The Similar Structures and Control Problems of Complex Systems

In this paper, the naturally evolving complex systems, such as biotic and social ones, are considered. Focusing on their structures, a feature is noteworthy, i.e., the similarity in structures. The relations between the functions and behaviors of these systems and their similar structures will be studied. Owing to the management of social systems and the course of evolution of biotic systems may be regarded as control processes, the researches will be within the scope of control problems. Moreover, since it is difficult to model for biotic and social systems, it will start with the control problems of complex systems, possessing similar structures, in engineering. The obtained results show that for either linear or nonlinear systems and for a lot of control problems similar structures lead to a series of simplifications. In general, the original system may be decomposed into reduced amount of subsystems with lower dimensions and simpler structures. By virtue of such subsystems, the control problems of original system can be solved more simply. At last, it turns round to observe the biotic and social systems and some analyses are given.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Management and Control of Outsourcing in Dispersed Network Manufacturing Systems

The trend of economic globalisation and advances in i nformation technology has led to the emergence of dispersed manufacturing system s as a form of the virtual organisation. New manufacturing strategy pays more at tention to the management of the total value chain and therefore puts emphasis o n outsourcing. In fact, outsourcing is an efficient way of utilizing available r esources and has become one key aspect of the manufacturing strategy. Improved d ecision and organization on outsourcing will result in cost production and short er lead-times. However, most concepts and practice of traditional outsourcing do not adapt to t he changing environment and meet increasing performance requirements. On the oth er hand, virtual organisations might display instability between pure outsourcin g and establishing alliance. Balance and trade-off between independent agents a nd creating alliance are thus required. Therefore, the purpose of this paper is to develop a model to support decision-making, management and control on outsou rcing in a dispersed network manufacturing system and to discuss several key iss ues that are relevant to the relationship between the agents of the network. Dev elopment of the model will deploy Applied System Theory and will be built on fou ndations of earlier research on industrial management such the theories of Outso urcing, Order Entry Points, Design of Organisations and Logistic Control. The is sues that will be addressed in this paper are: · The selection of suppliers and co-makers; · Communication between suppliers and clients; · The mechanisms for profit-sharing between agents; · The product data management to integrate the knowledge of the different agent s into product design. Industrial companies will benefit from this research by the practical methods an d implementation extending their business models beyond concepts for outsourcing and alliances. Additionally, the exploration will lead to proactive contributio n of manufacturing during engineering, which would improve management and contro l of dispersed manufacturing systems.

Special Issue on New Trends in Theory and Applications of Complex Control Systems

Complexity is one of the leading features of modem control systems. It is caused by the complex properties of controlled plants and the varied requirements in controller designs. In dealing with the control problems with complexity, in the past two decades, a lot of papers have been published that reported fruitful results on different theoretical backgrounds and with different methodologies, such as differential geometry-based design methods, hybrid system theory, switching control, neural network-based intelligent control, etc.

Management System Activities of the Operation and Utilization of GHARR-1

The Ghana Research Reactor-1 (GHARR-1) is a 34 kW low enriched uranium (LEU) Miniature Neutron Source Reactor (MNSR), tank-in-pool type and cooled by natural circulation under atmospheric pressure operating conditions. GHARR-1 is owned by Ghana Atomic Energy Commission (GAEC) and operated by National Nuclear Research Institute (NNRI), one of the institutes of GAEC. GHARR-1 is housed by Nuclear Reactors Research Centre (NRRC), one of the Centres of NNRI. Management/Administration, Radiation protection, Reactor operation and maintenance, Reactor utilization and Physical protection are the various systems/units that integrate to manage the activities of operation and utilization of GHARR-1 in addition to the quality assurance and quality control management system of the research reactor facility. The GHARR-1 which is currently in operation follows a robust maintenance culture adopted by the management system and this has made it possible to keep the reactor in operation with minimal interruption. The management system activities adopted at the Centre to ensure safety of the workers, public and the research reactor facility include authorization of the operation of the reactor for any experiments/modifications;providing material and financial resources for maintaining the research reactor facility;following standard procedures while carrying out Neutron Activation Analysis;participation in IAEA proficiency test;irradiation sites/positions characterization;following standard procedures while carrying out reactor operation and maintenance including reactor and pool water purification and other related activities;monitoring radiation levels in the controlled, supervised and uncontrolled areas of the research reactor facility as well as during reactor operation and maintenance;controlling the physical entry of the workers and public into the research reactor facility;and ensuring that the security structures provided to protect the reactor facility are functioning properly. The thorough knowledge on the functions of the various components that make up the electrical/electronic and control systems of the reactor has been observed to be important for continuous successful maintenance of the research reactor to keep the reactor in operation. This work provides some management system activities adopted to monitor the activities of the research reactor operation and utilization to guarantee safety of workers, public and the environment as well as to safeguard a continuous operation of the research reactor. These management system activities adopted among others, are in the form of Monitoring Forms provided for monitoring the activities of the research reactor operation and utilization in order to ensure standard procedures and specifications are followed and quality services are rendered to the public.

Research Progress of Parallel Control and Management

Based on ACP(artificial systems,computational experiments,and parallel execution)methodology,parallel control and management has become a popularly systematic and complete solution for the control and management of complex systems.This paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework,core technologies,and the application demonstration.The future research,application directions,and suggestions are also discussed.

Decentralized Variable Structure Control of Complex Giant Singular Uncertainty Systems

This paper, at the first time, considers the problem of decentralized variable structure control of complex giant singular uncertainty systems by using the property of diagonally dominant matrix and Frobenius-Person theorem. The splendid selection of switching manifold for each subsystem makes the design relatively straightforward and can be easily realized. An illustrate example is given.

Autonomous Multi-Factor Energy Flows Controller (AmEFC): Enhancing Renewable Energy Management with Intelligent Control Systems Integration

The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.

Structure identification of an uncertain network coupled with complex-variable chaotic systems via adaptive impulsive control

In this paper, structure identification of an uncertain network coupled with complex-variable chaotic systems is in- vestigated. Both the topological structure and the system parameters can be unknown and need to be identified. Based on impulsive stability theory and the Lyapunov function method, an impulsive control scheme combined with an adaptive strategy is adopted to design effective and universal network estimators. The restriction on the impulsive interval is relaxed by adopting an adaptive strategy. Further, the proposed method can monitor the online switching topology effectively. Several numerical simulations are provided to illustrate the effectiveness of the theoretical results.

Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

Lyapunov-Based Dynamic Neural Network for Adaptive Control of Complex Systems

In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.

The Development of the Production Management and Control System for 10th Assembly Workshop under XAC-CIMS

This paper gives brief introduction to the production management and control system (PMCS) for 10th assembly workshop under Xi’ an Aircraft Company (XAC)--CIMS. Its user requirements 9 structure, ftinctions, and implementation are described. It is showed that the development problems of PMCS can be further reduced to a development methodology for Chinese enterprises, which will make them competitive.

Robust Resource Management Control for CO2 Emission and Reduction of Greenhouse Effect: Stochastic Game Approach

With the increasingly severe global warming, investments in clean technology, reforestation and political action have been studied to reduce CO2 emission. In this study, a nonlinear stochastic model is proposed to describe the dynamics of CO2 emission with control inputs: clean technology, reforestation and carbon tax, under stochastic uncertainties. For the efficient resources management, a robust tracking control is designed to force resources tracking a desired reference output. The worst-case effect of stochastic parametric fluctuations, external disturbances and uncertain initial conditions on the tracking performance is considered and minimized from the dynamic game theory perspective. This stochastic game problem, in which one player (stochastic uncertainty) maximizes the tracking error and another player (control input) minimizes the tracking error, could be equivalent to a robust minimax tracking problem. To avoid solving the HJI, a fuzzy model is proposed to approximate the nonlinear CO2 emission model. Then the nonlinear stochastic game problem could be easily solved by fuzzy stochastic game approach via LMI technique.

Engineering complex systems applied to risk management in the mining industry

Related to complexity, there is a wide diversity of concepts, ranging from ‘‘systemic" to ‘‘complex", implying a need for a unified terminology. Per different authors, the main drivers of complexity can be found in human behaviour and uncertainty. This complexity, structural or dynamic can be organizational, technological, or nested in their relationship. ISO international standard 31000:2009 definition of risk management ‘‘coordinated activities to direct and control an organization with regard to risk", when applied to economic sectors, industry, services, project, or activity, it requires the use of models or theories as guidelines. Therefore, as its basic elements comprehend human behaviour and/or uncertainty, risk management to be effective and adapted as much as possible to reality, must be operational within complex systems, as already demonstrated in different R&D environments. Risk management faces demanding challenges when approaching specific and endogenous needs, such as the mining sector. This paper presents a multivariable function analysis methodology approach based on complex system modelling and through real data corresponding to a risk management tool in the mining sector.

The influence of CEO’s financial literacy on SMEs technological innovation:the mediating effects of MCS and risk‑taking

Previous literature showed mixed results about the impact of CEOs’financial literacy(CFL)on small and medium-sized enterprises’(SMEs)innovation.This relationship can be motivated by relevant variables,which are missing in the previous literature and make a difference as mediators.In this sense,based on the theoretical framework related to upper echelon theory and resource-based view,this study focuses on the mediating effect of risk-taking attitude and management control systems(MCS)varia-bles.Empirical data from 310 SMEs gathered using a qualitative research questionnaire are analyzed using structural equation modeling methodology.Specifically,estimations are carried out considering the partial least square method.Findings show that MCS and managers’risk attitudes fully mediate the relationship between financial literacy(FL)and innovation.Between these two mediating variables,the implementation of MCS stands out because it also enables the mediating effect of CEOs’risk-taking in the CFL–technological innovation relationship.As the results do not support the significant direct relationship between FL and risk attitude,they confirm an indirect effect through MCS.Furthermore,based on the study findings,SMEs’directors and owners,business associations,and public authorities can improve SMEs’technological innovation by implementing training programs and policies to foster CFL.They can also acknowledge the interdependency between organizational factors and individual characteristics to enhance SMEs’technological innovation.

Low-Cost Embedded Controller for Complex Control Systems

Because of limited resource of embedded platforms, the computational complexity of advanced control algorithms raises significant challenges for the use of embedded systems in complex control field. A Scilab/Scicos based embedded controller is developed on which various control software can be easily modeled, simulated, implemented, and evaluated to meet the ever-expanding requirements of industrial control applications. Built on the Cirrus Logic EP9315 ARM systems-on-chip board, this embedded controller is possible to develop complex embedded control systems that employ advanced control strategies in a rapid and cost-efficient fashion. Due to the free and open source nature of the software packages used, the cost of the embedded controller is minimized.

A Novel Evolutionary-Fuzzy Control Algorithm for Complex Systems

This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

Parameter Estimation and Topology Identification of Uncertain General Fractional-order Complex Dynamical Networks with Time Delay

Complex networks have attracted much attention from various fields of sciences and engineering in recent years.However, many complex networks have various uncertain information, such as unknown or uncertain system parameters and topological structure, which greatly affects the system dynamics.Thus, the parameter estimation and structure identification problem has theoretical and practical importance for uncertain complex dynamical networks. This paper investigates identification of unknown system parameters and network topologies in uncertain fractional-order complex network with time delays(including coupling delay and node delay). Based on the stability theorem of fractional-order differential system and the adaptive control technique, a novel and general method is proposed to address this challenge. Finally two representative examples are given to verify the effectiveness of the proposed approach.