Research on cubic polynomial acceleration and deceleration control model for high speed NC machining

To satisfy the need of high speed NC (numerical control) machining, an acceleration and deceleration (acc/dec) control model is proposed, and the speed curve is also constructed by the cubic polynomial. The proposed control model provides continuity of acceleration, which avoids the intense vibration in high speed NC machining. Based on the discrete characteristic of the data sampling interpolation, the acc/dec control discrete mathematical model is also set up and the discrete expression of the theoretical deceleration length is obtained furthermore. Aiming at the question of hardly predetermining the deceleration point in acc/dec control before interpolation, the adaptive acc/dec control algorithm is deduced from the expressions of the theoretical deceleration length. The experimental result proves that the acc/dec control model has the characteristic of easy implementation, stable movement and low impact. The model has been applied in multi-axes high speed micro fabrication machining successfully.

Study on the Control Model of Rural Non-point Source Pollution——Taking Ninghe County in Tianjin as an Example

[Objective] The aim was to study the control model of rural non-point source pollution.[Method] Taking Ninghe County(a typical agricultural county in Tianjin) as an example,the current development of local economy and society and characteristics of rural non-point source pollution were studied firstly,then the control model of rural non-point source pollution suitable for Ninghe County was constructed,and its environmental and economic benefits were analyzed finally.[Result] According to the sources of non-point source pollution,the control model of rural non-point source pollution in Ninghe County was divided into three modules from the aspects of planting industry,livestock raising industry and rural living.The main content of non-point source pollution control module of planting industry was composed of rational utilization of chemicals,water-saving irrigation,diversified utilization of straw etc.Non-point source pollution control module of livestock raising industry focused on the site selection of farm or raising zones,choice of raising mode,comprehensive utilization of livestock waste etc.In the non-point source pollution control module of rural living,villages were divided into two types(small town and ecological village) based on various geographical positions.In a word,these three modules were interrelated and targeted for non-point source pollution control in villages under different development situations.[Conclusion] The study could provide references for the rural non-point source pollution control in Hai River basin and other regions of Northern China.

Security analysis of access control model in hybrid cloud based on security entropy

To resolve the problem of quantitative analysis in hybrid cloud,a quantitative analysis method,which is based on the security entropy,is proposed.Firstly,according to the information theory,the security entropy is put forward to calculate the uncertainty of the system' s determinations on the irregular access behaviors.Secondly,based on the security entropy,security theorems of hybrid cloud are defined.Finally,typical access control models are analyzed by the method,the method's practicability is validated,and security and applicability of these models are compared.Simulation results prove that the proposed method is suitable for the security quantitative analysis of the access control model and evaluation to access control capability in hybrid cloud.

Causes and typical control model of wind-drift sandy lands in abandoned channel of the Yellow River

The historical formation and development of the abandoned channel of the Yellow River is reviewed and its causes of formation and present condition of prevention and control are analyzed in this paper. Based on this analysis, some ideas about control, critical problems and countermeasures in the next period are proposed with two typical control models as examples. We suggest that in preventing and controlling the wind-drift sandy lands in the region, the emphasis should be to develop, with a greatly expanded effort, a recycling economy. This should realize a combination of two ideas, i.e. integrate combating desertification with a structural adjustment of agricultural and an increase in the income of farmers.

A CONFERENCE CONTROL MODEL BETWEEN A WEB SERVER AND A TELECOM APPLICATION SERVER

The paper proposes a conference control model between a web server and a telecom application server,referred to as the Conference Directed Graph(CDG) ,and describes an asynchronous communication mechanism between them. The Corba Interface Definition Language(IDL) interfaces are defined,and a message sequence chart is illustrated. This web conference control model provides conference users with a new approach to manage and control a conference and the participants. The performance of the system prototype is analyzed and verified in the 863 project named "The Multi-media and Mobile Services Enabled Soft-switch System".

A Study of the system control model of caisson dewatering of the north anchorage of Taizhou Bridge

A caisson foundation is applied to the north anchorage of Taizhou Yangtze River Highway Bridge of which the initial caisson sinking requires dewatering. Since the caisson foundation is quite close to nearby buildings, a system control model is established with source (sink) distribution and intensity being the object function, minimum requirements of settlement and deformation of surroundings caused by dewatering and dynamic water levels during different working procedures being constraints, and the design parameter of pumping wells being the variable,so as to lower the jeopardizing of surrounding buildings, which provides a new method for active control over settlement during dewatering. Such a method of dewatering based on system control model should be of significance for similar projects involving dewatering.

TEMPERATURE FIELD CONTROL PRINCIPLE AND CONTROL MODELS FOR CRYOGENIC MACHINING SYSTEM

Cryogenic machining is a new technology which makes use of the special mechanicalprcperties of niaterials in a cryogenic or a 'super cold' state for machining. The control of acryogenic machining system(CMS)is one of the key problems which need to be solved for practi-cal cryogenic machining A temperature field control principle is presented which calculates theheat source temperature in the light of the tool temperature field information, and a control mod-el of three-dimensional dynamic temperature field for CMS is established, and the boundary con-ditions and the heat source of temperature field in orthogonal cutting are discussed. Based on theinvestigation of the control feature and technique. a prototype system for controlling CMS is crea-ted.

Research on Technological Process Control Model of Reverse Logistics in Manufacturing System

This paper has found out some important input factors of reverse logistics in manufacturing system throuth analysis and summary,and established four kinds of technological process control models of reverse logistics in manufacturing system according to different processing methods. These models embed each other that form a cubic control system of reverse logistics.

Simulation and control model for interactions among process parameters of directional solidification continuous casting

On the basis of analyzing the principles, equipment and control needs of directional solidification continuous casting (DSCC) process, the building and fulfilling methods of control model of DSCC procedure by neural network control (NNC) method were proposed and discussed. Combining the experimental researches, firstly the computer is used to simulate the effects of those solidification parameters on destination control variable (S/L interface) and the reactions among those parameters during DSCC procedure; secondly many training samples can be obtained. Moreover, after these samples are input into neural network software (NNs) and trained, the control model can be built.

Research on the Control Model of Coal Flotation

The Fuzzy Logic Control (FLC) was used in the nonlinear, multivariable, and time varying system to control coal flotation. The control algorithm includes two different methods of adding reagents. First, the reagent is coarsely adjusted according to the solid content in the feed and the reagent consumption per ton of coal; And then it is finely adjusted under the control of the Fuzzy Logic Algorithm according to the ash content of the clean coal. The industrial experiments and the site application show that this control model can keep a stable ash level of clean coal and cut down obviously the reagent consumption.

A New Fuzzy Control Model for Kidney Patients

The final diagnosis of some diseases depends on many factors and information. Considering all these factors and reviewing all of them is a difficult process, thus providing a mathematical model that can simultaneously consider all these factors is a great help for physicians to diagnose and treat these diseases. In this paper, we propose a new fuzzy control model for kidney transplantation patients. For the inference and conclusion of this model, we use Mamdani approach. Also we employ a new method to smooth the well-known non-smooth piecewise membership functions, i.e. trapezoidal and half trapezoidal membership functions.

The Research of Role Tree-Based Access Control Model

Towards the crossing and coupling permissions in tasks existed widely in many fields and considering the design of role view must rely on the activities of the tasks process,based on Role Based Accessing Control (RBAC) model,this paper put forward a Role Tree-Based Access Control (RTBAC) model. In addition,the model definition and its constraint formal description is also discussed in this paper. RTBAC model is able to realize the dynamic organizing,self-determination and convenience of the design of role view,and guarantee the least role permission when task separating in the mean time.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

Evaluation of the Impact of Refined Quality Control Management Model on the Qualified Rate of Disinfection and Sterilization of Surgical Instruments in the Sterilization Supply Center

Objective:To evaluate the application value of a refined quality control management model for a sterilization supply center.Methods:A retrospective analysis was conducted on the work situation of the sterilization supply center from January 2021 to January 2023.The work situation before January 31,2022,was classified as the control group;a routine quality control management model was implemented,and the work situation after January 31,2022,was classified as the observation group.The quality of medical device management and department satisfaction between the two groups were compared.Results:The timely recovery and supply rate,classification and cleaning pass rate,disinfection pass rate,packaging pass rate,sterilization pass rate,and department satisfaction score in the observation group were all higher than those of the control group(P<0.05).Conclusion:Implementing a refined quality control management model in the sterilization supply center can improve the quality management level of medical devices and department satisfaction and is worthy of promotion.

A Loss-model-based Efficiency Optimization Control Method for Induction Traction System of High-speed Train under Emergency Self-propelled Mode

Increasing attention has been paid to the efficiency improvement of the induction traction system of high-speed trains due to the high demand for energy saving. In emergency self-propelled mode, however, the dc-link voltage and the traction power of the motor are significantly reduced, resulting in decreased traction efficiency due to the low load and low speed operations. Aiming to tackle this problem, a novel efficiency improved control method is introduced to the emergency mode of high-speed train traction system in this paper. In the proposed method, a total loss model of induction motor considering the behaviors of both iron and copper loss is established. An improved iterative algorithm with decreased computational burden is then introduced, resulting in a fast solving of the optimal flux reference for loss minimization at each control period. In addition, considering the parameter variation problem due to the low load and low speed operations, a parameter estimation method is integrated to improve the controller's robustness. The effectiveness of the proposed method on efficiency improvement at low voltage and low load conditions is demonstrated by simulated and experimental results.

Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system

Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.

Finite-Time Stabilization for Constrained Discrete-time Systems by Using Model Predictive Control

In this paper, a model predictive control(MPC)framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system.Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically,and is supported by simulation examples.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

Due to the impact of source-load prediction power errors and uncertainties,the actual operation of the park will have a wide range of fluctuations compared with the expected state,resulting in its inability to achieve the expected economy.This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control(MPC).In the day-ahead stage,an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min.In order to cope with power fluctuations of photovoltaic,wind turbine and conventional load,MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park.Finally,the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.