A global piecewise smooth Newton method for fast large-scale model predictive control

In this paper, the strictly convex quadratic program (QP) arising in model predictive control (MPC) for constrained linear systems is reformulated as a system of piecewise affine equations. A regularized piecewise smooth Newton method with exact line search on a convex, differentiable, piecewise-quadratic merit function is proposed for the solution of the reformulated problem. The algorithm has considerable merits when applied to MPC over standard active set or interior point algorithms. Its performance is tested and compared against state-of-the-art QP solvers on a series of benchmark problems. The proposed algorithm is orders of magnitudes faster, especially for large-scale problems and long horizons. For example, for the challenging crude distillation unit model of Pannocchia, Rawlings, and Wright (2007) with 252 states, 32 inputs, and 90 outputs, the average running time of the proposed approach is 1.57 ms.     

仿真结果可信性分析方法及软件实现

模型验证是建模与仿真过程中很重要的一部分。该文介绍了模型验证软件包MSVSP,并分别引入了静态检验和动态检验两个例子对此软件进行验收。     

A novel self-tuning feedback controller for active queue management supporting TCP flows

Wireless access points act as bridges between wireless and wired networks. Since the actually available bandwidth in wireless networks is much smaller than that in wired networks, there is a bandwidth disparity in channel capacity which makes the access point a significant network congestion point. The recently proposed active queue management (AQM) is an effective method used in wired network and wired-wireless network routers for congestion control, and to achieve a tradeoff between channel utilization and delay. The de facto standard, the random early detection (RED) AQM scheme, and most of its variants use average queue length as a congestion indicator to trigger packet dropping. In this paper, we propose a Novel autonomous Proportional and Differential RED algorithm, called NPD-RED, as an extension of RED. NPD-RED is based on a self-tuning feedback proportional and differential controller, which not only considers the instantaneous queue length at the current time point, but also takes into consideration the ratio of the current differential error signal to the buffer size. Furthermore, we give theoretical analysis of the system stability and give guidelines for the selection of feedback gains for the TCP/RED system to stabilize the instantaneous queue length at a desirable level. Extensive simulations have been conducted with ns2. The simulation results have demonstrated that the proposed NPD-RED algorithm outperforms the existing AQM schemes in terms of average queue length, average throughput, and stability.     

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.     

A framework to determine bounds of maximum loss rate parameter of RED queue for next generation routers

Random early detection (RED) is expected to eliminate global synchronization by random active packet drop. Its packet drop probability is decided by the maximum packet drop probability in its drop function, buffer thresholds, and average queue length. It has been observed that for a large number of connections, a small value of the maximum packet drop probability may not eliminate global synchronization. Furthermore, since RED uses four parameters to regulate its performance, it is necessary to relate its maximum drop probability with those parameters. The objective of this paper is to develop a framework for the bounds of the maximum drop probability of RED, based on TCP channel model and traffic characteristics. The value of the maximum drop probability obtained by our model will make RED queue achieve its targeted goals.     

A fast network partition method for large-scale urban traffic networks

In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.     

A new congestion control algorithm for improving the performance of a broadcast-based multiprocessor architecture

Congestion occurring in the input queues of broadcast-based multiprocessor architectures can severely limit their overall performance. The existing congestion control algorithms estimate congestion based on a node’s output channel parameters such as the number of free virtual channels or the number of packets waiting at the channel queue. In this paper, we have proposed a new congestion control algorithm to prevent congestion on broadcast-based multiprocessor architectures with multiple input queues. Our algorithm performs congestion control at the packet level and takes into account the next input queue number which will be accessed by the processor, which form the fundamental differences between our algorithm and the algorithms based on the idea of virtual channel congestion control. The performance of the algorithm is evaluated by OPNET Modeler with various synthetic traffic patterns on a 64-node Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus) architecture employing the message passing protocol. Performance measures such as average input waiting time, average network response time and average processor utilization have been collected before and after applying the algorithm. The results show that the proposed algorithm is able to decrease the average input waiting time by 13.99% to 20.39%, average network response time by 8.76% to 20.36% and increase average processor utilization by 1.92% to 6.63%. The performance of the algorithm is compared with that of the other congestion control algorithms and it is observed that our algorithm performs better under all traffic patterns. Also, theoretical analysis of the proposed method is carried out by using queuing networks.     

STRESS: A simulator for hard real-time systems

The STRESS environment is a collection of CASE tools for analysing and simulating the behaviour of hard real-time safety-critical applications. It is primarily intended as a means by which various scheduling and resource management algorithms can be evaluated, but can also be used to study the general behaviour of applications and real-time kernels. This paper describes the structure of the STRESS language and its environment, and gives examples of its use.     

Model predictive control using reduced order models: Guaranteed stability for constrained linear systems

The problem of controlling a high-dimensional linear system subject to hard input and state constraints using model predictive control is considered. Applying model predictive control to high-dimensional systems typically leads to a prohibitive computational complexity. Therefore, reduced order models are employed in many applications. This introduces an approximation error which may deteriorate the closed loop behavior and may even lead to instability. We propose a novel model predictive control scheme using a reduced order model for prediction in combination with an error bounding system. We employ the explicit time and input dependent bound on the model order reduction error to achieve design conditions for constraint fulfillment, recursive feasibility and asymptotic stability for the closed loop of the model predictive controller when applied to the high-dimensional system. Moreover, for a special choice of design parameters, we establish local optimality of the proposed model predictive control scheme. The proposed MPC approach is assessed via examples demonstrating that a good trade-off between computational efficiency and conservatism can be achieved while guaranteeing constraint satisfaction and asymptotic stability.     

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.     

飞机环境控制系统分布式实时仿真

该文以整个环境控制系统为研究对象,根据其仿真的实际情况,根据传热学,热力学管理,建立了系统的数学模型,在此基础上,对系统进行分布式实时仿真。     

A three-dimensional probabilistic fuzzy control system for network queue management

A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol （TCP） networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control （PFC） system is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system. A three-dimensional （3-D） membership function （MF） is embedded in the PFC to express and describe the stochastic feature of network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among ＂fuzziness-randomness-state＂. The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior performance compared to traditional control schemes in a stochastic environment.     

Simulink用于现代矢量控制系统的仿真研究

对以往建立的异步机仿真模型进行了发展，重新推导了其状态方程形式的数学模型，并用S函数实现。最后通过仿真实验验证了模型的正确性，从而为进一步研究该领域打下了基础。     

Aluminium electrolytic cells: A computer simulator for training and supervision

A workstation-based simulator of an aluminium electrolytic cell has been constructed. A mathematical model of the cell is integrated with a database and a knowledge base, and the simulator serves as a tool for the training of personnel and for research related to cell dynamics and cell control. When used in conjunction with an expert system, it provides a powerful decision-making tool or an efficient supervisory system. The mathematical model, the simulator itself, the user environment, the interactive simulation procedure as well as examples of the use of the simulator are presented in detail.List of Symbols a _i parameters of electrolyte density calculation - AlF ₃ aluminium fluoride content [w%] - Al ₂O₃ alumina content of electrolyte [w%] - b _i parameters of electrolyte specific heat - c _i parameters of bath electrical resistivity - c _p ⁱ specific heat of electrolyte [J/kg°C] - c _p ² specific heat of freeze [J/kg°C] - c _p ³ specific heat of metal [J/kg°C] - C alumina content of electrolyte [w%] - C _s saturation concentration of alumina [w%] - CaF ₂ calcium fluoride content of electrolyte [w%] - CR cryolite ratio - H _F heat of fusion of electrolyte [J/kg] - I line current [kA] - k ₁ parameter of solution 1 [1/s w%] - k ₂ parameter of solution 2 [1/s w%] - k ₃ parameter of settling [1/s] - k ₄ parameter of alumina feeding [kg/s] - k ₅ parameter of metal production [kg/s kA] - k ₆ ratio of alumina-metal mass transformation - m ₁ mass of electrolyte [kg] - m ₂ mass of freeze [kg] - m ₃ mass of molten metal [kg] - m _a rate of alumina addition [kg/s] - m _m rate of metal production [kg/s] - m _t rate of metal tapping [kg/s] - Q total heat loss [kW] - Q _A heat flow from electrolyte to anode [kW] - Q _B heat flow from metal to cathode bottom [kW] - Q _F heat flow from freezing zone to freeze [kW] - Q _F1 heat flow from electrolyte to freezing zone [kW] - Q _F2 heat flow from metal to freezing zone [kW] - Q _Ha energy needed for alumina heatup [kW] - Q _K heat flow from electrolyte to cathode [kW] - Q _s heat flow from freeze to side-wall [kW] - S ₁ solution rate of disperesed alumina [kg/s] - S ₂ solution rate of settled alumina [kg/s] - S ₃ settling rate [kg/s] - T ₁ temperature of electrolyte [°C] - T ₂ temperature of freeze [°C] - T ₃ temperature of molten metal [°C] - T _F temperature of freezing zone [°C] - T _L cryolite liquidus temperature [°C] - U total cell voltage [V] - U _a voltage equivalent of enthalpy of metal production [V] - x ₁ mass of dispersed alumina [kg] - x ₂ mass of settled alumina [kg] - x ₃ mass of dissolved alumina [kg] - _i parameters of cryolite liquidus temperature - density of bath [kg/m³] - electrical resistivity of bath [m] - _c electrical conductivity of bath [1/m] - Note w% means percentage by weight     

天线伺服系统的快速辨识算法

天线伺服控制系统在移动卫星通讯中起着关键性作用,其对象模型的品质直接影响天线伺服控制系统的设计与性能.本文针对天线伺服系统的建模问题提出一种基于系统阶跃响应的快速优化建模方法.利用这一结果,本文建立了0.9 m小口径天线伺服系统的二阶模型.进一步,讨论了基于系统伪随机响应谱密度函数的模型有效性验证方法和模型品质的评价准则,并分别在频域和时域中将由本文方法及最小二乘法所得的模型与系统实际数据进行对比分析.本文辨识方法的优点是计算简单,易于工程应用,对系统阶次不敏感并且能较为精确地描述系统主谐振峰的特性,适用于实际工程中的参数在线整定.     

C3: A finite volume-finite difference hybrid model for tsunami propagation and runup

A numerical model that couples Finite Difference and Finite Volume schemes has been developed for tsunami propagation and runup study. An explicit leap-frog scheme and a first order upwind scheme has been considered in the Finite Difference module, while in the Finite Volume scheme a Godunov Type method based on the f-waves approach has been used. The Riemann solver included in the model corresponds to an approximate augmented solver for the Shallow Water Equations (SWE) in the presence of variable bottom surface. With this hybrid model some of the problems inherent to the Godunov type schemes are avoided in the offshore region, while in the coastal area the use of a conservative method ensures the correct computation of the runup and wave breaking. The model has been tested and validated using different problems with a known analytical solution and also with laboratory experiments, considering both non breaking and breaking waves. The results are very satisfactory, showing that the hybrid approach is a useful technique for practical usages.     

RTG: a recursive realistic graph generator using random typing

We propose a new, recursive model to generate realistic graphs, evolving over time. Our model has the following properties: it is (a) flexible, capable of generating the cross product of weighted/unweighted, directed/undirected, uni/bipartite graphs; (b) realistic, giving graphs that obey eleven static and dynamic laws that real graphs follow (we formally prove that for several of the (power) laws and we estimate their exponents as a function of the model parameters); (c) parsimonious, requiring only four parameters. (d) fast, being linear on the number of edges; (e) simple, intuitively leading to the generation of macroscopic patterns. We empirically show that our model mimics two real-world graphs very well: Blognet (unipartite, undirected, unweighted) with 27 K nodes and 125 K edges; and Committee-to-Candidate campaign donations (bipartite, directed, weighted) with 23 K nodes and 880 K edges. We also show how to handle time so that edge/weight additions are bursty and self-similar.     

Model validation for control and controller validation in a prediction error identification framework—Part II: illustrations

In this paper, we illustrate our new results on model validation for control and controller validation in a prediction error identification framework, developed in a companion paper (Gevers et al., Automatica (2003) 39(3) pii: S005-1098(02)00234-0), through two realistic simulation examples, covering widely different control design applications. The first is the control of a flexible mechanical system (the Landau benchmark example) with a tracking objective, the second is the control of a ferrosilicon production process with a disturbance rejection objective.     

Distributed joint dynamic maintenance and production scheduling in manufacturing systems: Framework based on model predictive control and Benders decomposition

Scheduling the maintenance based on the condition, respectively the degradation level of the system leads to improved system's reliability while minimizing the maintenance cost. Since the degradation level changes dynamically during the system's operation, we face a dynamic maintenance scheduling problem. In this paper, we address the dynamic maintenance scheduling of manufacturing systems based on their degradation level. The manufacturing system consists of several units with a defined capacity and an individual dynamic degradation model, seeking to optimize their reward. The units sell their production capacity, while maintaining the systems based on the degradation state to prevent failures. The manufacturing units are jointly responsible for fulfilling the demand of the system. This induces a coupling constraint among the agents. Hence, we face a large-scale mixed-integer dynamic maintenance scheduling problem. In order to handle the dynamic model of the system and large-scale optimization, we propose a distributed algorithm using model predictive control (MPC) and Benders decomposition method. In the proposed algorithm, first, the master problem obtains the maintenance scheduling for all the agents, and then based on this data, the agents obtain their optimal production using the distributed MPC method which employs the dual decomposition approach to tackle the coupling constraints among the agents. The effectiveness of the proposed method is investigated on two case studies.