Extremum-seeking control of state-constrained nonlinear systems

An extremum-seeking control problem is posed for a class of nonlinear systems with unknown dynamical parameters, whose states are subject to convex, pointwise inequality constraints. Using a barrier function approach, an adaptive method is proposed for generating setpoints online which converge to the feasible minimizer of a convex objective function containing the unknown dynamic parameters. A tracking controller regulates system states to the generated setpoint via state feedback, while maintaining feasibility of the state constraints. A simulation example demonstrates application of the method.     

Globally stabilizing control of fed-batch processes with Haldane kinetics using growth rate estimation feedback

The regulation of the biomass specific growth rate is an important goal in many biotechnological applications. To achieve this goal in fed-batch processes, several control strategies have been developed employing a closed loop version of the exponential feeding law, an estimation of the controlled variable and some error feedback term. In the case of non-monotonic kinetics, the specified growth rate can be achieved at two different substrate concentration values. Because of the inherent unstable properties of the system in the decreasing portion of the kinetics function, stabilization becomes a crucial problem in this high-substrate operating region. In this context, the dynamic behavior of fed-batch processes with Haldane kinetics is further investigated. In particular, some conditions for global stability and performance improvement are derived. Then, a stabilizing control law based on a partial state feedback with gain dependent on the output error feedback and gain saturation is proposed. Although particular emphasis is put on the critical case of high-substrate operation, low-substrate regulation is also treated.     

Optimization of fed-batch fermentation processes with bio-inspired algorithms

The optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.     

A practical robust control scheme for yeast fed-batch cultures – An experimental validation

Model-based control of bioprocess is a difficult task due to the challenges associated with bioprocess modeling and the lack of on-line measurements. In this study, a robust controller using minimal process knowledge and minimal measurement information is developed and applied to the ethanol regulation in cultures of Saccharomyces cerevisiae. A Youla parametrization is selected in order to reject the disturbance associated to the substrate demand for cell growth and to robustify the control scheme to unstructured uncertainties and measurement noise. The performance of this control scheme is illustrated in simulation and with real on-line experimental data.     

A new algorithm for solving convex parametric quadratic programs based on graphical derivatives of solution mappings

In this paper we derive formulas for computing graphical derivatives of the (possibly multivalued) solution mapping for convex parametric quadratic programs. Parametric programming has recently received much attention in the control community, however most algorithms are based on the restrictive assumption that the so called critical regions of the solution form a polyhedral subdivision, i.e. the intersection of two critical regions is either empty or a face of both regions. Based on the theoretical results of this paper, we relax this assumption and show how we can efficiently compute all adjacent full dimensional critical regions along a facet of an already discovered critical region. Coupling the proposed approach with the graph traversal paradigm, we obtain very efficient algorithms for the solution of parametric convex quadratic programs.     

Pseudosimulation: An algorithm for distributed simulation with limited memory

In this paper we present a new algorithm for the distributed simulation of systems that may be modeled as a network of processes which exchange event messages (e.g., computer networks, telephone systems). We focus upon the case of fully distributed processes with limited memory available to simulate each process. The synchronization algorithm employed is a blocking algorithm. The simulation of different processes is allowed to proceed in parallel until deadlock occurs, at which time a deadlock-breaking algorithm is invoked as in the Chandy/Misra scheme.⁽¹⁾ A central controller is emploled to detect and aid in the efficient resolution of deadlocks. We solve some problems which were not clearly addressed in the Chandy/Misra scheme. Our deadlock-breaking algorithm, christened Pseudosimulation, is a look-ahead algorithm, which undertakes to fill empty buffers with future event messages. Correctness and termination of the algorithm are proven. An analysis of the memory requirements and running time is performed. A lower bound on the memory requirements is also established which simplifies the deadlock-breaking algorithm.Research supported by grants from the National Sciences and Engineering Research Council of Canada and Centre de recherche informatique de Montréal.     

Tool support for the design of self-optimizing mechatronic multi-agent systems

Complex technical systems, such as mechatronic systems, can exploit networking as well as the computational power available today to achieve an automatic improvement of the technical system performance at run-time through self-optimization. To realize this vision, appropriate means for the design of such self-optimizing mechatronic systems are required. Well-established techniques and tools for the modeling of cognitive behavior, reflective behavior, and control behavior exist. However, to really enable self-optimization and its full potential, these different aspects have to be safely integrated in a manner that remains comprehensible to the designer. In this article, we present how this required integration has been realized at the semantic level by extending the unified modeling language (UML), and at the tool level by integrating the CAE tool CAMeL and the CASE tool Fujaba real-time tool suite. The presented Mechatronic UML approach supports the design of verifiable, complex, reconfigurable mechatronic systems using the multi-agent system metaphor. This work was developed in the course of the Special Research Initiative 614—self-optimizing concepts and structures in mechanical engineering—University of Paderborn, and was published on its behalf and funded by the Deutsche Forschungsgemeinschaft. Sven Burmester, Oliver Oberschelp, Florian Klein and Peter Scheideler are members of the respective research group which left after the paper was submitted.     

Approximation algorithm for periodic real-time tasks with workload-dependent running-time functions

This paper addresses the problem of resource allocation for distributed real-time periodic tasks, operating in environments that undergo unpredictable changes and that defy the specification of meaningful worst-case execution times. These tasks are supplied by input data originating from various environmental workload sources. Rather than using worst-case execution times (WCETs) to describe the CPU usage of the tasks, we assume here that execution profiles are given to describe the running time of the tasks in terms of the size of the input data of each workload source. The objective of resource allocation is to produce an initial allocation that is robust against fluctuations in the environmental parameters. We try to maximize the input size (workload) that can be handled by the system, and hence to delay possible (costly) reallocations as long as possible. We present an approximation algorithm based on first-fit and binary search that we call FFBS. As we show here, the first-fit algorithm produces solutions that are often close to optimal. In particular, we show analytically that FFBS is guaranteed to produce a solution that is at least 41% of optimal, asymptotically, under certain reasonable restrictions on the running times of tasks in the system. Moreover, we show that if at most 12% of the system utilization is consumed by input independent tasks (e.g., constant time tasks), then FFBS is guaranteed to produce a solution that is at least 33% of optimal, asymptotically. Moreover, we present simulations to compare FFBS approximation algorithm with a set of standard (local search) heuristics such as hill-climbing, simulated annealing, and random search. The results suggest that FFBS, in combination with other local improvement strategies, may be a reasonable approach for resource allocation in dynamic real-time systems. David Juedes is a tenured associate professor and assistant chair for computer science in the School of Electrical Engineering and Computer Science at Ohio University. Dr. Juedes received his Ph.D. in Computer Science from Iowa State University in 1994, and his main research interests are algorithm design and analysis, the theory of computation, algorithms for real-time systems, and bioinformatics. Dr. Juedes has published numerous conference and journal papers and has acted as a referee for IEEE Transactions on Computers, Algorithmica, SIAM Journal on Computing, Theoretical Computer Science, Information and Computation, Information Processing Letters, and other conferences and journals. Dazhang Gu is a software architect and researcher at Pegasus Technologies (NeuCo), Inc. He received his Ph.D. in Electrical Engineering and Computer Science from Ohio University in 2005. His main research interests are real-time systems, distributed systems, and resource optimization. He has published conference and journal papers on these subjects and has refereed for the Journal of Real-Time Systems, IEEE Transactions on Computers, and IEEE Transactions on Parallel and Distributed Systems among others. He also served as a session chair and publications chair for several conferences. Frank Drews is an Assistant Professor of Electical Engineering and Computer Science at Ohio Unversity. Dr. Drews received his Ph.D. in Computer Science from the Clausthal Unversity of Technolgy in Germany in 2002. His main research interests are resource management for operating systems and real-time systems, and bioinformatics. Dr. Drews has numerous publications in conferences and journals and has served as a reviewer for IEEE Transactions on Computers, the Journal of Systems and Software, and other conferences and Journals. He was Publication Chair for the OCCBIO’06 conference, Guest Editor of a Special Issue of the Journal of Systems and Software on “Dynamic Resource Management for Distributed Real-Time Systems”, organizer of special tracks at the IEEE IPDPS WPDRTS workshops in 2005 and 2006. Klaus Ecker received his Ph.D. in Theoretical Physics from the University of Graz, Austria, and his Dr. habil. in Computer Science from the University of Bonn. Since 1978 he is professor in the Department of Computer Science at the Clausthal University of Technology, Germany, and since 2005 he is visiting professor at the Ohio University. His research interests are parallel processing and theory of scheduling, especially in real time systems, and bioinformatics. Prof. Ecker published widely in the above mentioned areas in well reputed journals and proceedings of international conferences as well. He is also the author of two monographs on scheduling theory. Since 1981 he is organizing annually international workshops on parallel processing. He is associate editor of Real Time Systems, and member of the German Gesellschaft fuer Informatik (GI) and of the Association for Computing Machinery (ACM). Lonnie R. Welch received a Ph.D. in Computer and Information Science from the Ohio State University. Currently, he is the Stuckey Professor of Electrical Engineering and Computer Science at Ohio University. Dr. Welch performs research in the areas of real-time systems, distributed computing and bioinformatics. His research has been sponsored by the Defense Advanced Research Projects Agency, the Navy, NASA, the National Science Foundation and the Army. Dr. Welch has twenty years of research experience in the area of high performance computing. In his graduate work at Ohio State University, he developed a high performance 3-D graphics rendering algorithm, and he invented a parallel virtual machine for object-oriented software. For the past 15 years his research has focused on middleware and optimization algorithms for high performance computing. His research has produced three successive generations of adaptive resource management (RM) middleware for high performance real-time systems. The project has resulted in two patents and more than 150 publications. Professor Welch also collaborates on diabetes research with faculty at Edison Biotechnology Institute and on genomics research with faculty in the Department of Environmental and Plant Biology at Ohio University. Dr. Welch is a member of the editorial boards of IEEE Transactions on Computers, The Journal of Scalable Computing: Practice and Experience, and The International Journal of Computers and Applications. He is also the founder of the International Workshop on Parallel and Distributed Real-time Systems and of the Ohio Collaborative Conference on Bioinformatics. Silke Schomann graduated in 2003 with a M.Sc. in Computer Science from Clausthal University Of Technology, where she has been working as a scientific assistant since then. She is currently working on her Ph.D. thesis in computer science at the same university.     

非平衡有向网络下带约束的连续时间分布式优化算法设计EI北大核心CSCD

本文基于权重不平衡有向网络,对一类分布式约束优化问题进行研究,其中全局目标函数等于具有李普希兹梯度的强凸目标函数之和,并且每个智能体的状态都有一个局部约束集.每个智能体仅知道自身的局部目标函数和非空约束集.本文的目标是用分布式方法求解该问题的最优解.针对优化问题,提出了一种新的分布式投影梯度连续时间协调算法,利用拉普拉斯矩阵的零特征值对应的左特征向量消除了图的不平衡性.在某些假设下,结合凸分析理论和李雅普诺夫稳定性理论,证明了算法能够获得问题的最优解.最后,通过仿真验证了算法的有效性.     

An extremum seeking approach via variable-structure control for fed-batch bioreactors with uncertain growth rate

In this paper, we present an extremum-seeking scheme based on an approach to variable structure control for fed-batch bioreactors. The proposed scheme deals with uncertainty on the specific growth rate without assuming an explicit mathematical expression. The control approach exploits the inhibitory effect of the substrate concentration on the growth rate, in such a manner that the closed-loop system reaches the sliding regime on an optimal switching manifold, which is defined by maximizing biomass production. The control scheme comprises an estimation scheme consisting of a high-gain observer and a discrete gradient estimator which computes the unknown terms. The practical stabilizability for the closed-loop system around an unknown optimal set-point is analyzed. Numerical experiments illustrate the effectiveness of the proposed approach.     

Support software for the development of programmable logic controller applications

Enhancements to the software aids, used for the development of Programmable Logic Controller programs, are proposed in this work. A possible architecture of the software realizing these enhancements and the language constructs required for its configuration to a specific application are also presented. Based on this architecture, experimental software aids have been developed to demonstrate that it is quite feasible to provide the major services considered in this proposal. These services allow the program developer to emulate the dynamic operation of a specific programmable controller to alternative scenarios of input variations and relationships over a defined time horizon, and configure displays of graphics and timing diagrams which may assist him in following up and testing the program execution.     

Sensor selection for model-based real-time optimization: relating design of experiments and design cost

Real-time optimization systems have become a common tool, in the continuous manufacturing industries, for improving process performance. Typically, these are on-line, steady-state, model-based optimization systems, whose effectiveness depends on a large number of design decisions. The work presented here addresses one of these design decisions and proposes a systematic approach to the selection of sensors to be used by the RTO system. This paper develops a sensor system selection metric based on a trade-off between two approaches to the design of experiments, which is shown to be consistent with the design cost approach of Forbes and Marlin [Computers Chem Eng 20 (1996) 7/7]. The resulting design metric is incorporated into a systematic procedure for RTO sensor selection problem. Finally, the proposed RTO sensor selection procedure is illustrated with a case study using the Williams–Otto [AIEE Trans 79 (1960), 458] plant.     

蚁群算法求解函数优化中的参数设置

蚁群算法的参数设置一直是依靠经验和实验来确定,造成实验工作量大且难以得到最优的参数组合,影响了算法的使用。从基本蚂蚁算法出发,结合实验结果,讨论了α、β及ρ的变化对实验结果的影响,提出了相应的参数改进方案。并将经此方案修正的蚂蚁算法与基本蚂蚁算法同时运用于经典函数优化问题中,对仿真结果进行了对比。     

Hierarchical multi-pattern matching algorithm for network content inspection

Inspection engines that can inspect network content for application-layer information are urgently required. In-depth packet inspection engines, which search the whole packet payload, can identify the interested packets that contain certain patterns. Network equipment then utilizes the searching results from the inspection engines for application-oriented management. The most important technology for fast packet inspection is an efficient multi-pattern matching algorithm to perform exact string matching between packets and a large set of patterns. This paper proposes a novel hierarchical multi-pattern matching algorithm (HMA) for packet inspection. HMA builds hierarchical index tables from the most frequent common-codes, and efficiently reduces the amount of external memory accesses and memory space by two-tier and cluster-wise matching. Analysis and simulation results reveal that HMA performs much better than state-of-the-art matching algorithms. In particular, HMA can update patterns incrementally, thus creating a reliable network system.     

基于粒子群优化的有约束模型预测控制器

研究了模型预测控制(MPC)中解决带约束的优化问题时所用到的优化算法,针对传统的二次规划(QP)方法的不足,引入了一种带有混沌初始化的粒子群优化算法(CPSO),将其应用到模型预测控制中,用十解决同时带有输入约束和状态约束的控制问题.最后,引入了一个实际的带有约束的线性离散系统的优化控制问题,分别用二次规划和粒子群优化两种算法去解决,通过仿真结果的比较,说明了基于粒子群优化(PSO)的模型预测控制算法的优越性.     

Optimal control of parallel queues with impatient customers

We consider a queueing system with a number of identical exponential servers. Each server has its own queue with unlimited capacity. The service discipline in each queue is first-come-first-served (FCFS). Customers arrive according to a state-dependent Poisson process with an arrival rate which is a non-increasing function of the number of customers in the system. Upon arrival, a customer must join a server’s queue according to a stationary state-dependent policy, where the state is taken to be the number of customers in servers’ queues. No jockeying among queues is allowed. Each arriving customer is limited to a generally distributed patience time after which it must depart the system and is considered lost. Two models of customer behavior are considered: deadlines until the beginning of service and deadlines until the end of service. We seek an optimal policy to assign an arriving customer to a server’s queue. We show that, when the distribution of customer impatience satisfies certain property, the policy of joining shortest queue (SQ) stochastically minimizes the number of lost customers during any finite interval in the long run. This property is shown to always hold for the case of deterministic customer impatience.     

Anti-windup design with guaranteed regions of stability for discrete-time linear systems

The purpose of this paper is to study the determination of stability regions for discrete-time linear systems with saturating controls through anti-windup schemes. Considering that a linear dynamic output feedback has been designed to stabilize the linear discrete-time system (without saturation), a method is proposed for designing an anti-windup gain that maximizes an estimate of the basin of attraction of the closed-loop system in the presence of saturation. It is shown that the closed-loop system obtained from the controller plus the anti-windup gain can be locally modeled by a linear system with a deadzone nonlinearity. Then, based on the use of a new sector condition and quadratic Lyapunov functions, stability conditions in an LMI form are stated. These conditions are then considered in a convex optimization problem in order to compute an anti-windup gain that maximizes an estimate of the basin of attraction of the closed-loop system. Moreover, considering asymptotically stable open-loop systems, it is shown that the conditions can be slightly modified in order to determine an anti-windup gain that ensures global stability. An extension of the proposed results to the case of dynamic anti-windup synthesis is also presented in the paper.     

The design of a wireless real-time visual surveillance system

In this paper, we study the important issues in the design of an efficient wireless real-time visual surveillance system (WISES). Two important considerations are to minimize: (1) the video workload on the wireless network; and (2) the processing workload at the front-end video capturing unit. To achieve the first objective, we propose a cooperative framework for semantic filtering of video frames instead of forwarding every video frame to the back-end server for analysis and monitoring query evaluation. To minimize the processing workload at the front-end unit, a hierarchical object model (HOM) is designed to model the status of the objects, and their temporal and spatial properties in the video scene. With the information provided from the back-end server, the front-end unit pre-analyses the current status of the objects in the HOM by comparing the selection conditions in the submitted monitoring queries following the adaptive object-based evaluation (APOBE) scheme which is proposed to reduce the processing workload at the front-end unit. In APOBE, a higher evaluation frequency is given to the object which is closer to satisfy the condition in the monitoring queries. The performance of WISES has been studied to demonstrate the efficiency of the proposed scheme.