1、外文文献翻译 Advanced control algorithms embedded in a programmable Abstract This paper presents an innovative self -tuning nonlinear controller ASPECT (advanced control algorithms for programmable logic controllers). It is intended for the control of highly nonlinear processes whose properties change rad
2、ically over its range of operation, and includes three advanced control algorithms. It is designed using the concepts of agent -based systems, applied with the aim of automating some of the configuration tasks . The process is represented by a set of low -order local linear models whose parameters a
3、re identified using an online learning procedure. This procedure combines model identification with pre - and post identification steps to provide reliable operation. T he controller monitors and evaluates the control performance of the closed -loop system. The controller was implemented on a progra
4、mmable logic controller (PLC). The performance is illustrated on a field test application for control of pressure on a hydraul ic valves 2005 Elsevier Ltd. All rights reserved. Keywords: Control engineering； Fuzzy modelling； Industrial control； Model-based control； Nonlinear control； Programmable lo
5、gic controllers； Self tuning regulators 1. Introduction Modern control theory offe rs many control methods to achieve more efficient control of nonlinear processes than provided by conventional linear methods, taking advantage of more accurate process models (Bequette, 1991； Henson & Seborg, 1997； M
6、urray -Smith & Johansen, 1997). Surveys (Takatsu, Itoh, & Araki,1998； Seborg, 1999) indicate that while there is a considerable and growing market for advanced controllers, relatively few vendors offer turn -key products. Excellent results of advanced control concepts, based on fuzzy parameter sc he
7、duling (Tan, Hang, & Chai,1997； Babus ka, Oosterhoff, Oudshoorn, & Bruijn,2002), multiple-model control (Dougherty & Cooper,2003； Gundala, Hoo, & Piovoso, 2000), and adaptive control (Henson & Seborg, 1994； Ha gland & A strom,2000), have been reporte d in the literature. However, there are several r
8、estrictions for applying these methods in industrialapplications, as summarized below: （ 1） Because of the diversity of real -life problems, a single nonlinear control method has a relatively narrow field of ap plication. Therefore, more flexible methods or a toolbox of methods are required in indus
9、try. （ 2） New methods are usually not available in a ready -to use industrial form. Custom design requires considerable effort, time and money. （ 3） The hardware requirements are relatively high, due to the complexity of implementation and computational demands. （ 4） The complexity of tuning (Babus
10、ka et al., 2002) and maintenance makes the methods unattractive to nonspecialised engineers. （ 5） The reliability of nonlinear mode lling is often in question. （ 6） Many nonlinear processes can be controlled using the well -known and industrially proven PID controller. A considerable direct performa
11、nce increase (financial gain) is demanded when replacing a conventional control system with an advanced one. The maintenance costs of an inadequate conventional control solution may be less obvious. The aim of this work is to design an advanced controller that addresses some of the aforementioned pr
12、oblems by using the concepts of agent-based systems (ABS) (Wooldridge & Jennings, 1995). The main purpose is to simplify controller configuration by partial automation of the commissioning procedure, which is typically performed by the control engineer. ABS solve difficult problems by assigning task
13、s to networked software agents. The software agents are characterized by properties such as autonomy (operation without direct intervention of humans), social ability (interaction with other agents), reactivity (perception and response to the environment), pro-activeness (goal -directed behaviour,ta
14、king the initiative), etc. This work does not address issues of ABS theory, but rather the application of the basic concepts of ABS to the field of process systems engineering. In this context, a number of limits h ave to be considered. For example: initiative is restricted, a high degree of reliabi
15、lity and predictability is demanded, insight into the problem domain is limited to the sensor readings, specific hardware platforms are used, etc. The ASPECT controller is an efficient and user-friendly engineering tool for implementation of parameter -scheduling control in the process industry. The
16、 commissioning of the controller is simplified by automatic experimentation and tuning. A distinguishing feature of the controll er is that the algorithms are adapted for implementation on PLC or open controller Industrial hardware platforms. The controller parameters are automatically tuned from a
17、nonlinear process model. The model is obtained from operating process signals by experimental modelling,using a novel online learning procedure. This procedure is based on model identification using the local learning approach (Murray-Smith & Johansen,1997, p. 188). The measurement data are processe
18、d batch-wise. Additional steps are perfo rmed before and after identification in order to improve the reliability of modelling, compared to adaptive methods that use recursive identification continuously (Ha gland & A strom,2000).The nonlinear model comprises a set of local lowered linear models, ea
19、ch of which is valid over a specified operating region. The active local model(s) is selected using a configured scheduling variable. The controller is specifically designed for single -input, single output processes that may include a measured distur bance used for feed -forward. Additionally, the
20、application range of the controller depends on the selected control algorithm. A modular structure of the controller permits use of a range of control algorithms that are most suitable for different processes. The controller monitors the resulting control performance and reacts to detected irregular
21、ities. The controller comprises the run -time module (RTM) and the configuration tool (CT). The RTM runs on a PLC, performing all the main functionality of real -time control, online learning and control performance monitoring. The CT, used on a personal computer (PC) during the initial configuration phase, simplifies the configuration procedure by providing guidance and default parameter values. The outline of the pap er is as follows: Section 2 presents an overview of the RTM structure and describes
