Scheduling control of a deaerator plant

The deaerator of a power plant may experience severe two-phase steam/water problems especially during restart of the power plant following a unit trip. Unless suitable control strategies are adopted, cavitation in the suction area and choking in the deaerator can occur. Cavitation can cause expensive damage to the feedwater pumps, while choking will interrupt the operation of the deaerator. These all lead to failure of the restart. Some models and control schemes have been developed in recent years to study cavitation. A model that can predict the transient behaviour of the deaerator plant for possible cavitation and choking has been developed. In this paper, a scheduling control for deaerator operation based on this model is proposed. This scheme utilises operational status and process variables to predict possible cavitation or choking, and then takes the necessary action to avoid the loss of head in the suction area and high velocity of steam in the deaerator equalisers. The simulation results show that the control scheme will ensure safe operation, even during restart of the power plant.     

Development of PARS-EX pilot plant to study control strategies

A PARtially Simulated EXothermic chemical reactor (PARS-EX) pilot plant is developed in this work to carry out and evaluate various conventional and advanced control strategies. In this reactor, the heat generated from the assumed exothermic reaction was simulated through the use of a controlled steam flow rate into the reactor. Since there is no actual reaction involved, the system is defined as a ‘partially simulated’ reactor. The temperature of the reactor was regulated by an external plate heat exchanger that both cools the process fluid and recycles it back into the reactor. A software interface was also developed to exchange real online data and implement the various control strategies. The advanced control strategies used to control the temperature of the reactor in this work are the neural network-based controllers, which overcome the hassle in periodically tuning conventional controllers. An adaptive method is also incorporated to cater for changes in the process conditions. Tests involving set point tracking and various external and internal disturbance changes were carried out to evaluate and demonstrate the robustness of the neural network-based controllers on the PARS-EX plant. For all of the realistic online cases studied, the neural network-based controllers exhibit better control results compared to the conventional controllers.     

Adaptive control design under structured model information limitation: A cost-biased maximum-likelihood approach

Networked control strategies based on limited information about the plant model usually result in worse closed-loop performance than optimal centralized control with full plant model information. Recently, this fact has been established by utilizing the concept of competitive ratio, which is defined as the worst-case ratio of the cost of a control design with limited model information to the cost of the optimal control design with full model information. We show that an adaptive controller, inspired by a controller proposed by Campi and Kumar, with limited plant model information, asymptotically achieves the closed-loop performance of the optimal centralized controller with full model information for almost any plant. Therefore, there exists, at least, one adaptive control design strategy with limited plant model information that can achieve a competitive ratio equal to one. The plant model considered in the paper belongs to a compact set of stochastic linear time-invariant systems and the closed-loop performance measure is the ergodic mean of a quadratic function of the state and control input.     

提高分布式多预测控制器协调及稳定性的鲁棒区域控制

大型复杂化工程过程控制中,常规的集中式控制方式不利于实时性、灵活性和容错控制。而采用多预测控制器协调的分布式控制是解决这一问题的有效方法:。针对子系统间的动态耦合行为严重影响多预测控制器协调以及稳定性的问题,提出一种鲁棒区域控制策略。即在每个子系统的目标函数中加入松弛因子增加控制器间协调时的余量来达到分布式预测控制的稳定性。通过以反应器-存储器分馏器组成的过程为事例,仿真结果:表明该方法:的可行性和有效性。     

High-gain adaptive position control

High-gain adaptive position control is proposed for a stiff one-mass system (1MS) and an elastic two-mass system (2MS). The control objective is (load-side) position reference tracking and disturbance rejection (of load torques and friction). Position and speed are available for feedback. Two simple high-gain adaptive position control strategies are presented and applied to a laboratory setup: an adaptive λ-tracking controller and a funnel controller. Both controllers neither estimate nor identify the plant. The λ-tracking controller achieves tracking with prescribed asymptotic accuracy: for given λ?>?0 (arbitrary small) the error approaches the interval [?λ,?λ] asymptotically. Whereas the funnel controller assures tracking with prescribed transient accuracy: the error and its derivative are bounded by prescribed positive (possibly non-increasing) functions of time. A simple proportional-integral (PI)-like extension for the 1MS, and this extension in combination with a high-pass filter for the 2MS, allow for zero tracking errors in steady-state, respectively. Oscillations in the shaft of the 2MS can be suppressed.     

Application of multivariate virtual reference feedback tuning for wastewater treatment plant control

The objective of this paper is to apply the Virtual Reference Feedback Tuning (VRFT) to Multiple-Input Multiple-Output (MIMO) control strategies in wastewater treatment plants (WWTPs). Using the Benchmark Simulation Model No. 1 (BSM1) as a case study, the proposed data-driven approach provides reduced-order controllers only using a batch of input-output data points (i.e. manipulated variable—controlled variable), obtained from an open-loop experiment. The methodology also includes a pre-processing step that subtracts the impact of influent variability and sensor noise from the output signals. The possible interactions amongst different control loops are handled using a decoupling approach where each control signal is computed depending on the error signal of all the loops at the same time. In order to test the methodology, several control strategies are evaluated via simulation. The results show that substantial improvements in the plant performance can be obtained when controllers are implemented.     

Formation control of unmanned aerial vehicle swarms: A comprehensive review

The unmanned aerial vehicle formation plays a crucial role in numerous applications, such as reconnaissance, agricultural plant protection, and electric power inspection. This paper provides a comprehensive review and analysis of the unmanned aerial vehicle swarm communication networks and formation control strategies. First, the most commonly used unmanned aerial vehicles are introduced and compared. Next, the entire process of the formation task, from the formation assignment to the formation transformation, is detailed described. At last, the widely adopted communication networks are analyzed, and the existing formation control strategies of the UAV swarm are compared, which shows that the distributed formation control is superior to the centralized method and is the future development trend.     

Fault tolerant control of a simulated hydroelectric system

This paper analyses the application of two fault tolerant control schemes to a hydroelectric model developed in the Matlab and Simulink environments. The proposed fault tolerant controllers are exploited for regulating the speed of the Francis turbine included in the hydraulic system. The nonlinear behaviour of the hydraulic turbine and the inelastic water hammer effects are taken into account in order to develop a high-fidelity simulator of this dynamic plant. The first fault tolerant control solution relies on an adaptive control design, which exploits the recursive identification of a linear parametric time-varying model of the monitored system. The second scheme proposed uses the identification of a fuzzy model that is exploited for the reconstruction of the fault affecting the system under diagnosis. In this way, the fault estimation and its accommodation is possible. Note that these strategies, which are both based on identification approaches, are suggested for enhancing the application of the suggested fault tolerant control methodologies. These characteristics of the study represent key issues when on-line implementations are considered for a viable application of the proposed fault tolerant control schemes. The faults considered in this paper affect the electric servomotor used as a governor, the hydraulic turbine speed sensor, and the hydraulic turbine system, and are imposed both separately and simultaneously. Moreover, the complete drop of the rotational speed sensor is also analysed. Monte-Carlo simulations are also used for analysing the most important issues of the proposed schemes in the presence of parameter variations. Moreover, the performances achieved by means of the proposed solutions are compared to those of a standard PID controller already developed for the considered model. Finally, these strategies serve to highlight the potential application of the proposed control strategies to real hydraulic systems.     

蜡油催化裂化装置的先进控制应用策略

详细论述了催化裂化装置的控制策略,利用先进控制的模型预估控制功能,提高装置的平稳程度,多变量预估控制的应用可以减少MV变量的变化,同时保证CV变量的平稳,主要表现在温度、液位和产品质量的平稳上;提高装置处理量和掺渣量及高价值产品收率;实现产品质量卡边控制：包括汽油干点、柴油95％点、稳定汽油RVP等;降低能耗：主要是降低泵负荷以及蒸汽的消耗。实现催化裂化反应再生系统、主分馏塔和吸收稳定系统的前馈控制和动态解藕．提高了对装置的控制水平。镇海300万吨／年催化裂化联合装置的先进控制经过一年多的投用,降低了关键变量的波动方差,得到了操作人员的欢迎．由于先进控制与工艺的密切关系,随着工艺改造和生产方案的变化,周期性地对先进控制的维护是用好先进控制的基础,加大了用好先进控制的难度。     

Dynamical analysis of plant disease models with cultural control strategies and economic thresholds

In this paper plant disease models including impulsive cultural control strategies were developed and analyzed. The sufficient conditions under which the infected plant free periodic solution with fixed moments is globally stable are obtained. For the model with an economic threshold (ET) of infected plants, detailed investigations imply that the number of healthy plants either goes to extinction or tends to infinity, and the maximum value of infected plants is always less than the given ET. In order to prevent the healthy plant population going to extinction, we further propose a bi-threshold-value model, which has richer dynamical behavior including order 1-k or order k-1 periodic solutions with k≥1$k\ge 1$. Under certain parameter spaces, the infected plant free periodic solution is globally stable for the bi-threshold-value model. The modeling methods and analytical analysis presented can serve as an integrating measure to identify, evaluate and design appropriate plant disease control strategies.     

Characteristic model-based all-coefficient adaptive control for automatic train control systems

Safe and reliable automatic train control is a primary consideration for any advanced rail transit system. This paper introduces the characteristic model-based modeling method into ATC system and develops an all-coefficient adaptive control. Two characteristic models,namely speed characteristic model and position characteristic model,are established for analyzing both train traction and cruising dynamical characteristics.Control strategies are proposed using single speed feedback and speed/position bi-feedback. Numerical simulations are carried out to verify the effectiveness of the proposed strategies,showing that control objective and required dynamic performance are well satisfied. Moreover,the system shows robustness against time-varying model uncertainties and unknown operational environment dynamics.     

Gain-scheduling model predictive control of a Fresnel collector field

Model predictive control strategies have been applied successfully when controlling solar plants. If the control algorithm uses a linear model associated only to an operating point, when the plant is working far from the design conditions, the performance of the controller may deteriorate.In this paper, a gain scheduling model predictive control strategy is designed for the Fresnel collector field located at the Escuela Superior de Ingenieros de Sevilla. Simulation results are provided comparing the proposed strategy with another linear MPC controller showing a better performance. Furthermore, two real tests are presented showing the effectiveness of the proposed strategy.     

火电厂锅炉主汽温控制策略的比较研究

锅炉的主汽温被控对象是一个大惯性、大迟延、非线性且对象变化的系统,常用的汽温控制系统有串级PID控制和基于BP神经网络的PID控制。串级PID控制一般能将主汽温控制在允许的范围内;基于BP神经网络的PID控制将神经网络所具有的自学习能力与PID控制器的鲁棒性相结合,能实现对非线性、大时滞系统模型的控制。对这两种策略在不同负荷下进行了实验仿真,该结果对当前电厂的经济性和安全性有一定参考价值。     

非线性动态系统神经模糊建模与内模/PID双重控制系统设计

非线性动态系统的内模控制要求建立精确的对象正模型和逆模型,这对于大多数实际对象是难以做到.提出了基于一类神经模糊模型的非线性动态系统建模方法,并在此基础上研究了基于神经模糊模型的非线性系统的内模控制设计.基于输入输出数据辨识的对象正模型和逆模型存在着模型失配问题,导致神经模糊内模控制范围变窄和控制鲁棒性降低,为了改善系统的性能,提出了神经模糊内模控制与PID控制结合的双重控制策略.对CSTR的反应物浓度控制研究表明,双重控制策略能有效地拓宽系统可控范围,改善系统性能.仿真结果证明该控制策略简单而有效.     

Human processes of control: tracing the goals and strategies of control room teams

This study utilized a process tracing methodology to analyse the goals and strategies of control room teams in dealing with an unpredicted plant disturbance. The human processes of control used by operators and their supervisors, and interactions between them, were analysed during phases of detection, diagnosis, and control of a small plant leak. Five control room teams were videotaped tackling this simulated scenario on a full-scale simulator. The results found substantial differences both within and between teams in how the goals of monitoring and implementing procedures during the detection phase, and problem-solving and plant control during the diagnosis phase were achieved. The temporal patterning of the activities associated with these goals revealed that the teams used different strategies. The training implications of these findings are discussed, in particular with respect to the control room supervisor who had a pivotal role.     

感应电动机调速系统的自适应逆控制

针对电压源型逆变器供电的感应电动机调速系统,提出了一种自适应逆控制方法,实现了电磁转矩和定子磁链的解耦控制．应用得到的逆系统将多变量、非线性、强耦合的感应电动机调速系统解耦为电磁转矩和定子磁链两个一阶线性子系统,从而简化了各个闭环线性调节器的设计难度．为了提高系统的鲁棒性,利用递推最小二乘算法实时辨识电动机参数,降低了参数变化对系统性能的影响．使用MATLAB软件进行了仿真实验,实验结果验证了理论分析的正确性和控制方案的可行性．     

Robust and adaptive control: fidelity or an open relationship?

Robust and adaptive control are essentially meant to solve the same control problem. Given an uncertain LTI model set with the assumption that the controlled plant slowly drifts or occasionally jumps in the allowed model set, find a controller that satisfies the given servo and disturbance rejection specifications. Specifications on the transient response to a sudden plant change or “plant jump” are easily incorporated into the robust control problem, and if a solution is found, the robust control system does indeed exhibit satisfactory transients to plant jumps. The reason to use adaptive control is its ability, when the plant does not jump, to maintain the given specifications with a lower-gain control action (or to achieve tighter specifications), and also to solve the control problem for a larger uncertainty set than a robust controller. Certainly equivalence-based adaptive controllers, however, often exhibit insufficient robustness and unsatisfactory transients to plant jumps. It is therefore suggested in this paper that adaptive control always be built on top of a robust controller in order to marry the advantages of robust and adaptive control. The concept is called adaptive robust control. It may be compared with gain scheduling, two-time scale adaptive control, intermittent adaptive control, repeated auto-tuning, or switched adaptive control, with the important difference that the control is switched between robust controllers that are based on plant uncertainty sets that take into account not only the currently estimated plant model set but also the possible jumps and drifts that may occur until the earliest next time the controller can be updated.     

Low-gain integral control of continuous-time linear systems subject to input and output nonlinearities

Continuous-time low-gain integral control strategies are presented for tracking of constant reference signals for finite-dimensional, continuous-time, asymptotically stable, single-input single-output, linear systems subject to a globally Lipschitz and non-decreasing input nonlinearity and a locally Lipschitz, non-decreasing and affinely sector-bounded output nonlinearity. Both non-adaptive (but possibly time varying) and adaptive integrator gains are considered. In particular, it is shown that applying error feedback using an integral controller ensures asymptotic tracking of constant reference signals, provided that (a) the steady-state gain of the linear part of the plant is positive, (b) the positive integrator gain is ultimately sufficiently small and (c) the reference value is feasible in a very natural sense. The classes of actuator and sensor nonlinearities under consideration contain standard nonlinearities important in control engineering such as saturation and deadzone.     

Pseudo-decentralized switching control

The decentralized control of plants with uncertain mathematical models is studied in this paper. In particular, it is assumed that the plant is described by a continuous LTI model, which is contained in a specified family P? of plant models, and in this case it is assumed that a family of decentralized controllers has been found to satisfactorily control the models contained in P?. A switching control scheme is then proposed that uses the input-output information of each agent to switch between the local candidate controllers in each corresponding agent. A binary communication link is assumed to exist between the local control agents to assure that all agents switch to the next candidate controller, simultaneously. Simulation results, using the proposed decentralized scheme, are given and compared with the centralized scheme.     

The changing face of adaptive control: The use of multiple models

Adaptive systems that continuously monitor their own performance and adjust their control strategies to improve it, have been studied for over 50 years. The theory of such systems is now commonly referred to as classical adaptive control. Such control is now well established and is found to be satisfactory when the uncertainty in the system to be controlled (i.e. the plant) is small.During the past 15 years several attempts were made to extend this general methodology to systems with large uncertainties, by using multiple models to identify the plant. Among these, two general methods based on “switching” and “switching and tuning” have emerged as the leading contenders. Recently, a radically different approach was proposed by the authors (Han & Narendra, 2010b), in which the multiple models are used to play a significantly larger role in the decision making process, resulting in substantial improvement in performance.In this paper, which is tutorial in nature, the three methods based on multiple models are critically examined. At the same time, alternative methods using fixed and adaptive models are also proposed. In all cases, detailed simulation studies of adaptation in different environments are presented. Theoretical explanations are given, where available, for the wide spectrum of performances observed in the simulation studies.