CN111007716A - Alternating current servo motor variable discourse domain fuzzy PI control method based on prediction function - Google Patents
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Abstract
The invention discloses a variable-theory-domain fuzzy PI control method of an alternating current servo motor based on a prediction function, which comprises a PI controller, a variable-theory-domain fuzzy controller and a prediction function controller; the PI controller is used as a master controller to adjust the rotating speed of the servo motor; the prediction function controller outputs and predicts the future state of the system according to the current input instruction, the control signal and the feedback of the system; the variable-discourse-domain fuzzy controller takes the prediction information as input to adjust the gain of the PI controller on line; meanwhile, a variable domain expansion factor is designed, the domain of the fuzzy controller is adjusted in advance on line by using the prediction information, and a fuzzy rule for adjusting the PI control gain is indirectly increased. Compared with the traditional PI control and fuzzy PI control, the method has the advantages of high precision of variable-discourse-domain fuzzy control, improved dynamic response performance and disturbance resistance, and more outstanding advantages for application occasions with uncertain disturbance and large nonlinearity.
Description
Technical Field
The invention relates to the technical field of servo motor control, in particular to a fuzzy PI (proportional integral) control method for a variable-discourse domain of an alternating-current servo motor based on a prediction function.
Background
An ac servomotor is a machine that converts electrical energy into mechanical energy, consisting of an electromagnet winding or distributed stator winding for generating a magnetic field and a rotating armature or rotor. The alternating current servo motor has the advantages of stable operation, good speed controllability, high response speed and the like, and is widely applied to equipment such as numerical control machines, industrial robots, medical appliances and the like at present. With the continuous development of industrial production, higher and higher requirements are also put forward on the control of the alternating current servo motor, and the research on a high-performance servo control system becomes one of the key problems for improving the level of equipment manufacturing industry.
The main problems and deficiencies in the prior art include:
the PI controller has the characteristics of simple structure, strong robustness, easiness in implementation and the like, and is still the most common control strategy of the alternating current servo motor at present. However, the fixed-gain PI control parameter has a weak capability of processing a time-varying working state, and cannot effectively solve various non-linear problems such as sensor delay, time-varying key parameters, unknown load disturbance and the like in the operation process of the servo motor. Therefore, in order to further improve the control performance, the PI controller needs to be combined with other control strategies to form a composite control strategy, so as to realize online self-adaptation of the control parameters.
The fuzzy PI control strategy combines the nonlinear control capability of fuzzy control and the good adaptability of PI control, and is applied to the control of an alternating current servo system. The method can utilize expert knowledge to construct a fuzzy rule base, and adjust PI control gain on line based on fuzzy reasoning, thereby avoiding a complex and time-consuming system model identification process. The limitation is that the self-adaptive capacity of the fuzzy PI controller depends heavily on the number of fuzzy rules, and the fuzzy rules lack a theoretical design method. If the adjustment accuracy of the fuzzy control is further improved, a rich motor control experience is required to design a complex and detailed fuzzy rule. When the application working condition of the alternating current servo motor is complex, the design of a high-performance fuzzy PI controller is difficult, and further popularization of the fuzzy PI control in the field of motor control is limited. Therefore, a simple and effective control strategy needs to be explored to further improve the active disturbance rejection capability and dynamic response performance of the fuzzy PI control.
Disclosure of Invention
Aiming at the problems and the defects in the prior art, the invention provides a variable-discourse-domain fuzzy PI control method of an alternating-current servo motor based on a prediction function, which adopts a variable-discourse-domain strategy to dynamically adjust the control range of a fuzzy rule, changes the number of rules near the input of a current fuzzy controller, and overcomes the problem of insufficient control performance caused by difficult design of the fuzzy control rule; meanwhile, the prediction function is used for controlling and predicting the future state of the alternating current servo motor control system, input information is provided for the variable domain fuzzy controller, and parameters of the fuzzy controller and the PI controller can be adjusted rapidly.
Therefore, the invention adopts the following technical scheme:
a variable-discourse-domain fuzzy PI control method of an alternating current servo motor based on a prediction function comprises a PI controller, a variable-discourse-domain fuzzy controller and a prediction function control module, wherein the PI controller is used as a master controller and used for adjusting the rotating speed of the alternating current servo motor; the prediction function control module predicts the future state of the system according to the current input instruction, the control signal and the feedback output of the system; the variable discourse domain fuzzy controller takes the prediction information as input to adjust the gain of the PI controller on line; designing a variable domain expansion factor, using the prediction information to adjust the domain of the variable domain fuzzy controller in advance and on line, and indirectly increasing the fuzzy rule for adjusting the gain of the PI controller.
Further, the method comprises the following steps:
step one, aiming at the characteristics of an alternating current servo motor control system, a prediction function control equation is improved and used for predicting the future feedback speed omega of the systemp(t + p), a feedback error e (t + p), and a feedback error change rate Δ e (t + p);
selecting a prediction feedback error E (t + p) and a prediction feedback error change rate delta E (t + p) after the step p as input variables of the variable universe fuzzy controller, wherein the corresponding fuzzy variables are E and delta E;
analyzing the control process of the alternating current servo motor, and designing a fuzzy rule based on the change rule of the PI control parameter at different control stages; simultaneously determining a membership function and a defuzzification strategy of the fuzzy controller, thereby designing the Mamdani type fuzzy controller;
designing a scaling factor, and adjusting a fuzzy input discourse domain on line;
step five, carrying out fuzzy reasoning according to a Mamdani type fuzzy reasoning rule to obtain fuzzy output quantity;
step six, performing clarification operation on the fuzzy output quantity, and converting the fuzzy output into a clearness value;
and step seven, modifying the PI control parameters, and outputting the PI control parameters to a controlled object, so as to adjust the rotating speed of the motor according to the current and future system states.
Preferably, the specific process of step one is as follows:
the prediction function control takes a first-order autoregressive model as a prediction model of an alternating current servo motor control system: ω (t) ═ a ω (t-1) + biq(t-1) wherein a and b are parameters to be identified for the model, iq(t-1) and omega (t-1) are respectively the q-axis input current and the feedback output rotating speed of the alternating current servo motor at the time of t-1, and omega (t) is the output rotating speed of the alternating current servo motor at the time of t; in an alternating current servo motor control system, when the predicted time length does not exceed the closed loop response time of a servo system, the q-axis input current is kept unchanged, namely iq(t+i)＝iq(t), i 1,2,3.. times, p, wherein p is a prediction step length;
the output of the alternating current servo motor after p steps can be obtained according to the prediction model as follows:
and (3) predicting the deviation existing in the output of the prediction model at the t + p moment by using the deviation information before the t moment:
wherein H is { H (t-j) } ω (t-j) - ωm(t-j) | j ═ 1,2, a. W ═ W0,w1,...wj...wp-1]TIs a bias weight vector;
based on the above prediction deviation, the predicted output of the ac servo motor after p steps is corrected to:
ωp(t+p)＝ωm(t+p)+h(t+p)；
the feedback rotating speed error and the error change rate of the alternating current servo motor after the p steps are predicted as follows:
e(t+p)＝ωr(t+p)-ωp(t+p)，
Δe(t+p)＝e(t+p)-e(t+p-1)。
preferably, the specific process of step two is as follows:
transforming input variables from natural discourse
Mapping to a universe of ambiguity
With a quantization factor of ηeAnd ηΔeThe corresponding calculation formula is:
in the formula,
and
maximum, x, of the natural discourse domain and the fuzzy discourse domain, respectivelyiRepresents the input variable e (t + p) or Δ e (t + p), uiRepresenting the fuzzy variable E or Δ E, k representing the input quantisation factor ηeOr ηΔeAnd can be represented by formulas
To be determined.
Preferably, the fuzzy linguistic variable used in step three is { N, Z, P }, which respectively represents { negative, zero, positive }; before fuzzy reasoning, fuzzification processing is carried out on fuzzy numerical variables through a membership function, and the fuzzy numerical variables are converted into fuzzy linguistic variables; and triangular and Gaussian membership functions are respectively arranged near and at two sides of the zero point of the ambiguity domain.
Preferably, the formula for calculating the scaling factor in step four is as follows:
in the formula uiIs the input variable after the fuzzification,
is the maximum value of the symmetric modulo domain, ε is any nonzero minimum positive value and τ ∈ [0.5,1 ].
Preferably, the output of the paste in step five is yΔk(e (t + p), Δ e (t + p)), where yΔkFuzzy quantity representing PI regulation value
And
further, according to the fuzzy inference rule and formula of Mamdani type, the firing degree of each rule of the variable domain fuzzy controller can be calculated as:
wherein η (·) is the fuzzy variable EiAnd Δ EiIs a large operator, and the angular index delta k represents delta kpOr Δ ki；
According to the defuzzification strategy of the gravity center method, the fuzzy output of the fuzzy controller is
In the formula, yΔkRepresents
And
Cjdiscrete values representing the universe of fuzzy outputs.
Preferably, the specific process of step six is as follows:
according to output quantization factor α0And β0Performing sharpening operation on fuzzy output quantity, and converting fuzzy output into a sharpening value (delta k)p，Δki) (ii) a Design optimization coefficient ζkAnd performing online adjustment on the output quantization factor, wherein the calculation formula is as follows:
ζΔk＝γ+yΔk(e(t+p),Δe(t+p))，
where γ is the maximum value of the ambiguity domain with a midpoint of 0, and the corner mark Δ k represents the value used to generate Δ kpOr for generating Δ kiThe parameters of (1);
the online updating formula of the output quantization factor is as follows:
PI control parameter adjustment quantity (delta k)p，Δki) According to the following formulaNew:
preferably, the calculation formula of the control parameter in the step seven is as follows:
wherein k isp0、ki0Respectively, the initial parameters of the PI controller.
Firstly, designing a prediction model for controlling an alternating current servo motor based on a prediction function control theory, and predicting the motor rotating speed and the feedback rotating speed error after p steps; then, constructing a variable universe fuzzy controller, wherein the input universe of fuzzy controller realizes self-adaptation through a scaling factor, and the output quantization factor is adjusted on line by means of an optimized coefficient designed according to fuzzy output; and finally, taking the prediction information as an input variable of the variable domain fuzzy controller, and performing fuzzy inference to correct the PI control parameters on line.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention improves the control strategy of the prediction function aiming at the motion characteristic of the alternating current servo motor, and the designed prediction model has the advantage of small online calculation amount on the basis of keeping good prediction precision.
(2) The variable-theory-domain fuzzy controller can increase the number of fuzzy rules under current input through self-adaptive adjustment of the input fuzzy domain, and ensures that the variable-theory-domain fuzzy controller still has practical control precision under the condition of insufficient fuzzy rules; meanwhile, the output quantization factor is adjusted on line through the optimization coefficient, and the effect of fuzzy control is strengthened.
(3) The invention takes the prediction information as the input of the optimized fuzzy control parameter and the PI control parameter, effectively improves the dynamic response performance of the system, and particularly can adjust the control input in time to enable the system to return to a stable state when the system is disturbed.
(4) The method inherits the advantage of higher precision of variable-discourse-domain fuzzy control, improves the dynamic response performance and the disturbance resistance capability, and has more outstanding advantages for the application occasions with uncertain disturbance and larger nonlinearity.
Drawings
FIG. 1 is a schematic control structure diagram of an alternating current servo motor variable domain fuzzy PI control method based on a prediction function provided by the invention.
Fig. 2 is a schematic structural composition diagram of an ac servo motor speed regulation system in the ac servo motor variable domain fuzzy PI control method based on the prediction function according to the embodiment of the present invention.
Fig. 3 is a schematic control structure diagram of an alternating current servo motor variable domain fuzzy PI control method based on a prediction function according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of fuzzy rule components provided by the embodiment of the present invention.
FIG. 5 is a schematic diagram of a membership function according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and specific embodiments, which are provided for illustration only and are not to be construed as limiting the invention.
As shown in FIG. 1, the invention discloses a variable-discourse domain fuzzy PI control method of an alternating current servo motor based on a prediction function, which comprises a PI controller, a variable-discourse domain fuzzy controller and a prediction function control module, wherein the PI controller is used as a master controller and is used for adjusting the rotating speed of the alternating current servo motor; the prediction function control module predicts the future state of the system according to the current input instruction, the control signal and the feedback output of the system; the variable discourse domain fuzzy controller takes the prediction information as input to adjust the gain of the PI controller on line; designing a variable domain expansion factor, using the prediction information to adjust the domain of the variable domain fuzzy controller in advance and on line, and indirectly increasing the fuzzy rule for adjusting the gain of the PI controller.
The method specifically comprises the following steps:
(1) aiming at the characteristics of an alternating current servo motor control system, the method comprises the following stepsThe measurement function control equation is improved so as to better predict the future feedback speed omega of the systemp(t + p), feedback error e (t + p), and feedback error rate of change Δ e (t + p).
(2) And selecting the prediction feedback error E (t + p) and the prediction feedback error change rate delta E (t + p) after the p steps as input variables of the fuzzy controller, wherein the corresponding fuzzy variables are E and delta E. Since the input quantity in the natural theory domain is a distinct value and the input quantity in the ambiguity domain is an ambiguity value, it is necessary to move the input variables from the natural theory domain
Mapping to a universe of ambiguity
With a quantization factor of ηeAnd ηΔeThe corresponding calculation formula is:
in the formula (1), the reaction mixture is,
and
maximum, x, of the natural discourse domain and the fuzzy discourse domain, respectivelyiRepresents the input variable e (t + p) or Δ e (t + p), uiRepresenting the fuzzy variable E or Δ E, k representing the input quantisation factor ηeOr ηΔeAnd can be represented by formulas
To be determined.
(3) Analyzing the control process of the alternating current servo motor, and designing a fuzzy rule based on the change rule of PI control parameters at different control stages; and simultaneously determining a membership function and a defuzzification strategy of the fuzzy controller, thereby designing the Mamdani type fuzzy controller.
(4) Designing a scaling factor, adjusting a fuzzy input discourse domain on line, and calculating according to a formula
In the formula (2), uiIs the input variable after the fuzzification,
is the maximum value of the symmetric modulo domain, ε is any nonzero minimum positive value and τ ∈ [0.5,1 ].
(5) Fuzzy reasoning is carried out according to the Mamdani type fuzzy reasoning rule to obtain fuzzy output quantity yΔk(e (t + p), Δ e (t + p)), where yΔkFuzzy quantity representing PI regulation value
And
(6) according to output quantization factor α0And β0Performing sharpening operation on fuzzy output quantity, and converting fuzzy output into a sharpening value (delta k)p，Δki). Design optimization coefficient zeta of the inventionkAnd performing online adjustment on the output quantization factor, wherein the calculation formula is as follows:
ζΔk＝γ+yΔk(e(t+p),Δe(t+p)) (3)
in equation (3), γ is the maximum value of the ambiguity domain with a midpoint of 0, and the subscript Δ k represents the value used to generate Δ kpOr for generating Δ kiThe parameter (c) of (c).
Therefore, the online updating formula of the output quantization factor of the invention is as follows:
PI control parameter adjustment amount (Delta k) of the present inventionp，Δki) Updated according to the following formula:
(7) and modifying the PI control parameters, and outputting the PI control parameters to a controlled object so as to adjust the rotating speed of the motor according to the current and future system states.
In addition, as a further improvement of the present invention, the step (1) is specifically as follows:
the prediction function control of the invention takes a first-order autoregressive model as a prediction model of an alternating current servo motor control system: ω (t) ═ a ω (t-1) + biq(t-1) wherein a and b are parameters to be identified for the model, iqAnd (t-1) and omega (t-1) are the q-axis input current and the feedback output rotating speed of the alternating current servo motor at the time t-1 respectively. In an alternating current servo motor control system, when the predicted time length does not exceed the closed loop response time of a servo system, the q-axis input current is kept unchanged, namely iq(t+i)＝iq(t), i 1,2,3.
Therefore, in the present invention, the output of the ac servo motor after p steps according to the prediction model is:
the above prediction output inevitably deviates from the actual control output due to the existence of model identification errors and the influence of external interference. Therefore, the deviation existing in the output of the prediction model at the time t + p can be predicted by using the deviation information before the time t:
in formula (8), H ═ { H (t-j) ═ ω (t-j) - ωm(t-j) | j ═ 1,2, a. W ═ W0,w1,...wj...wp-1]TIs a bias weight vector.
Based on the above prediction deviation, the predicted output of the AC servo motor after p steps is corrected to
ωp(t+p)＝ωm(t+p)+h(t+p) (9)
Meanwhile, the feedback rotation speed error and the error change rate of the alternating current servo motor after p steps can be predicted as follows:
e(t+p)＝ωr(t+p)-ωp(t+p) (10)
Δe(t+p)＝e(t+p)-e(t+p-1) (11)
examples
A variable-discourse-domain fuzzy PI control method of an alternating current servo motor based on a prediction function is characterized in that a control structure shown in figure 2 is adopted in an alternating current servo motor speed regulation system based on a PI control strategy, and the motor is equivalent to a first-order transfer function
In the figure ωrIs an input command speed, ω is a feedback speed, iqIs the q-axis command current, KfIs the moment coefficient, J is the rotational inertia of the motor, B is the viscous friction coefficient, TlThe load disturbance includes a load moment, a friction torque, a cogging torque, and the like. Although the speed regulating system of the alternating current servo motor is a typical nonlinear complex system, linearization processing can be carried out on the premise of not influencing the control effect. In this embodiment, the ac servo motor speed control system is described by using a first-order autoregressive model (ARX):
ω(t)＝aω(t-1)+biq(t-1) (12)
in equation (12), a and b are model parameters to be identified. The effect of various types of disturbances on the system may be equivalent to changes in the model parameters.
The variable-discourse-domain fuzzy PI control strategy based on prediction function control in the embodiment is shown in FIG. 3, and a prediction function control module and a variable-discourse-domain fuzzy control module are introduced on the basis of the traditional PI control strategy. Feedback rotation speed omega (t) and input rotation speed instruction omegarAnd (t) comparing to obtain an error e (t), and converting the error into q-axis current for controlling the rotating speed of the motor through a PI control module. To improve this control processThe response speed and the accuracy of the system need to adjust the PI control parameters on line according to the state of the system. The method comprises the following specific steps:
step 1: firstly, a prediction function control module is used for predicting the future state of the system, and input reference information is provided for variable domain fuzzy control. Input current command i in closed loop response time in an AC servo motor control systemqWill remain unchanged. The predicted step size p is within the closed loop response time, then
iq(t+i)＝iq(t),i＝1,2,3......,p (13)
From the equations (12) and (13), the predicted rotational speed of the AC servo motor can be obtained by the mathematical induction method
In the formula (14), ap+1Represents the p +1 power of a.
The motor rotating speed predicted based on the model inevitably has an error with the actual rotating speed, and in order to further improve the prediction precision, the deviation information H between the previous predicted value and the actual value can be used for predicting omegamDeviation h (t + p) from the future actual output ω (t + p), and thus the deviation is based on ωm(t + p) is corrected by the formula:
ωp(t+p)＝ωm(t+p)+h(t+p) (15)
wherein,
in formula (16), H ═ { H (t-j) ═ ω (t-j) - ωm(t-j) | j ═ 1,2jSet of (1), W ═ W1,w2,...wj...wp]T。
Meanwhile, the input instruction omega after p steps can be predicted according to the input instruction trackr(t + p). Therefore, the difference between the actual rotating speed of the motor and the instruction rotating speed after the p steps can be predicted through the following formula:
e(t+p)＝ωr(t+p)-ωp(t+p) (17)
the corresponding error change rates are:
Δe(t+p)＝e(t+p)-e(t+p-1) (18)
in the formula (19), the compound represented by the formula (I),
and
maximum, x, of the natural discourse domain and the fuzzy discourse domain, respectivelyiRepresents the input variable e (t + p) or Δ e (t + p), uiRepresenting the fuzzy variable E or Δ E, k representing the input quantisation factor ηeOr ηΔeAnd can be represented by formulas
To be determined.
The fuzzy controller in this embodiment uses fuzzy rules as shown in fig. 4, which only includes 9 rules. According to the fuzzy reasoning principle of the Mamdani model, the basic structure of the rules is as follows: if the error E is A and the error change rate delta E is B, the proportional gain adjustment amount of the PI controller is C and the integral gain adjustment amount is D, wherein A, B, C and D represent fuzzy linguistic variables. The fuzzy linguistic variables used in this embodiment are { N, Z, P }, which represent { negative, zero, positive }, respectively.
Before fuzzy reasoning, fuzzy numerical variables need to be fuzzified through a membership function and converted into fuzzy linguistic variables. The membership function used in this embodiment is shown in fig. 5, and is a triangular membership function and a gaussian membership function near and on both sides of the zero point of the ambiguity domain. It can be seen that there are fewer fuzzy rules involved in the control near the zero point. In order to dynamically increase the number of fuzzy rules, the fuzzy domain can be adjusted by a scaling factor to realize that:
in the formula (20), ε is an arbitrary nonzero minimum positive value and τ ∈ [0.5,1 ].
Defining about input xiStretch function g (x)i) To be used to compute fuzzy variables after a domain of discourse:
according to the Mamdani type fuzzy inference rule and formula, the firing degree of each rule of the variable domain fuzzy controller can be calculated as follows:
in the formula (22), η (. cndot.) is a fuzzy variable EiAnd Δ EiIs a large operator, and the angular index delta k represents delta kpOr Δ ki。
According to the defuzzification strategy of the gravity center method, the fuzzy output of the fuzzy controller is
In the formula (23), yΔkRepresents
And
Cjdiscrete values representing the universe of fuzzy outputs.
By outputting the quantization factor α0And β0For fuzzy output quantity
A sharpening operation is carried out, which is converted into a sharpening value (Δ k) for optimizing the PI controllerp，Δki). To enhance the output effect of the fuzzy controller, the present embodiment designs an adaptive output quantization factor:
wherein,
in equation (25), γ is the maximum value of the ambiguity domain whose midpoint is 0.
According to the self-adaptive output quantization factor, the adjustment value of the PI parameter can be obtained:
through a prediction function control module and a variable discourse domain fuzzy controller, the parameters of the PI controller are subjected to online self-adaptive adjustment:
in the formula (27), kp0And ki0Is an initial parameter of the PI controller.
Aiming at nonlinear factors such as time lag, uncertain disturbance or parameter time variation and the like in an alternating current servo motor speed regulating system, the invention provides a variable discourse domain fuzzy PI control strategy based on prediction function control, and overcomes the defect that the traditional PI controller lacks adaptivity. Compared with the traditional fuzzy PI control strategy, the method has better response performance, precision and practicability.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and scope of the present invention are intended to be covered thereby.
Claims (10)
1. A variable-discourse-domain fuzzy PI control method of an alternating current servo motor based on a prediction function comprises a PI controller, a variable-discourse-domain fuzzy controller and a prediction function control module, and is characterized in that: the PI controller is used as a master controller and used for adjusting the rotating speed of the alternating current servo motor; the prediction function control module predicts the future state of the system according to the current input instruction, the control signal and the feedback output of the system; the variable discourse domain fuzzy controller takes the prediction information as input to adjust the gain of the PI controller on line; designing a variable domain expansion factor, using the prediction information to adjust the domain of the variable domain fuzzy controller in advance and on line, and indirectly increasing the fuzzy rule for adjusting the gain of the PI controller.
2. The alternating current servo motor variable-discourse domain fuzzy PI control method based on the prediction function according to claim 1, characterized in that: the method comprises the following steps:
step one, aiming at the characteristics of an alternating current servo motor control system, a prediction function control equation is improved and used for predicting the future feedback speed omega of the systemp(t + p), a feedback error e (t + p), and a feedback error change rate Δ e (t + p);
selecting a prediction feedback error E (t + p) and a prediction feedback error change rate delta E (t + p) after the step p as input variables of the variable universe fuzzy controller, wherein the corresponding fuzzy variables are E and delta E;
analyzing the control process of the alternating current servo motor, and designing a fuzzy rule based on the change rule of the PI control parameter at different control stages; simultaneously determining a membership function and a defuzzification strategy of the fuzzy controller, thereby designing the Mamdani type fuzzy controller;
designing a scaling factor, and adjusting a fuzzy input discourse domain on line;
step five, carrying out fuzzy reasoning according to a Mamdani type fuzzy reasoning rule to obtain fuzzy output quantity;
step six, performing clarification operation on the fuzzy output quantity, and converting the fuzzy output into a clearness value;
and step seven, modifying the PI control parameters, and outputting the PI control parameters to a controlled object, so as to adjust the rotating speed of the motor according to the current and future system states.
3. The alternating current servo motor variable-discourse domain fuzzy PI control method based on the prediction function as claimed in claim 2, characterized in that: the specific process of the step one is as follows:
the prediction function control takes a first-order autoregressive model as a prediction model of an alternating current servo motor control system: ω (t) ═ a ω (t-1) + biq(t-1) wherein a and b are parameters to be identified for the model, iq(t-1) and omega (t-1) are respectively the q-axis input current and the feedback output rotating speed of the alternating current servo motor at the time of t-1, and omega (t) is the output rotating speed of the alternating current servo motor at the time of t; in an alternating current servo motor control system, when the predicted time length does not exceed the closed loop response time of a servo system, the q-axis input current is kept unchanged, namely iq(t+i)＝iq(t), i 1,2,3.. times, p, wherein p is a prediction step length;
the output of the alternating current servo motor after p steps can be obtained according to the prediction model as follows:
and (3) predicting the deviation existing in the output of the prediction model at the t + p moment by using the deviation information before the t moment:
wherein H ═ H(t-j)＝ω(t-j)-ωm(t-j) | j ═ 1,2, a. W ═ W0,w1,...wj...wp-1]TIs a bias weight vector;
based on the above prediction deviation, the predicted output of the ac servo motor after p steps is corrected to:
ωp(t+p)＝ωm(t+p)+h(t+p)；
the feedback rotating speed error and the error change rate of the alternating current servo motor after the p steps are predicted as follows:
e(t+p)＝ωr(t+p)-ωp(t+p)，
Δe(t+p)＝e(t+p)-e(t+p-1)。
4. the alternating current servo motor variable domain fuzzy PI control method based on the prediction function according to claim 2 or 3, characterized in that: the specific process of the second step is as follows:
transforming input variables from natural discourse
Mapping to a universe of ambiguity
With a quantization factor of ηeAnd ηΔeThe corresponding calculation formula is:
in the formula,
and
maximum, x, of the natural discourse domain and the fuzzy discourse domain, respectivelyiRepresents the input variable e (t + p) or Δ e (t + p), uiRepresenting the fuzzy variable E or Δ E, k representing the input quantisation factor ηeOr ηΔeAnd can be represented by formulas
To be determined.
5. The alternating current servo motor variable-discourse domain fuzzy PI control method based on the prediction function according to claim 4, characterized in that: the fuzzy linguistic variables used in the third step are { N, Z, P }, which respectively represent { negative, zero, positive }; before fuzzy reasoning, fuzzification processing is carried out on fuzzy numerical variables through a membership function, and the fuzzy numerical variables are converted into fuzzy linguistic variables; and triangular and Gaussian membership functions are respectively arranged near and at two sides of the zero point of the ambiguity domain.
6. The alternating current servo motor variable-discourse domain fuzzy PI control method based on the prediction function according to claim 5, characterized in that: the formula for calculating the scaling factor in the fourth step is as follows:
in the formula uiIs the input variable after the fuzzification,
is the maximum value of the symmetric modulo domain, ε is any nonzero minimum positive value and τ ∈ [0.5,1 ].
7. The alternating current servo motor variable domain fuzzy PI control method based on the prediction function according to claim 2 or 3, characterized in that: the output of the paste in the fifth step is yΔk(e (t + p), Δ e (t + p)), where yΔkFuzzy quantity representing PI regulation value
And
8. the alternating current servo motor variable-discourse domain fuzzy PI control method based on the prediction function according to claim 7, characterized in that: according to the Mamdani type fuzzy inference rule and formula, the firing degree of each rule of the variable domain fuzzy controller can be calculated as follows:
wherein η (·) is the fuzzy variable EiAnd Δ EiIs a large operator, and the corner mark delta k represents delta kpOr Δ ki；
According to the defuzzification strategy of the gravity center method, the fuzzy output of the fuzzy controller is
In the formula, yΔkRepresents
And
Cjdiscrete values representing the universe of fuzzy outputs.
9. The alternating current servo motor variable-discourse domain fuzzy PI control method based on the prediction function according to claim 8, characterized in that: the concrete process of the step six is as follows:
according to output quantization factor α0And β0Performing sharpening operation on fuzzy output quantity, and converting fuzzy output into a sharpening value (delta k)p，Δki) (ii) a Design optimization coefficient ζkAnd performing online adjustment on the output quantization factor, wherein the calculation formula is as follows:
ζΔk＝Υ+yΔk(e(t+p),Δe(t+p))，
wherein γ is the maximum of the ambiguity domain with a midpoint of 0, and the corner mark Δ k represents the maximum for generating Δ kpOr for generating Δ kiThe parameters of (1);
the online updating formula of the output quantization factor is as follows:
PI control parameter adjustment quantity (delta k)p，Δki) Updated according to the following formula:
10. the alternating current servo motor variable-discourse domain fuzzy PI control method based on the prediction function according to claim 9, characterized in that: the calculation formula of the control parameters in the step seven is as follows:
wherein k isp0、ki0Respectively, the initial parameters of the PI controller.
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