With the increasing complexity of mobile robot systems, multi-robot collaboration has become essential. However, such collaboration places heavy demands on both computational power and communication bandwidth. Cloud computing offers elastic and scalable infrastructure, allowing robotic functions to be encapsulated and delivered as on-demand services. Meanwhile, edge computing provides low-latency processing near the data source. The integration of these paradigms enables efficient coordination between cloud and edge infrastructures, forming a solution for real-time, scalable, and intelligent robotic control. This paper presents a cloud-edge collaborative system for mobile robots. Based on cloud-native technologies, the cloud control platform is implemented on Kubernetes (K8s) to orchestrate containerized control services, enabling dynamic resource allocation and automated deployment. An edge offloading mechanism is designed to support real-time data exchange and task coordination between robots and the cloud. Furthermore, a collaborative mapping and control framework is developed, where computationally intensive tasks such as map construction and trajectory generation are offloaded to the cloud, while real-time control and data pre-processing are executed at the edge. Experimental results in outdoor environments verify that the proposed framework significantly enhances system scalability and performance. These findings confirm the feasibility and generality of the proposed approach, highlighting its strong potential for large-scale deployment in cloud-based robotic systems.

As more Internet of Things (IoT) applications gradually move toward the cloud-edge collaborative model, the containerized scheduling of workflows extends from the cloud to the edge. However, given the high delay of the communication network, loose coupling of structure, and resource heterogeneity between the cloud and the edge, workflow containerization scheduling in the cloud-edge scenarios faces the difficulty of resource collaboration and application collaboration management. To address these two issues, we propose a KubeEdge-cloud–edge-scheduling scheme named KCES. This workflow containerization scheduling scheme includes a cloud-edge workflow scheduling engine for KubeEdge and incorporates workflow scheduling strategies for tasks’ horizontal roaming and vertical offloading. This article proposes a cloud-edge workflow scheduling model and node model, as well as a workflow scheduling engine designed to maximize cloud-edge resource utilization under the constraint of workflow task delay. A cloud-edge resource hybrid management technology is used to devise the cloud-edge resource evaluation and resource allocation algorithms to achieve cloud-edge resource collaboration. Based on the ideas of distributed functional roles and the hierarchical division of computing power, the horizontal roaming among the edges and cloud-edge vertical offloading strategies for workflow tasks are designed to realize cloud-edge application collaboration. Experimental results using a customized IoT application workflow instance demonstrate that KCES outperforms three comparing algorithms in total workflow time, average workflow time, and resource usage and features horizontal roaming and vertical offloading of workflow tasks.

Nonlinear model predictive control (NMPC) is a promising approach for controlling large-scale complex systems (LSS) that exhibit nonlinearity and constraints. However, its computational and real-time limitations hinder its widespread adoption. To address this challenge, we propose a cloud-edge cooperative model predictive control (MPC) scheme that overcomes these limitations while ensuring the desired control performance. Specifically, our proposed approach involves designing a cloud-based NMPC with a high-fidelity nonlinear model for the cloud layer. Meanwhile, the edge layer is equipped with a simplified backup linear model predictive control (LMPC) that uses a linearized model based on constraint tightening to mitigate model mismatch errors. Additionally, we develop an automatic strategy that employs a sliding weighted average method to switch between the cloud and edge controllers, enhancing the system’s reliability under non-ideal networking conditions. We provide a thorough analysis of the recursive feasibility and asymptotic average performance of the control scheme with different prediction models in the cloud and edge layers. To validate our approach, we apply it to a charging system for plug-in hybrid electric vehicles (PHEVs). Furthermore, we compare the performance and computation efficiency of our proposed cloud-edge cooperative MPC scheme with four other MPC schemes.Note to Practitioners—This work aims to overcome the challenge of reducing the computational burden and increasing the applicability of nonlinear model predictive control (NMPC) in large-scale complex systems (LSS) with input nonlinearity. To address these issues, we propose a cloud-edge cooperative MPC scheme that involves deploying the cloud layer on a remote cloud server and the edge layer locally onboard the system. The proposed scheme not only provides a practical solution for LSS with input nonlinearity but is also applicable to general nonlinear systems. Further research will focus on exploring the relevant theory and practical aspects of the scheme.

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$\%$and 74.35$\%$for two real-time control examples, and by a maximum of 85.10$\%$for a high-dimensional control example.Note to Practitioners—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.

Subspace identification (SID) has been widely used in system identification and control fields, since it can estimate system models while only relying on the input and output data using reliable numerical operations. However, the high-dimension Hankel matrices are involved to store these data and used to obtain the system models, which increases the computation amount of SID and makes SID unsuitable for the large-scale or real-time identification tasks. In this paper, a novel fast SID method based on cloud workflow processing approach and container technology is proposed to accelerate the traditional algorithm. First, a workflow establishment method of SID is designed to match the distributed cloud environment, based on the computational feature of each calculation stage. Second, a containerised cloud workflow processing system is established to execute the logic-and data-dependent SID workflow mission based on the Kubernetes system. Finally, the experiments show that the computation time is reduced by at most$91.6\%$for the large-scale SID mission and decreased to within 20 ms for the real-time mission parameter.Note to Practitioners—Subspace identification has became a widely used method in various fields, including power grids, chemical processing, data-driven control, and fault detection. However, as systems become larger and more complex, the computational challenges increase. To address this issue, this paper proposes a workflow-based method for subspace identification that can be executed in a cloud environment to accelerate the process. This note outlines the steps that practitioners can take to apply this method. The first step is to design a workflow structure as proposed method in this paper. This structure should be customized to fit the specific needs of the practitioner’s application. The second step is to build a containerized cloud workflow processing system that can execute the workflow. This system should be based on the Kubernetes system and designed to handle the specific computational requirements of the workflow. Practitioners who work in fields where computational efficiency is crucial for system identification operations can benefit from the proposed method. By following the steps outlined above, practitioners can streamline the process of subspace identification and achieve improvements in computational efficiency.

With the development of cloud computing, a growing number of workflows are deployed in cloud platform that can dynamically provide cloud resources on demand for users. In clouds, one basic problem is how to schedule workflow under the deadline constraint and minimize the execution cost. As the capability of cloud resources getting higher, the required cost is also rising. Capability of some resources exceeds the need of users, which leads to higher cost, and the budget of users should be considered. In this paper, a novel scheduling algorithm, named DB-ACO, is proposed to minimize the execution cost for the workflow with deadline and budget constraints. DB-ACO is verified on four typical scientific workflows, and the experiments results show it outperforms four state-of-the-art methods, especially for CyberShake.Note to Practitioners—Budget and deadline are important requirements for users in cloud computing, which are used as constraints. Extensive works have been devoted to minimize the cost of workflows execution with different scheduling strategies. However, most of them only consider one single constraint and assume the constraint is simple and loose, which is impractical in actual scenarios due to higher requirement of users. This paper investigates a novel scheduling algorithm DB-ACO to optimize cost under budget and deadline. DB-ACO combines heuristic and meta-heuristic, it uses ant colony optimization to optimize the execution cost under the deadline and budget constraints: each ant sorts tasks on the basis of the combination of the pheromone trail and heuristic information, the deadline and budget are distributed fairly to each task by a novel distribution method, then the service selection rules are introduced to build solution.

In this paper, we propose a modular soft-rigid hybrid (MSRH) approach for designing a highly anthropomorphous and dexterous robotic hand. This MSRH approach allows the robotic hand to possess an inherently soft and compliant interface suitable for pHRI while maintaining the structural rigidity of the robot with the usage of rigid skeletons. We share the details of the design rationale, fabrication and assembly methods, and evaluation of the first prototype. Even though the presented prototype is scaled to be 125% of the average human hand, the mechanical components only weigh less than 450 g. The modular design approach allows the MSRH hand to have low manufacturing costs and a short lead time. Creation of the presented prototype costs less than ${\$}$100 CAD and can be built in three days from scratch with two-person labour. The usage of pneumatic soft robotic actuators also provides flexibility over choosing the number of controlled DOF and joint coupling. This hence provides a new angle to tackle spatial constraint that normally arises in robotic hand design with increased anthropomorphism. Lastly, the dexterity of the proposed hand is demonstrated by evaluating the hand using taxonomies highly relevant to replicating tasks humans perform daily.

Heavy computational load for solving nonconvex problems for large-scale systems or systems with real-time demands at each sample step has been recognized as one of the reasons for preventing a wider application of nonlinear model predictive control (NMPC). To improve the real-time feasibility of NMPC with input nonlinearity, we devise an innovative scheme called cloud-based computational model predictive control (MPC) by using an elaborately designed parallel multiblock alternating direction method of multipliers (ADMMs) algorithm. This novel parallel multiblock ADMM algorithm is tailored to tackle the computational issue of solving a nonconvex problem with nonlinear constraints. It is ensured that the designed algorithm converges to a locally optimal solution of the optimization problem under reasonable assumptions by using the Kurdyka–Łojasiewicz property. With the help of this distributed optimization algorithm, a computational MPC scheme is developed, which can transform the NMPC optimization problem into a set of subproblems only associated with the decision variables at one prediction step. Through the parallel computing algorithm, the computational MPC can deal with large computational loads caused by high-dimensional optimization problems, and improve computational efficiency. Furthermore, to allow for a more efficient implementation of the developed computational MPC and alleviate local calculation loads, a cloud-based computational MPC architecture is devised, which makes significantly better use of computational resources provided by a cloud server. An important advantage of this architecture with Docker container to implement parallelization is that it does not lead to large increases in the solution time regardless of how long the prediction horizon is set. Finally, the developed cloud-based computational MPC architecture is trialed on a group of plug-in hybrid electric vehicles (PHEVs).

Nowadays, more and more workflow applications with different computing requirements are migrated to clouds and executed with cloud resources. Workflow scheduling becomes a critical problem in the cloud environment, which focuses on meeting various quality of service (QoS) constraints. Workflow reliability and energy consumption are two essential parts in clouds and minimizing energy consumption for scheduling workflow with the reliability constraint is a challenging issue. In response to the challenge, we propose a workflow scheduling algorithm named REWS to reduce energy consumption and satisfy workflow reliability constraints. In REWS, a new sub-reliability constraint prediction strategy is adopted to break down the workflow reliability constraint to task sub-reliability constraints and the effectiveness of this strategy is proved. Moreover, an update method is adopted to adjust the task sub-reliability constraint for reducing energy consumption. In addition, a brief system framework which consists of five parts: workflow analyzer, reliability decomposer, resource manager, workflow scheduler and feedback processer is built to support the algorithm implementation of REWS. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed REWS approach. The results demonstrate the superiority of REWS as compared with the state-of-the-art algorithms.Note to Practitioners—Workflow scheduling is a challenging issue in emerging trends of the cloud environment that focuses on satisfying various QoS constraints. In this paper, we investigate a reliability-aware and energy-efficient workflow scheduling problem in cloud computing. A novel workflow scheduling algorithm called REWS, is designed to reduce the energy consumption and meet the workfolw reliability constraint. The basic idea of REWS is to divide the workflow reliability constraint into task sub-reliability constraints and schedule tasks with an energy-efficient scheduling strategy. We conduct the experiments to evaluate the proposed REWS and the results demonstrate that REWS outperforms the state-of-the-art algorithms.

With the migration of more and more workflows to clouds, the workflow scheduling in clouds (WSC) becomes a critical problem. Although many algorithms have been presented for WSC, there is still room and need for improvement. This paper formulates WSC as a constrained optimization problem that optimizes workflow execution cost within a workflow deadline constraint and proposes a fully hybrid workflow scheduling algorithm, called HPCP-PSO to solve it. Unlike previous works, HPCP-PSO is based on the repeated and alternated execution of two different methods, namely, the heuristic IaaS Cloud Partial Critical Paths (IC-PCP) and meta-heuristic Particle Swarm Optimization (PSO). Moreover, HPCP-PSO incorporates with two novel designs: 1) a new solution encoding strategy not only to sufficiently embody the elasticity of cloud resources, but also to reflect the scheduling relationship between assigned and unassigned tasks; 2) a solution repair strategy on each infeasible lease process to utilize a user-defined deadline more effectively and enhance the solution efficiency of the algorithm. Extensive experiments are conducted on four real-world scientific workflows and the results show that compared with IC-PCP, PSO, and HGSA, the proposed algorithm outperforms them on average by 35.83%, 70.53%, and 87.71% in terms of workflow execution cost.

This article considers the data-driven optimal control problem for unknown linear systems subject to system constraints from a computation efficiency improvement perspective. We propose a novel Computational Data-enabled Predictive Control (CDeePC) algorithm in a cloud environment, built on a recent work DeePC[1]. First, massive input–output data samples are precollected to describe the system input/output behavior through a behavioral systems theory approach, which might result in a large-scale online optimization problem with high dimensional decision variables. To solve it, in CDeePC, a multiblock alternating direction method of multipliers (ADMMs) algorithm is then employed, in which a solution to the original large problem can be calculated by solving iteratively a set of small subproblems. Next, to further improve the robustness of the algorithm, an online feedback CDeePC (OCDeePC) algorithm is proposed by utilizing real-time data samples to better capture the system characterization. Two analytic inversion formulas are derived for fast computation of time-varying controller parameters based on the result at the previous time. After that, we discuss the computational complexity and provide the convergence proof of proposed algorithms. Finally, after designing a cloud-based control architecture and formulating the iterative scheme to compute control actions as a workflow, we construct and deploy the proposed controllers as a service in the cloud. A case study of tracking control of wheeled mobile robots is provided to illustrate the efficacy of the proposed algorithms.

In this article, resilient strategy design is investigated for a cloud control system (CCS) subject to jamming attack. In the presence of jammer, signals, including measurements and control inputs between cloud server and physical plant may be interfered, which degrades the signal-to-interferencelus-noise ratio and further leads to packet dropout of signals. A Stackelberg game is applied to depict the interaction of transmitter and jammer. On account of game equilibria of two players, controller is devised for CCS with time-varying delay. A novel cross-layer dynamic pricing mechanism is devised to drive the disturbance attention performance of CCS to a desired region under constrained information and time-varying networks. The simulation results are provided to verify the effectiveness of the proposed methodology.