Dynamic hedging of basket options under proportional transaction costs using receding horizon control

In this article, we develop a semi-definite programming-based receding horizon control approach to the problem of dynamic hedging of European basket call options under proportional transaction costs. The hedging problem for a European call option is formulated as a finite horizon constrained stochastic control problem. This allows us to develop a receding horizon control approach that repeatedly solves semi-definite programmes on-line in order to dynamically hedge. This approach is competitive with Black–Scholes delta hedging in the one-dimensional case with no transaction costs, but it also applies to multi-dimensional options such as basket options, and can include transaction costs. We illustrate its effectiveness through a numerical example involving an option on a basket of five stocks.     

车载大功率DC/DC电源变换器半实物仿真验证测试方法

介绍了一种车载大功率DC/DC电源变换器(以下简称DC/DC)的测试方法,运用V模式理念并结合半实物仿真验证测试方法对DC/DC的技术方案进行了全数字仿真验证测试、对DC/DC的控制电路部分进行了快速控制原型仿真验证测试、对DC/DC的功率电路部分进行了硬件在环仿真验证测试、对集成功率及控制电路的DC/DC进行了功率硬件在环验证测试,并根据测试结果又进行了迭代验证测试。通过半实物验证测试,对DC/DC各项设计参数的正确性及性能指标符合性进行验证测试,保证DC/DC技术方案的合理可行。     

基于混合逻辑动态模型的混杂系统预测控制

针对过程工业控制对象的混杂特性，采用基于混合逻辑动态模型的预测控制策略。给出混杂系统的建模方法，并对其稳定性进行分析。仿真结果表明基于混合逻辑动态模型的预测控制能使混杂系统跟踪设定值并满足操作约束，为研究新一代复杂工业控制系统提供了新的思路。     

A multiple model approach for predictive control of nonlinear hybrid systems

This paper presents modeling and control of nonlinear hybrid systems using multiple linearized models. Each linearized model is a local representation of all locations of the hybrid system. These models are then combined using Bayes theorem to describe the nonlinear hybrid system. The multiple models, which consist of continuous as well as discrete variables, are used for synthesis of a model predictive control (MPC) law. The discrete-time equivalent of the model predicts the hybrid system behavior over the prediction horizon. The MPC formulation takes on a similar form as that used for control of a continuous variable system. Although implementation of the control law requires solution of an online mixed integer nonlinear program, the optimization problem has a fixed structure with certain computational advantages. We demonstrate performance and computational efficiency of the modeling and control scheme using simulations on a benchmark three-spherical tank system and a hydraulic process plant.     

Fuzzy modeling and stable model predictive tracking control of large-scale power plants

This paper develops a stable model predictive tracking controller (SMPTC) for coordinated control of a large-scale power plant. First, a Takagi–Sugeno (TS) fuzzy model is established to approximate the behavior of the boiler–turbine coordinated control system (CCS) using fuzzy clustering and subspace identification (SID). Then, an SMPTC is designed based on the fuzzy model to track the power and pressure set-points while guaranteeing the input-to-state stability and the input constraints of the system. An output-based objective function is adopted for the proposed SMPTC so that the controller could be directly applicable for the data-driven model. Moreover, the effect of modeling mismatches and unknown plant variations has been overcome by the use of a disturbance term and steady-state target calculator (SSTC). Simulation results for a 600 MW power plant show that an off-set free tracking performance can be achieved over a wide range load variation.     

A robust constrained model predictive control scheme for norm‐bounded uncertain systems with partial state measurements

A robust model predictive control scheme for a class of constrained norm‐bounded uncertain discrete‐time linear systems is developed under the hypothesis that only partial state measurements are available for feedback. The proposed strategy involves a two‐phase procedure. Initialization phase is devoted to determining an admissible, though not optimal, linear memoryless controller capable to formally address the input rate constraint; then, during on‐line phase, predictive capabilities complement the designed controller by means of N steps free control actions in a receding horizon fashion. These additive control actions are obtained by solving semidefinite programming problems subject to linear matrix inequalities constraints. As computational burden grows linearly with the control horizon length, an example is developed to show the effectiveness of the proposed approach for realistic control problems: the design of a flight control law for a flexible unmanned over‐actuated aircraft, where the states of the flexibility dynamics are not measurable, is discussed, and a numerical implementation of the controller within a nonlinear simulation environment testifies the validity of the proposed approach and the possibility to implement the algorithm on an onboard computer.     

自适应预测控制在纸浆浓度控制系统中的应用

本文将预测控制算法应用到纸浆浓度控制，该预测模型能够根据系统动态特性实行在线更新。文中介绍了控制系统模型及预测控制算法，并对纸浆浓度的白适应预测控制进行了仿真研究。     

Event‐driven model predictive control of timed hybrid Petri nets

Hybrid Petri nets represent a powerful modeling formalism that offers the possibility of integrating, in a natural way, continuous and discrete dynamics in a single net model. Usual control approaches for hybrid nets can be divided into discrete‐time and continuous‐time approaches. Continuous‐time approaches are usually more precise, but can be computationally prohibitive. Discrete‐time approaches are less complex, but can entail mode‐mismatch errors due to fixed time discretization. This work proposes an optimization‐based event‐driven control approach that applies on continuous time models and where the control actions change when discrete events occur. Such an approach is computationally feasible for systems of interest in practice and avoids mode‐mismatch errors. In order to handle modelling errors and exogenous disturbances, the proposed approach is implemented in a closed‐loop strategy based on event‐driven model predictive control. Copyright © 2013 John Wiley & Sons, Ltd.     

具有Hammerstein模型描述的非线性系统的基于混合神经网络的预测控制

本文针对多输入多输出Hamrnerstein模犁提出了一种基于混合神经网络的模犁预测控制策略,控制器采用线性优化机构和高斯径向基神经网络串联.该策略不需要假设Hammerstein模型的非线性部分由多项式构成,避免了已有研究在无根或重根情况下存在导致预测控制的优化特征丧失问题,而采用混合神经网络则避免了采用传统神经网络拟合动态映射时存在的网络规模大和实时性差的不足.     

应用模型预测控制的混合动力汽车模式切换动态协调控制

利用传统协调控制策略或模型预测控制(MPC)方法能够解决离合器模式切换的平顺性,但其改善效果不显著,且缺乏深入的细化研究.因此,为了改进混合动力汽车有离合器结合的模式切换过程中的平顺性,本文基于MPC制定有离合器模式切换过程的动态协调控制策略.在对混合动力系统有离合器模式切换模型进行简化的基础上,开展MPC在模式切换动态协调控制过程的原理描述,以减小有离合器模式间切换的冲击度进行基于MPC动态协调控制策略设计,并对不同权重下的冲击度进行了详细的对比.通过实验验证,其结果表明采用MPC的模式切换协调控制最大冲击度从26.3 m/s³下降至9.26 m/s³,降低了64.8%,明显的抑制了模式切换过程中的冲击度,有效的改善了模式切换的平顺性.     

Finite horizon optimal hybrid reference control for improving transient response of feedback control systems

Hybrid reference control (HRC) has been known to improve transient response of a stabilised closed-loop continuous tracking system. In this paper, an optimal HRC strategy, which minimises a finite horizon quadratic performance index in the control and state error, is investigated based on the assumption that the full plant states are available for measurement. A special case of constrained optimisation problems leads to the optimal deadbeat control strategy. Conditions for asymptotic stability to default reference signal are also derived.     

Predictive control for a class of distributed delay systems using Chebyshev polynomials

In this paper, the model predictive control (MPC) is developed for linear time-varying systems with distributed time delay in state. The Chebyshev operational matrices of product, integration and delay are utilized to transform the solution of distributed delay differential equation to the solution of algebraic equations. The Chebyshev functions are also applied to derive approximate solution of finite horizon optimal control problem involved in MPC. The proposed method is simple and computationally advantageous. Illustrative example demonstrates the validity and applicability of the technique.     

主动叶尖间隙模型预测控制系统设计与仿真

为将发动机全工况内叶尖间隙控制在最佳状态,首先,提出实现间隙主动控制的双闭环控制系统结构;其次,设计一种由多个电子机械式作动装置成的可变结构的机匣结构,并建立了其数学模型;然后,应用基于串级模型预测控制的主动间隙控制策略,完成了控制器的设计;最后,以某发动机叶尖间隙变化为被控对象,仿真验证了所设计系统.结果表明:所设计系统控制偏差≤0.01 mm,对对称性间隙变化和非对称性间隙变化的控制效果均较好,可为主动叶尖间隙控制的实现提供参考.     

负载台电液时变加载系统研究

该文在深入剖析运载火箭姿控系统仿真专用设备负载台加载系统的基础上,将现代计算机控制技术和液压控制技术相结合,研究和解决了负载台时变加载的关键技术问题,使半实物仿真进一步逼真化、通用化。     

DC/DC变换器的PWA模型及预测控制

利用υ步离散法,得到变换器输入控制变量与状态变量之间的直接映射关系,基于混杂系统理论分析系统的动态方程,建立其分段仿射模型。在此模型的基础上,结合非线性预测控制算法,通过模型预测系统的输出,利用反馈校正误差,给出二次型性能指标的优化计算方法,并由此设计预测控制器。最后,以Buck功率变换器为研究对象,通过与峰值电流控制算法的仿真结果进行比较,验证模型的正确性以及控制器设计的有效性。     

非线性HAMMERSTEIN系统的预测控制及其pH过程应用

本文基于非线性离散Hammerstein模型,开发了一种非线性Hammerstein系统预测控制(Non-Linear Hammerstein Predic- tive Control,NLHPC)算法。遵循预测控制策略,该算法利用Hammerstein模型进行输出预测。理论分析结果表明,该算法不仅具有好的稳定性和鲁棒性,而且其自身具有积分作用。在一台工业PC机上实现了该NLHPC算法,并用于具有强非线性的酸碱中和过程实验装置pH值的控制。实验结果表明NLHPC有着比工业界常用的非线性PID控制(nonlinear PID,NL-PID)更好的控制性能。     

Single and multiple property CD control of sheet forming processes via reduced order infinite horizon MPC algorithm

In this article, a reduced order infinite horizon model predictive control (MPC) algorithm for sheet forming processes is explained, and its closed loop performance is studied by using a paper production process as a typical sheet forming process. The proposed algorithm has a very light computational burden due to a significant decrease in the number of variables which is achieved by applying Karhunen–Loeve transformation (KLT) to the sheet profile and finding the lower order subspace which extracts the significant features of the disturbance. Then, the optimal control action in the lower order subspace is found by solving the infinite horizon MPC formulation, and the corresponding solution of the optimization problem is projected back to the original space to be implemented on the real plant. Also, a novel approach is introduced for the solution of multiple property control problem in sheet forming processes, where two or more sheet profiles with different process dynamics are to be controlled by using a single set of actuators. It is explained that the dimensional mismatch problem for the reduced order controller can be solved by using an intermediate subspace, and simulations show that the proposed algorithm performs successfully for paper production processes.     

Simultaneous Constrained Model Predictive Control and Identification of DARX Processes

In this work, we formulate a new approach to simultaneous constrained model predictive control and identification (MPCI). The proposed approach relies on the development of a persistent excitation (PE) criterion for processes described by DARX models. That PE criterion is used as an additional constraint in the standard on-line optimization of MPC. The resulting on-line optimization problem of MPCI is handled by successively solving a series of semi-definite programming problems. Advantages of MPCI in comparison to other closed-loop identification methods are (a) Constraints on process inputs and outputs are handled explicitly, (b) Deterioration of output regulation is kept to a minimum, while closed-loop identification is performed. The applicability of the method is illustrated by a number of simulation studies. Theoretical and computational issues for further investigation are suggested.     

Optimization management of hybrid energy source of fuel cell truck based on model predictive control using traffic light information

Energy optimization management can make fuel cell truck (FCT) power system more efficient, so as to improve vehicle fuel economy. When the structure of power source system and the torque distribution strategy are determined, the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources. The paper simulates the assistance of the intelligent transport system (ITS) and carries out the eco-velocity planning using the traffic signal light. On this basis, in order to further improve the energy efficiency of FCT, a model predictive control (MPC)-based energy source optimization management strategy is innovatively developed, which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption. Under the scenarios of signalized intersections, based on the planned eco-velocity, the off-line simulation results show that the proposed MPC-based energy source management strategy (ESMS) can reduce hydrogen consumption of fuel cell up to 7\% compared with the existing rule-based ESMS. Finally, the Hardware-in-the-Loop (HiL) simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.     

Evaluation of operators' performance for automation design in the fully digital control room of nuclear power plants

Recent technical developments in computer hardware and software have meant that human–machine systems can be automated in many respects. If automation fails, however, human operators can have difficulty in recognizing the existence of a problem, identifying what has failed, and taking corrective action to remedy these out‐of‐the‐loop (OOTL) performance problems. Several studies have suggested that taxonomies of levels of automation (LOAs) and types of automation (TOAs) can be used to solve OOTL problems. This study examined the impact of LOAs in process control automation within the context of nuclear power plants (NPPs). A simulation experiment in an NPP is performed to validate this framework using an automatic mode and a semiautomatic mode. Mental demand is found to be significantly reduced under the automatic mode; however, participants felt frustrated with this LOA. Situation awareness is found to be similar in the two modes. The results of an end‐of‐experiment subjective rating reveal that participants were evenly divided between the two modes with respect to generating and selecting functions. It is therefore suggested that human operators be involved in generating and selecting functions under an automatic mode. © 2009 Wiley Periodicals, Inc.