Tracking setpoint robust model predictive control for input saturated and softened state constraints

This paper starts with a brief review of robust model predictive control (RMPC) schemes for uncertain systems using linear matrix inequalities (LMIs) subject to input saturated and softened state constraints. However when RMPC has both input and state constraints, difficulties will arise due to the inability to satisfy the state constraints. In this paper, we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. A brief comparative simulation of the two new RMPC schemes is implemented via examples to demonstrate the ability of the new RMPC schemes.     

A Bidirectional Hetero-Associative Memory for True-Color Patterns

Classical bidirectional associative memories (BAM) have poor memory storage capacity, are sensitive to noise, are subject to spurious steady states during recall, and can only recall bipolar patterns. In this paper, we introduce a new bidirectional hetero-associative memory model for true-color patterns that uses the associative model with dynamical synapses recently introduced in Vazquez and Sossa (Neural Process Lett, Submitted, 2008). Synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. Propositions that guarantee perfect and robust recall of the fundamental set of associations are provided. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model with a benchmark of true-color patterns.     

Dynamically-Stable Motion Planning for Humanoid Robots

We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of the environment and a statically-stable desired posture, we search the configuration space of the robot for a collision-free path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a post-processing step to transform statically-stable, collision-free paths into dynamically-stable, collision-free trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using both simulated and real humanoid robots.     

基于依存分析的中文时间表达式类型判定

某些“基数词+时间单位词”组成的中文时间表达式在不同语境中可能表现为时点式,也可能表现为时段式。为自动判定中文时间表达式的类型,提出一种基于依存分析的判定方法。该方法首先借助依存分析考察中文时间表达式在句中所受的句法约束,然后将这些约束转化为具有可计算性的依存规则,最后利用依存规则对中文时间表达式的类型进行判定。实验结果显示,在此方法中,时间表达式确认的正确率、召回率、F值分别达到82.3%、88.1%和85.1%;时间表达式类型判定的正确率、召回率、F值分别达到77.1%、82.5%和79.7%。     

Static output-feedback control under information structure constraints

An important challenge in the static output-feedback control context is to provide an isolated gain matrix possessing a zero–nonzero structure, mainly in problems presenting information structure constraints. Although some previous works have contributed some relevant results to this issue, a fully satisfactory solution has not yet been achieved up to now. In this note, by using a Linear Matrix Inequality approach and based on previous results given in the literature, we present an efficient methodology which permits us to obtain an isolated static output-feedback gain matrix having, simultaneously, a zero–nonzero structure imposed a priori.     

On simultaneous optimization of truss geometry and topology

The paper addresses the classical problem of optimal truss design where cross-sectional areas and the positions of joints are simultaneously optimized. Se-veral approaches are discussed from a general point of view. In particular, we focus on the difference between simultaneous and alternating optimization of geometry and topology. We recall a rigorously mathematical approach based on the implicit programming technique which considers the classical single load minimum compliance problem subject to a volume constraint. This approach is refined leading to three new problem formulations which can be treated by methods of Mathematical Programming. In particular, these formulations cover the effect of melting end nodes, i.e., vanishing potential bars due to changes in the geometry. In one of these new problem formulations, the objective function is a polynomial of degree three and the constraints are bilinear or just sign constraints. Because heuristics is avoided, certain optimality properties can be proven for resulting structures. The paper closes with two numerical test examples.     

基于依存分析的离合触发词合法分离形式判定

离合触发词的构词语素可能因插入、颠倒、省略而产生多种合法分离形式,这些分离形式与原形一样也能表征事件。为完整抽取事件,提出一种基于依存分析的离合触发词合法分离形式判定算法。该方法首先借助依存分析考察离合触发词合法分离形式在句中所受的依存约束,然后将这些约束转化为可计算的判定规则,最后利用判定规则对离合触发词的合法分离形式进行判定。实验结果显示,排除稀疏数据前,此方法的正确率、召回率、F值分别为82.2%、88.3%、85.1%;排除稀疏数据后,正确率、召回率、F值提升到82.4%、88.7%、85.4%。方法已基本具备应用潜质。     

Undershoot and settling time tradeoffs for nonminimum phase systems

It has been known for some time that real nonminimum phase zeros imply undershoot in the step response of linear systems. Bounds on such undershoot depend on the settling time demanded and the zero locations. In this note, we review such constraints for linear time invariant systems and provide new stronger bounds that consider simultaneously the effect of two real nonminimum phase zeros. Using the concept of zero dynamics, we extend these results to a class of nonlinear systems.     

A bidirectional heteroassociative memory for binary and grey-level patterns

Typical bidirectional associative memories (BAM) use an offline, one-shot learning rule, have poor memory storage capacity, are sensitive to noise, and are subject to spurious steady states during recall. Recent work on BAM has improved network performance in relation to noisy recall and the number of spurious attractors, but at the cost of an increase in BAM complexity. In all cases, the networks can only recall bipolar stimuli and, thus, are of limited use for grey-level pattern recall. In this paper, we introduce a new bidirectional heteroassociative memory model that uses a simple self-convergent iterative learning rule and a new nonlinear output function. As a result, the model can learn online without being subject to overlearning. Our simulation results show that this new model causes fewer spurious attractors when compared to others popular BAM networks, for a comparable performance in terms of tolerance to noise and storage capacity. In addition, the novel output function enables it to learn and recall grey-level patterns in a bidirectional way.     

Ontology-driven visualization system for semantic searching

Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization.     

Velocity observers for linear mechanical systems subject to single non-smooth impacts

In this paper, we consider the problem of the velocity estimation from position measurement for linear mechanical systems (with multiple degrees of freedom) subject to single non-smooth impacts, both elastic and inelastic (i.e., with coefficient of restitution e=1 and e(0,1), respectively). Through a simple example, it is shown that a classical Luenberger observer is not able to reproduce instantaneously the jumps in the mass velocities, since it recovers the error induced by such jumps only asymptotically: an infinite sequence of impacts can prevent the estimation error to asymptotically go to zero. A new observer structure is proposed for linear mechanical systems subject to single unilateral constraints, that guarantees that the corresponding error dynamics are exponentially stable, also in presence of an infinite sequence of non-smooth impacts. The observer that we propose switches at the impact times, that can be recognized by position measurements only. To validate the proposed observer, both simulation and experimental tests have been carried out and are briefly reported, pointing out the drawbacks and advantages of the observer.     

Periodic optimal control of nonlinear constrained systems using economic model predictive control

In this paper, we consider the problem of periodic optimal control of nonlinear systems subject to online changing and periodically time-varying economic performance measures using model predictive control (MPC). The proposed economic MPC scheme uses an online optimized artificial periodic orbit to ensure recursive feasibility and constraint satisfaction despite unpredictable changes in the economic performance index. We demonstrate that the direct extension of existing methods to periodic orbits does not necessarily yield the desirable closed-loop economic performance. Instead, we carefully revise the constraints on the artificial trajectory, which ensures that the closed-loop average performance is no worse than a locally optimal periodic orbit. In the special case that the prediction horizon is set to zero, the proposed scheme is a modified version of recent publications using periodicity constraints, with the important difference that the resulting closed loop has more degrees of freedom which are vital to ensure convergence to an optimal periodic orbit. In addition, we detail a tailored offline computation of suitable terminal ingredients, which are both theoretically and practically beneficial for closed-loop performance improvement. Finally, we demonstrate the practicality and performance improvements of the proposed approach on benchmark examples.     

Analysis of General Ideal Proportional Navigation Guidance Laws

In this work, the capture region of the general ideal proportional navigation guidance law is analyzed with the following constraints: (i) the target's input acceleration is subject to independent or magnitude saturation, and (ii) the missile's input acceleration is subject to magnitude saturation. In addition to the case of unbounded maneuverability of missile with or without knowing the target acceleration, the necessary and sufficient condition for a bounded maneuvering missile that captures a nonmaneuvering target is also derived. Furthermore, the conditions that result in zero impact velocity are discovered and discussed in some of the cases. The capture regions that cannot be obtained analytically are determined numerically by a powerful classifier, the least square support vector machines. To have the capture region ready for least square support vector machines, all the state variables are transformed into modified polar variables and non‐dimensionalized to reduce the number of independent variables. In order to make the implementation possible in real time, an approximation of the Gaussian radial basis function is adopted to obtain the corresponding nonlinear feature mapping function. Through numerous numerical experiments, the proposed technique is found to be adequate for storing the capture region in an onboard fire control computer.     

Constraint satisfaction techniques in planning and scheduling

Over the last few years constraint satisfaction, planning, and scheduling have received increased attention, and substantial effort has been invested in exploiting constraint satisfaction techniques when solving real life planning and scheduling problems. Constraint satisfaction is the process of finding a solution to a set of constraints. Planning is the process of finding a sequence of actions that transfer the world from some initial state to a desired state. Scheduling is the problem of assigning a set of tasks to a set of resources subject to a set of constraints. In this paper, we introduce the main definitions and techniques of constraint satisfaction, planning and scheduling from the Artificial Intelligence point of view.     

中文金融新闻中公司名的识别

在金融领域信息抽取中,公司名扮演着非常重要的角色;因此如何正确识别文本中出现的公司名是一个非常重要的研究课题。在对金融新闻文本进行了深入地分析和研究的基础上,总结出了公司名的结构特征及其上下文信息,建立了六个用于识别公司名的知识库,并提出了一个基于两次扫描过程的识别策略。初步实验结果表明,在封闭测试中实验系统公司名识别的精确率可以达到97.3% ,召回率可达89.3%;在开放测试中精确率可以达到62.8% ,召回率可达62.1%。     

A probabilistic model for bounded elasticity tensor random fields with application to polycrystalline microstructures

In this paper, we address the construction of a prior stochastic model for non-Gaussian deterministically-bounded positive-definite matrix-valued random fields in the context of mesoscale modeling of heterogeneous elastic microstructures. We first introduce the micromechanical framework and recall, in particular, Huet’s Partition Theorem. Based on the latter, we discuss the nature of hierarchical bounds and define, under some given assumptions, deterministic bounds for the apparent elasticity tensor. Having recourse to the Maximum Entropy Principle under the constraints defined by the available information, we then introduce two random matrix models. It is shown that an alternative formulation of the boundedness constraints further allows constructing a probabilistic model for deterministically-bounded positive-definite matrix-valued random fields. Such a construction is presented and relies on a class of random fields previously defined. We finally exemplify the overall methodology considering an experimental database obtained from EBSD measurements and provide a simple numerical application.     

Circuit retiming applied to decomposed software pipelining

This paper elaborates on a new view on software pipelining, called decomposed software pipelining. The approach is to decouple the problem into resource constraints and dependence constraints. Resource constraints management amounts to scheduling an acyclic graph subject to resource constraints for which an efficiency bound is known, resulting in a bound for loop scheduling. The acyclic graph is obtained by cutting some particular edges of the (cyclic) dependence graph. In this paper, we cut edges in a different way, using circuit retiming algorithms, so as to minimize both the longest dependence path in the acyclic graph, and the number of edges in the acyclic graph. With this technique, we improve the efficiency bound given for Gasperoni and Schwlegelshohn algorithm, and we reduce the constraints that remain for the acyclic problem. We believe this framework to be of interest because it brings a new insight into the software problem by establishing its deep link with the circuit retiming problem     

On control of discrete-time state-dependent jump linear systems with probabilistic constraints: A receding horizon approach

In this article, we consider a receding horizon control of discrete-time state-dependent jump linear systems, a particular kind of stochastic switching systems, subject to possibly unbounded random disturbances and probabilistic state constraints. Due to the nature of the dynamical system and the constraints, we consider a one-step receding horizon. Using inverse cumulative distribution function, we convert the probabilistic state constraints to deterministic constraints, and obtain a tractable deterministic receding horizon control problem. We consider the receding horizon control law to have a linear state-feedback and an admissible offset term. We ensure mean square boundedness of the state variable via solving linear matrix inequalities off-line, and solve the receding horizon control problem on-line with control offset terms. We illustrate the overall approach applied on a macroeconomic system.     

A Permutation-Based Approach for Solving the Job-Shop Problem

In this paper, we deal with the famous job-shop scheduling problem, which has been being a constant subject of study for many years due to its high computational complexity (NP-hard in the strong sense). We present a permutation-based scheme for solving the problem, which in the abstraction level differs from the classical one of Jacques Carlier and Eric Pinson. In particular, we specify the differences both in the fashion of stating the constraints (the use of the generalized sorting constraint) and in the search strategy (splitting intervals of task orders). We will first give a constraint program for solving the problem, which involves only primitive constraints and which is clean and simple to understand. We then study some special techniques based on testing variable bounds that allow us to solve two hard instances la21 and la38. These two instances have been open problems recommended in a paper of David Applegate and William Cook in 1991.     

Minimizing mean flowtime and makespan on master–slave systems

The master–slave scheduling model is a new model recently introduced by Sahni. It has many important applications in parallel computer scheduling and industrial settings such as semiconductor testing, machine scheduling, etc. In this model each job is associated with a preprocessing task, a slave task and a postprocessing task that must be executed in this order. While the preprocessing and postprocessing tasks are scheduled on the master machine, the slave tasks are scheduled on the slave machines. In this paper, we consider scheduling problems on single-master master–slave systems. We first strengthen some previously known complexity results for makespan problems, by showing them to be strongly NP-hard. We then show that the problem of minimizing the mean flowtime is strongly NP-hard even under severe constraints. Finally, we propose some heuristics for the mean flowtime and makespan problems subject to some constraints, and we analyze the worst-case performance of these heuristics.