights for each analysis given their features, functionalities, and guidelines on the way to researchers’ preferred tools. Full article

(This article belongs to the Special Issue Advanced Technology of Intelligent Control and Simulation Evaluation)

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21 pages, 7201 KiB

Open AccessArticle

PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization

by Huaiqin Liu, Qinghe Yu and Qu Wu

Appl. Sci. 2023, 13(8), 4767; https://doi.org/10.3390/app13084767 - 10 Apr 2023

Cited by 3 | Viewed by 2079

Abstract

In processes of industrial production, the online adaptive tuning method of proportional-integral-differential (PID) parameters using a neural network is found to be more appropriate than a conventional controller with PID for controlling different industrial processes with varying characteristics. However, real-time implementation and high [...] Read more.

In processes of industrial production, the online adaptive tuning method of proportional-integral-differential (PID) parameters using a neural network is found to be more appropriate than a conventional controller with PID for controlling different industrial processes with varying characteristics. However, real-time implementation and high reliability require the adjustment of specific model parameters. Therefore, this paper proposes a PID controller that combines a back-propagation neural network (BPNN) and adversarial learning-based grey wolf optimization (ALGWO). To enhance the unpredictable behavior and capacity for exploration of the grey wolf, this study develops a new parameter-learning technique. Alpha gray wolves use the random walk of levy flight as their hunting method. In beta and delta gray wolves, a search strategy centering on the top gray wolf is employed, and in omega gray wolves, the decision wolves handle the confrontation strategy. A fair balance between exploration and exploitation can be achieved, as evidenced by the success of the adversarial learning-based grey wolf optimization technique in ten widely used benchmark functions. The effectiveness of different activation functions in conjunction with ALGWO were evaluated in resolving the parameter adjustment issue of the BPNN model. The results demonstrate that no unique activation function outperforms others in different controlled systems, but their fitnesses are significantly inferior to those of the conventional PID controller. Full article

(This article belongs to the Special Issue Advanced Technology of Intelligent Control and Simulation Evaluation)

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19 pages, 2468 KiB

Open AccessArticle

DAG Hierarchical Schedulability Analysis for Avionics Hypervisor in Multicore Processors

by Huan Yang, Shuai Zhao, Xiangnan Shi, Shuang Zhang and Yangming Guo

Appl. Sci. 2023, 13(5), 2779; https://doi.org/10.3390/app13052779 - 21 Feb 2023

Cited by 2 | Viewed by 1059

Abstract

Parallel hierarchical scheduling of multicore processors in avionics hypervisor is being studied. Parallel hierarchical scheduling utilizes modular reasoning about the temporal behavior of the upper Virtual Machine (VM) by partitioning CPU time. Directed Acyclic Graphs (DAGs) are used for modeling functional dependencies. However, [...] Read more.

Parallel hierarchical scheduling of multicore processors in avionics hypervisor is being studied. Parallel hierarchical scheduling utilizes modular reasoning about the temporal behavior of the upper Virtual Machine (VM) by partitioning CPU time. Directed Acyclic Graphs (DAGs) are used for modeling functional dependencies. However, the existing DAG scheduling algorithm wastes resources and is inaccurate. Decreasing the completion time (CT) of DAG and offering a tight and secure boundary makes use of joint-level parallelism and inter-joint dependency, which are two key factors of DAG topology. Firstly, Concurrent Parent and Child Model (CPCM) is researched, which accurately captures the above two factors and can be applied recursively when parsing DAG. Based on CPCM, the paper puts forward a hierarchical scheduling algorithm, which focuses on decreasing the maximum CT of joints. Secondly, the new Response Time Analysis (RTA) algorithm is proposed, which offers a general limit for other execution sequences of Noncritical joints (NC-joints) and a specific limit for a fixed execution sequence. Finally, research results show that the parallel hierarchical scheduling algorithm has higher performance than other algorithms. Full article

(This article belongs to the Special Issue Advanced Technology of Intelligent Control and Simulation Evaluation)

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24 pages, 5843 KiB

Open AccessArticle

Performance Evaluation of Load-Balancing Algorithms with Different Service Broker Policies for Cloud Computing

by Muhammad Asim Shahid, Muhammad Mansoor Alam and Mazliham Mohd Su’ud

Appl. Sci. 2023, 13(3), 1586; https://doi.org/10.3390/app13031586 - 26 Jan 2023

Cited by 14 | Viewed by 3530

Abstract

Cloud computing has seen a major boom during the past few years. Many people have switched to cloud computing because traditional systems require complex resource distribution and cloud solutions are less expensive. Load balancing (LB) is one of the essential challenges in cloud [...] Read more.

Cloud computing has seen a major boom during the past few years. Many people have switched to cloud computing because traditional systems require complex resource distribution and cloud solutions are less expensive. Load balancing (LB) is one of the essential challenges in cloud computing used to balance the workload of cloud services. This research paper presents a performance evaluation of the existing load-balancing algorithms which are particle swarm optimization (PSO), round robin (RR), equally spread current execution (ESCE), and throttled load balancing. This study offers a detailed performance evaluation of various load-balancing algorithms by employing a cloud analyst platform. Efficiency concerning various service broker policy configurations for load-balancing algorithms’ virtual machine load balance was also calculated using metrics such as optimized response time (ORT), data center processing time (DCPT), virtual machine costs, data transfer costs, and total cost for different workloads and user bases. Many of the past papers that were mentioned in the literature worked on round robin and equally spread current execution, and throttled load-balancing algorithms were based on efficiency and response time in virtual machines without recognizing the relation between the task and the virtual machines, and the practical significance of the application. A comparison of specific load-balancing algorithms has been investigated. Different service broker policy (SBP) tests have been conducted to illustrate the load-balancing algorithm capabilities. Full article

(This article belongs to the Special Issue Advanced Technology of Intelligent Control and Simulation Evaluation)

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13 pages, 3694 KiB

Open AccessArticle

Virtualization Airborne Trusted General Computing Technology

by Shuang Zhang, Yuanxun Wang, Xinyu Wan, Zhihui Li and Yangming Guo

Appl. Sci. 2023, 13(3), 1342; https://doi.org/10.3390/app13031342 - 19 Jan 2023

Cited by 1 | Viewed by 1307

Abstract

Aircraft information service systems, such as airborne information systems, airborne integrated maintenance management systems, and cabin management systems, have greatly improved the ease of use and maintenance of civil aircraft. The current computing platforms used for accommodating these systems are unable to satisfy [...] Read more.

Aircraft information service systems, such as airborne information systems, airborne integrated maintenance management systems, and cabin management systems, have greatly improved the ease of use and maintenance of civil aircraft. The current computing platforms used for accommodating these systems are unable to satisfy the multifaceted requirements of future information-based aircraft, such as energy conservation, emission reduction, high-performance computing, and information security protection, due to their high computing capacity, weight, and power consumption. Based on multi-core multi-threaded processors, a security hardware unit with microkernel virtualization technology and a virtualization airborne trusted general computing service architecture is proposed, and key technologies, including a high-performance processing and high-security hardware unit, virtualization management software unit, and virtualization security protection architecture were designed. By building a verification environment, the proposed platform was verified in terms of its application accommodation function, platform performance, and network security protection, for comparison with the existing platforms. The results showed that our method can fulfill the requirements of these technical indicators and is applicable, not only to new-generation civil aircraft, but also to unmanned aerial vehicles (UAVs) and emergency rescue aircraft with high-performance safety-critical computing needs. Full article

(This article belongs to the Special Issue Advanced Technology of Intelligent Control and Simulation Evaluation)

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16 pages, 4060 KiB

Open AccessArticle

UAV Cluster Behavior Modeling Based on Spatial-Temporal Hybrid Petri Net

by Xiaodong Wang, Yangming Guo, Nan Lu and Pei He

Appl. Sci. 2023, 13(2), 762; https://doi.org/10.3390/app13020762 - 5 Jan 2023

Cited by 1 | Viewed by 1236

Abstract

Currently, we are facing an increasing trend in the use of unmanned aerial vehicles (UAV) in various activities, both civilian and military. The application of UAVs in the battlefield has received extensive attention amid a global new military revolution. A UAV cluster is [...] Read more.

Currently, we are facing an increasing trend in the use of unmanned aerial vehicles (UAV) in various activities, both civilian and military. The application of UAVs in the battlefield has received extensive attention amid a global new military revolution. A UAV cluster is a large and complex real-time feedback system that integrates a communication and sensor network, control system, calculation, and physical process. The heterogeneous UAVs conduct complex behaviors, which requires a comprehensive description and analysis of UAV cluster modeling. The integrated modeling of the heterogeneous UAV cluster is of great significance and value to test and verify the new combat mode. In this paper, we present a novel representation framework based on the Petri nets. We used a spatial-temporal hybrid Petri net to illustrate the discrete state and continuous process of a heterogeneous UAV cluster system, and effectively achieved the fusion of a physical and computational process and interaction event modeling in the cluster system. Finally, the formal verification of UAV cluster attack mission modeling was carried out by UPPAAL, suggesting the proposed modeling method is feasible and effective. Full article

(This article belongs to the Special Issue Advanced Technology of Intelligent Control and Simulation Evaluation)

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16 pages, 13827 KiB

Open AccessArticle

Error Dynamics Based Dual Heuristic Dynamic Programming for Self-Learning Flight Control

by Xu Huang, Yuan Zhang, Jiarun Liu, Honghao Zhong, Zhaolei Wang and Yue Peng

Appl. Sci. 2023, 13(1), 586; https://doi.org/10.3390/app13010586 - 31 Dec 2022

Cited by 4 | Viewed by 1296

Abstract

A data-driven nonlinear control approach, called error dynamics-based dual heuristic dynamic programming (ED-DHP), is proposed for air vehicle attitude control. To solve the optimal tracking control problem, the augmented system is defined by the derived error dynamics and reference trajectory so that the [...] Read more.

A data-driven nonlinear control approach, called error dynamics-based dual heuristic dynamic programming (ED-DHP), is proposed for air vehicle attitude control. To solve the optimal tracking control problem, the augmented system is defined by the derived error dynamics and reference trajectory so that the actor neural network can learn the feedforward and feedback control terms at the same time. During the online self-learning process, the actor neural network learns the control policy by minimizing the augmented system’s value function. The input dynamics identified by the recursive least square (RLS) and output of the critic neural network are used to update the actor neural network. In addition, the total uncertainty term of the error dynamics is also identified by RLS, which can compensate for the uncertainty caused by inaccurate modeling, parameter perturbation, and so on. The outputs of ED-DHP include the rough trim surface, feedforward and feedback terms from the actor neural network, and the compensation. Based on this control scheme, the complete knowledge of system dynamics and the reference trajectory dynamics are not needed, and offline learning is unnecessary. To verify the self-learning ability of ED-DHP, two numerical experiments are carried out based on the established morphing air vehicle model. One is sinusoidal signal tracking at a fixed operating point, and the other is guidance command tracking with a morphing process at variable operating points. The simulation results demonstrate the good performance of ED-DHP for online self-learning attitude control and validate the robustness of the proposed scheme Full article

(This article belongs to the Special Issue Advanced Technology of Intelligent Control and Simulation Evaluation)

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16 pages, 3717 KiB

Open AccessArticle

A Multi-Level Fuzzy Evaluation Method for the Reliability of Integrated Energy Systems

by Pei He, Yangming Guo, Xiaodong Wang, Shiqi Zhang and Zhihao Zhong

Appl. Sci. 2023, 13(1), 274; https://doi.org/10.3390/app13010274 - 26 Dec 2022

Cited by 5 | Viewed by 1900

Abstract

With the increase in environmental pressure and rapid development of renewable energy technologies, an integrated energy system has been recognized as an effective approach to accommodate large-scale renewables and achieve environmental sustainability. While an integrated energy system significantly improves energy efficiency, the interaction [...] Read more.

With the increase in environmental pressure and rapid development of renewable energy technologies, an integrated energy system has been recognized as an effective approach to accommodate large-scale renewables and achieve environmental sustainability. While an integrated energy system significantly improves energy efficiency, the interaction between different energy systems may also bring multiple operational risks to its reliability, which necessitates an effective reliability assessment technique. In this paper, we proposed a multi-level fuzzy evaluation model based on combined empowerment for the reliability evaluation of an integrated energy system. The analytic hierarchy process method and entropy weight method were used to calculate the weight of each index in the evaluation model. Fuzzy evaluation matrix was constructed by the membership degree of a single factor, which was defined by the fuzzy comprehensive evaluation method. The multi-level fuzzy evaluation results were obtained based on single-level evaluation results. Finally, case studies were carried out based on a practical integrated energy system; we proposed 5 first-level indicators such as reliability and economy and 12 second-level indicators such as mean time of incapacity. The simulation results (85.15) showed the effectiveness and advantages of the proposed model. Full article

(This article belongs to the Special Issue Advanced Technology of Intelligent Control and Simulation Evaluation)

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