Yongsheng Ding

Optimal Control of a Fractional-Order HIV-Immune System With Memory

The fact that fractional-order models possess memory leads to modeling a fractional-order HIV-immune system. We discuss the necessary conditions for the optimality of a general fractional optimal control problem whose fractional derivative is described in the Caputo sense. Using an objective function that minimizes the infectious viral load and count of infected T cells, the optimal control problem is solved for the fractional-order optimality system with minimal dosage of anti-HIV drugs and the effects of mathematically optimal therapy are demonstrated. Simulation results show that the fractional-order optimal control scheme can achieve improved quality of the treatment.

An Intelligent Self-Organization Scheme for the Internet of Things

The Internet of Things (IoT) is emerging as the major trend in shaping the development of the next generation of information networks. The challenges of the enormous, dynamic, incredibly diverse and high complexity of the IoT urgently require novel self-organization scheme because most of the existing distributed self-organization schemes cannot be directly applied to it. In this paper, we propose an intelligent self-organizing scheme (ISOS) for the IoT inspired by the endocrine regulating mechanism. For each node in the network, an autonomous area is established, where the node can effectively interact with its peers and perform self-control according to its own status and dynamic circumstance in a decentralized infrastructure. By introducing the hormone mechanism as the medium for information transmission and data sharing, the nodes can collaborate with each other and work in a cooperative way. Through adjusting the release procedure of the hormones, the ability to effectively detect service randomly generated can also be guaranteed in the probabilistic partially-working IoT. Simulation results verify the performance of the proposed mechanism that entitles the IoT to the ability of maintaining its status in a globally stable status, while effectively discovering the random service requests in a resource-critical configuration. The ISOS would be of great significance for the practical implementation of the IoT.

An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink

The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.

An Intelligent Bi-Cooperative Decoupling Control Approach Based on Modulation Mechanism of Internal Environment in Body

In order to develop more effective decoupling control methods for complex coupling system, we present an intelligent bi-cooperative decoupling controller (IBCDC) inspired from the modulation mechanism of internal environment in body. The IBCDC consists of a coordination control unit (CCU), a model identification unit (MIU), a decoupling control evaluation unit (DEU), and two control units. The DEU includes a reinforcing decoupling control block (RDCB) and a suppression decoupling control block (SDCB). Every control unit or block including an original control block (OCB) and a decoupling compensation block (DCB) is designed according to different physiological organs or systems. Under the coordination of the CCU, all the control units or blocks communicate with each other to exchange the control or decoupling control information. Through adjusting the actuators harmoniously, the coupling influences among different control loops can be reduced or almost eliminated. The simulation results demonstrate that the IBCDC can almost eliminate the coupling influence with better control performance. Compared with other decoupling control methods, the IBCDC can be implemented more easily and practically, and be easily generalized to the multiple-input-multiple-output systems.

A Bioinspired Multilayered Intelligent Cooperative Controller for Stretching Process of Fiber Production

The stretching process is one of the key sections in fiber production, which is decisive to the quality of the final fiber products. Such a process raises high requirements on the control of the rollers with proper stretching ratios, and the large number of rollers with their special characteristics and the demand for synchronous running usually make the design of a good control scheme difficult. In this paper, a novel bioinspired multilayered intelligent cooperative controller (BMLICC) is proposed to provide a control plan for the interlinked rollers by organizing them into unified stretching units. Based on the multilayer regulation networks of neuroendocrine system in the human body, a networked controller structure is established. It consists of several components like rollers, distributed controllers, communication paths, and conversion units. The rollers in the same unit can exchange the working information rapidly to implement simultaneous response and cooperation. The stretching ratio can be kept stable and has strong resistance against the external disturbances on the stretching system. Both computer-simulation- and device-based experimental results demonstrate that the stretching unit with the proposed BMLICC can maintain its stretching ratio and effectively resist the external disturbances. This is beneficial to improve the performance of the stretched precursors and, furthermore, produce fibers with high quality. The proposed BMLICC can be easily extended to productions with multiple stretching units or industrial processes with similar mechanical structures for better control quality.

Self-organized swarm robot for target search and trapping inspired by bacterial chemotaxis

Target search and trapping using self-organized swarm robots have received increasing attention in recent years but control design of these systems remains a challenge. In this paper, we propose a decentralized control algorithm of swarm robot for target search and trapping inspired by bacteria chemotaxis. First, a local coordinate system is established according to the initial positions of the robots in the target area. Then the target area is divided into Voronoi cells. After the initialization, swarm robots start performing target search and trapping missions driven by the proposed bacteria chemotaxis algorithm under the guidance of the gradient information defined by the target. Simulation results demonstrate the effectiveness of the algorithm and its robustness to unexpected robot failure. Compared with other commonly used methods for distributed control of swarm robots, our simulation results indicate that the bacteria chemotaxis algorithm exhibits less vulnerability to local optimum, and high computational efficiency. Robots are less likely to get trapped in local optimums.The computational cost is low.Predefined global coordinate is not needed for accomplishing the tasks.

Data-Driven Cooperative Intelligent Controller Based on the Endocrine Regulation Mechanism

A data-driven mechanism can achieve effective control by utilizing the online/offline data of the target system, although its performance has not been tuned to a better level. The endocrine regulating mechanism in the human body establishes a rapid responding system to maintain the balance of the body, which can be mathematically derived and therefore provide an inspiration for optimizing the industrial controller. In this paper, a novel data-driven cooperative intelligent controller inspired by the regulating principle of the endocrine system in the human body is proposed. The data-driven component of the proposed controller optimizes the controller parameters by collecting and processing runtime data of the target system. The endocrine regulation-inspired enhancing component tunes the intensity of control signals adaptively. Both the components are further organized by an adaptive distributor so that their behaviors can be regulated dynamically. A dynamic tension control system for acrylic fiber production is taken to verify the performance of the proposed controller. Simulation results show that the proposed controller can realize effective control on systems with unknown or varying models, meanwhile featuring rapid response and effective regulation against external disturbance.

An endocrine-based intelligent distributed cooperative algorithm for target tracking in wireless sensor networks

In this paper, a novel endocrine-based intelligent distributed cooperative algorithm (EIDCA) for target tracking is proposed inspired by the regulating mechanisms of the human hormone systems. The EIDCA enables the nodes in the wireless sensor networks to self-organize themselves autonomously without a centralized control for target detection. A probability-based scheme for hormone transmission is also introduced to alleviate fluctuations of the network caused by frequent switches of the nodes. Meanwhile, a numerical evaluation method is designed to provide a quantitative metric for comparing the tracking performance of different algorithms. Simulation results show that the decentralized network controlled by the EIDCA can work efficiently and reliably without central control. It is also shown that the proposed EIDCA outperforms the compared algorithms in tracking targets.

An Intelligent Cooperative Decoupling Controller for Coagulation Bath in Polyacrylonitrile Carbon Fiber Production

The production of polyacrylonitrile carbon fiber (PANCF) consists of a set of industrial processes with high complexity. A key process is the coagulation of the PAN as-spun fiber in the coagulation bath which has a lot of variables coupling with each other. In this paper, an intelligent cooperative decoupling controller (ICDC) based on the neuroendocrine regulation principle of human body is proposed and applied to the coagulation bath in the PANCF production. Three important variables including the liquid-level, the temperature, and the concentration are considered. The ICDC consists of a control center unit, several control and decoupling units and their corresponding output units. The control center coordinates each control and decoupling unit and determines their control schemes. Each control and decoupling unit takes effect independently, and the control information is exchanged among them for decoupling. The control signals are then integrated at the output units and finally sent to the plant. Simulation results demonstrate that the ICDC can rapidly response to the variation of control variables, completely eliminate the coupling influence and make smooth regulation without overshoot, which has a better performance than the conventional control schemes.

Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system

The workflow scheduling with multiple objectives is a well-known NP-complete problem, and even more complex and challenging when the workflow is executed in cloud computing system. In this study, an endocrine-based coevolutionary multi-swarm for multi-objective optimization algorithm (ECMSMOO) is proposed to satisfy multiple scheduling conflicting objectives, such as the total execution time (makespan), cost, and energy consumption. To avoid the influence of elastic available resources, a manager server is adopted to collect the available resources for scheduling. In ECMSMOO, multi-swarms are adopted and each swarm employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective. To avoid falling into local optima which is common in traditional heuristic algorithms, an endocrine-inspired mechanism is embedded in the particles’ evolution process. Furthermore, a competition and cooperation technique among swarms is designed in the ECMSMOO. All these strategies effectively improve the performance of ECMSMOO. We compare the quality of the proposed method with other algorithms for multi-objective task scheduling by hybrid and parallel workflow jobs. The results highlight the better performance of the proposed approach than that of the compared algorithms.