Advanced methods of process computer control for industrial processes

In recent years, the requirements for the performance of multilevel process control, including feedforward and feedback control, monitoring and optimization have increased. Applying process computers and micro computers, the functions of analog equipment and hardwired logic devices cannot only be replaced. Extended or quite new methods can be realized improving the performance of multilevel process control. These advanced methods for process control are characterized by: more sophisticated, better adjusted control algorithms, forecasting of process variables, estimation of not directly measurable variables, computer aided design of algorithms and adaptive or selftuning algorithms. The basis of these advanced methods are mathematical models of the processes and their signals, often gained by the process computer itself during on-line operation.The present paper discusses first how process models in open and closed loop can be obtained by on-line identification methods. Then, based on these models, the computer aided design of control algorithms, adaptive control algorithms and adaptive steady-state on-line optimization will be regarded. Monitoring of not direct measurable variables will be mentioned. For some methods, practical results with real and simulated processes are shown. Interactive process computer software packages are used which can easily be transferred to other process computers.     

非参数模型控制在液位控制系统中的应用研究

针对工业控制过程中液位系统的时变和明显的滞后特征,研究了非参数模型控制方法在液位控制系统中的设计方案,讨论了控制算法中引入的伪偏导数的在线估计问题,实现了通过液位系统的输入输出信息并利用递归最小二乘法对伪偏导数进行在线估计的过程,仿真实验验证了非参数模型算法对液位控制的鲁棒性、快速性及抗干扰性,通过仿真比较,展示了该算法性能优于PID算法和模糊控制的结果.     

Adaptive input matching control of structurally varying plants

Adaptive control using input matching can avoid the identifiability and/or exact model-matching constraints of the bulk of existing adaptive control methods. The focus on input matchability allows the use of general controller structures capable of controlling a plant whose structure may vary arbitrarily within an allowable class. The applicability of this technique is demonstrated for two simulated plants: a MIMO linear network increasing its complexity due to component failure and an unstable SISO plant controlled by an actuator which gradually develops a nonlinearity. The sole need for pre-specification of an allowable plant structure re-focuses the thrust of system identification from parameter estimation to structure determination.     

Intelligent on-line system identification using a blackboard system

Adaptive control systems have been developed and used for a number of decades. However, there are still some problems with their operation, which limit wider industrial adoption. This paper addresses one aspect of adaptive control, namely the on-line system identification of the time-varying process being controlled. The approach adopted is to use a blackboard system to identify the time-varying process model. The blackboard system contains knowledge sources with, algorithmic, fuzzy-logic and evolutionary reasoning. The paper describes the design of a computer simulation of this approach, concentrating on the fuzzy reasoning used to validate the multiple models, and the evolutionary techniques used to reject poor models and introduce better models. The computer simulation is then evaluated, firstly using simulated plant data and then using real plant data from an experimental hot air dryer. It is concluded that the technique is practical, and worthy of further testing on a pilot plant.     

Experimental comparison and application of various parameter-adaptive control algorithms

Various recursive parameter estimation algorithms and controller design procedures can be combined to build up parameter-adaptive control algorithms. Two parameter estimation methods and six control algorithms have been selected, taking into account good convergence properties and small computational expense and regarding the conditions for closed-loop identification. The resulting 12 parameter-adaptive control algorithms are compared and tested with a process computer in on-line operation with analog simulated stable and unstable processes for stochastic disturbances and step changes of the reference signal. The results are very promising. In many cases a good control performance is achieved. As a priori knowledge only the sampling time, the process model order and time delay and in some cases a weighting factor for the process input signal are required. Some parameter-adaptive control algorithms with good properties are applied to digital adaptive control of an air heater. Conclusions are given for the selection of parameter-adaptive control algorithms, depending on the type of process and its disturbances.The adaptive control algorithms may be applied for adaptive control of constant and time variant, linear and weakly non-linear stable and unstable processes with process computers or micro computers or for self-tuning of control algorithms or tuning of conventional analog PID controllers, if external disturbances act on the loop.     

Digital parameter-adaptive control of an air-conditioning plant

Two parameter-adaptive (self-tuning) control algorithms for multivariable systems are described. The algorithms are designed on the basis of linear input-output system models by the combination of recursive parameter estimation and control algorithms: a parameter-adaptive deadbeat controller and a parameter-adaptive optimal state controller. These controllers are applied to a two-input two-output air-conditioning pilot plant, which consists of an air heater and an air humidifier and whose output variables are the temperature and the relative humidity of the air measured at the air outlet. The air-conditioning plant is a nonlinear system and its linearized static and dynamic behaviour is strongly dependent on the operating point characterized mainly by the output variables and by the air flow rate through the plant. The results of the real-time control experiments indicate that it is possible to use the self-tuning features of the parameter-adaptive controllers to stabilize the controlled system after a short adaption phase and to achieve at least a satisfactory control performance for time varying air flow rates and for time varying setpoints of the output variables.     

Distributed digital processing and closed loop computer control of wastewater treatment

The application of digital computer control to municipal wastewater treatment processes is steadily gaining popularity. Within the next few years this mode of control is expected to become a standard feature of larger wastewater treatment plants. The purpose of this paper is threefold: first, to acquiant those outside the wastewater industries of the general concepts of wastewater treatment so they may appreciate some of the requirements imposed on the control computer; second, to present the overall control laws that regulate the feedback control of pure oxygen activated sludge and anaerobic digestion processes; and third, to describe the control system architecture and present the general philosophy of on-line closed loop control of Metro Denver's high purity oxygen activated sludge process.     

Statistical Pattern Modeling in Vision-Based Quality Control Systems

Machine vision technology improves productivity and quality management and provides a competitive advantage to industries that employ this technology. In this article, visual inspection and quality control theory are combined to develop a robust inspection system with manufacturing applications. The inspection process might be defined as the one used to determine if a given product fulfills a priori specifications, which are the quality standard. In the case of visual inspection, these specifications include the absence of defects, such as lack (or excess) of material, homogeneous visual aspect, required color, predetermined texture, etc. The characterization of the visual aspect of metallic surfaces is studied using quality control chars, which are a graphical technique used to compare on-line capabilities of a product with respect to these specifications. Original algorithms are proposed for implementation in automated visual inspection applications with on-line execution requirements. The proposed artificial vision method is a hybrid between the two usual methods of pattern comparison and theoretical decision. It incorporates quality control theory to statistically model the pattern for defect-free products. Specifically, individual control charts with 6-sigma limits are set so the inspection error is minimized. Experimental studies with metallic surfaces help demonstrate the efficacy and robustness of the proposed methodology.     

A learning-based synthesis approach todecentralized supervisory control of discrete event systems with unknown plants

In this paper, we consider the problem of automatic synthesis of decentralized supervisor for uncertain discrete event systems. In particular, we study the case when the uncontrolled plant is unknown a priori. To deal with the unknown plants, we first characterize the conormality of prefix-closed regular languages and propose formulas for computing the supremal conormal sublanguages; then sufficient conditions for the existence of decentralized supervisors are given in terms of language controllability and conormality and a learning-based algorithm to synthesize the supervisor automatically is proposed. Moreover, the paper also studies the on-line decentralized supervisory control of concurrent discrete event systems that are composed of multiple interacting unknown modules. We use the concept of modular controllability to characterize the necessary and sufficient conditions for the existence of the local supervisors, which consist of a set of local supervisor modules, one for each plant module and which determines its control actions based on the locally observed behaviors, and an on-line learning-based local synthesis algorithm is also presented. The correctness and convergence of the proposed algorithms are proved, and their implementation are illustrated through examples.     

Quality control of polymer production processes

For the polymer production industries, the competitive edge will come from the technology that excels in controlling the polymer properties in a consistent way over the entire plant and in maximizing the production performance while keeping safety regulations. Based on the experience in applying advanced process control and scheduling schemes to industrial polyolefin polymerization plants, the state of the art in quality control systems for providing the polymer production plant with an enlarged capacity for product discrimination and flexibility is reviewed. On-line soft-sensing and optimal grade changeover control problems are the main focus of this paper. A quality control system for polymer production plants, which integrates optimal control with on-line sensing and scheduling techniques, is discussed making reference to an application of a prototype system to an industrial plant.     

Self-tuning optimum start control of heating plant

The problem of controlling the start-up of heating plant in industrial and domestic buildings is considered and the need for a self-tuning start control scheme is explained. Using a simple semi-empirically derived relationship between the time required to preheat the building before occupancy and the measured temperatures, a self-tuning start controller is developed based on an on-line adaptive predictor. The controller assumes no prior knowledge of the thermal characteristics of the heating plant and building, and is suitable for low-cost microprocessor implementation. Results obtained from a hybrid simulation of a heating plant, building and environment show that the self-tuning controller behaves well and has superior performance characteristics compared to conventional start controllers. The results of preliminary experimental tests, using a microprocessor-based controller, suggest that, in certain circumstances, further simplifications to the control algorithms can be made so that implementation on small single-chip microcomputer systems, which are suitable for the control of domestic heating equipment, is feasible.     

Dual-mode predictive control algorithm for constrained Hammerstein systems

In the process industry, there exist many systems which can be approximated by a Hammerstein model. Moreover, these systems are usually subjected to input magnitude constraints. In this paper, a multi-channel identification algorithm (MCIA) is proposed, in which the coefficient parameters are identified by least squares estimation (LSE) together with a singular value decomposition (SVD) technique. Compared with traditional single-channel identification algorithms, the present method can enhance the approximation accuracy remarkably, and provide consistent estimates even in the presence of coloured output noises under relatively weak assumptions on the persistent excitation (PE) condition of the inputs. Then, to facilitate the following controller design, this MCIA is converted into a two stage single-channel identification algorithm (TS-SCIA), which preserves most of the advantages of MCIA. With this TS-SCIA as the inner model, a dual-mode non-linear model predictive control (NMPC) algorithm is developed. In detail, over a finite horizon, an optimal input profile found by solving a open-loop optimal control problem drives the non-linear system state into the terminal invariant set; afterwards a linear output-feedback controller steers the state to the origin asymptotically. In contrast to the traditional algorithms, the present method has a maximal stable region, a better steady-state performance and a lower computational complexity. Finally, simulation results on a heat exchanger are presented to show the efficiency of both the identification and the control algorithms.     

Exponential ARX model-based long-range predictive control strategy for power plants

For nonlinear thermal power plants whose dynamics vary with load demand, a load-dependent exponential ARX (Exp-ARX) model, which can effectively describe the nonlinear properties of the plants, is presented. The Exp-ARX model requires only off-line identification. Based on the model, a constrained multivariable generalized predictive control (CMGPC) strategy is designed and implemented in a simulation of 375 MW thermal power plants. This CMGPC algorithm does not resort to on-line parameter estimation and can more exactly predict the future outputs of the nonlinear plants, so it shows better reliability and control performance than the usual GPC algorithm.     

Explicit stochastic predictive control of combustion plants based on Gaussian process models

Energy production is one of the largest sources of air pollution. A feasible method to reduce the harmful flue gases emissions and to increase the efficiency is to improve the control strategies of the existing thermoelectric power plants. This makes the Nonlinear Model Predictive Control (NMPC) method very suitable for achieving an efficient combustion control. Recently, an explicit approximate approach for stochastic NMPC based on a Gaussian process model was proposed. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation, which is an essential issue in safety-critical applications. This paper considers the application of an explicit approximate approach for stochastic NMPC to the design of an explicit reference tracking NMPC controller for a combustion plant based on its Gaussian process model. The controller brings the air factor (respectively the concentration of oxygen in the flue gases) on its optimal value with every change of the load factor and thus an optimal operation of the combustion plant is achieved.     

Contribution to the indirect decentralized adaptive control of manipulation robots

In this paper, efficient approaches to the synthesis of indirect decentralized adaptive control for manipulation robots are presented. The first part of control synthesis consists of the estimation of unknown dynamic robot parameters using the methods of recursive identification and fast dynamic as well as identification models in a symbolic form. The second part of synthesis includes the self-tuning control strategy which is a basis for adaptive control synthesis according to the estimates of the unknown dynamic parameters. Using the theory of decentralized systems, a new robust algorithm for adaptive control with the ability of adaptation in the feedforward or feedback loop are proposed. A complete stability and convergence analysis is presented. A special part of the paper represents an analysis of practical implementation of the proposed control algorithms on modern microprocessor-based robot controllers. Based on this analysis, an efficient application of indirect adaptive algorithms in real time with high-quality system performance is shown. Adaptive algorithms are verified through simulation of trajectory tracking for an industrial robot with unknown dynamic parameters of payload.     

Implementation and application of microprocessor-based self-tuners

The implementation of a class of self-tuning controllers using a portable microcomputer system is described. The self-tuning control theory is shown to provide a variety of control objectives such as model-reference, optimal Smith prediction and the minimization of a general k-step-ahead cost-function. Hardware and software details of the portable computer, SESAME, are presented with particular reference to the use of a new high-level language, Control Basic. Studies of the application of self-tuning to the control of room-temperature, acid neutralization, and batch chemical reactors in industry are outlined.     

On-line identification and optimal control of continuous-time systems

In this paper, a moving algorithm for on-line identification of continuous-time systems is developed. With the proposed algorithm, the observed input–output data can be directly used to estimate the system parameters without any numerical pre-processing, and by means of a recursive formula the estimates can be updated step by step without repeatedly computing the matrix inversion. In this way, the use of both computer memory and computing time can be reduced. Besides, the computations are simple and straightforward. From the moving identification algorithm, a linear moving model can be obtained to represent the control systems. The on-line optimal control algorithm is also developed via the linear moving model. A slider-crank motion control system is used to illustrate that the proposed on-line identification and optimal control algorithms can give satisfactory results.     

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.     

Genetic Tuning of PID Controllers Using a Neural Network Model: A Seesaw Example

When genetic algorithms (GAs) are applied for PID parameter tuning, since the PID parameters are adjusted almost randomly, it is possible that the plant will be damaged due to abrupt changes in PID parameters. To solve this problem, a neural network will be used to model the plant and the genetic tuning procedure will be performed on the neural network instead of the plant. After determining the PID parameters in this off-line manner, these gains are then applied to the plant for on-line control. Moreover, considering that the neural network model may not be accurate enough, a method is also proposed for on-line fine-tuning of PID parameters. To show the validity of the proposed method, a seesaw system that has one input and two outputs will be used for experimental evaluation     

pH值中和反应过程的无模型学习自适应控制

根据pH值处理过程的时变和非线性特性,将基于紧格式线性化的单入单出非线性离散时间系统的无模型学习自适应控制方法应用在带有时滞的pH值中和反应过程中。控制器的设计是无模型的,是直接基于称为伪偏导数的向量,此伪偏导数是通过一种新型参数估计算法,根据酸碱中和反应系统的输入输出信息在线导出的。此无模型控制方法非常适用于实际的模型参数难以辨识,且是时变的非线性系统。仿真控制验证了该方法对不确知动态的非线性pH值的控制具有鲁棒性强、响应速度快和控制精度高的优点,性能好于传统的PID控制。