Model predictive control of an activated sludge process: A case study

This paper details a case study application of model predictive control for a wastewater treatment process in Lancaster, North England. The control system was implemented in real time, together with a plant monitoring system for the purposes of process supervision. Following implementation, the model predictive control system provided significant benefits compared with the previously applied control system. These benefits included a reduction of over 25% in power usage and a similar increase in plant efficiency. The system therefore represents a useful tool in helping the water industry to reach its goal of significantly reducing its carbon footprint.     

带有迭代学习外环的快速路入口匝道无模型自适应预测控制

针对快速路交通系统复杂时变以及难以建模的特点,首先,本文设计了基于无模型自适应预测控制的快速路入口匝道控制方案.其次,根据快速路交通系统具有重复性特点,本文在无模型自适应预测控制方法的基础上引入开环迭代学习控制,提出一种带有迭代学习前馈外环的无模型自适应入口匝道预测控制方案.相比无模型自适应预测控制方案,该方案可以利用迭代学习前馈控制器补偿系统可重复扰动,实现系统的完全跟踪.值得说明的是,预测控制器和学习控制器可以独立工作也可以联合工作.最后,文章给出了控制方案的收敛性分析,并通过交通流仿真验证了所提控制方案的有效性.     

Analysis of artificial neural network models for freeway ramp metering control

Traffic along a freeway varies not only with time but also with space. It is thus essential to model dynamic traffic patterns on the freeway in order to derive appropriate metering control strategies. Existing methods cannot fulfill this task effectively. Due to the learning capability, artificial neural network models are developed to simulate typical time series traffic data and then expanded to capture the inherent time–space interrelations. The augmented-type network is proposed that includes several basic modules intelligently affiliated according to traffic characteristics on the freeway. Inputs to neural network models are traffic states in each time period on the freeway segments while outputs correspond to the desired metering rate at each entrance ramp. The simulation outcomes indicate very encouraging achievements when the proposed neural network model is employed to govern the freeway traffic operations. Also discussed are feasible directions for further improvements.     

高速公路主线限速与匝道融合的协调控制

为缓解高速公路的交通拥挤,主线限速、匝道融合等常被应用,因主线限速和匝道融合经各自优化获得的控制策略可能存在矛盾,故二者协调是必须的,而如何建立和求解二者的协调控制模型还没有有效方法.本文基于宏观交通流理论和多agent技术研究了此协调控制问题.为此首先阐述了高速公路的一般宏观交通流模型;然后分析主线限速、匝道融合的交通特性,建立了主线限速-匝道融合交通流模型;并协调主线限速和匝道融合,建立了协调控制模型.最后,基于多agent技术和分层递阶结构提出了协调控制模型的求解算法,并给出了应用此方法控制仿真高速公路的一个实例.     

On ramp metering: towards a better understanding of ALINEA via model-free control

ALINEA, which was introduced almost 30 years ago, remains certainly the most well-known feedback loop for ramp metering control. A theoretical proof of its efficiency at least when the traffic conditions are rather mild is given here, perhaps for the first time. It relies on tools stemming from the new model-free control and the corresponding ‘intelligent’ proportional controllers. Several computer experiments confirm our theoretical investigations.     

On optimal ramp control of traffic jam queues

A queuing theory model is developed for the controlled merging of vehicles entering an expressway ramp. The flow of ramp vehicles is restricted in response to downstream congestion or traffic jams. An optimal on-off controller is found for the stochastic problem with random stopping time which results from minimizing the mean sum of the number of cars stopped in the jam and the number of cars diverted from the ramp.     

Multiphase rotor current observers for current predictive control: A five-phase case study

The use of multiphase drives has gained importance in recent times due to some advantages that they provide over conventional three-phase ones. High performance stator current control can be achieved by means of direct command of voltage source inverter. In this context finite-state model predictive control is a very flexible strategy that has been recently proposed and analyzed. Nevertheless, its implementation must solve the problem of estimating rotor quantities, being the conventional solution a simple backtracking procedure. In this respect, observers appear as an attractive alternative. However, while they have been used with FOC, sensorless drives and for fault detection, they have not been used yet for predictive control of drives as a way to provide rotor values estimates. In this paper the authors propose to incorporate a full-order rotor current observer in a finite-state model predictive controller of a five-phase induction machine. Pole placement design based on Butterworth filters is used. The new estimation scheme and the standard procedure are compared. By means of experimental tests, the differences between both approaches and the benefits of including a rotor observer are illustrated and verified.     

On decentralized estimation and control with application to freeway ramp metering

This paper presents an approach to the suboptimal design of time invariant, infinite time horizon, L-Q-G regulator in which decision making and state estimation are decentralized into "local" subsystems. For a prespecified information pattern (set of admissible intercommunications) the problem of finding a best linear control law and state estimator is converted to a parameter optimization which can be solved off line. A modified Davidon-Fletcher-Powell algorithm is used to obtain numerical solutions to the static optimization. A design is illustrated by means of a 12 state freeway corridor ramp metering problem; behavior of centralized and decentralized designs in a nonlinear macroscopic simulation of the freeway are provided.     

快速路入口匝道的非参数自适应迭代学习控制

基于快速路交通系统重复性和周期性的特征, 引入“拟伪偏导数”概念, 给出了宏观交通流模型沿迭代轴的非参数动态线性化形式. 进一步, 提出了快速路入口匝道的非参数自适应迭代学习控制(NP-AILC)方案. 该控制方法本质上是无模型的, 并且学习增益可迭代调节. 收敛性分析表明当系统初始状态随迭代次数随机变化时, 该方法可实现几乎完全跟踪性能. 仿真结果进一步验证了方法的有效性.     

Handling uncertainty in economic nonlinear model predictive control: A comparative case study

In the last years, the use of an economic cost function for model predictive control (MPC) has been widely discussed in the literature. The main motivation for this choice is that often the real goal of control is to maximize the profit or the efficiency of a certain system, rather than tracking a predefined set-point as done in the typical MPC approaches, which can be even counter-productive. Since the economic optimal operation of a system resulting from the application of an economic model predictive control approach drives the system to the constraints, the explicit consideration of the uncertainties becomes crucial in order to avoid constraint violations. Although robust MPC has been studied during the past years, little attention has yet been devoted to this topic in the context of economic nonlinear model predictive control, especially when analyzing the performance of the different MPC approaches. In this work, we present the use of multi-stage scenario-based nonlinear model predictive control as a promising strategy to deal with uncertainties in the context of economic NMPC. We make a comparison based on simulations of the advantages of the proposed approach with an open-loop NMPC controller in which no feedback is introduced in the prediction and with an NMPC controller which optimizes over affine control policies. The approach is efficiently implemented using CasADi, which makes it possible to achieve real-time computations for an industrial batch polymerization reactor model provided by BASF SE. Finally, a novel algorithm inspired by tube-based MPC is proposed in order to achieve a trade-off between the variability of the controlled system and the economic performance under uncertainty. Simulations results show that a closed-loop approach for robust NMPC increases the performance and that enforcing low variability under uncertainty of the controlled system might result in a big performance loss.     

Model predictive control for networked control systems

This paper investigates the problem of model predictive control for a class of networked control systems. Both sensor‐to‐controller and controller‐to‐actuator delays are considered and described by Markovian chains. The resulting closed‐loop systems are written as jump linear systems with two modes. The control scheme is characterized as a constrained delay‐dependent optimization problem of the worst‐case quadratic cost over an infinite horizon at each sampling instant. A linear matrix inequality approach for the controller synthesis is developed. It is shown that the proposed state feedback model predictive controller guarantees the stochastic stability of the closed‐loop system. Copyright © 2008 John Wiley & Sons, Ltd.     

Intelligent automation systems for predictive maintenance: A case study

A case study is presented, where a predictive maintenance solution for non-critical machinery (such as elevators and machine tools) was sought. Both cases are different. There is no experience in elevator monitoring and diagnosis, and modeling has been performed using Neural Networks. On the other hand, machine tools were monitored through vibration systems where some experience exists. In this case, Bayesian Networks are the paradigm of choice as it was also recommended to include some ‘adaptation’ mechanism for the knowledge modeled in the network. The final system also includes a sensor processing unit and a remote maintenance module system that provides an automated remote condition monitoring system, for both applications. Results indicate the feasibility of partial solutions in monitoring and diagnosis, though future enhancements are needed to compose a complete solution. This paper explains the characteristics of the Bayesian Network solution finally developed for high-speed machine tools, evaluate their strengths and weaknesses, and indicate the future enhancements.     

城市交通信号的ANN自校正预测控制

提出一种基于人工神经网络的城市交通信号的自校正预测控制方法.充分考虑相邻交叉路口之间交通流的强耦合性,在此基础上建立关于队长的交通模型;其中,受控路口下一周期到达的车辆数用人工神经网络(ANN)来预测;通过该ANN还可获得确定最佳周期长度所需要的交通参量,因此还可预测下一周期的长度;上述预测值均用实测信息进行反馈校正,在此基础上即可给出带约束的预测控制算法,从而确定下一周期的控制策略.仿真实例表明该方法具有较好的控制效果.     

Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information

This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems(ITS). Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.     

基于遗传算法优化的高速公路匝道PI控制器

研究遗传算法优化PI控制器的参数,并应用到高速公路匝道控制中。阐述了匝道控制目标,建立了高速公路交通流模型,给出了遗传算法优化的步骤,并对入口匝道PI控制器的参数进行了优化。仿真结果表明,该方法性能优越,用于高速公路入口匝道控制中效果良好。     

监督交通量变化的多模型预测自适应交通信号灯控制

因信号设定时间和车流量动态行为引起的交通量变化是现代交通控制系统存在高度不确定性的主要因素.根据交通流量具有高峰期、正常期及突发超流量期的特点,本文提出了一种监督多模型交通流量建模方法,结合模型预测控制技术对交通信号灯进行优化式智能控制,对不同交通模式下交通流量的实时变化作出反应,在优化的模式下对关键主干道交叉路口的信号灯进行自适应调节,达到实现通行次数合理,车辆延误时间以及停车时间都减少的目的.仿真示例说明了该方法的有效性.     

Model predictive control design for linear parameter varying systems: A survey

Motivated by the fact that many nonlinear plants can be represented through Linear Parameter Varying (LPV) embedding, and being this framework very popular for control design, this paper investigates the available Model Predictive Control (MPC) policies that can be applied for such systems. This paper reviews the available works considering LPV MPC design, ranging from the sub-optimal, simplified, yet Quadratic Programming (QP) algorithms, the tube-based tools, the set-constrained procedures, the Nonlinear Programming procedures and the robust ones; the main features of the recent research body on this topic are examined. A simulation example is given comparing some of the important techniques. Finally, some suggestions are given for future investigation threads, seeking further applicability of these methods.     

From scenarios to code: An air traffic control case study

There has been much recent interest in synthesis algorithms that generate finite state machines from scenarios of intended system behavior. One of the uses of such algorithms is in the transition from requirements scenarios to design. Despite much theoretical work on the nature of these algorithms, there has been very little work on applying the algorithms to practical applications. In this paper, we apply the Whittle & Schumann synthesis algorithm [32] to a component of an air traffic advisory system under development at NASA Ames Research Center. We not only apply the algorithm to generate state machine designs from scenarios but also show how to generate code from the generated state machines using existing commercial code generation tools. The results demonstrate the possibility of generating application code directly from scenarios of system behavior.     

Contention-resolving model predictive control for an intelligent intersection traffic model

Discrete Event Dynamic Systems - We address the problem of optimally scheduling automated vehicles crossing an intelligent intersection by assigning vehicles with priorities and desired speed. An...