CN113141138B - Self-tuning fuzzy control method for asynchronous motor drive - Google Patents
Self-tuning fuzzy control method for asynchronous motor drive Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P21/001—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using fuzzy control
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
- H02P21/18—Estimation of position or speed
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
- H02P21/20—Estimation of torque
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P2205/00—Indexing scheme relating to controlling arrangements characterised by the control loops
- H02P2205/07—Speed loop, i.e. comparison of the motor speed with a speed reference
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
- H02P2207/01—Asynchronous machines
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Abstract
The invention discloses a self-tuning fuzzy control method for asynchronous motor driving, which is characterized in that a proportional factor GE of a PI fuzzy controller, a proportional factor GC of the PI fuzzy controller, a proportional factor Gdu of the PI fuzzy controller, a parameter KP of the PI controller and a parameter KI of the PI controller are optimized and adjusted based on a self-tuning optimization algorithm. The control method can adaptively improve the steady-state response of the system.
Description
Technical Field
The invention relates to a self-tuning fuzzy control method for asynchronous motor driving, and belongs to the technical field of power transmission control.
Background
Motor drives for motion control must have fast torque response, controllability of torque, and speed response over a wide range of operating conditions. Vector control is known to solve the control problem of ac motor drives. With the known rotor time constant, the indirect vector control method decouples the flux and torque of the motor current components by estimating slip speed. The accuracy of this method depends to a large extent on the accuracy of the motor parameters, in particular the rotor time constant Tr. However, variations in the rotor time constant Tr tend to cause field misorientation and reduce system performance, especially for large, high efficiency asynchronous motor systems. While the indirect field-oriented method is very sensitive to changes in the motor parameters, it is generally preferred over the direct field-oriented method. This is because the direct process requires modification or special design of the machine. Furthermore, the vulnerability of the flux sensors tends to reduce the inherent robustness of the asynchronous motor drive.
The traditional PI controller is one of the most commonly used speed control methods in industrial electric drives. This introduces additional complexity to the modeling process, as most industrial processes are typically complex time-varying, model-uncertain nonlinear systems. It is well known that PI controllers may not be sufficient to handle severely disturbed systems. In fact, the main drawbacks of PI controllers are sensitivity to system parameter variations, insufficient damping of external disturbances and load variations, robustness to inertia increase and rotor resistance variations in the case of indirect rotor flux guidance. Therefore, the parameters of the controller must be constantly adjusted according to the current trend of the system.
The coefficient setting of the PI controller has various self-adaptive control technologies, such as model reference self-adaptive control MRAC, sliding mode control SMC, variable structure control VSC, self-setting PI control and the like. The design of the controller described above depends on the exact mathematical model of the system. However, it is often difficult to build accurate mathematical models of the system due to system saturation, temperature variations, and system disturbances.
Fuzzy logic control is the product of fuzzy logic technology combined with automatic control systems. Due to the popularity of PI controllers and PID controllers in industrial applications, the development of fuzzy controllers has mostly centered around fuzzy PID, PI, or PD controllers in the past decade. In standard fuzzy control systems, the scaling factor of the fuzzy controller is determined and selected under nominal conditions that do not allow for both dynamic and steady state performance of the wide speed range drive system.
In order to realize the control of the driving system of the IFOC asynchronous motor in the large rotating speed range by the indirect magnetic field directional control and obtain good dynamic and steady-state performance, a plurality of new control methods and control technologies are required to be adopted.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art and provide a self-tuning fuzzy control method for driving an asynchronous motor, which is a control method for directionally controlling a large-rotating-speed-range driving system of an IFOC asynchronous motor by an indirect magnetic field so as to obtain good dynamic and steady-state performance, quick torque response, torque controllability and speed response under a wide-range operation condition.
In order to achieve the above object, the present invention provides a self-tuning fuzzy control method for asynchronous motor driving, comprising:
and optimizing and adjusting a proportional factor GE of the PI fuzzy controller, a proportional factor GC of the PI fuzzy controller, a proportional factor Gdu of the PI fuzzy controller, a parameter KP of the PI controller and a parameter KI of the PI controller based on a self-adjusting and optimizing algorithm.
Preferentially, optimizing and adjusting the scale factor GE of the PI fuzzy controller based on a self-tuning optimization algorithm comprises the following steps:
GE(k)＝(1/GE(k-1))×α(k)，
GE (k) is a scale factor GE of the PI fuzzy controller of the kth sampling example, GE (k-1) is a scale factor GE of the PI fuzzy controller of the kth sampling example, and alpha (k) is a preset gain updating coefficient.
Preferentially, optimizing and adjusting the scale factor GC of the PI fuzzy controller based on a self-tuning optimization algorithm comprises the following steps:
the speed controller structure is represented as:
u(k)＝u(k-1)+du(k)，
i qs * ＝Gdu(k)×u(k)，
in the formula, k is a sampling example, u (k) is the output of the PI fuzzy controller, and du (k) is the incremental change of the output of the PI fuzzy controller;
i qs * is the output of the speed controller;
GC(k)＝(1/GC(k-1))/α(k),
wherein, GC (k) is a scale factor GC of the PI fuzzy controller of the kth sampling example, GC (k-1) is a scale factor GC of the PI fuzzy controller of the kth sampling example, and alpha (k) is a preset gain updating coefficient.
Preferably, the adjusting the scale factor Gdu of the PI fuzzy controller is optimized based on a self-tuning algorithm, which includes:
Gdu(k)＝Gdu(k-1)×α(k)，
wherein gdu (k) is the scale factor Gdu of the PI fuzzy controller of the kth sampling instance, Gdu (k-1) is the scale factor Gdu of the PI fuzzy controller of the kth sampling instance, and α (k) is a preset gain update coefficient.
Preferentially, optimizing the parameter KP of the PI controller based on a self-tuning algorithm comprises the following steps:
KP(k)＝KP(k-1)/α(k)，
KP (k) is a parameter KP of the PI controller of the kth sampling instance, KP (k-1) is a parameter KP of the PI controller of the kth sampling instance, and α (k) is a preset gain update coefficient.
Preferentially, optimizing the parameter KI of the PI controller based on a self-tuning algorithm comprises:
KI(k)＝KP(k-1)×α(k),
wherein, KI (k) is a parameter KI of the PI controller of the kth sampling example, KP (k-1) is a parameter KP of the PI controller of the kth sampling example, and α (k) is a preset gain updating coefficient.
The invention achieves the following beneficial effects:
the invention provides a self-setting fuzzy control method for driving an asynchronous motor, which is used for a control method for directionally controlling a large-rotating-speed-range driving system of an IFOC asynchronous motor by an indirect magnetic field so as to obtain good dynamic and steady-state performances, quick torque response, torque controllability and speed response under a wide-range operating condition. According to the current state of the controlled process of the asynchronous motor, the proportional factors GE, GC and Gdu of the PI fuzzy controller and the parameters KP and KI of the PI controller are adjusted on line through a self-tuning device, and the final aim is to obtain better control performance of the IFOC asynchronous motor through a gain updating coefficient alpha.
Drawings
FIG. 1 is a block diagram of a self-tuning fuzzy controller;
fig. 2 is a speed controller structure of an IFOC induction motor.
Detailed Description
The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The self-tuning fuzzy controller adopts a defuzzification method based on a center of area (COA) method and adopts a Mamdani type fuzzy inference method to calculate a gain updating coefficient alpha so as to improve the overall control performance. And the self-tuning fuzzy controller is adjusted and optimized by the following steps. The system is a system corresponding to the control method, and overshoot is the maximum deviation value. The method comprises the following steps:
the definition of the switching mechanism is
E in the formula (1) is an input error signal, the value of the threshold w depends on the sampling frequency of the PI fuzzy controller, and the threshold w of the selection switch control is larger than the maximum value of the steady-state error of the PI fuzzy controller. With the increase of the sampling frequency of the PI fuzzy controller, the threshold value w is reduced as the steady-state error of the PI fuzzy controller is reduced along with the increase of the sampling frequency; and when the absolute value of the output speed error of the fuzzy system is larger than w, the PI fuzzy controller enables the output response to be faster and the overshoot to be lower.
The first step is as follows: adjusting and optimizing the scale factors GE, GC and Gdu of the PI fuzzy controller;
firstly, input error signals e and change rates delta e of the error signals e are divided by respective scale factors GE and GC, input of a PI fuzzy controller is normalized, and the error e covers the whole domain (-1, 1), so that a rule base is effectively utilized;
the GE and GC are then optimized to make the transient response of the system as good as possible.
The second step is that: under the condition of no gain tuning mechanism and no PI fuzzy controller, only setting parameters KP and KI of the PI controller; and (5) adjusting KP and KI by using a formula and KI (k) × KP (k-1) × alpha (k), so that the steady-state response of the system is as good as possible.
The third step: and determining a w value of the switching mechanism, enabling the PI controller to be in a stable state, enabling the PI fuzzy controller to be in a transient state, enabling the self-tuning fuzzy controller to be in an adjusting state, and obtaining better control performance of the IFOC asynchronous motor by obtaining a gain updating coefficient alpha (k).
A self-tuning fuzzy control method for asynchronous motor driving comprises the following steps:
and optimizing and adjusting a proportional factor GE of the PI fuzzy controller, a proportional factor GC of the PI fuzzy controller, a proportional factor Gdu of the PI fuzzy controller, a parameter KP of the PI controller and a parameter KI of the PI controller based on a self-adjusting optimization algorithm.
Preferentially, optimizing and adjusting the scale factor GE of the PI fuzzy controller based on a self-tuning optimization algorithm comprises the following steps:
GE(k)＝(1/GE(k-1))×α(k)，
GE (k) is a scale factor GE of the PI fuzzy controller of the kth sampling example, GE (k-1) is a scale factor GE of the PI fuzzy controller of the kth sampling example, and alpha (k) is a preset gain updating coefficient.
Preferentially, optimizing and adjusting the scale factor GC of the PI fuzzy controller based on a self-tuning optimization algorithm comprises the following steps:
in order to maintain the expected performance of the drive of the asynchronous motor, the scaling factor GE of the PI fuzzy controller, the scaling factor GC of the PI fuzzy controller and the scaling factor Gdu of the PI fuzzy controller need to be adjusted in real time,
the speed controller structure is represented as:
u(k)＝u(k-1)+du(k)，
i qs * ＝Gdu(k)×u(k)，
in the formula, k is a sampling example, u (k) is the output of the PI fuzzy controller, and du (k) is the incremental change of the output of the PI fuzzy controller;
i qs * is the output of the speed controller;
GC(k)＝(1/GC(k-1))/α(k),
wherein, GC (k) is a scale factor GC of the PI fuzzy controller of the kth sampling example, GC (k-1) is a scale factor GC of the PI fuzzy controller of the kth sampling example, and alpha (k) is a preset gain updating coefficient.
Preferably, the adjusting the scale factor Gdu of the PI fuzzy controller is optimized based on a self-tuning algorithm, which includes:
Gdu(k)＝Gdu(k-1)×α(k)，
wherein gdu (k) is the scale factor Gdu of the PI fuzzy controller of the kth sampling instance, Gdu (k-1) is the scale factor Gdu of the PI fuzzy controller of the kth sampling instance, and α (k) is a preset gain update coefficient.
Preferentially, optimizing the parameter KP of the PI controller based on a self-tuning algorithm comprises the following steps:
KP(k)＝KP(k-1)/α(k)，
KP (k) is a parameter KP of the PI controller of the kth sampling instance, KP (k-1) is a parameter KP of the PI controller of the kth sampling instance, and α (k) is a preset gain update coefficient.
Preferentially, optimizing the parameter KI of the PI controller based on a self-tuning algorithm comprises the following steps:
KI(k)＝KP(k-1)×α(k),
wherein, KI (k) is a parameter KI of the PI controller of the kth sampling example, KP (k-1) is a parameter KP of the PI controller of the kth sampling example, and α (k) is a preset gain updating coefficient.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (2)
1. A self-tuning fuzzy control method for driving an asynchronous motor is characterized by comprising the following steps:
optimizing and adjusting a proportional factor GE of the PI fuzzy controller, a proportional factor GC of the PI fuzzy controller, a proportional factor Gdu of the PI fuzzy controller, a parameter KP of the PI controller and a parameter KI of the PI controller based on a self-adjusting optimization algorithm;
optimizing and adjusting the scale factor GE of the PI fuzzy controller based on a self-adjusting optimization algorithm, comprising the following steps:
GE(k)＝(1/GE(k-1))×α(k)，
GE (k) is a scale factor GE of the PI fuzzy controller of the kth sampling example, GE (k-1) is a scale factor GE of the PI fuzzy controller of the kth sampling example, and alpha (k) is a preset gain updating coefficient;
optimizing and adjusting a scale factor GC of the PI fuzzy controller based on a self-adjusting optimization algorithm, comprising the following steps:
the speed controller structure is represented as:
u(k)＝u(k-1)+du(k)，
i qs * ＝Gdu(k)×u(k)，
in the formula, k is a sampling example, u (k) is the output of the PI fuzzy controller, and du (k) is the incremental change of the output of the PI fuzzy controller; i.e. i qs * Is the output of the speed controller;
GC(k)＝(1/GC(k-1))/α(k),
wherein, GC (k) is a scale factor GC of the PI fuzzy controller of the kth sampling example, GC (k-1) is a scale factor GC of the PI fuzzy controller of the kth sampling example, and alpha (k) is a preset gain updating coefficient;
the method for optimizing and adjusting the scale factor Gdu of the PI fuzzy controller based on the self-adjusting optimization algorithm comprises the following steps:
Gdu(k)＝Gdu(k-1)×α(k)，
gdu (k) is a scale factor Gdu of the PI fuzzy controller of the kth sampling example, Gdu (k-1) is a scale factor Gdu of the PI fuzzy controller of the kth sampling example, and alpha (k) is a preset gain updating coefficient;
optimizing a parameter KP of the PI controller based on a self-tuning algorithm, comprising the following steps:
KP(k)＝KP(k-1)/α(k)，
KP (k) is a parameter KP of the PI controller of the kth sampling instance, KP (k-1) is a parameter KP of the PI controller of the kth sampling instance, and α (k) is a preset gain update coefficient.
2. The self-tuning fuzzy control method for the asynchronous motor drive according to claim 1, characterized in that optimizing the parameter KI of the PI controller based on the self-tuning algorithm comprises:
KI(k)＝KP(k-1)×α(k),
wherein, KI (k) is a parameter KI of the PI controller of the kth sampling example, KP (k-1) is a parameter KP of the PI controller of the kth sampling example, and α (k) is a preset gain updating coefficient.
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| CN103414415A (en) * | 2013-07-05 | 2013-11-27 | 石成富 | Motor control method based on PI parameter self-tuning |
| CN110138299A (en) * | 2019-05-22 | 2019-08-16 | 河南科技大学 | Induction-type bearingless motor reversed decoupling control system based on rotor resistance on-line identification |
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| CN103414415A (en) * | 2013-07-05 | 2013-11-27 | 石成富 | Motor control method based on PI parameter self-tuning |
| CN110138299A (en) * | 2019-05-22 | 2019-08-16 | 河南科技大学 | Induction-type bearingless motor reversed decoupling control system based on rotor resistance on-line identification |
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| 基于模糊自适应算法的异步电机矢量控制系统;何永勃等;《中国民航大学学报》;20170228;第35卷(第1期);第47-51页 * |
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