Cloud-based Control System Research Articles

Private outsourced model predictive control via secure multi-party computation

Protecting data privacy is essential in developing practical cloud-based control systems to benefit from remote and distributed computation while avoiding the risks of disclosing sensitive information to potentially untrusted parties. This paper proposes a privacy-preserving framework to outsource the computation of model predictive control law to several untrusted cloud-based servers. A secret sharing scheme is employed to maintain the privacy of the system’s data in all the stages of the control loop in a secure multi-party computation environment. In this regard, a privacy-preserving algorithm is derived to securely implement a projected gradient method to solve the underlying optimization problem, without revealing private data to external eavesdroppers and curious cloud-based servers. The proposed method is solely based on secret sharing, and all computations can be performed securely by cloud servers without the need to designate target nodes or engage the system’s actuator in computing the intermediate steps. Therefore, the proposed method is less computationally demanding than the existing results based on homomorphic encryption and is applicable to systems with limited computing resources. Information-theoretic privacy assessments based on mutual information measures are provided. The efficacy of the proposed method is investigated on the cruise control problem of a freight train system.

Solar-Powered Smart Buildings: Integrated Energy Management Solution for IoT-Enabled Sustainability

The increasing demand for energy-efficient and sustainable solutions in the building sector has driven the need for innovative approaches that integrate renewable energy sources and advanced control systems. This paper presents an integrated energy management solution for solar-powered smart buildings, combining a multifaceted physical system with advanced IoT- and cloud-based control systems. The physical system includes a heat pump, photovoltaics, solar thermal panels, and an innovative low-enthalpy radiant wall and ceiling, providing self-sufficient heating and cooling. The control system makes use of advanced IoT and communication engineering technologies, using Modbus, HTTP, and MQTT protocols for seamless interconnectivity, monitoring, and remote management. The successful implementation of this solution in an average-sized model house in Paris and a deep energy retrofit of a semidetached single-family house in Oviedo, northern Spain, demonstrates increased energy efficiency, improved thermal comfort, and reduced environmental impact compared with conventional alternatives. This study illustrates the potential of integrating solar energy, IoT, and communication technologies into smart buildings, contributing to the global effort to reduce the environmental impact of the building sector.

Workflow-Based Fast Data-Driven Predictive Control With Disturbance Observer in Cloud-Edge Collaborative Architecture

Data-driven predictive control (DPC) has been studied and applied in various scenarios. However, the challenge of computational efficiency remains. With the development of cloud computing, it provides potential solutions to the computational problem. Hence, this paper proposes a workflow-based fast DPC method in cloud-edge collaborative architecture. First, a workflow construction method of DPC is designed to make full use of the distributed computing ability of cloud computing. Next, to tackle the uncertainty in the cloud workflow processing, we design a cloud-edge collaborative scheme. In this scheme, a edge data-driven disturbance observer is proposed to estimate and compensate the uncertain event with guaranteed UUB stability. Then, An autonomous cloud control experimental system based on container technology is designed and implemented to execute the workflow-based DPC controller. Evaluations demonstrate that computation times are reduced by 45.19 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> and 74.35 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> for two real-time control examples, and by a maximum of 85.10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> for a high-dimensional control example. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the challenge of the further combination of cloud computing and control system such as DPC. In the existing cloud-based control system, the computation mission of native control algorithm is deployed in a single cloud server directly. However, the structure of cloud environment is distributed, and the existing computation mode could not make full use of the parallel computing of cloud computing. Thus, the computation time could not be reduced significantly, and would still have serious effects on the control quality. In this work, a novel workflow-based DPC system in cloud-edge collaborative architecture is proposed, which decompose the native control mission into distributed cloud workflow with multiple smaller computation tasks. Then, an edge disturbance observer is designed to compensate the uncertainty occurring in the cloud workflow processing. As the evaluations show, the proposed workflow-based control system in cloud-edge collaborative architecture could be applied in the control mission with real-time requirement and the high-dimension control mission with intensive data.

A Physics-Based Modelling and Control of Greenhouse System Air Temperature Aided by IoT Technology

The need to reduce energy consumption in greenhouse production has grown. Thermal heating demand alone accounts for 80% of conventional greenhouse energy consumption; this significantly reduces production profit. Since microclimate affects crop metabolic processes and output, it is essential to monitor and control it to achieve both quantity and quality production with minimum energy consumption for maximum profit. The Internet of Things (IoT) is an evolving technology for monitoring and controlling environments that have recently been adopted to boost greenhouse efficiency in many applications by integrating hardware and software solutions; therefore, its adoption is thus critical in enabling greenhouse energy consumption minimisation. The first objective of this study is to improve and validate a greenhouse dynamic air temperature model required to simulate or predict indoor temperature. To achieve the first objective, therefore, an existing model was enhanced and a closed loop test experimental data from the IoT cloud-based control system platform deployed in the prototype greenhouse built in Cranfield University was used to validate the model using an optimisation-based model fitting approach. The second goal is to control the greenhouse air temperature in simulation using relatively simple PI and on-off control strategies to maintain the grower’s desired setpoint irrespective of the inevitable disturbances and to verify the potential of the controllers in minimising the total energy input to the greenhouse. For the second objective, the simulation results showed that the two controllers maintained the desired setpoint; however, the on-off strategy retained a sustainable oscillation, and the tuned PI effectively maintained the desired temperature, although the average energy used by the controllers is the same.

Cloud-based control systems: a systematic literature review

Control systems and computer science are two distinct and important fields of engineering. The development of cloud computing in computer science has become an enabler for the widely used controller in control systems to migrate to the cloud and has created a new field of research in cloud-based control systems (CCS). The paper used the systematic literature review approach to obtain insight into current CCS research. The objectives include a review in areas such as the demographics, topics of the research, evaluation method, and application domain. To that end, systematic literature review (SLR) has been conducted. The study obtained 64 primary studies from 581 articles. The CCS has a distinct characteristic; despite the fact that the cloud and network dynamics system, when coupled with the controlled plant, is inherently nonlinear, research efforts have used linear models with optimal control to approach it successfully in a limited case of control objectives. Furthermore, cloud-centric and cloud-fog network architecture approaches are considered in the studies—whereas, the quantitative method mainly uses simulation and discussion. Finally, the SLR summarizes open challenges for CCS in the future.

Attack Resilient Cloud-Based Control Systems for Industry 4.0

In recent years, since the cloud can provide tremendous advantages regarding storage and computing resources, the industry has been motivated to move industrial control systems to the cloud. However, the cloud also introduces significant security challenges since moving control systems to the cloud can enable attackers to infiltrate the system and establish an attack that can lead to damages and disruptions with potentially catastrophic consequences. Therefore, some security measures are necessary to detect these attacks in a timely manner and mitigate their impact. In this paper, we propose a security framework for cloud control systems that makes them resilient against attacks. This framework includes three steps: attack detection, attack isolation, and attack mitigation. We validate our proposed framework on a real testbed and evaluate its capability by subjecting it to a set of attacks. We show that our proposed solution can detect an attack in a timely manner and keep the plant stable, with high performance during the attack.

Cloud-based control system: a bibliometric analysis

Network control system (NCS) approaches for distributed closed-loop control systems have been established in industrial control. However, recent advancements in cloud computing provide scalable, elastic, and low-cost networked computing capabilities over the internet, which can be utilized as an extension of NCS, in this term, cloud-based control systems (CCS) with a potential replacement of the controller. The main objective of this research is to use bibliometric analysis to obtain insight into diachronic productivity, the significant effect of the published information on the research network, and research trends based on term co-occurrences of the CCS domain. The literature study employs the PRISMA method to construct necessary inclusion criteria such as keywords, databases, publication year, accessibility, and primary article, then Publish or Perish is used to generate RIS formatted file for network analysis of co-authorship and term co-occurrence with VOSviewer. The results showed that Yuanqing Xia was the most prolific author in terms of the total published article and total citations received, China was the country with the highest publication output, and Elsevier was the publisher with the most significant impact factor. CCS emerged in 2012, and current research trends include control system architecture, controller, algorithm, stability, and approach on intelligent manufacturing.

Addressing the range anxiety of battery electric vehicles with charging en route

Battery electric vehicles (BEVs) have emerged as a promising alternative to traditional internal combustion engine (ICE) vehicles due to benefits in improved fuel economy, lower operating cost, and reduced emission. BEVs use electric motors rather than fossil fuels for propulsion and typically store electric energy in lithium-ion cells. With rising concerns over fossil fuel depletion and the impact of ICE vehicles on the climate, electric mobility is widely considered as the future of sustainable transportation. BEVs promise to drastically reduce greenhouse gas emissions as a result of the transportation sector. However, mass adoption of BEVs faces major barriers due to consumer worries over several important battery-related issues, such as limited range, long charging time, lack of charging stations, and high initial cost. Existing solutions to overcome these barriers, such as building more charging stations, increasing battery capacity, and stationary vehicle-to-vehicle (V2V) charging, often suffer from prohibitive investment costs, incompatibility to existing BEVs, or long travel delays. In this paper, we propose Peer-to-Peer Car Charging (P2C2), a scalable approach for charging BEVs that alleviates the need for elaborate charging infrastructure. The central idea is to enable BEVs to share charge among each other while in motion through coordination with a cloud-based control system. To re-vitalize a BEV fleet, which is continuously in motion, we introduce Mobile Charging Stations (MoCS), which are high-battery-capacity vehicles used to replenish the overall charge in a vehicle network. Unlike existing V2V charging solutions, the charge sharing in P2C2 takes place while the BEVs are in-motion, which aims at minimizing travel time loss. To reduce BEV-to-BEV contact time without increasing manufacturing costs, we propose to use multiple batteries of varying sizes and charge transfer rates. The faster but smaller batteries are used for charge transfer between vehicles, while the slower but larger ones are used for prolonged charge storage. We have designed the overall P2C2 framework and formalized the decision-making process of the cloud-based control system. We have evaluated the effectiveness of P2C2 using a well-characterized simulation platform and observed dramatic improvement in BEV mobility. Additionally, through statistical analysis, we show that a significant reduction in carbon emission is also possible if MoCS can be powered by renewable energy sources.

Устройства вычислительной техники облачных систем управления и контроль их состояния

Проанализированы типовые устройства вычислительной техники для реализации облачных систем управления на основе оборудования центров обработки данных. Поставлена задача сформулировать виды контроля исследуемых устройств; найти расчетные соотношения для определения показателей, характеризующих виды контроля; а также определить средства технического мониторинга, контролирующие облачные системы управления. Проанализированы характеристики широко применяющегося в облачных системах управления сервиса резервного копирования и восстановления данных. Разработаны оценки для контроля параметров облачных систем управления. Typical computing devices for implementing cloud-based control systems based on data center equipment are analyzed. The task is to formulate the types of control of the studied devices; find the calculated ratio to determine indicators characterizing the types of control; and identify technical monitoring tools that control cloud-based management systems. The characteristics of the backup and data recovery service widely used in cloud management systems are analyzed. Estimates have been developed to control the parameters of cloud-based management systems.

Intelligent Traffic Control System Based on Cloud Computing and Big Data Mining

This article aims at discussing problems such as complex object types, large amount of data collection, high demand for transmission and calculation, and weak real-time scheduling and control ability in the construction of modern intelligent traffic information physical fusion network, cloud-based control system theory, modern intelligent traffic control network as the research object, and the physical design of the intelligent transportation information fusion cloud control system scheme. The scheme includes intelligent transportation edge control technology and intelligent transportation network virtualization technology. Based on intelligent traffic flow data, in the center of the cloud control management server using deep learning and overrun learning machine intelligence study methods, such as the forecast of traffic flow data for training, to predict urban road short-term traffic flow and congestion. Further up in the air by using intelligent optimization scheduling algorithm for real-time traffic flow control strategy, the simulation results show the effectiveness of the proposed method.

Cloud-Based Control Systems: Basics and Beyond

Using advanced information technologies (IT) and Internet of things (IOT) methodologies, cloud-based control systems (CCS) emerge as natural paradigm of networked control systems (NCS) and it being under development by enforcing the concept of control as a service (CaaS). Day by day NCS methods strongly promoted for distributed closed-loop control and industrial automation systems and extremely developed to study the influence of bidirectional communication constraints. In this paper, we examine the basics of cloud-based control systems (CCS) where the controller methods and algorithms are remotely placed in cloud far from the physical system. This creates a two parts: cyber part and physical part. There are several issues arose including networking delays, data dropouts and vulnerability to attacks. Research activities into these and related directions are discussed.