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CONTROL-ID: A Demonstration Prototype for Control-Relevant Identification of Process Systems
Copyright e IFAC System Identification. Copenhagen. Denmark. 1994
CONTROL-ID: A Demonstration Prototype for Control-Relevant Identification of Process Systems D. E. Rivera, S.V. Gaikwad, X. Chen and S. Bhatnagar Dept. of Chemical, Bio, and Materials Engineering and Computer-Integrated Manufacturing Systems Research Center Arizona State University Tempe, AZ 85287-6006 Description During the past four years. the Control Systems Engineering Laboratory at ASU has been engaged in controlrelevant identification research with the goal of developing technology that is both fundamental in nature yet meets the needs of practicing process control engineers. CONTROL-ID is a demonstration prototype package written in MATLAB/SIMULINK that presents an integrated, synergistic view of the control-relevant identification problem for SISO plants, starting from input design and ending with controller commissioning and refinement. CONTROL-ID has been developed in both a standalone MATLAB/SIMULINK version and a real-time version interfaced to a Honeywell TDC3000 distributed control system. The latter demonstrates vividly the benefits of control-relevant identification technology for extending the lifecycle of control designs and allowing the migration of advanced control technology to the distributed control layer. The tool displays the following novel features: 1. It addresses the need for technology that meets the skill level of B.S. engineers who comprise the bulk of control engineers in industry. This is done through gUidelines that translate a priori information commonly available to the user (e.g., dominant system time cOWitants, desired closed-loop speed of response) into judicious choices of design variables in the identification procedure. FUrthermore, the package supports the use of restricted complexity models for low-order discrete time control laws, which are readily implemented using the programming capability of many commercial distributed control systems. 2. It relies on the use of periodic, deterministic signals (PRBS and Schroeder-phased) which display properties that closely resemble those of white noise. These signals permit identification under low signal-te>-noise ratios, a scenario where step testing (the traditional input of choice in system identification for process eontrol) is inadequate. 3. It effectively combines both nonparametric and parametric system identification techniques. Nonparametric estimation includes estimates of frequency-domain additive uncertainty bounds which are derived for finite data sets, not based on asymptotic approximations.
References [1] Rivera, D.E., J.F. Pollard, and C.E. Garcia, UControl-relevant prefiltering: a systematic design approach and case study," IEEE nuns. ·Autom. Cntrl., 37, 964, 1992. [21 Ri....era, D.E., X. Chen, and D.S. Bayard, uExperimental Design for Robust Process Control Using SchroederPhased Inputs," Pree. 1993 American Control Conference, San Francisco, June, 1993. [31 Rivera, D.E. and S. Bhatnagar. uClosed-loop identification of restricted complexity models via iterative refinement," Proc. 1993 American Control Conference, San Francisco. June, 1993.
Hardware and software requirements The program will execute under any platform that runs MATLAB with SIMULINK. Development of the standalone prototype was done on MacIntosh computers under System 7 using SIMULINK Version 1.2.
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CONTROL-ID: A Demonstration Prototype for Control-Relevant Identification of Process Systems D. E. Rivera, S.V. Gaikwad, X. Chen and S. Bhatnagar Dept. of Chemical, Bio, and Materials Engineering and Computer-Integrated Manufacturing Systems Research Center Arizona State University Tempe, AZ 85287-6006 Description During the past four years. the Control Systems Engineering Laboratory at ASU has been engaged in controlrelevant identification research with the goal of developing technology that is both fundamental in nature yet meets the needs of practicing process control engineers. CONTROL-ID is a demonstration prototype package written in MATLAB/SIMULINK that presents an integrated, synergistic view of the control-relevant identification problem for SISO plants, starting from input design and ending with controller commissioning and refinement. CONTROL-ID has been developed in both a standalone MATLAB/SIMULINK version and a real-time version interfaced to a Honeywell TDC3000 distributed control system. The latter demonstrates vividly the benefits of control-relevant identification technology for extending the lifecycle of control designs and allowing the migration of advanced control technology to the distributed control layer. The tool displays the following novel features: 1. It addresses the need for technology that meets the skill level of B.S. engineers who comprise the bulk of control engineers in industry. This is done through gUidelines that translate a priori information commonly available to the user (e.g., dominant system time cOWitants, desired closed-loop speed of response) into judicious choices of design variables in the identification procedure. FUrthermore, the package supports the use of restricted complexity models for low-order discrete time control laws, which are readily implemented using the programming capability of many commercial distributed control systems. 2. It relies on the use of periodic, deterministic signals (PRBS and Schroeder-phased) which display properties that closely resemble those of white noise. These signals permit identification under low signal-te>-noise ratios, a scenario where step testing (the traditional input of choice in system identification for process eontrol) is inadequate. 3. It effectively combines both nonparametric and parametric system identification techniques. Nonparametric estimation includes estimates of frequency-domain additive uncertainty bounds which are derived for finite data sets, not based on asymptotic approximations.
References [1] Rivera, D.E., J.F. Pollard, and C.E. Garcia, UControl-relevant prefiltering: a systematic design approach and case study," IEEE nuns. ·Autom. Cntrl., 37, 964, 1992. [21 Ri....era, D.E., X. Chen, and D.S. Bayard, uExperimental Design for Robust Process Control Using SchroederPhased Inputs," Pree. 1993 American Control Conference, San Francisco, June, 1993. [31 Rivera, D.E. and S. Bhatnagar. uClosed-loop identification of restricted complexity models via iterative refinement," Proc. 1993 American Control Conference, San Francisco. June, 1993.
Hardware and software requirements The program will execute under any platform that runs MATLAB with SIMULINK. Development of the standalone prototype was done on MacIntosh computers under System 7 using SIMULINK Version 1.2.
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