“模糊控制”模型在疾病流行预测中的应用

本文将预测过程模拟成一个“模糊控制系统”,以预测因子作为系统的输入,预测量作为系统的输出。先根据历史资料确定系统的特性,建立预测模型;然后,在一定输入条件下,通过系统特性的作用,即可求得预报输出。这一模型本质上属语言控制模型,不需要知道输入量与输出量之间的函数关系,与经典的数学模型相比更具有广泛的适应性,文中用某地1977～1987年的流脑发病资料建立模型,对该地1988～1991四年的流脑年发病率进行预测,收到了良好效果,实际年发病率均在预测区间内。

Support vector machine based nonlinear model multi-step-ahead optimizing predictive control

A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.

A practical nonlinear controller for levitation system with magnetic flux feedback

This work proposes a practical nonlinear controller for the MIMO levitation system. Firstly, the mathematical model of levitation modules is developed and the advantages of the control scheme with magnetic flux feedback are analyzed when compared with the current feedback. Then, a backstepping controller with magnetic flux feedback based on the mathematical model of levitation module is developed. To obtain magnetic flux signals for full-size maglev system, a physical method with induction coils installed to winding of the electromagnet is developed. Furthermore, to avoid its hardware addition, a novel conception of virtual magnetic flux feedback is proposed. To demonstrate the feasibility of the proposed controller, the nonlinear dynamic model of full-size maglev train with quintessential details is developed. Based on the nonlinear model, the numerical comparisons and related experimental validations are carried out. Finally, results illustrating closed-loop performance are provided.

Leaching kinetics of antimony-bearing complex sulfides ore in hydrochloric acid solution with ozone

The leaching kinetics of Sb and Fe from antimony-bearing complex sulfides ore was investigated in HCl solution by oxidation?leaching with ozone.The effects of temperature,HCl concentration,stirring speed and particle size on the process were explored.It is found that the recoveries of Sb and Fe reach86.1%and28.8%,respectively,when the reaction conditions are4.0mol/L HCl,900r/min stirring speed at85°C with<0.074mm particle size after50min leaching.XRD analysis indicates that no new solid product forms in the leaching residue and the leaching process can be described by shrinking core model.The leaching of Sb corresponds to diffusion-controlled model at low temperature(15?45°C)and mixed-controlled model at high temperature(45?85°C),and the apparent activation energies are6.91and17.93kJ/mol,respectively.The leaching of Fe corresponds to diffusion-controlled model,and the apparent activation energy is1.99kJ/mol.Three semi-empirical rate equations are obtained to describe the leaching process.

Modeling and simulation of a mini AUV in spatial motion

Accurate modeling and simulation of autonomous underwater vehicle （AUV） is essential for autonomous control and maneuverability research. In this paper, a mini AUV- ＂MAUV-Ⅱ＂ was researched and the nonlinear mathematic model of the AUV in spatial motion was derived based on momentum theorem. The forces acting on AUV were resolved to several modules which were expressed in matrix form. Based on the motion model and combined with virtual reality technology, a motion simulation system was constructed. Considering the characteristic of ＂MAUV-Ⅱ ＂, the heading control and depth control were simulated by adopting S-surface control method. A long distance traveling simulation experiment based on target planning was also done. The simulation results show that the ＂MAUV-Ⅱ＂ has good spatial maneuverability, and verify the feasibility and reliability of control software.

Dynamic modeling and simulation for nonholonomic welding mobile robot

Based on the Newton-Euler method, the dynamic behaviors of the left and right driving wheels and the robot body for the welding mobile robot were derived. In order to realize the combination control of body turning and slider adjustment, the dynamic behaviors of sliders were also investigated. As a result, a systematic and complete dynamic model for the welding mobile robot was constructed. In order to verify the effectiveness of the above model, a sliding mode tracking control method was proposed and simulated, the lateral error stabilizes between -0.2 mm and ＋0.2 mm, and the total distance of travel for the slider is consistently within 4-2 ram. The simulation results verify the effectiveness of the established dynamic model and also show that the seam tracking controller based on the dynamic model has excellent performance in terms of stability and robustness. Furthermore, the model is found to be very suitable for practical applications of the welding mobile robot.

SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control

A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control （ILC） and model predictive control （MPC） is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By min- imizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type （P- t-we） ILC despite the model error and disturbances.

Dynamic modelling of 2-DOF luffing mechanism with serial closed kinematic chains

Luffing mechanism is a key component of the construction machinery.This paper proposes a two degree of freedom(2-DOF)luffing mechanism,which has one more pair of driving cylinders than the single DOF luffing mechanism,to improve the performance of the machinery.To establish the dynamic model of the 2-DOF luffing mechanism,firstly,we develop a hierarchical method to deduce the Jacobian matrix and Hessian matrix for obtaining the kinematics equations.Subsequently,we divide the luffing mechanism into six bodies considering actuators,and deduce the kinetic equations of each body by the Newton-Euler method.Based on the dynamic model,we simulate the luffing process.Finally,a prototype is built on a pile driver to validate the model.Simulations and experiments show that the dynamic model can reflect the dynamic properties of the proposed luffing mechanism.And the control strategy that the front cylinders retract first shows better mechanical behavior than the other two control strategies.This research provides a reference for the design and application of 2-DOF luffing mechanism on construction machinery.The modeling approach can also be applied to similar mechanism with serial closed kinematic chains,which allows to calculate the dynamic parameters easily and exactly.

Min-max fuzzy model predictive tracking control of boiler-turbine system for ultra-supercritical units

To improve the control performance of nonlinear ultra-supercritical(USC)thermal power units,an improved min-max fuzzy model predictive tracking control(FMPTC)strategy is proposed.First,a T-S fuzzy model is established to approximate the dynamics of the nonlinear boiler-turbine system.Then,based on an extended fuzzy model containing state variables and output variables,a min-max FMPTC is derived for output regulation while ensuring the closed-loop system stability and the inputs in their given constraints.For greater controller design freedom,the developed controller adopts a new state-and output-based objective function.In addition,the observer estimation error is regarded as a bounded disturbance,ensuring the stability of the entire closed-loop control system.Simulation results on a 1000 MW USC boiler-turbine model illustrate the effectiveness of the proposed approach.

Cavitation Passive Control on Immersed Bodies

This paper introduces a new idea of controlling cavitation around a hydrofoil through a passive cavitation controller called artificial cavitation bubble generator （ACG）. Cyclic processes, namely, growth and implosion of bubbles around an immersed body, are the main reasons for the destruction and erosion of the said body. This paper aims to create a condition in which the cavitation bubbles reach a steady-state situation and prevent the occurrence of the cyclic processes. For this purpose, the ACG is placed on the surface of an immersed body, in particular, the suction surface of a 2D hydrofoil. A simulation was performed with an implicit finite volume scheme based on a SIMPLE algorithm associated with the multiphase and cavitation model. The modified k-ε RNG turbulence model equipped with a modification of the turbulent viscosity was applied to overcome the turbulence closure problem. Numerical simulation of water flow over the hydrofoil equipped with the ACG shows that a low-pressure recirculation area is produced behind the ACG and artificially generates stationary cavitation bubbles. The location, shape, and size of this ACG are the crucial parameters in creating a proper control. Results show that the cavitation bubble is controlled well with a well-designed ACG.

Behavior Measurement Model Based on Prediction and Control of Trusted Network

In order to construct the trusted network and realize the trust of network behavior,a new multi-dimensional behavior measurement model based on prediction and control is presented.By using behavior predictive equation,individual similarity function,group similarity function,direct trust assessment function,and generalized predictive control,this model can guarantee the trust of an end user and users in its network.Compared with traditional measurement model,the model considers different characteristics of various networks.The trusted measurement policies established according to different network environments have better adaptability.By constructing trusted group,the threats to trusted group will be reduced greatly.Utilizing trusted group to restrict individuals in network can ensure the fault tolerance of trustworthiness of trusted individuals and group.The simulation shows that this scheme can support behavior measurement more efficiently than traditional ones and the model resists viruses and Trojans more efficiently than older ones.

A state estimation based constrained model predictive control system

In this paper,the model predictive control based on the state estimation for a constrained system isinvestigated.By modifying the constraints for the predictive state,the control sequence becomes feasiblefor the real system,i.e.,the system state is guaranteed to be in the constraint domain.It s also provedthat the close-loop system is asymptotically stable and the system state converges to the origin.The conclusionis verified through simulation.

Analysis of the development and application of incomplete contract theory based on the basic theory of economics

Incomplete contract theory （GHM-model）, namely, GHM model, Grossman-Hart-Moore model, GHM model or called ownership - control model by Grossman and Hart （Grossman ＆ Hart, 1986）, it is established by Hart and Moore （Hart ＆ Moore, 1990） and other cofounder, so this theory is also known as GHM theory or GHM model. Domestic scholars generally call their theory incomplete contract theory or incomplete contract theory, because the theory is ba^ed on the following analytical framework： They use the incompleteness of contracts for research as starting point to property or （ residual ） optimal control with Ge for research purposes. It is the most important analytical tool for analyze business theory and corporate governance structures with Ge control incentives to obtain the information. GHM model directly inherit the transaction cost theory by Coase, and Williamson, and it is a critical development. Among them, the 1986 model is mainly to solve the integration problem of assets, in 1990, the model evolved into a general model of asset ownership.

Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy

The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an online SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush–Kuhn–Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately.The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for largemagnitude set point changes and variations in process parameters.

Nonlinear model predictive control with guaranteed stability based on pseudolinear neural networks

A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor.It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.

A New Model Parameter Identification Technique for Magnetorheological Dampers

Magnetorheological （MR） Dampers offer rapid variation in damping properties, making them ideal in semi-active control of structures. They potentially offer highly reliable operation and can be viewed as fail safe, in that in the worst case, they become passive dampers. Perfect understanding of the response is necessary when implementing these in operation in conjunction with a control mechanism. There are many models used to predict the behavior of MR dampers. One of these is the Bouc-Wen model. It is extremely popular as it is numerically tractable, very versatile and can exhibit a wide range of hysteretic behavior. It is necessary to first identify the characteristic parameters of the model before response prediction is possible. However, characteristic parameters identification of the Bouc-Wen model needs an experimental base, which has its own limitations. The extraction of these characteristic parameters by trial and error and optimization techniques leaves significant difference between observed and simulated results. This paper deals with a new approach to extract characteristic parameters for the Bouc-Wen model.

Dynamic alarm prediction for critical alarms using a probabilistic model

Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper,we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.

Study on Control Strategy of Sulfur Dioxide Concentration in the Urban Area of Shijiazhuang

Two Gaussian air quality dispersion models, the industrial source complex short-term model (ISCST3) with and without modification have been used to simulate the pollutant concentration distribution in urban areas based on the meteorological data and the emissions distribution of sulfur dioxide. The verified data show that the modified model is more accurate in the urban area of Shijiazhuang. Using the modified model predictions, the control strategies of sulfur dioxide in the urban area have been studied, and the result show that the second long-term (to 2010) strategy can mitigate air pollution significantly and maintain pollution levels within permissible limits.

A leveling mechanism for the platform based on booms-constraint control of aerial vehicle

In order to achieve an automatic leveling function for work platforms of aerial vehicles with mixed-booms( MAV) in full elevating domain,an auto-leveling mechanism for the platform is proposed based on a control method of booms-constraint,where mixed-boom structures and elevating characteristics are considered. Three models of constraint strategies include non-constraint model,elevating constraint model and lowering constraint model,which is designed to meet the leveling requirements in full working extent. Through the hydro-mechatronic unified modeling,a virtual prototype model is set up based on the auto-leveling mechanism,and leveling performances of the platform are studied during booms elevating to the maximum working height and extent. Simulation results show that the control method of booms-constraint can realize auto-leveling of the platform under two typical working conditions,meanwhile a leveling deviation appears at the constrained point,but the platform inclination is adjusted in the permissible range. The control method does not only restrict booms' freedom elevating to a certain extent,but also impacts the booms extending to the maximum working range. Experimental results verify that the auto-leveling mechanism based on booms-constraint control is valid and rational,which provides an effective technology approach for development of the platform leveling of MAV.