纸浆浓度流量计算机控制系统设计

针对制浆生产过程中与产品质量密切相关的纸浆浓度和流量的稳定性,为解决纸浆浓度控制和测量存在的问题,设计了基于模糊控制技术的纸浆浓度流量计算机控制系统。该系统根据生产工艺要求采用计算机控制和浓度、流量模糊控制器,利用组态王6．0编制了相应的控制程序,对制浆生产过程需要的纸浆浓度、流量以及计算绝干量进行控制。实际运行结果表明,系统运行可靠,浓度、流量等控制量波动小、精度高,能够满足生产要求,达到了设计目的。     

Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information

This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems(ITS). Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.     

Development Of Cvt Control System And Its Use For Fuel‐Efficient Operation Of Engine

Continuously variable transmission (CVT) provides an automobile with the ability to change the gear ratio continuously, which can then improve not only ride quality such as acceleration performance but also fuel‐efficiency. However, to take advantage of the ability, a control system that can precisely control the gear ratio is required. This paper proposes such a control system for a belt‐driven CVT system. For controller design, first the CVT system is modeled by analytical and experimental approaches. The resultant static and dynamic characteristics provide a nonlinear first‐order model with an uncertain time constant and time delay. The nonlinear steady‐state gain is adjusted to one by a gain‐scheduled pre‐compensator. Thereby the plant model becomes a linear first‐order lag system with a dead time. The next step is controller design using the plant model. To guarantee stability and control performance against the parameter variation and time delay, the μ‐synthesis, a robust control method, is employed for feedback control. In addition, a feedforward controller is incorporated into the feedback control system to obtain better output response. The feedforward controller is given by a combination of the inverse system of the plant and a reference model that gives desired output response. As a result, the control system becomes a two‐degree‐of‐freedom control system. To evaluate the performance of the control system and its effectiveness on the fuel‐efficiency, computer simulation and driving tests were conducted. The simulation and experiment results prove that the proposed control system can make the gear ratio track a reference output quickly and precisely in the presence of the uncertainties. The results also show that the control system improves fuel‐efficiency by changing the gear ratio so that the engine torque and its revolution speed can satisfy optimum‐efficiency operating condition.     

基于MATLAB的轿车动力性和燃油经济性仿真

轿车的动力性和燃油经济性是其重要的使用性能之一,直接影响其商品性。计算机仿真方法为汽车动力性和燃油经济性预测提供了快速、准确、有效的工具,消除了实车道路试验中司机、道路环境、气候等因素对汽车使用性能测定的影响,具有可比性好、重复性强的优点。该文利用最小二乘法和线性回归原理建立了发动机外特性和万有特性模型,利用建立的汽车动力性和燃油经济性的数学模型,推导了汽车动力性和燃油经济性的参数灵敏度公式。用MATLAB编程,对某经济型轿车进行了动力性和燃油经济性计算机仿真,并分析了其参数灵敏度,指出了改进的方向。     

Nonlinear model predictive control for the ALSTOM gasifier

In this work a nonlinear model predictive control based on Wiener model has been developed and used to control the ALSTOM gasifier. The 0% load condition was identified as the most difficult case to control among three operating conditions. A linear model of the plant at 0% load is adopted as a base model for prediction. A nonlinear static gain represented by a feedforward neural network was identified for a particular output channel—namely, fuel gas pressure, to compensate its strong nonlinear behaviour observed in open-loop simulations. By linearising the neural network at each sampling time, the static nonlinear model provides certain adaptation to the linear base model at all other load conditions. The resulting controller showed noticeable performance improvement when compared with pure linear model based predictive control.     

纸浆间歇蒸煮过程的分数阶建模及内模控制

纸浆间歇蒸煮是在高温高压密封的蒸煮锅中进行，是一个复杂的黑箱过程.针对间歇蒸煮过程的非线性和系统参数不确定性问题，在机理建模的基础上，通过函数拟合及数值逼近对模型的非线性部分进行数学近似，建立了间歇蒸煮过程的分数阶模型.针对分数阶系统，采用内模控制原理设计分数阶PID控制器，以简化分数阶控制器的可调参数，并基于最大灵敏度及稳定裕度整定控制器参数，使系统在保证期望的动态性能条件下，获得较好的鲁棒性.仿真结果表明，间歇蒸煮过程的分数阶内模控制在超调、响应速度和鲁棒性方面优于整数阶控制.     

燃油调节器防喘装置试验台测控系统的设计与应用

燃油调节器防喘装置是实现发动机工作过程中控制发动机主燃烧室主、付油路供油的执行装置;为燃油调节器防喘试验装置设计的测控系统实现了试验过程的自动化;该系统采用面向对象的原理和虚拟仪器技术设计。集成数据处理、数据库、自动控制等技术,在高速计算机环境下,实现对防喘试验器性能参数的采集、存储、监测、数据处理、分析、控制、报表输出及试验数据管理,具有对试验全过程的分析处理能力;系统在试验台的应用实践表明,操作使用简便、控制准确、测试精度高、完全满足生产试验的要求。     

Adaptive Decoupling Control of Pulp Levels in Flotation Cells

Control of the pulp levels in flotation cells directly affects the grade of the concentrate and the tailings in a concentration plant. Nevertheless, with strong coupling among cell levels and nonlinearities in the flotation process, conventional control strategies cannot achieve satisfactory control performance. In this paper, a nonlinear multi‐model adaptive decoupling control strategy based on adaptive‐network‐based fuzzy inference systems (ANFIS) is proposed for the flotation process, which includes a linear adaptive decoupling controller, an ANFIS‐based nonlinear adaptive decoupling controller, and a switching mechanism. The proposed method not only improves the transient performance and mitigates effects of the nonlinearities on the system, but also guarantees the input‐output stability of the closed‐loop system. Successful application to the flotation process has been made in a concentration plant in China, and the feasibility and efficiency of the proposed method have been validated.     

A new approach to adaptive control: no nonlinearities

In adaptive control the goal is to design a controller to control an uncertain system whose parameters may be changing with time. Typically the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that good asymptotic behaviour is achieved, such as model matching (for minimum phase systems) or stability. However, a typical adaptive controller does not track time-varying parameters very well, and it is often highly nonlinear, which can result in undesirable behaviour, such as large transients or a large control signal. Furthermore, most adaptive controllers provide only asymptotic tracking, with no ability to design for a pre-specified settling time.Here we propose an alternative approach, which yields a linear periodic controller. Rather than estimating the plant or compensator parameters, instead we estimate what the control signal would be if the plant parameters were known. In this paper we argue the utility of this approach and then examine the first order case in detail, including a simulation. We also explore the benefits and limitations of the approach.     

Understanding the Difference Between Robust Control and Optimal Control in a Linear Discrete-Time System with Time-Varying Parameters

Robust control has been a very popular area of research in the last two decades. The goal of this paper is to investigate the assumptions implicit in the “nonprobabilistic nature” of the a priori information used to derive the linear-quadratic robust control in discrete-time. This is done by comparing robust control with the optimal control for a linear system with time-varying parameters. First the theoretical differences between the two approaches are discussed. Then they are used in two numerical examples: a simple model with one control, one state and a time horizon of two periods, which is suitable for hand calculations, and the permanent income model. The main conclusion is that the decision maker applying robust control is indeed assuming a very restricted class of true, unknown models. JEL classification: C61     

Realization of online optimizing control in an industrial semi-batch polymerization

In this work, the realization of an online optimizing control scheme for an industrial semi-batch polymerization reactor is discussed in detail. The goal of the work is the automatic minimization of the duration of the batch without violating the tight constraints for the product specification which translate into stringent temperature control requirements for a highly exothermic reaction. Crucial factors for a successful industrial implementation of the control scheme are the development and the validation of a process model that is suitable for process optimization purposes and the estimation of unmeasured process states and the online compensation of model uncertainties. Two implementations are proposed, a direct online optimizing control scheme and a simplified scheme that combines a model-predictive temperature controller and a monomer feed controller that steers the cooling power to a predefined value in a cascaded fashion. We show by simulation results with a validated process model that both schemes achieve the goals of tight temperature control and reduction of the batch time. The performance of the NMPC controller is superior, on the other hand the cascaded scheme could be directly implemented into the DCS of the plant and is in daily operation while the online optimizing scheme requires an additional computer and is currently in the test phase.     

Supervisory control of air–fuel ratio in spark ignition engines

The problem of air–fuel ratio stabilization in spark ignition engines is addressed in this paper. The proposed strategy consists of proper switching among two control laws to improve quality of the closed-loop system. The first control law is based on an a priori off-line identified engine model and ensures robust and reliable stabilization of the system at large, while the second control law is adaptive, it provides on-line adaptive adjustment to the current fluctuations and improves accuracy of the closed-loop system. The supervisor realizes a switching rule between these control laws providing better performance of regulation. Results of implementation on two vehicles are reported and discussed.     

一种新型的造纸过程计算机检测与控制系统

介绍了一种采用红外线纸浆浓度传感器和单片机组成的在线实时检测控制系统的组成及设计方法。纸浆浓度是影响纸质量及成本消耗的主要指标，采用准确度高的红外线纸浆浓度传感器是该控制系统的主要环节。     

一种基于进程的访问控制模型的研究

病毒、木马在互联网环境下对终端计算机系统造成的危害越来越严重。一个主要的原因是传统访问控制模型将进程作为用户的代理赋予其用户的全部权限,从而导致用户权力被滥用。基于进程的访问控制模型遵循最小特权原则,通过约束进程的权限,避免权限滥用,从而达到阻止计算机病毒、木马在终端中传播和扩散的目的。     

计算机控制系统在峨山化肥厂干冰生产线中的应用

干冰生产过程自动监控是基于PLC的集散式控制系统;系统由上位计算机和PLC组成,对生产过程进行全程控制和管理;上位计算机通过监控画面对PLC进行参数设置,接受参数后PLC执行闭环控制;使用Citect组态软件设计各种监控画面;计算机与PLC间采用SYSMAC WAY协议进行通信;该系统配置灵活,控制可靠．编程简单且成本低;通过现场实际应用,控制效果良好,达到了预期的控制目的。     

Handling uncertainty in economic nonlinear model predictive control: A comparative case study

In the last years, the use of an economic cost function for model predictive control (MPC) has been widely discussed in the literature. The main motivation for this choice is that often the real goal of control is to maximize the profit or the efficiency of a certain system, rather than tracking a predefined set-point as done in the typical MPC approaches, which can be even counter-productive. Since the economic optimal operation of a system resulting from the application of an economic model predictive control approach drives the system to the constraints, the explicit consideration of the uncertainties becomes crucial in order to avoid constraint violations. Although robust MPC has been studied during the past years, little attention has yet been devoted to this topic in the context of economic nonlinear model predictive control, especially when analyzing the performance of the different MPC approaches. In this work, we present the use of multi-stage scenario-based nonlinear model predictive control as a promising strategy to deal with uncertainties in the context of economic NMPC. We make a comparison based on simulations of the advantages of the proposed approach with an open-loop NMPC controller in which no feedback is introduced in the prediction and with an NMPC controller which optimizes over affine control policies. The approach is efficiently implemented using CasADi, which makes it possible to achieve real-time computations for an industrial batch polymerization reactor model provided by BASF SE. Finally, a novel algorithm inspired by tube-based MPC is proposed in order to achieve a trade-off between the variability of the controlled system and the economic performance under uncertainty. Simulations results show that a closed-loop approach for robust NMPC increases the performance and that enforcing low variability under uncertainty of the controlled system might result in a big performance loss.     

A new robust model predictive control method I: theory and computation

The success of the single-model MPC (SMPC) controller depends on the accuracy of the process model. Modeling errors cause sub-optimal control performance and may cause the control system to become closed-loop unstable. The goal of this paper is to examine the control performance of the robust MPC (RMPC) method proposed by Wang and Rawlings [34] on several illustrative examples. In this paper, we show the RMPC method successfully controls systems with time-varying uncertainties in the process gain, time constant and time delay and achieves offset-free non-zero set point tracking and non-zero disturbance rejection subject to input and output constraints.     

橡胶厂硫化过程的集散控制

本文利用Intel8098单片机和IBMPC-286微机组成二级控制系统成功地完成了橡胶厂小平板硫化车间的生产控制与管理,在直接过程制中对橡胶制品硫化实施等效硫化控制,上位机能够方便地调度生产直接更改硫化产品的型号,并能有效地监督多至38台流化机的操作过程,可以方便地显示和打印出班、日、月、年报表,为工厂实际生产、质量管理、计划调度,提供了方便有效的工具,本系统已在工厂实际投运,效果良好。     

Nonlinear model predictive control with relevance vector regression and particle swarm optimization

In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable random exploration velocity （PSO-CREV） is applied to a catalytic continuous stirred tank reactor （CSTR） process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function （RBF） kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control （MPC） using a deterministic sparse kernel learning technique called Least squares support vector machines （LS-SVM） regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.     

A control design and calibration reduction methodology for AFR control in gasoline engines

A control architecture for air to fuel ratio (AFR) control of gasoline engines designed to work with switching and/or wide range oxygen sensors, with the goal of minimizing calibration effort while meeting performance requirements, is described. A high bandwidth, dithered inner-loop reference tracking controller with pre-catalyst oxygen sensor feedback coupled with a low bandwidth setpoint tracking outer-loop with post catalyst oxygen sensor feedback, is used to control engine exhaust and O₂ storage in the three-way catalyst (TWC), respectively. A total synthesis inspired design ensures that significant non-linearity in the system is handled through a coordinated and corrective action and expected response blocks in the open-loop, without burdening the closed loop controller. Calibration is achieved offline, through closed loop optimization using genetic algorithms, while simultaneously meeting performance and stability criteria with significantly reduced need for in-vehicle tuning. Experimental results show comparable emissions performance with the stock OEM AFR controller under warmed up conditions over a standard drive cycle.