Statistical identification for optimal control of supercritical thermal power plants

The use of a multivariate autoregressive model for the implementation of a new practical optimal control of a supercritical thermal power plant is discussed. The control is realized by identifying the system characteristics of the plant under the conventional PID control by the autoregressive model fitting and then implementing the digital control to correct the defect of the analog control. The procedure of identification and the controller implementation is described in detail by using the experimental results of a real plant. The results clearly demonstrate the advantage of the new controller over the conventional PID controller. The experience of the commercial operation of the plant confirms that the new controller is extremely robust against the gradual change of the plant characteristics, and this shows the practical utility of the identification procedure on which the design of the controller is based.     

Constrained linear quadratic gaussian control with process applications

Constrained linear quadratic Gaussian control is considered. Important practical design constraints including restrictions in control signal variations and in regulator structure are introduced quantitatively into the control problem formulation. Various topics in the resulting extension of the standard LQG design procedure are discussed, for instance optimality conditions, design of optimal low-order controllers and variance-constrained self-tuning control. Numerical algorithms for solving the constrained LQG control problems are given facilitating the application of the design procedure. Three industrial applications of linear quadratic Gaussian design are described. The examples are taken from the cement industry and from a process for the production of plastic film.     

Improving power network stability and unit stress with adaptive generator control

The ‘steady state stability’ of the French power transmission system has been greatly improved by supplying additional signals to the voltage regulator. The control system parameters, including the additional signals, have been chosen in order to cover all the possible operating conditions and network configurations. These additional signals lead to an increase in the ‘steady state stability’ domain even with a regulator having constant parameters. Better results have been obtained using the model reference adaptive control method with an adjustable regulator. A method based on the ‘hyperstability’ approach has been used for the design of the adaptive control system because of its efficiency. Moreover, the adaptive control of generator excitation, using voltage, speed and active power as input signals, reduces the oscillations of the H.P. turbine valves when the generator is affected by network perturbations. Therefore, the wear of the units, in particular of nuclear units, could be reduced. The principles of this new type regulator are discussed and experimental results are presented.     

Adaptive nuclear reactor control for integral quadratic cost functions

The problem of optimally controlling the power level changes of a nuclear reactor is considered. The model of an existing power plant is used, which is a ninth-order nonlinear system, having time-varying parameters. A closed form solution of the optimal feedback controller is not known for such a system, and numerical solutions require prohibitive amounts of computer time and/or storage for on-line applications. The approach in the present work is, therefore, to approximate the response of the reactor by that of a low-order linear model in a piece-wise manner. The model parameters are chosen to minimize the deviations, in any desired sense, between the system and model responses. The optimal feedback parameters are readily computed for the model, and the same controller results in suboptimal system performance. The difference, however, has been found negligible in the examples considered. Since the model parameters are updated to reflect the plant nonlinearities as well as changes in the plant parameters, the resultant control system is adaptive. It has been found, that using a second-order model, optimal performance could be approached to within practical limits, and the necessary computer time is realizable for on-line applications.     

Distributed control of spatially invariant systems

We consider distributed parameter systems where the underlying dynamics are spatially invariant, and where the controls and measurements are spatially distributed. These systems arise in many applications such as the control of vehicular platoons, flow control, microelectromechanical systems (MEMS), smart structures, and systems described by partial differential equations with constant coefficients and distributed controls and measurements. For fully actuated distributed control problems involving quadratic criteria such as linear quadratic regulator (LQR), H₂ and H_∞, optimal controllers can be obtained by solving a parameterized family of standard finite-dimensional problems. We show that optimal controllers have an inherent degree of decentralization, and this provides a practical distributed controller architecture. We also prove a general result that applies to partially distributed control and a variety of performance criteria, stating that optimal controllers inherit the spatial invariance structure of the plant. Connections of this work to that on systems over rings, and systems with dynamical symmetries are discussed     

Constrained predictive control for multivariable systems with application to power systems

Generalized predictive control (GPC) and dynamic performance predictive control (DPC) algorithms are introduced for industrial applications. Constraints on plant input rate, plant absolute input and plant absolute output can be implemented and are demonstrated on an application of these algorithms. A standard quadratic programming algorithm performs the calculation of the optimal control. A MATLAB/Simulink toolbox environment has been developed where controllers can be designed, linear and non-linear plant models can be embedded, discrete- and continuous-time loop parts can be mixed and simulation results can be managed and evaluated by graphical and statistical tools. This package utilises a graphical user interface. Finally, a case study design example is presented where a linear gas turbine model for power generation is examined with constrained GPC and DPC, and the advantages and drawbacks of the approach are the discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

基于DCS尿素装置三级计算机优化控制系统的研究与实现

针对集散控制系统（DCS）在尿素装置控制中的应用状况，研究了一个实际尿素装置的DCS—微机—微机三级计算机优化控制系统，开发了一整套的系统应用软件，完成了尿素生产管理—优化—控制一体化的组织和实现。文中的思想和方法在某尿素生产过程中得到了应用，实施结果令人满意。     

焦炉加热微型机最优控制系统

本文介绍了焦炉加热微型机控制系统的设计及应用,叙述了该系统的结构及控制方法所 使用的随机采样二次型最优调节器的计算机辅助设计问题.     

基于混合逻辑动态的混杂系统研究及应用

综述了一种在工业领域中使用的混杂系统建模方法,即基于混合逻辑动态MLD（Mixed Logic Dynamic）的建模方法。分析了在该建模框架下系统的可观性、可控性、形式验证、稳定性、状态估计及故障检测、最优控制和预测控制,并且以水电厂为例讨论了该方法在实际工程中的应用。基于MLD的混杂系统研究目前仍处于起步阶段,在理论上混杂系统预测控制、最优控制、多目标控制及混合整数二次规划求解都有待研究,在应用上如何根据不同问题有效地建模混杂系统也有待研究。     

A multivariable selftuning regulator to control a double effect evaporator

A multivariable selftuning regulator has been applied in control of a pilot plant double effect evaporator. The selftuning regulator consists of a recursive least squares algorithm combined with a single step optimal control strategy which minimizes a quadratic criterion. The algorithm is very fast and can be adjusted by an external operator in order to obtain satisfactory stationary regulator performance.     

Steady-state target optimization designs for integrating real-time optimization and model predictive control

In industrial practice, the optimal steady-state operation of continuous-time processes is typically addressed by a control hierarchy involving various layers. Therein, the real-time optimization (RTO) layer computes the optimal operating point based on a nonlinear steady-state model of the plant. The optimal point is implemented by means of the model predictive control (MPC) layer, which typically uses a linear dynamical model of the plant. The MPC layer usually includes two stages: a steady-state target optimization (SSTO) followed by the MPC dynamic regulator. In this work, we consider the integration of RTO with MPC in the presence of plant-model mismatch and constraints, by focusing on the design of the SSTO problem. Three different quadratic program (QP) designs are considered: (i) the standard design that finds steady-state targets that are as close as possible to the RTO setpoints; (ii) a novel optimizing control design that tracks the active constraints and the optimal inputs for the remaining degrees of freedom; and (iii) an improved QP approximation design were the SSTO problem approximates the RTO problem. The main advantage of the strategies (ii) and (iii) is in the improved optimality of the stationary operating points reached by the SSTO-MPC control system. The performance of the different SSTO designs is illustrated in simulation for several case studies.     

Multivariable integral linear quadratic Gaussian robust control of islanded microgrid to mitigate voltage oscillation for improving transient response

A novel robust integral linear quadratic Gaussian (ILQG) controller is presented in this paper to control the voltage of islanded microgrid and improves its transient response. Microgrid is a small grid that consists of number of distributed generator units, power‐electronic components with inductor‐capacitor (LC) filters and loads. The loads are parametrically uncertain and unknown that produces the voltage or power oscillation. The ILQG controller is capable to compensate for the voltage oscillation and exhibits the tracking of grid voltage against the different load dynamics. The design of ILQG controller is carried out by augmenting the plant dynamics with an integrator. The robustness of the ILQG controller is studied by considering a number of uncertainties within the plant model. The performance of ILQG controller is compared with linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) controller in terms of rise time, settling time, bandwidth and tracking error. The comparison results ensure the high bandwidth and tracking performance of ILQG controller as compared to other controllers.     

Novel hybrid MRAC-LQ control schemes: synthesis,analysis and applications

LQ optimal controllers are widely used in many applications as they provide a simple and effective solution to optimal control problems. There are many examples in the literature that show the lack of robustness of such controllers when the process is subject to parameter variations or when some dynamics are neglected. The hybrid algorithms proposed in this work extend the family of model reference minimal control synthesis adaptive controllers and address the issue of enhancing the robustness of LQ regulators. The main idea behind the approach is to seek a simple and alternative route to implement the LQ regulator via the MCS algorithm which is effective also in those practical cases when the LQ action itself fails. The stability of all the proposed control schemes is proved and the performance on a set of representative examples shows the effectiveness of the approach.     

Optimal PID automatic voltage regulator for synchronous machines

The design and testing of an optimal PID automatic voltage regulator for synchronous machines is treated. The proposed digital PID regulator combines automatic voltage regulation with the function of a power system stabilizer. The PID and stabilizing signal parameters are optimized based on a linear quadratic performance index using the Simplex method. The design of the regulator is achieved directly without using a linearised mathematical model, and only impulse responses of the system are required during the optimization procedure. The microcomputer-based regulator with digital filters has been tested on a laboratory model turbogenerator system. Simulation and experimental results are presented, showing that the regulator provides very good performance, which is superior to that obtained with a conventional automatic voltage regulator with power system stabilizer.     

A robust optimal sliding‐mode control approach for magnetic levitation systems

This paper presents a robust optimal sliding‐mode control approach for position tracking of a magnetic levitation system. First, a linear model that represents the nonlinear dynamics of the magnetic levitation system is derived by the feedback linearization technique. Then, the robust optimal sliding‐mode control developed from the linear model is proposed. In the proposed control scheme, the integral sliding‐mode control with robust optimal approach is developed to achieve the features of high performance in position tracking response and robustness to the matched and unmatched uncertainties. Simulation and experimental results from the computer‐controlled magnetic levitation system are illustrated to show the validity of the proposed control approach for practical applications. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Automatic Voltage Regulator Design by Modified Discrete Integral Variable Structure Model Following Control

In power systems, the automatic voltage regulator (AVR) is designed to make the terminal voltage to accurately track the deviation of the reference voltage. To achieve the desired requirements, this paper proposes the modified discrete integral variable structure model following control (MDIVSMFC) approach. The MDIVSMFC approach comprises a reference model part for specifying the design requirements and a MDIVSC part for minimizing the errors between the plant and model. The non-ideal sliding motion is introduced to take the place of the ideal sliding motion in discrete-time systems. Its existence conditions are used to adapt the proposed approach to the discrete systems. The application of MDIVSMFC to an AVR design is developed. Procedures for determining the control function, the coefficients of the sliding surface and the integral control gain are also proposed in detail. Simulation results are compared with the IEEE type-I voltage regulator to demonstrate that the proposed scheme can achieve fast and robust responses.     

车辆追踪运行的离散模型与最优控制

对车辆追踪运行进行控制,是实现车辆安全、快捷运行的重要手段之一.将车辆追踪运行控制问题归结为“速度差”调节和标准车距跟踪的混合系统进行研究.建立了离散模型,分析了其能控性和能观性,运用最优控制理论求解其反馈控制律,给出了算法步骤.对具体算例进行了仿真实现.     

Output constrained IMC controllers in control systems of electromechanical actuators

Electromechanical actuators are widely used in many industrial applications. There are usually some constraints existing in a designed system. This paper proposes a simple method to design constrained controllers for electromechanical actuators. The controllers merge the ideas exploited in internal model control and model predictive control. They are designed using the standard control system structure with unity negative feedback. The structure of the controllers is relatively simple as well as the design process. The output constraint handling mechanism is based on prediction of the control plant behavior many time steps ahead. The mechanism increases control performance and safety of the control plant. The benefits offered by the proposed controllers have been demonstrated in real-life experiments carried out in control systems of two electromechanical actuators: a DC motor and an electrohydraulic actuator.     

Experimental evaluation of model predictive control with excitation (MPC-X) on an industrial depropanizer

It is commonly observed that over the lifetime of most model predictive controllers, the achieved performance degrades over time. This effect can often be attributed to the fact that the dynamics of the controlled plant change as the plant ages, due to wear and tear, refurbishment and design changes of the plant, to name a few factors. These changes mean that re-identification is necessary to restore the desired performance of the controller. An extension of existing predictive controllers, capable of producing signals suitable for closed loop re-identification, is presented in this article. The main contribution is an extensive experimental evaluation of the proposed controller for closed loop re-identification on an industrial depropanizer distillation column in simulations and in real experiments. The plant experiments are conducted on the depropanizer during normal plant operations. In the simulations, as well as in the experiments, the updated models from closed loop re-identification result in improvement of the performance. The algorithm used combines regular model predictive control with ideas from applications oriented input design and linear matrix inequality based convex relaxation techniques. Even though the experiments show promising result, some implementation problems arise and are discussed.     

高精度恒温控制系统的设计

为了开展光学实验,设计了一种高精度恒温控制系统,应用于搭建光栅制作的实验环境.该方案通过Matlab仿真建立了恒温室水平面温度梯度数学模型,确定恒温室热源的分布方式,采用西门子S7系列模块作为主控CPU,前端利用Ptl00传感器,经过A/D转换后输入到PLC,运用PID算法控制D/A模块至调压器的输出,从而决定执行机构的工作功率,最终实现温度的闭环控制.实验结果表明,该恒温控制系统控制精度高、稳定可靠,恒温室控制精度≤±0.1℃,能够满足精密仪器测量与光学加工的温控要求,具有很高的实用价值.