Complex Environmental Systems Research Articles

Life cycle sustainability assessment and circularity of geothermal power plants

Geothermal power generation is expected to increase fivefold worldwide by 2040 compared to 2018. The upcoming growth of geothermal power plants (GPPs) requires assessments of its role in tackling climate change and other impacts within complex environmental, economic and social systems. This study presents the first literature review of GPPs, including comparisons among different GPP technologies, based on life cycle sustainability and circular economy perspectives. A total of 76 core literature studies on geothermal-related topics are reviewed, including technology choices, and critically discussed in terms of their environmental, economic, social and circular economy aspects. Firstly, seven life cycle environmental impact indicators (global warming, acidification, eutrophication, human toxicity, ozone depletion, photochemical oxidation, and cumulative energy demand) are compared both within GPP technologies and to other conventional electricity generation technologies (such as coal and hydro). Secondly, economic sustainability is considered via life cycle costing, and the results show that geothermal could be economically competitive when compared to solar photovoltaic (PV), hydro, and even wind energy sources. Thirdly, social aspects are discussed considering 15 articles that evaluated concerns such as public acceptance, technology safety and local employment rate, although none rigorously considered a life cycle approach. Lastly, a total of 12 articles were found linking the circular economy with GPPs and elaborating on some of the ‘9Rs’ framework. To improve the effectiveness of future research, studies should focus on fulfilling major data gaps in literature such as the lack of detailed documentation for specific materials and background process choices in life cycle assessment databases. The development of GPPs can be an important alternative in efforts to decrease climate change impacts and pursue cleaner energy sources in countries where geothermal energy is more easily available.

The Importance of Intelligent Colouring for Simulation Decomposition in Environmental Analysis

“Real world” risk analysis in environmental contexts frequently requires the need to contrast numerous uncertain factors simultaneously and to communicate difficult-to-capture interactions. Monte Carlo simulation modelling of complex environmental sytems is frequently employed to integrate uncertain inputs and to construct probability distributions of the resulting outputs. Visual analytics and data visualization can then be employed for the processing, analyzing, and communicating of the influence of any multi-variable uncertainties on the system. The simulation decomposition (SimDec) analytical technique has recently been employed in the complex assessments of environmental systems. SimDec has proved to be beneficial in revealing interdependencies in complex models, lowering computational burdens, facilitating decision-maker perceptions, and especially, making analytical components visualizable. It has been demonstrated that many analytical findings would not have been revealed without the coloured visualizations provided by SimDec. However, an ad hoc colouring scheme of the distribution output is neither sufficient nor capable of producing much of the key visualizable information requisite for an effective SimDec analysis. Instead, an approach that has recently been referred to as an intelligent colouring has been proposed. This paper outlines, highlights, and demonstrates the importance of and best-practices in an intelligent colouring scheme needed for an effective SimDec analysis of complex environmental systems.

Research on AGV positioning method combined with IMU and UWB

Aiming at the problem that automated guided vehicle (AGV) is difficult to locate accurately due to the influence of environment and time drift when it works in the indoor intelligent storage system. In this paper, an extended kalman filtering (EKF) framework is designed. In order to make full use of the original ranging values of ultra wideband (UWB) and inertial measurement unit (IMU), the framework realizes the fusion positioning between UWB module and IMU module in a tight coupling manner, so as to ensure that the system can still work when the available base station signal is inaccurate. Firstly, for the problem that the traditional UWB positioning method is easily affected by the non-line of sight (NLOS) error indoors, the calculated positioning coordinate value is unstable. With the help of different NLOS probability distribution curves of different obstacles, the weighted least square method is applied to the UWB positioning method to determine the positioning coordinate value of UWB, which improves the sudden change of AGV positioning coordinate in the static environment. Then the data fusion algorithm is optimized, and the error value of IMU and UWB coordinate is taken as the observation value of EKF, which reduces the influence of cumulative error on IMU positioning results, provides the global optimal estimation of the system optimal state, and improves the fusion positioning accuracy. Finally, the measured data of UWB and IMU systems in indoor complex environment are simulated in MATLAB. The experimental results show that when NLOS signal seriously affects the positioning effect, the UWB and IMU combined positioning system can provide more reliable positioning results than the single IMU positioning system. It improves the positioning accuracy of AGV and provides a new idea for indoor positioning mode.

Facies variations in response to tectonic evolution, climate and sea-level changes since the Late Cretaceous in Wuhu region, Eastern China

Eastern China has a complex environmental dynamics system in the western Pacific tectonic domain, and the study of its sedimentary records controlled by tectonic movements is distinctly significant for exploring sea-land interactions, global climate change and sea level fluctuations. A reliable OSL (Optically Stimulated Luminescence) chronostratigraphic framework was established based on a systematic investigation of the stratigraphic lithology of the boreholes in Wuhu area, Eastern China, and the depositional environment since the Late Cretaceous was reconstructed by multiple environmental proxies. Significant regional changes in sedimentary activity since the Mesozoic indicate that the Yanshan movement and the Neotectonic movement controlled the evolution of sedimentary basins and fracture tectonics in the study area and influenced the paleo-geographic environment and sedimentary patterns in a regional geotectonic context. Since the Middle Pleistocene, the temperature and sea level trends were split into six stages, four of which (MIS 6, 4, 3 and 2) can be categorized as periods of decline, with MIS 1 being a period of significant increase. The other phase (MIS 5) was characterized by violent fluctuations in climate and sea level, with periods of increase in MIS 5a, 5c and 5e and decrease in MIS 5b and 5d. Sedimentary process in eastern China are mainly controlled by regional geotectonic activity, and the specific evolution of the depositional environment is also influenced by the combined effects of regional climate and sea level.

Fabrication and Underwater Testing of a Vector Hydrophone Comprising a Triaxial Piezoelectric Accelerometer and Spherical Hydrophone.

A vector hydrophone is an underwater acoustic sensor that can detect the direction of a sound source. Wide-band characteristics and high sensitivity enhance the performance of underwater surveillance systems in complex environments. A vector hydrophone comprising a triaxial piezoelectric accelerometer and spherical hydrophone was fabricated and tested in the air and underwater. The vector hydrophone was designed to exceed the quantitative figures of merit (i.e., receiving voltage sensitivity and bandwidth) of commercially available hydrophones. Accelerometer performance was enhanced by placing a pair of piezoelectric single crystals on each axis and modifying the seismic mass material. The receiving voltage sensitivity of the omnidirectional hydrophone was approximately -160 dB relative to 1 V/μPa with the amplifier in water; the sensitivity of the accelerometer exceeded 300 mV/g in air and -215 dB relative to 1 V/μPa underwater over the frequency range of interest. The receiving directivity of the vector hydrophone was validated underwater, which confirmed that it could detect the direction of a sound source.

Nonlinear modeling and multi-scale influence characteristics analysis of organic Rankine cycle (ORC) system considering variable driving cycles

The reasonable construction of the organic Rankine cycle (ORC) system model under road conditions is the key to analyze, evaluate, and optimize the performance of the ORC system. However, due to the variability of high-temperature waste heat source and the strong coupling correlation of operating parameters, the operation characteristics of the ORC system show evident time-varying characteristics. Based on the coupling correlation and redundancy characteristics of the ORC system in complex environment, this paper presents a nonlinear modeling framework for the multi-scale influence analysis of the ORC system under road conditions. The nonlinear modeling framework can improve the prediction accuracy of ORC model by at least 68.36% and reduce the time cost by 53.37%. Based on the nonlinear model, the synergistic effects of multiple variables on ORC system performance at different scales are studied. The frequent fluctuation of vehicle speed enhances the coupling correlation of operating parameters, resulting in nonlinear, hysteretic dynamic characteristics of ORC thermal efficiency. The maximum thermal efficiency of ORC is only 3.28%. The nonlinear modeling framework proposed in this paper can provide a practical solution for constructing the intelligent analysis, design, and optimization models of ORC systems under complex road conditions.

Identification of Intrusion Events Based on Distributed Optical Fiber Sensing in Complex Environment

With the development of economy, the construction of modern infrastructures has been gradually improved. Some infrastructures, such as factories and airports, have large construction volume and high security requirements. Once an intrusion occurs, it will pose a great threat to the safety of personnel and property. Therefore, the perimeter security system based on Internet of Things (IoT) devices and technology is important. Distributed optical fiber sensor (DOFS) is an emerging infrastructure for IoT. Owing to multipoint sensing at the same time, it has attracted extensive attention in the field of perimeter security. However, with the ambient interference, intrusion signals and ambient signals form mixed signals, which brings great difficulty to the efficient and correct identification of intrusion signals. In order to establish a perimeter security system in complex environment, this article proposes an intrusion detection scheme based on DOFS, IoT, and pattern separation technology. The scheme realizes the separation of ambient signals and intrusion signals for the first time. Experiments have proved that this method can separate and recover intrusion signals from mixtures with the average SDR and SI-SDR of 12.35 dB and 11.61 dB, which effectively lays a foundation for subsequent feature extraction and identification. Besides, the response time is only about 0.5 s. This work makes a significant step toward achieving high performance of the perimeter security system in complex environment.

Nimble adaptation: Tailoring monitoring, evaluation, and learning methods to provide actionable data in complex environments

AbstractThis chapter examines good practices in implementing effective Monitoring, Evaluation, and Learning (MEL) systems within complex international development Democracy, Human Rights, and Governance (DRG) programs, which are characterized by challenges of non‐linearity, limited evidence of theories of change, and contextual and politically contingent nature of outcomes. The chapter presents three cases of MEL systems in complex projects implemented by Pact across distinct and diverse operating contexts – Zimbabwe, Cambodia, and Somalia – to illustrate those projects’ MEL approaches that enabled continuous adaptation. The authors analyze the cases to respond to two questions: (1) What are the key elements of effective adaptive management‐focused MEL systems in complex environments? (2) What is practical guidance for designing and enabling complexity‐responsive and effective adaptive management‐focused MEL systems? The case studies illustrate three key elements: (1) Information gathering that closely links context, research, and performance data; (2) Systems for reflection that offer scheduled learning moments of varying frequency and intensity, as well as multiple feedback mechanisms; and (3) Enabling structures that promote adaptive mindsets and attitudes within project teams.

Distributionally Robust Optimal Sensor Placement Method for Site-Scale Methane-Emission Monitoring

Recent research in deterministic sensor placement optimization technologies has improved the capability of monitoring large-scale field environments with a limited budget. In traditional stochastic mixed-integer linear programming formulations, minimizing the expectation of detection time can lead to a detector placement with good average behavior but unexpected worst case behavior. The uncertainty factors in the complex environment and sensor system significantly challenge the effects of the placement strategy provided by stochastic programming (SP). These factors include unknown leakage rate and location, sensor delay, and primary uncertainty of wind conditions. This article introduces a distributionally robust optimization (DRO) formulation of sensor placement under the uncertainty of wind conditions and improves a sensor network’s detection robustness. The method is demonstrated using the atmospheric simulation with site-specific methane-emission scenarios that capture partial natural wind conditions and emission characteristics. DRO techniques are employed to determine sensor locations that minimize the detection time expectation of the emission scenarios with a significantly better worst case behavior. Experiment results show that the proposed DRO method outperforms the sensor placement methods based on SP.

Novel methods for wind speeds prediction across multiple locations

This article provides two unique methodologies that may be coupled to study the dependability of multidimensional nonlinear dynamic systems. First, the structural reliability approach is well suited for multidimensional environmental and structural reactions and is either measured or numerically simulated over sufficient time, yielding lengthy ergodic time series. Second, a unique approach to predicting extreme values has technical and environmental implications. In the event of measurable environmental loads, it is also feasible to calculate the probability of system failure, as shown in this research. In addition, traditional probability approaches for time series cannot cope effectively with the system's high dimensionality and cross-correlation across dimensions. It is common knowledge that wind speeds represent a complex, nonlinear, multidimensional, and cross-correlated dynamic environmental system that is always difficult to analyze. Additionally, global warming is a significant element influencing ocean waves throughout time. This section aims to demonstrate the efficacy of the previously mentioned technique by applying a novel method to the Norwegian offshore data set for the greatest daily wind cast speeds in the vicinity of the Landvik wind station. This study aims to evaluate the state-of-the-art approach for extracting essential information about the extreme reaction from observed time histories. The approach provided in this research enables the simple and efficient prediction of failure probability for the whole nonlinear multidimensional dynamic system.

Equilibrium Approximating and Online Learning for Anti-Jamming Game of Satellite Communication Power Allocation

Satellite communication systems are increasingly facing serious environmental challenges such as malicious jamming, monitoring, and intercepting. As a current development of artificial intelligence, intelligent jammers with learning ability can effectively perceive the surrounding spectrum environment to dynamically change their jamming strategies. As a result, the current mainstream satellite communication anti-jamming technology based on wide interval high-speed frequency hopping is unable to deal with this problem effectively. In this work, we focus on anti-jamming problems in the satellite communication domain, and reformulate the power allocation problem under two kinds of confrontation scenarios as one-shot and repeated games model. Specifically, for the problem of multi-channel power allocation under a one-shot confrontation scenario, we firstly model the problem of allocating limited power resource between communication parties and a jammer on multi-channel based on a BG (Blotto Game) model. Secondly, a DO-SINR (Double Oracle-Signal to Interference plus Noise Ratio) algorithm is designed to approximate the Nash equilibrium of the game between two parties. Experiments show that the DO-SINR algorithm can effectively obtain the approximate Nash equilibrium of the game. For the problem of multi-channel power allocation under a repeated confrontation scenario, we firstly transform the problem into an online shortest path problem with a graph structure to make the problem solving process more intuitive, and then design the Exp3-U (Exp3-Uniform) algorithm which utilizes the graph structure to solve the multi-channel power allocation problem. Experiments show that our algorithm can minimize the expected regret of communication parties during online confrontation, while maintaining good operating efficiency. The two power allocation problems constructed in this paper are common problem formed in confrontation scenarios. Our research and analysis can simulate some actual confrontation scenarios of the satellite communication power allocation, which can be used to improve the adaptability of satellite communication systems in complex environments.

Computing dissipative particle dynamics interactions to render molecular structure and temperature-dependent properties of simple liquids

Simulating structural and thermodynamical properties of liquids has always been a challenge. Typical examples of liquids that demonstrate particular structure and properties are water and the low molecular weight alcohols, for which hydrogen bond interactions lead to their distinctive properties, such as cage-like structures and temperature-dependent properties. Modeling these materials at the coarse-grained level is even a bigger challenge due to the loss of atomistic-level interactions. Nevertheless, one is interested in mimicking these typical properties at the coarse-grained level due to the relevance of these systems in complex environments, for which fully atomistic simulations still remain a challenge. In this paper, we introduce a mesoscopic level parameterization of DPD interactions to study the particular structural and thermodynamic properties of liquid water, methanol, ethanol and 1-propanol. The conservative repulsive DPD interactions are explicitly computed by a bottom-up parameterization, in which experimental thermodynamics data are used. A previously developed statistical mechanics approach is used to compute the hydrogen bond strength. The transport properties, such as viscosity, and thermodynamical properties, such as isothermal compressibility, are found to agree reasonably well with experimental data. Moreover, the structure as characterized by the radial distribution function and angular distributions of three neighboring molecules are in line with the atomistic simulations performed in this work. Furthermore, the temperature-dependency of the repulsive DPD interactions is modeled by incorporating the experimental isothermal compressibilities at different temperatures. The effect of the temperature on the hydrogen bond strengths is considered as well and the structural properties are predicted via the DPD simulations. In general, our work can be viewed as an attempt to model systems by the DPD simulations, where hydrogen bonds play a crucial role. The computed parameterization of DPD interactions is believed to pave the way towards extending the current applicability of DPD method to more complex systems.

Enhanced generation of reactive oxygen species by pyrite for As(III) oxidation and immobilization: The vital role of Fe(II)

The migration and conversion of arsenic in the environment usually accompany by the redox of iron-bearing minerals. For instance, the oxidation of pyrite can generate reactive oxygen species (ROS) affecting the species of arsenic, but the types and roles of ROS have been unclear. This paper demonstrated the vital role of Fe(II) in the pyrite for the formation of ROS. Results showed that exogenous addition of Fe(II) significantly enhanced the removal rate of As(III) by pyrite. 2,2′-bipyridine (BPY) decreased the oxidation of As(III) by complexing with Fe2+ in solution, whilst EDTA enhanced the oxidation of As(III) by boosting the autoxidation of Fe2+. In addition, neutral pH is superior for the oxidation of As(III) and removal of total arsenic. Importantly, Methanol, SOD enzyme and PMOS inhibited 54%, 28% and 17.5% of As(III) oxidation, respectively, which indicated O2•− and •OH were the main contributors to As(III) oxidation, and Fe(IV) contributed a small part of As(III) oxidation. The content of As(V) in the FeS2–Fe2+-As(III) system was higher than that in the FeS2–As(III) system, further confirming the vital role of Fe(II) for As(III) oxidation. Lepidocrocite was produced in a single Fe2+ system, which was not detected in the FeS2–As(III) system. Thus, the presence of mineral surfaces changed the oxidation products of Fe2+ and accelerated the oxidation and immobilization of As(III). FA (Fulvic Acid) and HA (Humic Acid) accelerated the oxidation of As(III), but the oxidation of As(III) by pyrite was inhibited to a certain extent, with increasing phenolic hydroxyl groups in phenolic acid. Our findings provide new insight into the oxidative species in the pyrite-Fe(II) system and will help guide the remediation of arsenic pollution in complex environmental systems.

Ability of machine learning models to identify preferred habitat traits of a small indigenous fish (Chanda nama) in a large river of peninsular India.

Physical and chemical parameters of river influence the habitat of fish species in aquatic ecosystems. Fish showed a complex relationship with different aquatic factors in river. Machine learning modeling is a useful tool to identify relationships between components of a complex environmental system. We identified the preferred habitat indicators of Chanda nama (a small indigenous fish), in the Krishna River located in peninsular India, using machine learning modeling. Using data on Chanda nama fish distribution (presence/absence) and associated ten physical and chemical parameters of water at 22 sampling sites of the river collected during the year 2001-2002, machine learning models such as random forest, artificial neural network, support vector machine, and k-nearest neighbors were used for classification of Chanda nama distribution in the river. The machine learning model efficiency was evaluated using classification accuracy, Cohen's kappa coefficient, sensitivity, specificity, and receiver-operating-characteristics. Results showed that random forest is the best model with higher classification accuracy (82%), Cohen's kappa coefficient (0.55), sensitivity (0.57), specificity (0.76), and receiver-operating-characteristics (0.72) for prediction of the occurrence of Chanda nama in the Krishna River. Random forest model identified three preferred physicochemical habitat traits such as altitude, temperature, and depth for Chanda nama distribution in Krishna River. Our results will be helpful for researcher and policy maker to understand important physical and chemical variables for sustainable management of a small indigenous fish (Chanda nama) in a large tropical river.

Robust affine maneuver formation control based of second-order multi-agent grid inspection systems

Grid formation inspection is the future development trend of grid inspection. Aim to the robust control problem of multi-agent grid inspection system in complex environment, this paper proposes a formation control algorithm with prescribed performance. The dynamic model of follower distance error is given in the leader-follower framework using a symbolic Laplace matrix. Performance bounds are set on the sliding mode error by prescribed performance control. Then, the convergence and stability of the proposed control algorithm are not only proved by the Lyapunov stability theorem, but also the error state of the system converges to the prescribed performance bound. Finally, the simulation results show that the follower can follow the leader despite external interference and the validity of the proposed algorithm.

Transdisciplinary multistage system modeling: migrant entrepreneurship in the digital economy

PurposeThe dynamics of modern life lead to societal changes that affect innovation systems. Entrepreneurship is an important driver for fostering adaptive capacities of innovation systems in such uncertain and complex environments. This study aims to gain a detailed understanding of how (innovative) entrepreneurship can promote innovation systems, leading to more sustainable societies. A particular focus is placed on migrant entrepreneurship in the digital economy, as a concrete implication of innovative entrepreneurship, and its role within the Austrian innovation system.Design/methodology/approachIn order to develop a shared system understanding from a scientific and practical perspective, transdisciplinary multistage system modeling was applied. The transdisciplinary discourse involved 14 experts, and several system models were iteratively co-created during the course of the research.FindingsThe main result demonstrates the interrelationship between the innovation system and migrant entrepreneurship in the digital economy, which includes six core reinforcing loops: (1) the mindsets of entrepreneurs, (2) the role of international collaboration, (3) the role of entrepreneurial education, the financial sphere in regard to (4) government and (5) private funding, as well as (6) the impact of formal procedures.Originality/valueThe authors present and discuss the relational dynamics of this complex phenomenon as well as the applied transdisciplinary approach, with the aim of identifying a potential way to improve the sustainable impact of (migrant) entrepreneurship considering the case of the Austrian innovation system.

Developing, evaluating and scaling learning agents in multi-agent environments

The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks. A signature aim of our group is to use the resources and expertise made available to us at DeepMind in deep reinforcement learning to explore multi-agent systems in complex environments and use these benchmarks to advance our understanding. Here, we summarise the recent work of our team and present a taxonomy that we feel highlights many important open challenges in multi-agent research.

Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems.

Rapid progress in various advanced analytical methods, such as single-cell technologies, enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares data set-wise distributional differences with no requirements on human intervention or prior knowledge-based preclassification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of data sets with both linear and nonlinear relationships. The model was then validated in a case study with Single-cell Raman Spectroscopy (SCRS) phenotyping data sets from eight different enhanced biological phosphorus removal (EBPR) activated sludge communities located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages enable the extension of the capability to other single-cell technologies or environmental applications with data sets exhibiting continuous features.

The Role of Hydrogeological Monitoring in a Multidisciplinary Context for the Preservation of the Critical Zone in the Natural Reserve of Castelporziano Estate

Critical Zone (CZ) science has developed in recent years, involving different disciplines that vary depending on the specific research focus. This multidisciplinary approach highlights the relevance of the Underground component of the Critical Zone (UCZ) in regulating the water cycle, which can influence the complex equilibrium of the whole CZ. In this study, we analyze evolution during the time of different parameters, characterizing the saturated and unsaturated parts of the UCZ of the Castelporziano Estate, a natural reserve located in a coastal area close to Rome. The purposes of these activities are to monitor the potential depletion of groundwater resources and understand the recharge mechanism processes characterizing the aquifer in the framework of occurring climate changes, net of anthropogenic pressure. The long-term analyses of water table variations carried out over the last 25 years, allowed us to preliminarily identify four different ranges of the slope coefficient of the water table, characterizing different areas of the Estate. Specifically, the Northern, Central, and Coastal areas have shown a general depletion trend in piezometric levels, while in the Eastern area, a positive trend has been recognized. Additional long-term analysis of piezometric level variations allowed us to confirm the presence of the four recharge areas and compare annual recharge and water table levels to assess the relationship between the saturated UCZ and meteoric recharge in the identified areas. To evaluate the role of the unsaturated UCZ in recharge mechanisms, the water content in the first meter of soil has also been analyzed, showing different responses of outcropping sediments in capturing rainfall during different periods of the year and under different rainfall input conditions, highlighting the pivotal role of rainfall for the Castelporziano UCZ, both for deep recharge of the water table and for feeding the forest roots. Stable isotopes confirm that Castelporziano UCZ feeding is strongly dependent on local meteoric recharge, also highlighting that evaporation processes are active in a limited way. The obtained results assess that the monitoring of UCZ has a crucial role in the correct preservation of more complex environmental systems, which include groundwater resources and the coastal Mediterranean forest.

Design and Implementation of Multimedia Network Intelligent Control Robot Based on Software Definition

In order to solve the problem that the delay of wireless network and complex operating environment affects the stability and operating performance of teleoperation system, a method of intelligent control robot based on multimedia network defined by software is proposed in this paper. In the network environment established based on the software definition, the gain of the system control is increased according to the network delay to improve the operating performance of the system, and the output of parameters is dynamically adjusted to adapt to the stability of the system in complex environment. The experimental results show that the robot control system can obtain the best control stability by continuously adjusting the relevant parameters. After the simulation test, the final setting is k p = 0.8 , k i = 0.001 , k d = 0 . Conclusion. Based on the intelligence of gain scheduling control algorithm, the control effect of fuzzy control can be significantly improved when the network delay is large.