Design of output constraints for model-based non-square controllers using interval operability

A steady-state interval operability methodology is introduced here for multivariable non-square systems with fewer inputs than output variables to be used in the design of model-based constrained controllers (MPC, DMC). For such systems, set-point control is not possible for all the outputs and interval control is needed. The proposed iterative approach enables the selection of the needed interval constraints systematically, so that the tightest possible control is achieved without rendering the control problem infeasible. The application of this methodology to high-dimensional industrial problems characterizing processes of Air Products and Chemicals and DuPont shows that very significant reduction of the constrained region can be achieved from the steady-state point of view. Ratios of the initial to the calculated volume of the constrained regions examined range between 10⁴ and 10⁸.     

A primary influence vertex approach to identify driving factors in complex integrated agri-industrial systems – an example from sugarcane supply and processing systems

Integrated agri-industrial systems (IAISs), such as sugarcane supply and processing systems, are complex systems and hence generally difficult to understand and manage. The large number factors in IAISs coupled with the complex interrelationships among the factors make it challenging to identify the points of intervention for improving their overall performance. Several approaches, such as the network theory and the Theory of Constraints have been used to identify important factors in systems with variations in success. This paper demonstrates a primary influence vertex approach for identifying and ranking the factors that drive the performance of IAISs. The approach is based on comprehensive causal network analyses and was tested in four relatively diverse large-scale sugarcane milling operations in South Africa. Results from the analyses were found to be consistent with the literature and external knowledge of the milling areas as at the time of the study. It is concluded that the approach can proffer a sound basis from which deeper rooted problems in systems can be identified on an ongoing basis. It is, however, recommended that the approach should be systematically compared with other relevant methods that are used to analyse complex systems.     

Two nonlinear optimization methods for black box identification compared

In this paper, two nonlinear optimization methods for the identification of nonlinear systems are compared. Both methods estimate the parameters of e.g. a polynomial nonlinear state-space model by means of a nonlinear least-squares optimization of the same cost function. While the first method does not estimate the states explicitly, the second method estimates both states and parameters adding an extra constraint equation. Both methods are introduced and their similarities and differences are discussed utilizing simulation data. The unconstrained method appears to be faster and more memory efficient, but the constrained method has a significant advantage as well: it is robust for unstable systems of which bounded input-output data can be measured (e.g. a system captured in a stabilizing feedback loop). Both methods have successfully been applied on real-life measurement data.     

具有约束多目标优化的进化算法

针对带有约束多目标优化问题,提出一种多目标优化进化算法。在选择过程中,采用约束的Pareto支配和聚集距离定义适应值,根据适应值挑选出有代表性的个体。在变异过程中,沿着权重梯度方向搜索来寻找可行的Pareto最优解。最后,采用两个数值算例测草算法的性能,结果表明该算法能获得多目标约束优化问题的可行Pareto最优解并且具有较好的分散性。     

Barrier Lyapunov Functions for the control of output-constrained nonlinear systems

In this paper, we present control designs for single-input single-output (SISO) nonlinear systems in strict feedback form with an output constraint. To prevent constraint violation, we employ a Barrier Lyapunov Function, which grows to infinity when its arguments approach some limits. By ensuring boundedness of the Barrier Lyapunov Function in the closed loop, we ensure that those limits are not transgressed. Besides the nominal case where full knowledge of the plant is available, we also tackle scenarios wherein parametric uncertainties are present. Asymptotic tracking is achieved without violation of the constraint, and all closed loop signals remain bounded, under a mild condition on the initial output. Furthermore, we explore the use of an Asymmetric Barrier Lyapunov Function as a generalized approach that relaxes the requirements on the initial conditions. We also compare our control with one that is based on a Quadratic Lyapunov Function, and we show that our control requires less restrictive initial conditions. A numerical example is provided to illustrate the performance of the proposed control.     

Semantic Web Constraint Language and its application to an intelligent shopping agent

Semantic Web society was initially focused only on data, but then gradually moved toward knowledge. If a vision of the Semantic Web is to enhance humans' decision-making assisted by machines, a missing but important part is knowledge about constraints on data and concepts represented by ontology. This paper proposes a Semantic Web Constraint Language (SWCL) based on OWL, and shows its effectiveness in representing and solving an internet shopper's decision-making problems by implementing a shopping agent in the Semantic Web environment.     

字符分布特征对带有通配符串匹配问题的影响

近年来,字符串匹配问题被不断扩展。其中,具有代表性的是在模式中引入可变长度的通配符,本文称之为PMWL问题。针对此问题,已有工作分析了在不同的模式特征下,匹配数Ω随文本长度增加呈指数级增长。本文同时考虑文本分布特征和模式特征,建立了期望模型E(Ω)＝n*D*π(P),其中n为文本长度,D为模式中各通配符跨度的乘积,π(P)为基于字符分布的模式出现概率。实验部分,在人工随机数据和DNA真实数据上验证了E(Ω)的准确性,得到预测误差率分别为1.8%~3.2%和4.7%~7.8%;在不同字符分布中,分析了模式模长和通配符跨度对匹配数Ω的影响。E(Ω)模型揭示了Ω的增长趋势不一定呈指数级,而取决于π(P)和D的共同影响。此外,E(Ω)模型能够在线性时间内得到近似完备解。     

Constrained motion planning of nonholonomic systems

This paper addresses the constrained motion planning problem for nonholonomic systems represented by driftless control systems with output. The problem consists in defining a control function driving the system output to a desirable point at a given time instant, whereas state and control variables remain over the control horizon within prescribed bounds. The state and control constraints are handled by extending the control system with a pair of state equations driven by the violation of constraints, and adding regularizing perturbations. For the regularized system a Jacobian motion planning algorithm is designed, called imbalanced. Solutions of example constrained motion planning problems for the rolling ball illustrate the theoretical concepts.     

参数驱动法的刚体研究

本文在基于约束的参数驱动法的基础上,对图形设计中经常遇到的循环约束现象提出了平面刚体的概念,并对颊刚体的成因、条件作了详细的分析,给出平面刚体的数据结构,并将其思想具体应用于CAD系统的开发中。     

Constraint-based deductive model checking

We show that constraint logic programming (CLP) can serve as a conceptual basis and as a practical implementation platform for the model checking of infinite-state systems. CLP programs are logical formulas (built up from constraints) that have both a logical interpretation and an operational semantics. Our contributions are: (1) a translation of concurrent systems (imperative programs) into CLP programs with the same operational semantics; and (2) a deductive method for verifying safety and liveness properties of the systems which is based on the logical interpretation of the CLP programs produced by the translation. We have implemented the method in a CLP system and verified well-known examples of infinite-state programs over integers, using linear constraints here as opposed to Presburger arithmetic as in previous solutions. Published online: 18 July 2001