Global Transmission Dynamics of a Schistosomiasis Model and Its Optimal Control

Drug treatment, snail control, cercariae control, improved sanitation and health education are the effective strategies which are used to control the schistosomiasis. In this paper, we consider a deterministic model for schistosomiasis transmission dynamics in order to explore the role of the several control strategies. The global stability of a schistosomiasis infection model that involves mating structure including male schistosomes, female schistosomes, paired schistosomes and snails is studied by constructing appropriate Lyapunov functions. We derive the basic reproduction number R0 for the deterministic model, and establish that the global dynamics are completely determined by the values of R0. We show that the disease can be eradicated when R0?≤1;otherwise, the system is persistent. In the case where ?R0?>1, we prove the existence, uniqueness and global asymptotic stability of an endemic steady state. Sensitivity analysis and simulations are carried out in order to determine the relative importance of different control strategies for disease transmission and prevalence. Next, optimal control theory is applied to investigate the control strategies for eliminating schistosomiasis using time dependent controls. The characterization of the optimal control is carried out via the Pontryagins Maximum Principle. The simulation results demonstrate that the insecticide is important in the control of schistosomiasis.

MODELING, VALIDATION AND OPTIMAL DESIGN OF THE CLAMPING FORCE CONTROL VALVE USED IN CONTINUOUSLY VARIABLE TRANSMISSION

Associated dynamic performance of the clamping force control valve used in continuously variable transmission （CVT） is optimized. Firstly, the structure and working principle of the valve are analyzed, and then a dynamic model is set up by means of mechanism analysis. For the purpose of checking the validity of the modeling method, a prototype workpiece of the valve is manufactured for comparison test, and its simulation result follows the experimental result quite well. An associated performance index is founded considering the response time, overshoot and saving energy, and five structural parameters are selected to adjust for deriving the optimal associated performance index. The optimization problem is solved by the genetic algorithm （GA） with necessary constraints. Finally, the properties of the optimized valve are compared with those of the prototype workpiece, and the results prove that the dynamic performance indexes of the optimized valve are much better than those of the prototype workpiece.

Dynamics modeling and optimal control for multi-information diffusion in Social Internet of Things

As an ingenious convergence between the Internet of Things and social networks,the Social Internet of Things(SIoT)can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information.Nevertheless,SIoT is characterized by high openness and autonomy,multiple kinds of information can spread rapidly,freely and cooperatively in SIoT,which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion.To this end,with the aim of exploring multi-information cooperative diffusion processes in SIoT,we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper.Subsequently,the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated,and the diffusion trend is predicted.On this basis,to further control the multi-information cooperative diffusion process efficiently,we propose two control strategies for information diffusion with control objectives,develop an optimal control system for the multi-information cooperative diffusion process,and propose the corresponding optimal control method.The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory.Finally,extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model,strategy and method.

Application of Dynamic Programming Algorithm Based on Model Predictive Control in Hybrid Electric Vehicle Control Strategy

A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle,but also effectively save fuel and reduce emissions.In this paper,the construction of model predictive control in hybrid electric vehicle is proposed.The solving process and the use of reference trajectory are discussed for the application of MPC based on dynamic programming algorithm.The simulation of hybrid electric vehicle is carried out under a specific working condition.The simulation results show that the control strategy can effectively reduce fuel consumption when the torque of engine and motor is reasonably distributed,and the effectiveness of the control strategy is verified.

Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution,few combine the connected vehicle technologies into braking velocity planning.If the braking intention is accessed by the vehicle-to-everything communication,the electric vehicles(EVs)could plan the braking velocity for recovering more vehicle kinetic energy.Therefore,this paper presents an energy-optimal braking strategy(EOBS)to improve the energy efficiency of EVs with the consideration of shared braking intention.First,a double-layer control scheme is formulated.In the upper-layer,an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm,which could derive the energy-optimal braking trajectory.In the lower-layer,the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system,then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety.Several simulations are conducted by jointing MATLAB and CarSim,the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy.Finally,the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration,battery charging power,and motor efficiency,which could be a guide to real-time control.

CONTROLLING ROBOT MANIPULATORS BY DYNAMIC PROGRAMMING

A certain number of considerations should be taken into account in the dynamic control of robot manipulators as highly complex non-linear systems.In this article,we provide a detailed presentation of the mechanical and electrical impli- cations of robots equipped with DC motor actuators.This model takes into account all non-linear aspects of the system.Then,we develop computational algorithms for optimal control based on dynamic programming.The robot's trajectory must be predefined,but performance criteria and constraints applying to the system are not limited and we may adapt them freely to the robot and the task being studied.As an example,a manipulator arm with 3 degrees of freedom is analyzed.

The global stability and optimal control of the COVID-19 epidemic model

This paper considers stability analysis of a Susceptible-Exposed-Infected-Recovered-Virus(SEIRV)model with nonlinear incidence rates and indicates the severity and weakness of control factors for disease transmission.The Lyapunov function using Volterra-Lyapunov matrices makes it possible to study the global stability of the endemic equilibrium point.An optimal control strategy is proposed to prevent the spread of coronavirus,in addition to governmental intervention.The objective is to minimize together with the quantity of infected and exposed individuals while minimizing the total costs of treatment.A numerical study of the model is also carried out to investigate the analytical results.

Dynamic modeling and optimal control of cystic echinococcosis

Background:Cystic echinococcosis is one of the most severe helminth zoonosis with a drastic impact on human health and livestock industry.Investigating optimal control strategy and assessing the crucial factors are essential for developing countermeasures to mitigate this disease.Methods:Two compartment models were formulated to study the dynamics of cystic echinococcosis transmission,to evaluate the effectiveness of various control measures,and to find the optimal control strategy.Sensitive analyses were conducted by obtaining PRCCs and contour plot was used to evaluate the effect of key parameters on the basic reproduction number.Based on forward-backward sweep method,numerical simulations were employed to investigate effects of key factors on the transmission of cystic echinococcosis and to obtain the optimal control strategy.Results:The food resources of stray dog and invalid sheep vaccination rate,which are always neglected,were significant to the transmission and control of cystic echinococcosis.Numerical simulations suggest that,the implementation of optimal control strategy can significantly reduce the infections.Improving the cost of health education and domestic dog deworming could not decrease human infections.Conclusions:Our study showed that only a long-term use of the optimal control measures can eliminate the disease.Meanwhile,during the intervention,sheep vaccination and stray dogs disposing should be emphasized ahead of domestic dogs deworming to minimize the control cost.Simultaneously reducing other wild intermediate hosts and strengthening the sheep vaccination as well as disposing the stray dogs would be most effective.

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

Substructure design optimization and nonlinear responses control analysis of the mega-sub controlled structural system (MSCSS) under earthquake action

The newly proposed mega sub-controlled structure system(MSCSS)and related studies have drawn the attention of civil engineers for practice in improving the performance and enhancing the structural effectiveness of mega frame structures.However,there is still a need for improvement to its basic structural arrangement.In this project,an advanced,reasonable arrangement of mega sub-controlled structure models,composed of three mega stories with different numbers and arrangements of substructures,are designed to investigate the control performance of the models and obtain the optimal model configuration(model with minimum acceleration and displacement responses)under strong earthquake excitation.In addition,the dynamic parameters that affect the performance effectiveness of the optimal model of MSCSS are studied and discussed.The area of the relative stiffness ratio RD,with different mass ratio MR,within which the acceleration and displacement of the optimal model of MSCSS reaches its optimum(minimum)value is considered as an optimum region.It serves as a useful tool in practical engineering design.The study demonstrates that the proposed MSCSS configuration can efficiently control the displacement and acceleration of high rise buildings.In addition,some analytical guidelines are provided for selecting the control parameters of the structure.

Distributed model predictive control based on adaptive sampling mechanism

In this work,an adaptive sampling control strategy for distributed predictive control is proposed.According to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function.Then,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is repeated.Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object.It can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

Relationship between Maximum Principle and Dynamic Programming in Stochastic Differential Games and Applications

This paper is concerned with the relationship between maximum principle and dynamic programming in zero-sum stochastic differential games. Under the assumption that the value function is enough smooth, relations among the adjoint processes, the generalized Hamiltonian function and the value function are given. A portfolio optimization problem under model uncertainty in the financial market is discussed to show the applications of our result.

风浪联合作用下分布式调谐质量阻尼器对海上半潜漂浮式风机的减振控制

海上半潜漂浮式风机在复杂深海环境下产生有害振动会威胁风机的安全性和耐久性,针对该问题并结合美国NREL的5 MW样机的漂浮平台几何结构构造,提出利用分布式调谐质量阻尼器(Tuned Mass Dampers,TMDs),即分别在漂浮平台的3根浮筒中布置TMD,形成等边三角形布置,对随机风浪联合作用下海上半潜漂浮式风机的平台纵摇振动进行控制。为了更好地描述分布式TMDs对海上半潜漂浮式风机的减振效果,基于拉格朗日方程和模态叠加法,对海上半潜漂浮式风机-TMDs耦合系统提出并建立了9自由度多体动力学模型。基于H_(∞)算法,即以平台纵摇频响函数的峰值为优化目标,对分布式TMDs的参数进行优化设计,优化设计中考虑了3个TMDs之间的耦合关系。对风机-TMDs耦合系统开展了风浪联合作用下的数值模拟,分析了分布式TMDs对平台纵摇响应的减振效果。结果表明:最优设计下的分布式TMDs对海上半潜漂浮式风机平台纵摇振动具有良好的减振性能;在三种不同工况的随机风浪荷载作用下,分布式TMDs对平台纵摇固有频率附近的功率谱密度曲线峰值减振率和标准差减振率能分别达到39%和52%以上。

基于EGA优化的农用UTV半主动悬架最优控制

针对UTV在恶劣路面行驶引起的车辆振动,以某款农用UTV悬架系统为对象,建立包含俯仰的四自由度(4-DOF)半车半主动悬架动力学模型,并提出一种EGA-LQR复合控制策略,设计满足物理约束的悬架系统自适应最优控制器。利用EGA算法的全局寻优与快速收敛特性,对LQR最优控制器的权重矩阵寻优,输出悬架系统最优控制阻尼力。在Matlab/Simulink中搭建UTV的路面与悬架模型进行时域仿真,仿真分析结果表明,EGA-LQR控制显著减小了车体质心垂向振动加速度、车体俯仰角加速度、前后轮动位移以及前后悬架动行程的均方根值,有效保证了UTV在农田路面下行驶的舒适性与安全性。

基于优化动力学模型的路径跟踪控制研究

针对一般路径跟踪模型预测控制器在高速大曲率工况下适应性差的问题,提出了一种基于优化动力学模型的自适应预测时域控制策略。首先,为解决经典动力学模型在较高侧向加速度工况下精度不足的问题,建立了包含侧倾转向和变形转向特性的优化模型,实现了车辆状态的较高精度预测;其次,为解决高速大曲率工况下固定预测时域控制效果不佳的问题,提出了基于二维高斯函数的自适应预测时域策略,以低算法复杂度实现了预瞄距离的实时调整;最后,通过CarSim/Simulink联合仿真实验验证了控制器在双移线道路上的控制效果,结果表明,横向位置峰值误差降低45.1%,横摆角峰值误差降低72.4%,设计的控制器对极限工况有更好的适应性。

基于MPC的光电热联合系统建模与控制优化

为解决我国北方地区冬季采暖产生的能源消耗与环境污染问题,改善光资源丰富、较丰富地区光能利用率不足的现状,充分利用谷电和日照优势,针对太阳能利用与建筑采暖相结合的分布式能源系统性问题,提出一种基于TRNSYS动态建模与数值建模相结合的光电热联合供暖系统。综合考虑小范围内供暖温度的时滞性以及系统各设备的出力情况,联合Matlab搭建模型预测控制器(MPC),提出一种基于MPC的误差实时校正优化控制策略。分析表明:采用MPC的控制优化,在热负荷跟踪方面,最大误差降低4.16%,平均误差降低2.79%;在室内温度控制方面,最大偏差降低1.2℃,平均偏差降低0.2℃;在太阳能利用占比方面,太阳辐射强度趋近于800 W/m^(2)时,太阳能利用占比差距达最大8.9%。分析结果说明该系统可以更快速、更准确地跟踪建筑热负荷波动,并且有效抑制室内温度波动,提高清洁能源的利用率。

高空风电系统的优化控制研究进展与挑战

为了实现“碳达峰,碳中和”的战略目标,我国将持续推动可再生能源的高比例发展,构建以新能源为主的新型电力系统.作为可再生的清洁源,风能的开发和利用已成为研究的重要方向.研究表明远离地面的高空中风速更加强劲并且更均匀,风向也更稳定,因此,风力发电的进一步突破可以通过用风筝捕获高空风能来实现.为了确保高空风能系统安全、经济、高效地运行,对其控制系统设计的要求极高.本文阐述了国际上几种主流高空风电技术的发电原理、发展进程以及现状.通过对典型的Yo-Yo式结构进行动力学建模,分析了各类非线性控制技术的原理和特点,具体描述了非线性模型预测控制原理和轨迹跟踪控制的仿真结果.总结了未来高空风能控制技术面临的控制算法计算量大,控制系统可靠性研究缺乏以及智能化水平不高等关键问题.

基于模型预测控制的电-气互联系统动态优化调度研究

在当前电气、天然气与人们生活密不可分的背景下,电—气互联综合能源系统(IPGES)的调度运行研究具有极其重要的价值和意义。目前电—气互联系统的运行策略大多基于日前概况设计,而负荷预测误差往往导致次优日前调度运行。针对这种情况,模型预测控制因为其实时性和鲁棒性的优点,在处理不确定性问题方面表现出卓越效果。本研究在长时间尺度上对日前调度进行了优化,并基于模型预测控制方法进行了实时在线优化调度。在日内调度中,采用卡尔曼滤波对负荷进行短期预测,并根据最新预测信息建立滚动优化和实时反馈校正模型以进行多时间尺度的调度。最后通过案例分析验证了该方法在动态优化调度电力系统和天然气系统组成的电—气互联系统中具有更好经济性和供需匹配性,并能有效应对负荷波动。

基于模型预测控制的高比例可再生能源电力系统多时间尺度动态可靠优化调度

提出基于模型预测控制的高比例可再生能源电力系统多时间尺度动态可靠优化调度方法,通过日前优化调度、日内滚动优化、运行风险计算及反馈校正几个环节,对可再生能源发电和系统内可调资源进行最优调度。日内实时调度阶段,基于日前调度计划和实时运行状况,采用回归预测算法,自适应选择重要变量预测系统未来运行状态,通过吉布斯抽样得到关键变量的概率密度,快速量化系统下一时刻的运行风险,并将风险反馈至日内滚动优化阶段,重复进行可靠优化调度。仿真算例结果验证所提方法的适应性和可行性。

基于MPC的球形机器人轨迹跟踪方法研究

单摆驱动式球形机器人运动存在欠驱动和非线性特性,导致轨迹跟踪难度较大。为此,提出了一种基于模型预测控制(MPC)的球形机器人轨迹跟踪控制方法。该方法首先利用拉格朗日动力学模型和参考运动轨迹,结合泰勒线性展开和时域离散处理,得到球形机器人运动状态偏差的预测模型。随后,以未来特定时段内轨迹跟踪误差最小为目标,在满足控制量和控制增量约束的条件下,利用二次规划对球形机器人长轴和短轴电机转矩进行滚动优化,同时在特定控制步长下不断根据优化结果进行电机转矩控制,最终实现对机器人参考轨迹的跟踪。仿真结果表明,与传统PID控制方法相比,所提MPC方法横向跟踪最大误差减少了0.664 4 m,纵向跟踪最大误差减少了0.112 1 m,跟踪误差曲线基本在0附近波动,具有明显的轨迹跟踪精度与稳定性优势。