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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (24 January 2024) | Viewed by 11082

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Special Issue Editors

Dr. Jin Tao

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Guest Editor

1. College of Artificial Intelligence, Nankai University, Tianjin 300350, China
2. Silo AI, 00100 Helsinki, Finland
Interests: complex system dynamics and control; dynamics modeling and intelligent control of parafoil UAV; acoustic wave manipulation; reinforcement learning; deep learning and swarm intelligent control

Dr. Hao Sun

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Guest Editor

College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Interests: active disturbance rejection control; deep reinforcement learning
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Prof. Dr. Mingwei Sun

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Guest Editor

College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Interests: flight guidance and control; model predictive control; active disturbance rejection control; nonlinear optimization
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qinglin Sun

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Guest Editor

College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Interests: adaptive control; embedded control systems; aircraft modeling and intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce a new Special Issue of the journal Sustainability, entitled “Complex System Dynamics and Intelligent Control for Sustainable Engineering”.

Today, in every walk of life, we encounter various complex systems, whether it is the transportation system, autonomous vehicles, communication networks, power grids, or the financial markets. Complex systems have distinct properties, such as highly nonlinear dynamics, emergence, adaptation, and self-organization, which threaten the model's accuracy. These properties make it difficult to understand the behaviors of complex systems, model them accurately, control them precisely, and make them work sustainably. This necessitates adopting novel and environmentally friendly technologies to design or operate systems that use energy and resources sustainably. Artificial intelligence techniques, such as neural networks, evolutionary computation, and machine learning, have proven to have the potential to offer practical solutions for promoting sustainable engineering.

This Special Issue of Sustainability is intended to serve as a platform for sharing research findings and insights in "Complex System Dynamics and Intelligent Control for Sustainable Engineering".

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Dynamics analysis and modeling of complex systems;
Composite learning control of complex dynamical systems;
AI-based control method of complex systems;
Intelligent control of multi-agent control systems;
Intelligent sustainable production system modeling, simulation, and optimization;
Stability and robustness analysis of intelligent control systems;
Modeling and intelligent control for complex systems of renewable energy systems, smart grids, wastewater treatment systems and autonomous systems.

Dr. Jin Tao
Dr. Hao Sun
Prof. Dr. Mingwei Sun
Prof. Dr. Qinglin Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

complex system
system dynamics
modeling
intelligent control
neural networks
machine learning
sustainable engineering

Published Papers (7 papers)

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Research

22 pages, 6520 KiB

Open AccessArticle

Research on Design and Control Strategy of Novel Independent Metering System

by Jing Yang, Jiadong Li, Yuhang Zhong, Yingjie Gao, Rui Guo and Jingyi Zhao

Sustainability 2023, 15(18), 13359; https://doi.org/10.3390/su151813359 - 6 Sep 2023

Viewed by 708

Abstract

The independent metering system used in the combination of traditional cartridge proportional valves employs an excessive number of components, which increases the complexity of the control strategy. To address this problem, a novel independent metering system based on pilot hydraulic control was developed. Following the pressure and flow requirements, the structure and valve body size of the two spools were designed. The effect of the parameter change in the control valve on the dynamic response characteristics of the main spool was investigated by simulation. A control strategy was developed based on load force direction prediction and two-chamber pressure switching to verify the feasibility of working mode switching during load direction change. As indicated by the results, compared with the mode switching control strategy of the traditional independent metering system, the proposed control strategy could effectively reduce the number of mode switching and ensure the continuity of the actuator operation. Compared with the traditional load-sensitive valve control system, the proposed pilot-controlled independent metering system achieved an average energy-saving efficiency of 47.27%. This study provides technical reference for the low energy consumption, high efficiency, and sustainable development of hydraulic systems. Full article

(This article belongs to the Special Issue Complex System Dynamics and Intelligent Control for Sustainable Engineering)

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23 pages, 7842 KiB

Open AccessArticle

Auxiliary Steering Control of Vehicle Driving with Force/Haptic Guidance

by Xiaobo Shi, Dingxuan Zhao, Yuhang Zhong, Jinming Chang, Tao Ni and Xiangxian Chen

Sustainability 2023, 15(16), 12366; https://doi.org/10.3390/su151612366 - 14 Aug 2023

Viewed by 1067

Abstract

The rapid development of the automobile industry has resulted in the development of many vehicles, increased traffic, and frequent accidents. The complexity of road conditions is a major contributor to the occurrence of traffic accidents. Drivers are distracted and hence unable to fully observe all road information and make optimal and timely driving decisions. This study proposes an auxiliary steering control system with force/tactile guidance (ASCFT) and its corresponding control strategy to address this problem. We combined vehicle autonomous path planning based on road condition information and the human–machine sharing control strategy, which integrated the manipulative force of the driver and a virtual guidance force on the steering wheel. Consequently, the ASCFT eliminated the mechanical connection between the steering wheel and the steering wheels in favor of a force/tactile-assisted steering structure, providing the driver with a sense of steering force based on road information. Additionally, we proposed a smooth vehicle trajectory optimization method based on the improved RRT algorithm and a path-following controller based on the forecast information to achieve auxiliary safety driving. The ASCFT’s performance was confirmed through constructing a fixed-base simulator experimental platform with the ASCFT. The results revealed that at the vehicle speed of 60 km/h and a handwheel rotation of 60°, the steering wheel was instantly released and turned back in about 3.5 s. Furthermore, predictive haptic feedback warned the driver of an upcoming obstacle. Full article

(This article belongs to the Special Issue Complex System Dynamics and Intelligent Control for Sustainable Engineering)

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21 pages, 4717 KiB

Open AccessArticle

Research on Excavator Trajectory Control Based on Hybrid Interpolation

by Jing Yang, Yingjie Gao, Rui Guo, Qingshan Gao and Jingyi Zhao

Sustainability 2023, 15(8), 6761; https://doi.org/10.3390/su15086761 - 17 Apr 2023

Cited by 1 | Viewed by 1692

Abstract

In this study, to address the issues of tooth tip operation discontinuity and jitter during autonomous excavator operation, a multi-segment mixed interpolation method utilizing different higher-order polynomials has been proposed. This approach is designed to optimize the tooth tip trajectory of the excavator under multiple constraints, resulting in a smoother trajectory. Specifically, the single-bucket excavator was chosen as the research object, and three different high-order mixed polynomials were utilized to interpolate the trajectory of the digging discrete points. Through a comparative analysis under multiple constraints, this study explored and analyzed the joint angle, angular velocity, and angular acceleration curves of each excavator’s joint. An experimental platform was established to investigate the hydraulic system of an excavator, and the optimal trajectory was controlled using a high-order mixed polynomial interpolation. The results of this study demonstrate that the tracking accuracy of the excavator’s actuator under the optimal interpolation strategy is high, with a maximum displacement deviation of ±3 mm. Additionally, during operation, the excavator manipulator runs smoothly and continuously with minimal flexible impact and vibration. Full article

(This article belongs to the Special Issue Complex System Dynamics and Intelligent Control for Sustainable Engineering)

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17 pages, 6078 KiB

Open AccessArticle

Effect of Shear Modulus on the Inflation Deformation of Parachutes Based on Fluid-Structure Interaction Simulation

by Hong Zhu, Jin Tao, Qinglin Sun, Hao Sun, Feng Duan, Zengqiang Chen, Xianyi Zeng and Damien Soulat

Sustainability 2023, 15(6), 5396; https://doi.org/10.3390/su15065396 - 17 Mar 2023

Cited by 1 | Viewed by 1370

Abstract

Parachutes and other inflatable aerodynamic decelerators usually use flexible fabrics due to their lightweight and high load-carrying capacity. The behavior of fabrics during complex deformations is mainly influenced by their shear properties. The shear properties of fabric can be explained by the shear stiffness or shear modulus. The design optimization of these inflatable structures relies on a detailed knowledge of the mechanical properties of the fabric material. To investigate the effect of shear modulus on the inflatable shapes of parachute canopies, an arbitrary Lagrangian–Eulerian coupling method based on the incompressible computational fluid dynamics solver and structural solver LS-DYNA is proposed. Finite element methods are used to describe continuous materials such as fabrics and airflow fields. The effects of the shear modulus on the inflated parachute shapes are investigated from the macroscopic and microscopic scales. A comparison analysis reveals that different shear moduli have little effect on the overall shape and in-plane shear strain of the parachute, while they have significant effects on the in-plane stress distribution and wrinkles of the parachute. The methods and conclusions of this paper can provide some reference for the materials design of parachutes in preforming stage. Full article

(This article belongs to the Special Issue Complex System Dynamics and Intelligent Control for Sustainable Engineering)

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18 pages, 3903 KiB

Open AccessArticle

Deep-Reinforcement-Learning-Based Active Disturbance Rejection Control for Lateral Path Following of Parafoil System

by Yuemin Zheng, Jin Tao, Qinglin Sun, Hao Sun, Zengqiang Chen, Mingwei Sun and Feng Duan

Sustainability 2023, 15(1), 435; https://doi.org/10.3390/su15010435 - 27 Dec 2022

Cited by 4 | Viewed by 1769

Abstract

The path-following control of the parafoil system is essential for executing missions, such as accurate homing and delivery. In this paper, the lateral path-following control of the parafoil system is studied. First, considering the relative motion between the parafoil canopy and the payload, an eight-degree-of-freedom (DOF) model of the parafoil system is constructed. Then, a guidance law containing the position deviation and heading angle deviation is proposed. Moreover, a linear active disturbance rejection controller (LADRC) is designed based on the guidance law to allow the parafoil system to track the desired path under internal unmodeled dynamics or external environmental disturbances. For the adaptive tuning of the controller parameters, a deep Q-network (DQN) is applied to the LADRC-based path-following control system, and the controller parameters can be adjusted in real time according to the system’s states. Finally, the effectiveness of the proposed method is applied to a parafoil system following circular and straight paths in an environment with wind disturbances. The simulation results show that the proposed method is an effective means to realize the lateral path-following control of the parafoil system, and it can also promote the development of intelligent controllers. Full article

(This article belongs to the Special Issue Complex System Dynamics and Intelligent Control for Sustainable Engineering)

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18 pages, 4300 KiB

Open AccessArticle

Deep Reinforcement Learning Car-Following Model Considering Longitudinal and Lateral Control

by Pinpin Qin, Hongyun Tan, Hao Li and Xuguang Wen

Sustainability 2022, 14(24), 16705; https://doi.org/10.3390/su142416705 - 13 Dec 2022

Cited by 4 | Viewed by 1756

Abstract

The lateral control of the vehicle is significant for reducing the rollover risk of high-speed cars and improving the stability of the following vehicle. However, the existing car-following (CF) models rarely consider lateral control. Therefore, a CF model with combined longitudinal and lateral control is constructed based on the three degrees of freedom vehicle dynamics model and reinforcement learning method. First, 100 CF segments were selected from the OpenACC database, including 50 straight and 50 curved road trajectories. Afterward, the deep deterministic policy gradient (DDPG) car-following model and multi-agent deep deterministic policy gradient (MADDPG) car-following model were constructed based on the deterministic policy gradient theory. Finally, the models are trained with the extracted trajectory data and verified by comparison with the observed data. The results indicate that the vehicle under the control of the MADDPG model and the vehicle under the control of the DDPG model are both safer and more comfortable than the human-driven vehicle (HDV) on straight roads and curved roads. Under the premise of safety, the vehicle under the control of the MADDPG model has the highest road traffic flow efficiency. The maximum lateral offset of the vehicle under the control of the MADDPG model and the vehicle under the control of the DDPG model in straight road conditions is respectively reduced by 80.86% and 71.92%, compared with the HDV, and the maximum lateral offset in the curved road conditions is lessened by 83.67% and 78.95%. The proposed car following model can provide a reference for developing an adaptive cruise control system considering lateral stability. Full article

(This article belongs to the Special Issue Complex System Dynamics and Intelligent Control for Sustainable Engineering)

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13 pages, 3141 KiB

Open AccessArticle

Modeling of Acoustic Vibration Theory Based on a Micro Thin Plate System and Its Control Experiment Verification

by Xiaodong Jiao, Jin Tao, Hao Sun and Qinglin Sun

Sustainability 2022, 14(22), 14900; https://doi.org/10.3390/su142214900 - 11 Nov 2022

Cited by 3 | Viewed by 1285

Abstract

As a novel control method, acoustic manipulation technology shows extraordinary talents in culturing of tissue and cell, microchip processing and research on material chemistry, which is closely relevant to the vibration modes and the driving signals of the acoustic system. In this paper, bringing up reasonable assumptions, from the perspective of vibration force analysis of a thin plate, the response function of the forced vibration thin plate is derived combining with the Green’s function. Simultaneously, the effective vibration frequencies of micro thin plate are determined. Using the finite element simulation software Comsol 5.6 building thin plate geometry in 2D, the vibration modes of a thin plate are numerically analyzed from the top view and the side view. Additionally, an experimental platform is established, and the vibration experiments of a square micro thin plate (5 cm × 5 cm × 0.625 mm) driven by a central acoustic source is conducted. By comparison, the corresponding experimental results are in good agreement with simulations. Furthermore, single particle motion control is also realized based on the presented platform, and the underlying mechanism is the effects of nodes and anti-nodes on particle motion. The vibrating platform here will become an effective manipulation tool for many scientific fields with the advantage of micro size, good compatibility, and multipurpose. Full article

(This article belongs to the Special Issue Complex System Dynamics and Intelligent Control for Sustainable Engineering)

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