Robust process identification and auto-tuning control

Details Robust process identification and auto-tuning control Robust process identification and auto-tuning control

2001-10-18

2004-02-24

Hoff, Marc S. (Department: 2857)

Data processing: measuring, calibrating, or testing

Measurement system

Measured signal processing

C702S179000, C702S181000, C702S190000

Reexamination Certificate

active

06697767

ABSTRACT:

BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to system identification and control design for single-variable and multi-variable plants. More particularly, the invented schemes permit improved identification accuracy and control performance over conventional methods. The present invention provides general, systematic, effective, and applicable methods for process identification and control for a wide range of industries, such as process and chemical plants, food processing, waste water treatment and environmental systems, oil refinery, servo and mechatronic systems, e.g. anywhere a system model is needed for analysis, prediction, filtering, optimization and management, and/or where control or better control is required for their systems
2. Description of the Background Art
Identifying an unknown system has been an active area of research in control engineering for a few decades and it has strong links to other areas of engineering including signal processing, system optimization and statistics analysis. Identification can be done using a variety of techniques and the step test and relay feedback test are dominant. Though there are many methods available for system identification from a step test (Strejc 1980), most of them do not consider the process delay (or dead-time) or just assume knowledge of the delay. It is well known that the delay is present in most industrial processes, and has a significant bearing on the achievable performance for control systems. Thus there has been continuing interest in identification of delay processes. In the context of process control, control engineers usually use a first-order plus dead-time (FOPDT) model as an approximation of the process for control practice:
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For such a FOPDT model, area-based methods are more robust than other methods such as the graphical method or two-point method (Astrom and Hagglund 1995). This FOPDT model is able to represent the dynamics of many processes over the frequency range of interest for feedback controller design. Yet, there are certainly many other processes for which the model (1) is not adequate to describe the dynamics, or for which higher-order modeling could improve accuracy significantly. Graphical methods (Mamat and Fleming 1995, Rangaiah and Krishnaswamy 1996a, Rangaiah and Krishnaswamy 1996b) have been proposed to identify a second-order plus dead-time (SOPDT) model depending on different types of response, i.e., underdamped, mildly underdamped, or overdamped. However, such a SOPDT model does not adequately describe non-minimum-phase systems. Further, though model parameters can be easily calculated using several points from the system response, such graphical methods may not be robust to noise. Further, though most tests can be done in open-loop, the plant model identification in closed-loop operation is also an important practical issue. In some cases the plant can be operated in open-loop with difficulties or a control may already exist and it is not possible to open the loop. However, most existing closed-loop identification methods suffer from one or more of the following inherent drawbacks: not robust to measurement noise; an FOPDT model can only be reconstructed; and/or the controller is restricted to the proportional type.
Since a relay feedback test is successful in many process control applications, it has been integrated into commercial controllers. Through years of experience, it is realized that the main problems with the standard relay test are as follows. (i) Due to the adoption of describing function approximation, the estimation of the critical point is not accurate in nature. (ii) Only crude controller settings can be obtained on the single point identified. Several modified relay feedback identification methods have been reported (Li and Eskinat 1991, Leva 1993, Palmor and Blau 1994, Wang et al. 1999a). Additional linear components (or varying hysteresis width) have to be introduced and additional relay tests have to be performed to identify two or more points on the process frequency response. These methods are time consuming and the resulting estimation is still approximate in nature since they actually make repeated use of the standard method. Consequently, it is important to develop techniques for identifying multiple and accurate points on the process frequency response from a single relay test, which is necessary for enhancement of control performance and for auto-tuning of advanced controllers.
Over and above the subject of single variable process identification, multivariable counterpart is another topic of strong interest and need to be studied more thoroughly. Processes inherently having more than one variable to be controlled at the output are frequently encountered in the industries and are known as multivariable or multi-input multi-output (MIMO) processes. Interactions usually exist between control loops, and this causes the renowned difficulty in their identification and control compared with single-input single-output (SISO) processes. Though there are many methods available for single-variable process identification from the relay or step test operated in open-loop or in closed-loop, most of them show no extension to multivariable cases. Further, most of the existing multivariable identification methods assume the absence of inverse response (non-minimum-phase behaviour), oscillating behaviour and/or time delays (Ham and Kim 1998). In Melo and Friedly (1992), a frequency response technique for a single-variable system is extended to a multivariable system, where frequency response characteristics are obtained utilizing a closed-loop approach. However, no process model is generated from the procedures. In Ham and Kim (1998), a closed-loop procedure of the process identification in multivariable systems using a rectangular pulse set-point change is proposed. The test signal is not so widely used and understandable to control engineers and only a FOPDT model can be obtained for each element. Therefore, there is high demand for a general identification scheme for multivariable systems, which can cover many different experimental tests in a unified framework.
The identification of the process dynamics may not be the final target in many applications. Further, control engineers are typically interested in the controller design based on the process information obtained. The majority of the single-loop controllers used in industry are of PID type (Astrom and Hagglund 1984a). However, despite the fact that the use of PID control is well established in process industries, many control loops are still found to perform poorly. In spite of the enormous amount of research work reported in the literature, tuning a good PID controller is still recognized as a rather difficult task. It would hence be desirable to develop a design method that works universally with high performance for general processes. Simple controllers like PLD are adequate for benign process dynamics. They will rapidly lose their effectiveness when the process deadtime is much larger than the process time constant. Advanced control strategies such as the internal model control (IMC) would have to be considered to achieve higher performance. The advantages of IMC are exploited in many industrial applications (Morari and Zafiriou 1989). In spite of the effectiveness of the control scheme, the internal model control has disadvantages that hindered its wider use in industry. In cases with large modeling errors, its performance will not be satisfactory. Besides, for a high-order model, the resulting controller derived will be of high order, and the implementation of the IMC control scheme is sometimes costly. Therefore, a new internal model control scheme which has a simpler design and implementation will be welcomed by the related art.
The goal of controller design to achieve satisfactory loop performance has also posed a great challenge in the area of multivariable control design. For multivariable PID control, Koivo and Tan

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