Shortest path stochastic control for hybrid electric vehicles

When a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP‐SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite‐horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time‐invariant causal state‐feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite‐horizon case, and for the same level of complexity as a discounted future‐cost controller, the shortest‐path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs. Copyright © 2007 John Wiley & Sons, Ltd.     

Decoupling two-degree-of-freedom control strategy for cascade control systems

In this paper, two new two-degree-of-freedom control structures are proposed for cascade control systems, both of which are identical in the controller design procedure. The primary controller used for setpoint tracking is derived in terms of H₂ optimal performance objective. The secondary controller is responsible for rejecting load disturbances that seep into the intermediate process and therefore is called a load disturbance estimator, which is inversely figured out by proposing the desired complementary sensitivity function of the inner loop for disturbance rejection. As a result, the nominal setpoint response is decoupled with the load disturbance response of the inner loop, both of which can be optimized independently. Based on the control system stability analysis, the rule of thumb for tuning the single adjustable parameter of each controller is provided to cope with the process uncertainty in practice. Several illustrative examples are included to demonstrate the superiority of the proposed method.     

供应链环境下库存控制策略研究

库存控制与管理是现代企业生产经营管理过程中不可或缺的组成部分,也是供应链管理的主要活动之一。传统的库存管理仅将注意力放在对自身库存物资进行控制与管理上,但在供应链环境下,库存管理需将视野从自身扩大到整个供应链网络上来,相互协调共同管理库存,实现整个供应链库存的优化,达到供应链上各节点企业的共赢。     

Vessel motion control using rope tension control strategy

In this paper, the problem of designing a Position Mooring (PM) system for a barge ship is addressed. A mathematical model of a system comprising a barge ship and mooring system is derived. Hydrodynamic coefficients of the low speed model for PM ship are identified by suitable experiments. A PID control scheme is implemented to achieve PM for the vessel by changing the tension of the mooring ropes. The proposed strategy is finally tested on station keeping, and desired positions of a 1/50 scale model ship are obtained.     

自适应仿人智能控制控制策略研究

在分析自适应仿人智能控制结构的基础上,重点研究了多模态控制策略,充分利用系统误差、误差的变化、被控对象的输入量的变化和模型误差四个信息,并以被控对象的输入量的变化和模型误差作为仿人智能控制器和内模控制器之间的协调参数,使控制器的快速性、平稳性、鲁棒性和抗扰性得到极大改善。仿真试验证明在该控制结构下的控制策略非常有效。     

交通信号公交优先控制策略及智能控制方法

针对公交优先交通信号控制问题,研究了公交优先的信号控制策略,提出了一种变论域模糊神经网络公交优先智能控制方法.提出了基于相位优先度值的公交优先相位选择方法,并给出了其数学描述.建立了绿灯时间的3层模糊控制模型,分别为红灯排队疏散时间、绿灯延长时间和论域调节因子模糊控制器,其中红灯排队疏散时间和绿灯延长时间两个模糊控制器的输出变量均采用变论域,论域的变化考虑混合交通、天气情况、车流转向等因素由论域调节因子模糊控制器确定.模糊控制器采用粒子群优化神经网络实现.仿真结果表明该方法具有较好的公交优先控制效果.     

时序最短路径算法

最短路径是指网络中两结点间阻碍强度最小的一条路径。传统的最短路径是在静态网络上进行研究的,然而现实生活中很多网络是动态的、有时序性的,因此传统的最短路径算法并不能用于解决所有最短路径问题。为了寻找时序网络上的最短路径,在Dijkstra算法思想基础上,提出一种时序最短路径的精确算法。文中利用严格的数学推导证明了本算法的可行性,并通过对构建的网络做实证分析验证了算法的正确性。     

On Shortest Linear Recurrences

This is an expository account of a constructive theorem on the shortest linear recurrences of a finite sequence over an arbitrary integral domainR. A generalization of rational approximation, which we call “realization”, plays a key role throughout the paper.We also give the associated “minimal realization” algorithm, which has a simple control structure and is division-free. It is easy to show that the number ofR-multiplications required isO(n²), wherenis the length of the input sequence.Our approach is algebraic and independent of any particular application. We view a linear recurring sequence as a torsion element in a naturalR[X]-module. The standardR[X]-module of Laurent polynomials overRunderlies our approach to finite sequences. The prerequisites are nominal and we use short Fibonacci sequences as running examples.     

Shortest Path Geometric Rounding

Exact implementations of algorithms of computational geometry are subject to exponential growth in running time and space. In particular, coordinate bit-complexity can grow exponentially when algorithms are cascaded : the output of one algorithm becomes the input to the next. Cascading is a significant problem in practice. We propose a geometric rounding technique: shortest path rounding . Shortest path rounding trades accuracy for space and time and eliminates the exponential cost introduced by cascading. It can be applied to all algorithms which operate on planar polygonal regions, for example, set operations, transformations, convex hull, triangulation, and Minkowski sum. Unlike other geometric rounding techniques, shortest path rounding can round vertices to arbitrary lattices, even in polar coordinates, as long as the rounding cells are connected. (Other rounding techniques can only round to the integer grid.) On the integer grid, shortest path rounding introduces less combinatorial change and geometric error than the other rounding methods. Three algorithms are given for shortest path rounding, one of which we have used in industrial application software since 1992. In combination with recent advances in exact floating point evaluation of numerical primitives, shortest path geometric rounding yields a practical solution to numerical issues in computational geometry. Geometric algorithms can be implemented exactly on floating point input coordinates; the exact output coordinates can be rounded to accurate floating point approximations; and the cost of each arithmetic operation is only a little more than if it were implemented as a single hardware floating point operation. Received February 7, 1997; revised September 9, 1998.     

一种多变量预测控制的分程控制策略实现方法

针对包含分程控制回路的控制系统具有 非线性特征,提出一种适用于多变量模型预测控制的分程控制实现方法.在分程控制的各线性区间内根据 各分程阀的不同动作组合建立预测模型,通过在线比较控制器输出信号值与相应定义的中间阈值选择模型,模型 发生切换时保留切换前模型中的输出预测值作为当前模型下的初始输出预测值;解决了控制过程存在分程 控制需求时,多变量预测控制器的设计问题,提高了线性预测控制器的适用范围.仿真结果验证了所提出 方法的有效性.     

A hybrid model predictive control strategy for nonlinear plant-wide control

A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy flows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlinear subsystems. A simple controller coordination strategy that counteracts interaction effects is proposed for the case of one linear subsystem and one nonlinear subsystem. A reactor/separator process with recycle is used to compare the hybrid method to conventional LMPC and NMPC techniques.     

Bioreactor control using a probing feeding strategy and mid-ranging control

The paper presents a fed-batch fermentation technique for bioreactors operating close to their maximum oxygen transfer capacity. The method combines the advantages of the probing feeding strategy and the temperature limited fed-batch technique. When the maximum oxygen transfer capacity of the reactor is reached, the temperature is decreased to lower the oxygen demand. To achieve a good control of the dissolved oxygen a mid-ranging controller manipulating the stirrer speed and the temperature is used. The feeding strategy is analysed and it is also illustrated by simulations and an experiment.     

A scalable correlation aware aggregation strategy for wireless sensor networks

Sensors-to-sink data in wireless sensor networks (WSNs) are typically characterized by correlation along the spatial, semantic, and/or temporal dimensions. Exploiting such correlation when performing data aggregation can result in considerable improvements in the bandwidth and energy performance of WSNs. In this paper, we first identify that most of the existing upstream routing approaches in WSNs can be translated to a correlation-unaware data aggregation structure – the shortest-path tree. Although by using a shortest-path tree, some implicit benefits due to correlation are possible, we show that explicitly constructing a correlation-aware structure can result in considerable performance improvement. Toward this end, we present a simple, scalable and distributed correlation-aware aggregation structure that addresses the practical challenges in the context of aggregation in WSNs. Through simulations and analysis, we evaluate the performance of the proposed approach with centralized and distributed correlation-aware and -unaware structures.     

Active steering control strategy for articulated vehicles

To improve maneuverability and stability of articulated vehicles, we design an active steering controller, including tractor and trailer controllers, based on linear quadratic regulator (LQR) theory. First, a three-degree-of-freedom (3-DOF) model of the tractor-trailer with steered trailer axles is built. The simulated annealing particle swarm optimization (SAPSO) algorithm is applied to identify the key parameters of the model under specified vehicle speed and steering wheel angle. Thus, the key parameters of the simplified model can be obtained according to the vehicle conditions using an online look-up table and interpolation. Simulation results show that vehicle parameter outputs of the simplified model and TruckSim agree well, thus providing the ideal reference yaw rate for the controller. Then the active steering controller of the tractor and trailer based on LQR is designed to follow the desired yaw rate and minimize their side-slip angle of the center of gravity (CG) at the same time. Finally, simulation tests at both low speed and high speed are conducted based on the TruckSim-Simulink program. The results show significant effects on the active steering controller on improving maneuverability at low speed and lateral stability at high speed for the articulated vehicle. The control strategy is applicable for steering not only along gentle curves but also along sharp curves.     

An adaptive control strategy for mechanical manipulators

This paper focuses on the study of an adaptive perturbation control which tracks a desired time-based trajectory as close as possible for all times over a wide range of manipulator motion and payloads. The proposed adaptive control is based on the linearized perturbation equations in the vicinity of a nominal trajectory. The controlled system is characterized by feedforward and feedback components which can be computed separately and simultaneously. The feedforward component computes the nominal torques from the Newton-Euler equations of motion to compensate all the interaction forces among the various joints. The feedback component consisting of recursive least-square identification and an optimal adaptive self-tuning control algorithm for the linearized system computes the perturbation torques which reduce the position and velocity errors of the manipulator along the nominal trajectory. A computer simulation study was conducted to evaluate the performance of the proposed adaptive control.     

模块化多电平逆变器预充电控制策略

针对矿井提升机运行特点,采用由模块化多电平整流器和模块化多电平逆变器构成的背靠背模块化多电平变流器用于矿井提升机控制;针对目前通过直流侧对模块化多电平逆变器进行预充电的控制研究较少的问题,提出了一种模块化多电平逆变器预充电控制策略。该控制策略通过控制充电电流来控制子模块充电状态,使子模块电容充电至模块化多电平变流器稳定运行时的电压要求,同时将充电电流和充电过程中产生的过电压限定在安全范围内,使充电时间最短。仿真结果验证了该控制策略的有效性。     

A feedforward control strategy for distillation columns

The prime objective of this work is to demonstrate the potential of neural network modeling for advanced nonlinear control applications. In particular, for the case of a single composition distillation column, a model-based neural controller is developed to regulate the composition of the distillate stream. The neural controller relies on process inversion for the evaluation of the actuator action on the manipulated variable (reflux flowrate) to maintain the controlled variable (distillate composition) at the prescribed value.The performance of the neural controller is assessed and compared with that of a conventional temperature control loop and of a neural inferential control structure. The neural controller by far outperforms the other two in terms of the response speed by which the upsetting loads are compensated.     

A cyclic switching strategy for parameter-adaptive control

This paper introduces a new strategy, called “cyclic switching,” to deal with the well-known certainty equivalence control synthesis problem which arises in the design of identifier-based adaptive controllers because of the existence of points in parameter space where the design model Σ_D, upon which certainty equivalence synthesis is based, loses stabilizability. Unlike most previously suggested methods for handling this problem, the technique proposed here can be employed with or without process excitation. For the technique to work it is not necessary for there to be a mechanism for moving tuned parameters away from values at which Σ_D loses stabilizability, and no such mechanism is used     

A new control strategy for IKN-type coolers

This paper presents a new system for the optimized control of IKN-type cement coolers based on adaptive predictive methodology. The new system introduces the concepts of ’layer on grate’ and ’thickness of slope’, which allow a decoupled multivariable control of the layer of clinker over the grate, and the flow rate of the cooler fans, solving a fundamental control problem for this type of process and maximizing the heat transfer from the cooling process to the kiln. The experimental results obtained in the Gador mill of Holcim, in Almeria (Spain), are analyzed.