对“一业为主、多种经营”的三点认识

浦市化工总厂１５年多种经营的体会有三条：由于中小矿山规模小、交通不便，从事多种经营既是自身生存发展的必由之路，又要着眼于填补市场空白，不断优化产品结构，开发新产品的同时必须狠抓经营管理；不断完善规章制度。面对变化、竞争的市场，缺乏足够的产品开发资金和快速反应的机制，还有一些其他不利因素，必须由国家采取调控手段才能加以解决。     

复分解四步循环法制取硫酸钾新工艺

介绍了制取硫酸钾的一种新工艺－－复分解四步循环法，即氯化钾与硫酸铵反应生成硫酸钾铵与氯化铵钾，硫酸钾铵再与氯化钾溶液反应生成硫酸钾，氯化铵钾再与硫酸铵溶液反应生成氯化铵。整个工艺全封闭循环生产，产品硫酸钾K2O质量分数≥50％，钾收率达92％。     

动力学控制结晶法拆分1,1''''-联二萘酚工艺研究

对外消旋1,1'-联二萘酚(BINOL)的拆分进行了研究.采用动力学控制结晶的方法改进了(S)-2-吡咯烷酮-5-羧酰苯胺拆分BINOL的工艺,得到一种实用有效的获得光学纯BINOL的方法.采用该方法可以同时得到BINOL的R和S两种异构体,它们的ee值均大于99%.采用正交实验的方法研究了反应时间、拆分剂用量、溶剂组成以及溶剂体积在拆分过程中对(R)和(S)-BINOL的产率的影响.在最佳条件下,(R)-BINOL和(S)-BINOL的收率分别为18.0%和35.2%,外消旋BINOL的回收率为58.8%,拆分剂的回收率为68.6%.     

光亮镀镍液pH值计算机控制系统

光亮镀镍液的ｐＨ值控制得稳定否，对于镀层质量有重要的影响，本文介绍了在电镀生产过程中，对镀镍液的ｐＨ值进行自动控制的一个计算机系统，该系统在广州五羊自行车厂电镀车间投入使用后，镀件质量有了明显的提高。     

Distributed receding horizon control of large-scale nonlinear systems: Handling communication delays and disturbances

This paper studies the robust distributed receding horizon control (DRHC) problem for large-scale continuous-time nonlinear systems subject to communication delays and external disturbances. A dual-mode robust DRHC strategy is designed to deal with the communication delays and the external disturbances simultaneously. The feasibility of the proposed DRHC and the stability of the closed-loop system are analyzed, and the sufficient conditions for ensuring the feasibility and stability are developed, respectively. We show that: (1) the feasibility is affected by the bounds of external disturbances, the sampling period and the bound of communication delays; (2) the stability is related to the bounds of external disturbances, the sampling period, the bound of communication delays and the minimum eigenvalues of the cooperation matrices; (3) the closed-loop system is stabilized into a robust invariant set under the proposed conditions. A simulation study is conducted to verify the theoretical results.     

Adaptive tracking control of leader–follower systems with unknown dynamics and partial measurements

In this paper, a decentralized adaptive tracking control is developed for a second-order leader–follower system with unknown dynamics and relative position measurements. Linearly parameterized models are used to describe the unknown dynamics of a self-active leader and all followers. A new distributed system is obtained by using the relative position and velocity measurements as the state variables. By only using the relative position measurements, a dynamic output–feedback tracking control together with decentralized adaptive laws is designed for each follower. At the same time, the stability of the tracking error system and the parameter convergence are analyzed with the help of a common Lyapunov function method. Some simulation results are presented to validate the proposed adaptive tracking control.     

Multivehicle coverage control for a nonstationary spatiotemporal field

This paper presents a multivehicle sampling algorithm to generate trajectories for nonuniform coverage of a nonstationary spatiotemporal field characterized by spatial and temporal decorrelation scales that vary in space and time, respectively. The sampling algorithm described in this paper uses a nonlinear coordinate transformation that renders the field locally stationary so that existing multivehicle control algorithms can be used to provide uniform coverage. When transformed back to the original coordinates, the sampling trajectories are concentrated in regions of short spatial and temporal decorrelation scales. For fields with coupled spatial statistics, i.e., the spatial decorrelation scales are functions of both spatial dimensions, the coordinate transformation is implemented numerically, whereas for decoupled spatial statistics, the transformation is expressed analytically. We show that the analytical transformation results in vehicle motion that preserves the vehicle sampling speed (which is a measure of vehicle speed scaled by the ratio of the spatial and temporal decorrelation scales), in the original domain; the sampling speed determines the minimum number of vehicles needed to cover a spatiotemporal domain. Theoretical results are illustrated by numerical simulations.     

Averaged control

We analyze the problem of controlling parameter-dependent systems. We introduce the notion of averaged control according to which the quantity of interest is the average of the states with respect to the parameter.First we consider the problem of controllability for linear finite-dimensional systems and show that a necessary and sufficient condition for averaged controllability is an averaged rank condition, in the spirit of the classical rank condition for linear control systems, but involving averaged momenta of any order of the matrices generating the dynamics and representing the control action.We also describe some open problems and directions of possible research, in particular on the average controllability of evolution partial differential equations. In this context we analyze also the averaged version of a classical optimal control problem for a parameter dependent elliptic equation and derive the corresponding optimality system.     

Asynchronous decentralized event-triggered control

In this paper we propose an approach to the implementation of controllers with decentralized strategies triggering controller updates. We consider set-ups with a central node in charge of the computation of the control commands, and a set of not co-located sensors providing measurements to the controller node. The solution we propose does not require measurements from the sensors to be synchronized in time. The sensors in our proposal provide measurements in an aperiodic way triggered by local conditions. Furthermore, in the proposed implementation (most of) the communication between nodes requires only the exchange of one bit of information (per controller update), which could aid in reducing transmission delays and as a secondary effect result in fewer transmissions being triggered.     

Adaptive receding horizon control for constrained MIMO systems

An adaptive control algorithm for open-loop stable, constrained, linear, multiple input multiple output systems is presented. The proposed approach can deal with both input and output constraints, as well as measurement noise and output disturbances. The adaptive controller consists of an iterative set membership identification algorithm, that provides a set of candidate plant models at each time step, and a model predictive controller, that enforces input and output constraints for all the plants inside the model set. The algorithm relies only on the solution of standard convex optimization problems that are guaranteed to be recursively feasible. The experimental results obtained by applying the proposed controller to a quad-tank testbed are presented.