1、PDF外文：http:/ 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
2、 change radically 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 para
3、meters are identified using an online learning procedure. This procedure combines model identification with pre- and post identification steps to provide reliable operation. The controller monitors and evaluates the control performance of the closed-loop system. The controller was implemented on a p
4、rogrammable logic controller (PLC). The performance is illustrated on a field test application for control of pressure on a hydraulic valves 2005 Elsevier Ltd. All rights reserved. Keywords: Control engineering； Fuzzy modelling； Industrial control； Model-based control； Nonlinear control； Programmabl
5、e logic controllers； Self tuning regulators 1. Introduction Modern control theory offers 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, 1
6、997； Murray-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 par
7、ameter scheduling (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 reported in the literatu
8、re. However, there are several restrictions 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 application. Therefore, more flexible methods or a toolbox of
9、 methods are required in industry. （ 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
10、 complexity of tuning (Babus ka et al., 2002) and maintenance makes the methods unattractive to nonspecialised engineers. （ 5） The reliability of nonlinear modelling is often in question. （ 6） Many nonlinear processes can be controlled using the well-known and industrially proven PID controller. A c
11、onsiderable direct performance 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 addres
12、ses some of the aforementioned problems 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
13、 difficult problems by assigning tasks 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-ac
14、tiveness (goal-directed behaviour,taking the initiative), etc. This work does not address issues of ABS theory, but rather the applicationof the basic concepts of ABS to the field of process systems engineering. In this context, a number of limits have to be considered. For example: initiative
15、 is restricted, a high degree of reliability 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-scheduli
16、ng control in the process industry. The commissioning of the controller is simplified by automatic experimentation and tuning. A distinguishing feature of the controller is that the algorithms are adapted for implementation on PLC or open controllerIndustrial hardware platforms. The controller
17、 parameters are automatically tuned from a 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,1
18、997, p. 188). The measurement data are processed batch-wise. Additional steps are performed 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
19、comprises a set of local lowered linear models, each 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
20、bance used for feed-forward. Additionally, the 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 contr
21、ol performance and reacts to detected irregularities. 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 per
22、sonal computer (PC) during the initial configuration phase, simplifies the configuration procedure by providing guidance and default parameter values. The outline of the paper is as follows: Section 2 presents an overview of the RTM structure and describes its most important modules； Section 3 gives
23、 a brief description of the CT； and finally, Section 4 describes the application of the controller to a pilot plant where it is used for control of the pressure difference on a hydraulic valve in a valve test apparatus. 2. Run-Time Module The RTM of the ASPECT controller comprises a set of modules,
24、linked in the form of a multi-agent system. Fig. 1 shows an overview of the RTM and its main modules: the signal pre-processing agent (SPA), the online learning agent (OLA), the model information agent (MIA), the control algorithm agent (CAA), the control performance monitor (CPM), and the operation
25、 supervisor (OS). 2.1. Multi-faceted model (MFM) The ASPECT controller is based on the multi-faceted model concept proposed by Stephanopoulus, Henning, and Leone (1990) and incorporates several model forms required by the CAA and the OLA. Specifically, the MFM includes a set of local first- and second-order discrete-time linear models with time delay and offset, which are specified by a given scheduling variable s(k).The model equation of first order local models is
