Digital twin-enabled adaptive scheduling strategy based on deep reinforcement learning

The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method,such as self-regulation and self-learning capabilities.While traditional scheduling methods cannot meet these needs due to their rigidity.Self-learning is an inherent ability of reinforcement learning(RL) algorithm inhered from its continuous learning and trial-and-error characteristics.Self-regulation of scheduling could be enabled by the emerging digital twin(DT) technology because of its virtual-real mapping and mutual control characteristics.This paper proposed a DT-enabled adaptive scheduling based on the improved proximal policy optimization RL algorithm,which was called explicit exploration and asynchronous update proximal policy optimization algorithm(E2APPO).Firstly,the DT-enabled scheduling system framework was designed to enhance the interaction between the virtual and the physical job shops,strengthening the self-regulation of the scheduling model.Secondly,an innovative action selection strategy and an asynchronous update mechanism were proposed to improve the optimization algorithm to strengthen the self-learning ability of the scheduling model.Lastly,the proposed scheduling model was extensively tested in comparison with heuristic and meta-heuristic algorithms,such as wellknown scheduling rules and genetic algorithms,as well as other existing scheduling methods based on reinforcement learning.The comparisons have proved both the effectiveness and advancement of the proposed DT-enabled adaptive scheduling strategy.

Client-Centric Adaptive Scheduling of Service-Oriented Applications

The paper proposes a client-centric computing model that allows for adaptive execution of service-oriented applications. The model can flexibly dispatch application tasks to the client side and the network side, dynamically adjust an execution scheme to adapt to environmental changes, and thus is expected to achieve better scalability, higher performance and more controllable privacy. Scheduling algorithms and the rescheduling strategies are proposed for the model. Experiments show that with the model the performance of service-oriented application execution can be improved.

Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

In order to deal with the dynamic production environment with frequent fluctuation of processing time,robotic cell needs an efficient scheduling strategy which meets the real-time requirements.This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process.The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler,which can adjust the scheduling rules according to the current production status.In the process of establishing scheduler,how to choose essential attributes is the main difficulty.In order to solve the low performance and low efficiency problem of embedded feature selection method,based on the application of Extreme Gradient Boosting model(XGBoost)to obtain the adaptive scheduler,an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization(PSO)is employed to acquire the optimal subset of features.The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules.At the same time,it can meet the demand of real-time scheduling.

Using Adaptive Gain Scheduling LQR Method Control of Arm Driven Inverted Pendulum System Based on PIC18F4431

The arm driven inverted pendulum system is a highly nonlinear model, muhivariable and absolutely unstable dynamic system so it is very difficult to obtain exact mathematical model and balance the inverted pendulum with variable position of the ann. To solve this problem, this paper presents a mathematical model for arm driven inverted pendulum in mid-position configuration and an adaptive gain scheduling linear quadratic regulator control method for the stabilizing the inverted pendulum. The proposed controllers for arm driven inverted pendulum are simulated using MATLAB-SIMULINK and implemented on an experiment system using PIC 18F4431 mieroeontroller. The result of experiment system shows the control performance to be very good in a wide range stabilization of the arm position.

Self-configuring scheduling scheme for IPv6 traffic with multiple QoS classes

This paper proposes a new queuing model and adaptive scheduling scheme which realizes multi-class QoS mechanism under DiffServ architecture. The queuing model is composed of two parallel output subqueues, each output sub-queue adopts random drop algorithm by setting different buffer threshold for different class traffic, so it can provide multi-class QoS. The new proposed scheduling scheme which adaptively changes the parameter A can guarantee the performance target of high class traffic, in the mean time, improve the QoS of low classes traffic.

Computer Control Techniques of Phased Array Radars

The computer control techniques applicable to electronically scanned multifunction radars are presented. The software and hardware architecture for the real time control and the data processing within a phased array radar are described. The software system comprising a number of tasks is written in C language and implemented. The results show that the algorithm for the multitask adaptive scheduling and the multitarget data processing is suitable for multifunction phased array radars.

A PSL Ontology-based Shop Floor Dynamical Scheduler Design

Due to the complex,uncertainty and dynamics in the modern manufacturing environment,a flexible and robust shop floor scheduler is essential to achieve the production goals.A design framework of a shop floor dynamical scheduler is presented in this paper.The workflow and function modules of the scheduler are discussed in detail.A multi-step adaptive scheduling strategy and a process specification language,which is an ontology-based representation of process plan,are utilized in the proposed scheduler.The scheduler acquires the dispatching rule from the knowledge base and uses the build-in on-line simulator to evaluate the obtained rule.These technologies enable the scheduler to improve its fine-tune ability and effectively transfer process information into other heterogeneous information systems in a shop floor.The effectiveness of the suggested structure will be demonstrated via its application in the scheduling system of a manufacturing enterprise.

A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations

The parallel computation capabilities of modern graphics processing units （GPUs） have attracted increasing attention from researchers and engineers who have been conducting high computational throughput studies. However, current single GPU based engineering solutions are often struggling to fulfill their real-time requirements. Thus, the multi-GPU-based approach has become a popular and cost-effective choice for tackling the demands. In those cases, the computational load balancing over multiple GPU ＂nodes＂ is often the key and bottleneck that affect the quality and performance of the real=time system. The existing load balancing approaches are mainly based on the assumption that all GPU nodes in the same computer framework are of equal computational performance, which is often not the case due to cluster design and other legacy issues. This paper presents a novel dynamic load balancing （DLB） model for rapid data division and allocation on heterogeneous GPU nodes based on an innovative fuzzy neural network （FNN）. In this research, a 5-state parameter feedback mechanism defining the overall cluster and node performance is proposed. The corresponding FNN-based DLB model will be capable of monitoring and predicting individual node performance under different workload scenarios. A real=time adaptive scheduler has been devised to reorganize the data inputs to each node when necessary to maintain their runtime computational performance. The devised model has been implemented on two dimensional （2D） discrete wavelet transform （DWT） applications for evaluation. Experiment results show that this DLB model enables a high computational throughput while ensuring real=time and precision requirements from complex computational tasks.

Low-cost fault tolerance in evolvable multiprocessor systems:a graceful degradation approach

The evolvable multiprocessor (EvoMP), as a novel multiprocessor system-on-chip (MPSoC) machine with evolvable task decomposition and scheduling, claims a major feature of low-cost and efficient fault tolerance. Non-centralized control and adaptive distribution of the program among the available processors are two major capabilities of this platform, which remarkably help to achieve an efficient fault tolerance scheme. This letter presents the operational as well as architectural details of this fault tolerance scheme. In this method, when a processor becomes faulty, it will be eliminated of contribution in program execution in remaining run-time. This method also utilizes dynamic rescheduling capability of the system to achieve the maximum possible efficiency after processor reduction. The results confirm the efficiency and remarkable advantages of the proposed approach over common redundancy based techniques in similar systems.