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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.1 P.1-3


Complex system and intelligent control: theories and applications

Author(s):  Jie Chen, Ben M. Chen, Jian Sun

Affiliation(s):  School of Automation, Beijing Institute of Technology, Beijing 100081, China; more

Corresponding email(s):   chenjie@bit.edu.cn

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Jie Chen, Ben M. Chen, Jian Sun. Complex system and intelligent control: theories and applications[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(1): 1-3.

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Complex systems are the systems that consist of a great many diverse and autonomous but interacting and interdependent components whose aggregate behaviors are nonlinear. As phased by Aristotle, “the whole is more than the sum of its parts;” properties of complex systems are not a simple summation of their individual parts.
Complex systems are widespread. Typical examples of complex systems can be found in the human brain, flocking formation of migrating birds, power grid, transportation systems, autonomous ve-hicles, social networks, and communication net-works.
Complex systems have some distinct properties, such as highly nonlinear dynamics, emergence, ad-aptation, and self-organization, which are difficult to model precisely. Such properties lead to difficulties in understanding the behaviors of complex systems, to model them accurately, to control them, and to make them work in a specific way we desire.
There is no generally agreed definition of intel-ligent control. Generally speaking, intelligent control is a class of control methods that use artificial intel-ligence techniques, such as fuzzy logic, neural net-works, evolutionary computation, machine learning, and swarming intelligence. Intelligent control tries to borrow ideas from the sciences of physics, mathe-matics, biology, neuroscience, and others to develop a control system to manage a complex process in an uncertain environment. Intelligent control is a kind of interdisciplinary new technology, which integrates the knowledges of control theory, computer science, artificial intelligence, information theory, and other areas. Intelligent control has been shown to be effec-tive in controlling complex systems where traditional control methods can hardly perform well.
In this context, the Chinese Academy of Engi-neering (CAE) organized a special issue on complex system and intelligent control in Frontiers of Infor-mation Technology and Electronic Engineering. This special issue aims to promote research on complex systems and intelligent control and reflect the most recent advances, with emphasis on both theories and applications. After rigorous review process, 11 papers by researchers worldwide have been selected for this special issue, including one survey paper and 10 research papers.
Electric power grid as a typical example of complex systems has brought a great change in our daily lives. An accurate, fast, and robust power sys-tem state estimation is undoubtedly significant. Gang WANG et al. gave a comprehensive review on some of the recent advances in power system state estima-tion with an emphasis on the solvers that can effi-ciently obtain optimal or near-optimal solutions to nonconvex state estimation tasks. Some directions for future research were also discussed.
Motion coverage is an important real-world ap-plication for mobile robots. To deal with the multi- robot coverage motion planning problem, Guan-qiang GAO and Bin XIN proposed an auction-based spanning tree coverage (A-STC) algorithm. Compared with existing methods, this algorithm is effective, time-efficient, and complete, especially in large complex configuration spaces.
A snake robot is a biomorphic robot to imitate snake’s morphology. To make a snake robot as agile as a biological snake, locomotion generation and body motion control are two important issues. Wenjuan OUYANG et al. proposed a biomimetic control approach for steering a snake robot. The proposed method includes an artificial central pattern generator, a cerebellar model articulation controller, and a proportional-derivative controller. Their simulations and experiments demonstrated good performance and adaptability of the method.
Ning-shi YAO et al. presented an autonomous robotic blimp equipped with only one monocular camera, named Georgia Tech Miniature Autonomous Blimp (GT-MAB). It was designed to support human robot interaction experiments in an indoor space. The blimp is capable of effectively detecting the face of a human subject, following the human, and recognizing hand gestures by employing a deep neural network based algorithm. Their experimental results demon-strated that GT-MAB has reliable human-detection and human-following capabilities.
Execution control is a critical task of robot ar-chitecture design, and has important influence on the quality of the final system. Martin MOLINA et al. presented a general method for execution control. The proposed method was integrated in the Aerostack open-source framework. Their experiments demon-strated the validity, feasibility, and effectiveness of the proposed method.
In recent years, multi-agent systems have re-ceived much attention since their wide applications in mobile sensor networks, formation of unmanned flight vehicles, smart grids, etc. Formation control is an important topic in cooperative control for multi- agent systems. Mao-peng RAN et al. considered the time-varying formation tracking problem for multi- agent systems subject to unknown nonlinear dynamics and external disturbances. An extended state observer (ESO) was designed to estimate the total uncertainty of the system. An ESO-based time-varying formation tracking protocol was proposed. Effectiveness of the theoretical results was demonstrated by application to the target enclosing problem of a group of unmanned aerial vehicles (UAVs). Mao-bin LU and Lu LIU studied the leader-following consensus problem of a class of heterogeneous second-order nonlinear multi-agent systems subject to disturbances. A class of distributed control laws that depend on the relative state of the system were proposed to solve the prob-lem.
In recent years, UAVs have been widely applied in many fields, such as reconnaissance, search and rescue, surveillance, environment monitoring, large- accident investigation, parcel delivery, and agricul-ture service. Among all the UAVs, the quadrotor platform is the most popular one. Control and navi-gation of quadrotor UAV has received much attention. Yu-jiang ZHONG et al. presented a reliable active fault-tolerant tracking control method for a quadrotor UAV with model uncertainties and actuator faults. A radial basis function neural network was introduced to estimate the model uncertainties online, modify the reference model adaptively, and incorporate it into the model reference adaptive control scheme. To deal with actuator faults, a fault detection and diagnosis estimator and a fault compensation term were intro-duced into the control law. Shu-peng LAI et al. pro-posed a computationally efficient safe flying corridor navigation method for a quadrotor UAV. This method can generate a jerk-limited trajectory within a safe corridor. Unlike the existing methods, the proposed method can generate a smooth non-stop trajectory with safety guarantee but does not require the vehicle stop at each intersected point of the connected boxes.
A flywheel suspended on active magnetic bear-ings as a kind of complex system has been found to have many applications in energy storage devices. However, the controller design problem for a flywheel system is still challenging. Xujun LYU et al. applied the characteristic model based all-coefficient adaptive control method to active magnetic bearings suspended flywheel systems. An extensive simulation showed that the characteristic model based all-coefficient adaptive control method has considerable robustness with respect to plant uncertainties, external disturbances, and time delay.
Ze-zhi TANG et al. investigated the disturbance rejection problem of active magnetic bearing systems. A disturbance observer based iterative learning con-trol strategy that combines the extended state observer with a classic iterative learning control law was proposed. Their simulation and comparison con-firmed that the proposed method can yield good tracking performance.
The papers included in this special issue cover a broad spectrum of current research topics on complex systems and intelligent control, including formation control of complex multi-agent systems, control and navigation of a quadrotor UAV, robust control of robots, interaction between human and robot, control of systems with model uncertainties and disturbances. We hope that this special issue will benefit the re-searchers in these fields and foster research on com-plex systems and intelligent control.
This special issue would not be possible without the support of many people, including the authors and reviewers. We are indebted to the editorial staff of the journal for their enormous assistances, and to the Editors-in-Chief, Profs. Yun-he PAN and Xi-cheng LU, for this great opportunity.


概要: 复杂系统是指由多个不同的、自治的且相互作用的子系统组成的系统。正如亚里士多德所说,“整体大于部分之和”,复杂系统的性质并非其子系统性质的简单加和。复杂系统概念非常广泛,包括人脑、鸟类编队飞行、电网、交通系统、社交网络、通讯网络,等等。复杂系统具有高度非线性、涌现性、自适应性、自组织等特征,这些特征使得复杂系统难于精确建模,难于理解,难于控制。
执行控制是机器人结构设计中的关键环节,对机器人系统最终质量具有重要影响。Martin Molina等提出一种通用执行控制方法。实验结果验证了该方法的正确性、可行性与有效性。


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