Data processing: generic control systems or specific application – Generic control system – apparatus or process – Optimization or adaptive control
Reexamination Certificate
1993-08-11
2001-12-11
Grant, William (Department: 2121)
Data processing: generic control systems or specific application
Generic control system, apparatus or process
Optimization or adaptive control
C700S030000, C700S037000, C700S038000, C700S042000, C706S900000
Reexamination Certificate
active
06330484
ABSTRACT:
BACKGROUND OF THE INVENTION
Fuzzy logic control (FLC) has been widely applied to the industrial environment in recent years. Although many of the applications are relatively small in scale, such as in washing machines, elevators, automobiles and video cameras, there is a considerable amount of interest in applying fuzzy logic systems to process control. In the field of process control, research has been conducted into the use of FLCs similar to conventional Proportional, Integral, Derivative (PID) controllers.
In typical applications, an operator will set up a FLC by adjusting certain control parameters that exist within the controller, in order to attempt to optimize the response performance of the controller. While manual tuning of fuzzy logic controllers is possible, it is often tedious and error prone. Moreover, the process under control may change over time, thus requiring retuning.
Automatic tuning approaches have been applied to process controllers and their variants. For example, in U.S. Pat. No. 4,549,123, and in application Ser. No. 08/070,090, now U.S. Pat. No. 5,453,925, filed May 28, 1993 (assigned to the same assignee as the present application), the disclosures of each of which are incorporated herein by reference, a controlled induced oscillation procedure is used in order to determine the ultimate gain, ultimate period and time delay of the process under control. Once these process characteristics are derived, tuning rules are used to determine the process control parameters of the PID controller.
However, to date, there have been no methods or means proposed for the automatic tuning of fuzzy logic controllers, thus leaving an operator with only trial and error manual tuning.
SUMMARY OF THE INVENTION
The present invention provides a method and apparatus for fuzzy logic control which incorporates automatic self-tuning, thus satisfying the above-noted discrepancies in prior approaches.
In general, the present invention contemplates an automatically tunable fuzzy logic controller. The dynamic characteristics of the process under control are determined and are used to calculate process control parameters for application to the fuzzy logic controller to control the process.
In one embodiment a fuzzy logic controller is employed for controlling a process by selectively connecting the output of the fuzzy logic controller to the process under control. The fuzzy logic controller is tuned by disconnecting the fuzzy logic controller from the process, and by applying to the process a controllable signal generator which causes the process to undergo controlled induced oscillation. During induced oscillation, the output of the process is monitored by a tuning module and dynamic characteristics of the process are determined. These dynamic characteristics are then used by the tuning module to calculate control parameters within the fuzzy logic controller in order to optimally tune the fuzzy logic controller to control the process under consideration. The signal generator is then disconnected, and the tuned fuzzy logic controller is then reconnected in order to control the process.
In accordance with another embodiment of the present invention, a fuzzy logic controller which is employed for process control is tuned by injecting a perturbation signal into the closed loop including the fuzzy logic controller and process to cause the process to undergo controlled induced oscillation. Then, during induced oscillation, the process is monitored by a tuning module and dynamic characteristics of the process are determined. These characteristics are then used to calculate control parameters within the fuzzy logic controller in order to optimally tune the fuzzy logic controller to control the process under consideration. After tuning, the perturbation signal is removed, and the tuned fuzzy logic controller then controls the process in a closed-loop fashion.
In accordance with yet another embodiment of the present invention, a fuzzy logic controller is tuned by a tuning module by determining the dynamic process characteristics using a pattern recognition tuning method which analyzes the response of the process to process upset conditions, and from the response calculates dynamic process characteristics. Then, from these dynamic process characteristics, control parameters for the fuzzy logic controller are determined in order to optimally tune the fuzzy logic controller to control the process. In the alternative, the tuning module may employ a model matching tuning method to determine dynamic process characteristics.
The dynamic process characteristics determined during the tuning procedure may include, for example, the ultimate gain, ultimate period and time delay of the process. The control parameters which are determined from the dynamic process characteristics may include, for example, a control error scaling factor, a change in control error scaling factor and a control action scaling factor.
REFERENCES:
patent: 3798426 (1974-03-01), Bristol, II
patent: 4441151 (1984-04-01), Hayashibe
patent: 4481567 (1984-11-01), Kaya et al.
patent: 4549123 (1985-10-01), Hägglund et al.
patent: 4602326 (1986-07-01), Kraus
patent: 4754391 (1988-06-01), Suzuki
patent: 4758943 (1988-07-01), Aström et al.
patent: 4768143 (1988-08-01), Lane et al.
patent: 4814968 (1989-03-01), Fukumoto
patent: 4855674 (1989-08-01), Murate et al.
patent: 4882526 (1989-11-01), Iino et al.
patent: 4954975 (1990-09-01), Kalata
patent: 4959767 (1990-09-01), Buchner et al.
patent: 5057993 (1991-10-01), Kanda
patent: 5179634 (1993-01-01), Matsunaga et al.
patent: 5231335 (1993-07-01), Mega et al.
patent: 5241651 (1993-08-01), Ueda
patent: 5295061 (1994-03-01), Katayama et al.
patent: 0 241 286 A1 (1987-10-01), None
patent: A 2 211 324 (1989-06-01), None
patent: 2 211 324 (1989-06-01), None
Zheng, J et al; “STFC—Self-Tuning Fuzzy Controller”; IEEE, 1992; New York.*
Takahashi et al, H; “Automatic Speed Control Device Using Self Tuning Fuzzy Logic,”; IEEE; 1988; New York.*
David G. Burkhardt and Piero P. Bonissone, “Automated Fuzzy Knowledge Base Generation and Tuning,” IEEE International Conference on Fuzzy Systems, pp. 179-188, Mar. 8-12, 1992.
Chen et al., “A Self-Learning Fuzzy Controller,” IEEE International Conference on Fuzzy Systems, pp. 189-196, Mar. 8-12, 1992.
Daugherity et al., “Performance Evaluation of a Self-Tuning Fuzzy Controller,” IEEE International Conference on Fuzzy Systems, pp. 389-397, Mar. 8-12, 1992.
Hoon Kang and George Vachtsevanos, “Adaptive Fuzzy Logic Control,” IEEE International Conference on Fuzzy Systems, pp. 407-414, Mar. 8-12, 1992.
A. Boscolo, “Computer Aided Tuning and Validation of Fuzzy Systems,” IEEE International Conference on Fuzzy Systems, pp. 605-614, Mar. 8-12, 1992.
Samuel M. Smith and David J. Comer, “An Algorithm for Automated Fuzzy Logi Controller Tuning,” IEEE International Conference on Fuzzy Systems, pp. 615-622, Mar. 8-12, 1992.
Tseng et al., “Fuzzy Servocontroller: The Hierarchical Approach,” IEEE International Conference on Fuzzy Systems, pp. 623-631, Mar. 8-13, 1992.
Li Zheng, “A Practical Guide to Tune of Proportional and Integra(PI) Like Fuzzy Controllers,” IEEE International Conference on Fuzzy Systems, pp. 633-640, Mar. 8-13, 1992.
M. Mizumoto, “Realization of PID Controls by Fuzzy Control Methods,” IEEE International Conference on Fuzzy Systems, pp. 709-715, Mar. 8-12, 1992.
Seborg et al.,Process Dynamics and Control, Chapters 12, 13 and 18, John Wiley & Sons, Inc., 1989, U.S.A.
Hong et al., “A Design of Auto-Tuning PID Controller Using Fuzzy Logic,”1992 International Conference on Industrial Electronics, Control, Instrumentation and Automation, vol. 2, pp. 971-976 (Nov. 9, 1992).
Hed et al., “Fuzzy Self-Tuning of PID Controllers,”Fuzzy Sets and Systems, vol. 56, No. 1, pp. 37-46 (May 25, 1993).
Daugherity et al., “Performance Evaluation of a Self-Tuning Fuzzy Controller,”IEEE International Conference on Fuzzy Systems, pp. 389-397 (Mar. 8, 1992).
Ying et al., “Fuzzy Control Theory: A Nonlinear Case*,”Automatica, vol. 26, No. 3, pp. 513-520, 1990.
Jyh-Shing R
Fisher-Rosemount Systems Inc.
Fulbright & Jaworski
Garland Steven R.
Grant William
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