基于模型预测控制的微电网优化调度

模型预测控制(model predictive control,MPC)作为一种滚动优化方法,能够很好的处理可再生能源出力不确定的问题。经典模型预测控制的预测时域长度固定,不能很好的适应经济调度问题。文章提出了变预测时域长度的模型预测方法,根据日前预测光伏风力发电、负荷数据,使用遗传算法合理配置每个时刻的预测时域长度值,获得更低的运行成本。     

Neural-network-based predictive learning control of ram velocity in injection molding

In this paper, we develop a predictive learning controller for ram velocity of injection molding based on neural networks. We first introduce a model of describing the injection molding, including the time horizon and the batch index. The feedback control plus biased function is proposed for controlling this plant. More specifically, a radial basis function (RBF) network is used to approximate the biased function based on the time horizon. The weights in the RBF are determined by a predictive control scheme based on the batch index. For this algorithm, relevant convergence is investigated. Simulation results reveal that the proposed control can achieve our claims.     

Distributed nonlinear model predictive control of an autonomous tractor–trailer system

This paper addresses the trajectory tracking problem of an autonomous tractor–trailer system by using a fast distributed nonlinear model predictive control algorithm in combination with nonlinear moving horizon estimation for the state and parameter estimation in which constraints on the inputs and the states can be incorporated. The proposed control algorithm is capable of driving the tractor–trailer system to any desired trajectory ensuring high control accuracy and robustness against environmental disturbances.     

基于Toeplitz方程的改进广义预测PID控制

针对具有较大时滞的复杂被控对象,研究了改进的广义预测(GPC)PID控制算法。在保证经典PID控制性能的前提下,用滚动预测优化原理,自整定PID控制参数,同时解决系统时滞引起控制问题。通过最后对仿真结果的分析来看,运用Toeplitz方程的改进算法能有效提高系统的稳定性和求解计算的快速性。     

Evaluating generalized predictive control for a brushless DC drive

This paper proposes a new control approach for a brushless DC motor drive using the generalized predictive control (GPC) algorithm. Based on the same least-squares framework as in the controller design, we further develop the method to design an observer. The GPC algorithm uses the receding horizon approach whereby the control signals are determined by minimizing a quadratic cost function. Our study shows that the rise time and settling time of the servo system have an approximate linear relationship with the prediction horizon. Thus, it is used to tune the controller of the drive. Moreover, the control weighting factor can be used to smooth the controller output. The proposed algorithm has been implemented using a digital signal processor (DSP) and tested in real time with a prototype system. The performance and robustness of the algorithms have been evaluated both in simulation and experiment. The results show that the drive performs reasonably well despite load changes and step changes in the position setpoint. Furthermore, it is fairly robust against motor parameters change     

Predictive Control of Constrained Linear Systems with Multiple Missing Measurements

This paper investigates the problem of predictive control for constrained control systems, in which the measurement signal may be multiply missing. An augmented stochastic model is firstly introduced to describe and compensate these missing measurements. By means of an infinite horizon quadratic performance objective, a state-feedback predictive control law involving missing probability is designed by minimizing the upper bound on performance objective at each sampling instant. It is shown that the on-line optimization problem subject to input and state constraints can effectively be solved in terms of linear matrix inequalities. The designed predictive controller can achieve the desired control performance and also guarantee the closed-loop stability in mean square sense. Finally, an example is given to illustrate the proposed results.     

基于遗传算法的制冷系统动态矩阵控制

根据数据采用最小二乘法辨识得到制冷系统多变量传递函数模型,随后对制冷系统设计动态矩阵控制器,使系统蒸发温度和过热度在满足约束条件下达到控制要求;考虑到DMC控制器的控制时域、预测时域、控制量权重矩阵等参数直接影响系统的稳定性和输出跟踪效果,而一般试凑法选取参数不可避免地存在主观性和随机性,因此给出一种基于遗传算法的DMC参数寻优方法,并应用于制冷系统的控制问题中。最后的仿真试验表明了算法的有效性。     

Robust model predictive control and observer for direct driveapplications

In this paper, robust position control of a direct drive using a state space model predictive control (MPC) algorithm is presented. The proposed controller consists of a state feedback regulator and a feedforward controller. Their gains are obtained by minimizing a cost function that is a sum of the position tracking errors and the control cost over some user defined time horizons. The effects of the controller parameters on the dynamic performance and the robustness of the direct drive are investigated. To provide good estimates of the state variables in the presence of load disturbance, a new observer based on the receding horizon concept is also formulated. Experimental results are presented to demonstrate the effectiveness of the approach     

约束滚动时域预测控制的鲁棒稳定性分析

研究了一类约束滚动时域预测控制系统的鲁棒稳定问题。利用性能指标值关于状态量的泰勒展开,对存在模型误差时的鲁棒稳定性进行了分析,给出了闭环系统鲁棒稳定的充分条件及其鲁棒稳定的上界。     

An active model-based prototype for predictive network management

If current trends continue, the next generation of enterprise networks is likely to become a more complex mixture of hardware, communication media, architectures, protocols, and standards. One approach toward reducing the management burden caused by growing complexity is to integrate management support into the inherent function of network operation. In this paper, management support is provided in the form of network components that, simultaneously with their network function, collaboratively project and adjust projections of future state based upon actual network state. It is well known that more accurate predictions over a longer time horizon enables better control decisions. This paper focuses upon improving prediction; the many potential uses of predictive capabilities for predictive network control will be addressed in future work.     

One-Step Modulation Predictive Current Control Method for the Asymmetrical Dual Three-Phase Induction Machine

Multiphase (more than three phases) drives exhibit interesting advantages over conventional three-phase drives. Over the last years, topics related to the extension of control schemes to these specific drives have been covered in depth in the literature. Direct torque control and predictive current control are normally used in conventional ac drives when fast electrical dynamic performance is required. In this paper, a one-step modulation predictive current control technique is proposed for asymmetrical dual three-phase ac drives. Based on the use of a predictive model including the motor and the inverter, the control algorithm determines the switching state which minimizes errors between predicted and reference state variables. The period of application of the selected switching state is then obtained, resulting in a submodulation method. The proposed predictive current control algorithm uses a prediction horizon of one sampling period; however, two switching states are applied during the sampling period. The switching states are the selected optimum active vector and a null voltage combination. Simulation and experimental results are provided to examine the features of the control method. Performances, advantages, and limitations are also discussed.      

MPC-based yaw stability control in in-wheel-motored EV via active front steering and motor torque distribution

This paper focuses on yaw stability control of in-wheel-motored electric vehicle (EV), and a model predictive controller is designed based on holistic control structure via active front steering and motor torque distribution. By designing a suitable reference model, the controller stabilizes a vehicle along the desired states while rejecting skid and fulfilling its physical constraints, so this is described as a constrained tracking problem. To solve this, the holistic control scheme is built to simplify the hierarchical structure of the controller and directly optimize the control inputs of system. Based on holistic control structure and MPC method, an objective function with constraints is designed over a receding horizon to meet the control requirements. Finally, the proposed nonlinear model predictive controller is evaluated on eight degrees of freedom (8DOF) EV model offline simulation platform. Simulation results of different road maneuver on slippery surfaces show the benefits of the control methodology used.     

不确定环境中多无人机协同搜索算法

多无人机(Unmanned Aerial Vehicle,UAV)协同搜索是多UAV协同控制的一个重要研究内容。多架UAV同时对一个未知区域进行搜索,目的在于获取搜索区域的信息,降低环境的不确定度。该文提出了一种基于模型预测控制(MPC)理论和遗传算法(GA)的多UAV协同搜索算法。首先,建立搜索环境的规则描述,然后将多个UAV建模为一个控制系统,建立系统的预测模型,考虑到UAV传感器测量的不确定性和环境自身的不确定性,建立搜索概率图描述搜索环境的不确定性,给出了基于Bayes准则的搜索概率图更新方法,继而基于搜索概率图定义信息增益来衡量搜索效果,并将预测周期内的优化目标定为最大化信息增益,采用遗传算法进行求解,得到最优解作为被控系统的输入。仿真结果验证了算法的有效性。     

采用预测控制进行纵向塞曼幅值热稳频的研究

产生纵向塞曼频率分裂的He—Ne激光器输出左、右旋圆偏振光,其光强差和激光频率呈稳定的调谐曲线。装置利用单片机采集幅值信号,采用预测控制理论进行数字调节,输出PWM(PulseWidth Modulation)信号补偿激光管的温度以实现热稳频控制。为实现对热稳频大滞后、非线性这一复杂系统的高质量控制,提出了预测控制方法,该方法克服了传统PID(Proportional IntegralDifferential)调节中存在的调整工作量大,控制后果不能及时反馈的不足,适用于各种热稳频过程。数字仿真和实验验证预测控制是稳定的,对模型误差的适应能力强。对10s采样周期,估算得系统频率稳定度达6.7×10~(-9)。     

Linear quadratic Gaussian control of 2-dimensional systems

The linear quadratic Gaussian (LQG) control for one-dimensional (1D) systems has been known to be one of the fundamental and significant methods in linear system theory. However, the LQG control problem for two-dimensional (2D) systems has not been satisfactorily solved due to their structural and dynamical complexity. In this paper, sufficient conditions for evaluation of the quadratic performance indices of 2D systems in terms of the system state and control variables are proposed. Using these conditions, systematic design methods for finite horizon and infinite horizon LQG controls of 2D systems are developed using a convex optimization method.     

凸优化理论在非线性滚动时域控制中的应用

滚动时域控制是一种考虑整个固定时域范围的控制方法,对于每次由优化算法得到的最优控制序列,仅取序列中的第一个元素作为当前此时刻的控制率。滚动时域控制是一种基于模型、滚动优化并结合反馈校正的优化控制算法。在由控制自由变量的非线性函数组成的非线性优化问题中,该控制自由变量的求取可采用凸优化理论中的相关算法来获取,进而采用滚动时域的思想来重新组合控制变量,以得到最优的控制变量。对于非线性系统中滚动时域控制所得到的最优化问题,分析非线性系统的滚动时域控制是否存在最优解,利用凸优化理论中的基本知识分别推导出此最优化问题在无和有集合约束条件下存在最优解的充要条件,并将此充要条件与经典优化理论中现有的最优条件进行对比,得出该充要条件的优势,最后将凸优化理论在无人机航迹规划算例中进行仿真验证。     

激光水平仪理论模型的建立

激光水平仪是一种新型的智能光电仪器，该仪器在二维空间实时产生一条激光扫描线，这这条的发描线总是与地平线平行。本文对激光水平仪的扫描控制模型进行了详细的介绍，采用该模型已成地完成了仪器的设计制做。     

广义预测控制系统的仿真软件设计与开发

介绍了基于广义预测控制的模型预测控制（MPC）系统仿真软件的开发与实现过程。该软件包含了MAC，IMAC，DMC，DDMC和GPC五种预测控制算法，可以用不同的方法实现对预测控制系统的综合性仿真以及控制器参数的调整。利用该软件可对模型预测控制系统的作用机理做进一步仿真分析，并有助于定性分析各种算法中设计参数对系统性能的影响以及不同算法之间的区别。     

Evaluation of a long-range adaptive predictive controller forcomputerized drug delivery systems

A closed-loop adaptive control system, based on the generalized predictive control law with a terminal matching condition, has been developed for computerized drug delivery. The control law is a minimization of the squares of prediction errors over a small future prediction horizon plus weighted square of the prediction error at steady-state. A control relevant, long-range identification algorithm is used for on-line parameter estimation. Since the control and identification are mutually compatible, the system truly satisfies the approximate dual control criterion. The system has been applied to the control of mean arterial pressure (MAP) by automatic infusion of sodium nitroprusside in the presence of physical and physiological constraints. Experimental evaluation on six mongrel dogs, in an ethics-approved manner, included setpoint tracking and regulation of MAP in the presence of unpredictable disturbances. The system was found to he capable of inducing hypotension in an average of 2.44±0.31 min (mean±standard error of mean) after probing without any overshoots in mean arterial pressure. The nitroprusside infusion was also free of any ringing. When the subjects were not disturbed, 96.2% of mean arterial pressure remained within 5 mm Hg of the target pressure. A series of disturbances introduced in the presence and absence of closed-loop control affirms the robustness and effectiveness of this control system     

一种基于STK的地平仪数值仿真方法

介绍了红外 地平仪探头的布局和姿态解算公式,然后提出了一种利用卫星工具包软件(Satellite Tool Kit, STK)建立地平仪仿真模型的方法。利用 该模型对地平仪进行了动态仿真并获取了探测图像。最后,利用获取图像解算出了卫星姿态并给出 了不同工况下的仿真结果。该方法可以在项目开发早期帮助确定地平仪方案,以避免不必要的 重复工作。