Min-Max MPC based on a computationally efficient upper bound of the worst case cost

Min-Max MPC (MMMPC) controllers [P.J. Campo, M. Morari, Robust model predictive control, in: Proc. American Control Conference, June 10–12, 1987, pp. 1021–1026] suffer from a great computational burden which limits their applicability in the industry. Sometimes upper bounds of the worst possible case of a performance index have been used to reduce the computational burden. This paper proposes a computationally efficient MMMPC control strategy in which the worst case cost is approximated by an upper bound based on a diagonalization scheme. The upper bound can be computed with O(n³) operations and using only simple matrix operations. This implies that the algorithm can be coded easily even in non-mathematical oriented programming languages such as those found in industrial embedded control hardware. A simulation example is given in the paper.     

Expert identification and selection: Legal liability concerns and directions

Legal liabilities pertaining to the identification and selection of domain experts is an issue that could adversely impact expert systems developers. Problems pertaining to flawed knowledge, improperly defined expertise, and behavioural and psychological impediments are just some of the issues. This paper examines the torts of strict products liability and negligence that system developers could incur as a result of expert-related difficulties. Parallels from legal scholars and federal and state court decisions are discussed relevant to expert system projects and developers. The paper concludes with a presentation of steps that systems developers can take to minimize potential legal liability.     

Tandem queues with general blocking: A unified model and comparison results

We study tandem queues with finite intermediate buffers and develop a general blocking scheme that unifies and generalizes various blocking schemes previously studied in the literature, such as manufacturing blocking, communication blocking, kanban blocking and its variations. In the general blocking scheme, job movement and service at each stage are controlled by three parameters, which represent, respectively, the upper limits (at that stage) on work-in-process inventory, finished goods inventory, and buffer space. We derive a set of recursive equations that characterize the dynamics of the system, in terms of the job completion and departure processes, and establish comparison results for these processes in the settings of stochastic ordering, variability ordering and stochastic convexity. Free of distributional assumptions, the results provide characterization of system behavior with respect to the control parameters as well as to the arrival and service processes. We also compare system performance under two different modes of operation: make-to-order versus make-to-stock, and demonstrate the trade-off between improving service and reducing inventory. Numerical studies are also presented to illustrate the diversity of performance trade-off, including composition of inventory, offered by the general model.Supported in part by NSF under grant ECS-89-96201.     

改善PZT微位移器线性的一种方法

根据锆钛酸铅压电陶瓷的逆压电效应。设计了以８０３１单片机为核心的压电陶瓷堆微位移器提高了ＰＺＴ的线性，从而满足了在天文自适应系统中所要求的精确微位的应用。     

实时跟踪控制系统中的数字滤波技术

在高精度的实时跟踪控制系统中，由探测器等其他一些因素给系统带来的噪声将不容忽视，数字滤波将是滤除噪声的有效手段，，由于系统实时性和采样频率的要求，本文介绍几种时跟踪系统中常用的数字滤波方法，并对它们在实时跟踪系统中的作用进行了和比较。     

基于Legendre多项式的随机连续系统的Markov参数估计

本文在讨论连续Ｗｉｓｅｎｒ过程的Ｌｅｇｅｎｄｒｅ多项式逼近值的相关性和Ｗｉｅｎｅｒ过程扰动下连续线性系统基于该正交多项的最小二乘估计有偏性后，提出了无偏一致的且数估计误差方差最小的Ｍａｒｋｏｖ估计（最小方差估计）算法，并给出本文方法的仿真结果。     

离散系统LQ调节器的鲁棒性分析

本文研究了离散ＬＱ调节器系统鲁棒稳定性问题，稳定的不确定参数是通过考虑参数摄动的方向而求得的，这种方法比现有文献提供的结果具有更好的稳定范围。     

全金属反射光学系统结构的有限元分析

用有限元分析方法对全金属反射光学系统在各种受力状态下的变形、应力及固有频率做了全面的分析，并给出了结构多次优化后的分析结果。     

Artificial intelligence and symbols

The introduction of massive parallelism and the renewed interest in neural networks gives a new need to evaluate the relationship of symbolic processing and artificial intelligence. The physical symbol hypothesis has encountered many difficulties coping with human concepts and common sense. Expert systems are showing more promise for the early stages of learning than for real expertise. There is a need to evaluate more fully the inherent limitations of symbol systems and the potential for programming compared with training. This can give more realistic goals for symbolic systems, particularly those based on logical foundations.