CN111367168A - Feedforward parameter design method based on fuzzy logic - Google Patents

Abstract

The invention discloses a feedforward parameter design method based on fuzzy logic, which is characterized in that an initialized servo drive servo system executes a motion, the maximum dynamic error value of the motion and the absolute difference value of the maximum dynamic error and the average error are calculated, and the two calculated values are used as indexes reflecting dynamic precision to carry out fuzzification processing and are converted into corresponding logic; establishing a rule base based on the corresponding logic, and designing corresponding logic output according to the rule base; adjusting the feed forward gain by deblurring based on the logic output; the maximum dynamic error value is updated with the adjustment of the feedforward gain, and the above process is repeated until the maximum dynamic error value and the absolute difference between the maximum dynamic error and the average error reach the minimum value. The feedforward parameter design method based on the fuzzy logic design has the advantages that the optimization period is short, parameters can be automatically adjusted along with the change of working conditions, the dynamic precision is greatly improved, and a servo system meets the requirement of high precision.

Description

Feedforward parameter design method based on fuzzy logic

Technical Field

The invention relates to the field of control methods of servo drive systems, in particular to a feedforward parameter design method based on fuzzy logic.

Background

The servo driving system is widely applied to the fields of numerical control machines, electromechanical integration, automobiles, aerospace, robots and the like. With the trend of mature control theory of servo drive, the requirements of wide speed regulation range, high precision and high reliability are provided in application. Feedback control is based on a control mode that eliminates errors, and theoretically, the positioning error can be converged to zero after enough time. For precise servo applications, the dynamic error during motion should also be limited to a certain range. In order to reduce dynamic error, feedforward of command speed and command acceleration is added in a position loop, and the feedforward gain K is used_v，K_aThe magnitude of the feed forward action is adjusted. Generally, a set of feedforward parameters cannot guarantee optimum performance in the zero to rated speed range, and therefore, the appropriate feedforward parameters are selected according to the operating conditions.

Disclosure of Invention

The invention aims to: the feed-forward parameter design method based on the fuzzy logic is provided, and the problem that the operation performance of a servo drive system is poor due to improper adjustment of the feed-forward parameters is solved.

The technical scheme of the invention is as follows: the feedforward parameter design method based on fuzzy logic comprises the following steps: executing a motion by the initialized servo drive servo system, calculating the maximum dynamic error value of the motion and the absolute difference value of the maximum dynamic error and the average error, fuzzifying the two calculated values as indexes reflecting dynamic precision, and converting the two calculated values into corresponding logics; establishing a rule base based on the corresponding logic, and designing corresponding logic output according to the rule base; adjusting the feed forward gain by deblurring based on the logic output; the maximum dynamic error value is updated with the adjustment of the feedforward gain, and the above process is repeated until the maximum dynamic error value and the absolute difference between the maximum dynamic error and the average error reach the minimum value.

Preferably, the absolute difference between the maximum dynamic error and the average error is the difference between the absolute value of the maximum dynamic error and the absolute value of the average error.

Preferably, the corresponding logic includes thirteen different logic states, and the thirteen different logic states are divided according to the definition domain.

Preferably, each logic output in the rule base corresponds to an adjustment to the feedforward parameter.

Preferably, the thirteen logical states include: positive big, positive big and middle, positive big, positive small and middle, positive small, zero, negative big and middle, negative big and small, negative small and middle, negative small and small.

Preferably, the maximum dynamic error value is updated with the adjustment of the feedforward gain, so that the updated calculation formula of the feedforward gain at the next moment is:

wherein,

for the feed-forward gain at the previous moment, Δ GainK_vFor the feedforward parameter adjustment, K, corresponding to the logic output in the rule base_conIs the amplification factor.

The invention has the advantages that: fuzzy logic can be formulated according to expert experience and experimental guidance, so that the optimization period is shorter, and the adaptability of the algorithm is strong by combining the strong robustness of fuzzy control and the advantage of a mathematical model which is not specific to a controlled object. The feedforward parameter based on fuzzy logic design has short optimization period, can automatically adjust along with the change parameter of the working condition, greatly improves the dynamic precision, and ensures that a servo system meets the requirement of high precision.

Drawings

The invention is further described with reference to the following figures and examples:

FIG. 1 is a schematic diagram of a fuzzy logic based feed forward parameter design method;

Detailed Description

Fuzzy control is a nonlinear control mode which classifies data by adopting logics such as 'large', 'medium', 'small', and the like commonly used in human thinking and converts data operation into logic operation on the basis of not knowing a mathematical model of a controlled object. The method comprises the following four steps: fuzzification, rule base establishment, fuzzy reasoning and fuzzy solution. In the application of the present solution, fuzzy logic is used to design the feedforward gain K_v，K_aThe purpose of keeping the highest dynamic precision under different working conditions is achieved. The basic principle is as follows: the dynamic error of one motion is taken, the maximum dynamic error value can be calculated through data analysis and is recorded as the difference value PUS of the DPE, the maximum dynamic error value and the average error, the DPE and the maximum dynamic error value are used as indexes reflecting dynamic precision, corresponding logic is obtained through fuzzification processing, corresponding logic output is obtained according to a rule base, then feedforward gain is adjusted through defuzzification processing, the DPE and the PUS are updated after adjustment and increase, and the feedforward gain is adjusted again when next adjustment is carried out until the DPE and the PUS are minimum, namely the dynamic error is minimum.

As shown in the attached figure 1, the feedforward parameter design method based on fuzzy logic comprises the following steps: the method comprises the following specific steps:

first, initializing parameters.

Second, an initial value of the feedforward gain is set empirically, a motion is performed, and data of the result of the motion is saved.

Thirdly, data processing is carried out on the motion result, and DPE and PUS are calculated. The DPE is the maximum dynamic error value, namely the data with the maximum absolute value of the error in the motion result data; meanwhile, the average value of errors in the motion result data is obtained and recorded as PEMean; PUS is the difference between the absolute values of DPE and PEmean, i.e., PUS ═ DPE | - | PEmean |, rather than a simple arithmetic difference.

Fourthly, fuzzification processing is carried out on the DPE and the PUS, a rule base and corresponding logic output are established, and the method is embodied as follows:

a. and carrying out size fuzzification treatment on the DPE and the PUS, and converting into one of thirteen logic states of NBB, NBM, NBS, NSB, NSM, NSS, ZO, PSS, PSM, PSB, PBS, PBM and PBB. The fuzzification is to divide the definition domain of the input into thirteen states, and the variables after fuzzification processing are no longer physical quantities but logical states. Fuzzification needs to be carried out according to physical characteristics of input variables, for example, when DPE is fuzzified, a reasonable threshold value fstepDPE is selected, and when fstepDPE < DPE <2fstepDPE, the current logic state is recorded as PSS (positive and small); DPE >20 fstepDPPE, noted PBB (positive Large). The fuzzification process is required to be in accordance with logic rules, and detailed fuzzification is a precondition of high performance, but the complexity of the system is increased, so that the step is required to be simplified as much as possible within the range of performance requirements.

The following table is an fuzzification logic table of the DPE, and the fuzzification logic table of the PUS and the fuzzification logic table of the DPE are the same in division principle, and therefore are not described in detail.

DPE≥16fstepDPE	Is just large
		12fstepDPE≤DPE<16fstepDPE	Zheng Da Zhong
8fstepDPE≤DPE<12fstepDPE	Positive size
		4fstepDPE≤DPE<8fstepDPE	Big and small
2fstepDPE≤DPE<4fstepDPE	Zheng Xiao Zhong
		fstepDPE≤DPE<2fstepDPE	Positive small cell
-fstepDPE≤DPE<fstepDPE	Zero
		-2fstepDPE≤DPE<-fstepDPE	Negative small cell
-4fstepDPE≤DPE<-2fstepDPE	Negative small and middle
		-8fstepDPE≤DPE<-4fstepDPE	Big and small
-12fstepDPE≤DPE<-8fstepDPE	Negative size
		-16fstepDPE≤DPE<-12fstepDPE	Greater negative and middle
DPE≤-16fstepDPE	Great negative

TABLE 1 fuzzy logic table for DPE

b: and inputting the logic states of the DPE and the PUS into a rule base, and reasoning out the logic state for correcting the gain. The rule base is a two-dimensional data table, and deduces how to act, increase or decrease, and whether the amplitude of the action is large or small for each fuzzy subset. Since there are 13 possible states of DPE and PUS, 13 × 13 in total, which are 169 outputs in the two-dimensional table, the 169 outputs may be classified or processed individually. The establishment of the two-dimensional table during the independent processing can be completed according to expert experience or experimental guidance, and the establishment of the two-dimensional table directly influences the dynamic performance. Adjustment K as shown in Table 2_vThe two-dimensional logic table of (a),will adjust the amplitude delta gain_vIs taken as-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5. K_aThe operation process is the same.

TABLE 2 for adjustment K_vTwo-dimensional logic table

c. And performing deblurring operation, and correcting the gain according to the adjustment amplitude. For example: the DPE is negative and the PUS is negative, and K is known by looking up the table_vThe correction amplitude of (1) is 5, K of the feedforward parameter operation period of the next moment_vThe parameters are updated as:

wherein,

for the feed-forward gain at the previous moment, Δ GainK_vFor the feedforward parameter adjustment, K, corresponding to the logic output in the rule base_conIs the amplification factor.

Fifthly, when the dynamic error is minimum in the gain combination state, the correction can be stopped, otherwise, the correction is continued to search for a better gain combination.

Sixthly, if a forced stopping condition is triggered, immediately stopping correction; otherwise, executing one movement again and continuing the design.

The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical concepts disclosed herein be covered by the appended claims.

Claims (6)

1. The feedforward parameter design method based on fuzzy logic is to execute a motion on the servo-driven servo system after initialization, and is characterized in that: calculating the maximum dynamic error value of the motion and the absolute difference value between the maximum dynamic error and the average error, and performing fuzzification processing and converting the two calculated values into corresponding logics by taking the two calculated values as indexes reflecting dynamic precision; establishing a rule base based on the corresponding logic, and designing corresponding logic output according to the rule base; adjusting the feed forward gain by deblurring based on the logic output; the maximum dynamic error value is updated with the adjustment of the feedforward gain, and the above process is repeated until the maximum dynamic error value and the absolute difference between the maximum dynamic error and the average error reach the minimum value.

2. A fuzzy logic based feed forward parameter design method as claimed in claim 1 wherein: the absolute difference between the maximum dynamic error and the average error is the difference between the absolute value of the maximum dynamic error and the absolute value of the average error.

3. A fuzzy logic based feed forward parameter design method according to claim 1 or 2, characterized by: the corresponding logic comprises thirteen different logic states, and the thirteen different logic states are divided according to the definition domain.

4. A fuzzy logic based feed forward parameter design method as claimed in claim 3 wherein: each logic output in the rule base corresponds to an adjustment amount aiming at the feedforward parameter.

5. A fuzzy logic based feed forward parameter design method as claimed in claim 4 wherein: the thirteen logical states include: positive big, positive big and middle, positive big, positive small and middle, positive small, zero, negative big and middle, negative big and small, negative small and middle, negative small and small.

6. A fuzzy logic based feed forward parameter design method according to claim 1 or 5, wherein: the maximum dynamic error value is updated with the adjustment of the feedforward gain, so the updating calculation formula of the feedforward gain at the next moment is:

wherein,

for the feed-forward gain at the previous moment, Δ GainK_vFor the feedforward parameter adjustment, K, corresponding to the logic output in the rule base_conIs the amplification factor.

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