CN109145522B - Feeding system servo optimization method based on scalable dynamic performance evaluation function - Google Patents

Abstract

The invention discloses a feed system servo optimization method based on a scalable dynamic performance evaluation function, which comprises the following steps: establishing a servo simulation model of a feed system based on MATLAB; For the scalable dynamic performance evaluation function optimized for the system servo, the corresponding response characteristic index evaluation function is selected according to some dynamic response performances to be considered comprehensively, and combined with the actual optimization requirements, it is substituted into the scalable dynamic performance evaluation function. The objective evaluation function of servo optimization is obtained; based on the intelligent optimization algorithm, the performance optimization of the feed system is realized through the optimization of servo parameters for the objective evaluation function constructed. By constructing a scalable dynamic performance evaluation function for servo optimization, based on the scalability of the dynamic response characteristic index evaluation function, the real-time adjustment and further integration of the servo optimization target evaluation function can be easily realized, thereby realizing the servo system performance according to need to be optimized.

Description

Feed system servo optimization method based on scalable dynamic performance evaluation function

technical field

The invention relates to a feed system servo optimization method, in particular to a feed system servo optimization method based on a scalable dynamic performance evaluation function.

Background technique

At present, the servo parameter optimization method of the feed system usually aims at improving the dynamic response characteristics of the servo system, and uses the intelligent optimization search algorithm to optimize the servo control parameters. According to different optimization design problems, the selection method of dynamic response characteristic evaluation index function is also different. Commonly used response characteristic evaluation index functions include IAEI, ISE, ITAE, ITSE, ISTAE, ISTS, etc. When selecting the above different response characteristic evaluation indicators as the objective function to optimize the servo control parameters, the closed-loop control effect of the system obtained is different. For example, when optimizing the servo parameter setting according to the ITAE criterion, it pays more attention to the transient response oscillation. After system optimization, the transient response oscillation is smaller, but less attention is paid to the response speed of the system; when the servo parameter setting optimization is performed according to the ISE criterion, The system often has a fast response speed, but its relatively large oscillation and poor stability.

It can be seen that in the existing research on the optimization of the servo parameters of the feed system based on the intelligent optimization search algorithm, the response characteristic evaluation index function is relatively single, and only one aspect of the system can be realized. The control effect optimization; and it is impossible to adjust and further integrate the selected response characteristic evaluation index objective function conveniently in real time according to the system performance optimization requirements, so as to achieve the on-demand optimization of the servo system performance.

Contents of the invention

Purpose of the invention: Aiming at the problem that the existing feed system servo optimization method cannot realize multi-faceted simultaneous optimization of servo system performance and real-time on-demand optimization, the present invention provides a feed system servo optimization method based on a scalable dynamic performance evaluation function .

Technical solution: The feed system servo optimization method based on the scalable dynamic performance evaluation function of the present invention includes the following steps:

(1) Establish the servo simulation model of the feed system based on MATLAB;

(2) Determine the servo parameter optimization variable of the feed drive;

(3) Construct a scalable dynamic performance evaluation function for servo optimization of the feed system, select the corresponding response characteristic index evaluation function according to certain dynamic response performances to be considered comprehensively in the feed system, and combine the actual optimization of the feed system Requirements are substituted into the scalable dynamic performance evaluation function to obtain the target evaluation function of servo optimization;

(4) Based on the intelligent optimization algorithm, the performance optimization of the feed system is realized through the optimization of the servo parameters for the constructed target evaluation function.

By constructing a scalable dynamic performance evaluation function for servo optimization, according to the actual optimization requirements, based on the scalability of the dynamic response characteristic index evaluation function, the real-time adjustment and further integration of the servo optimization target evaluation function can be realized conveniently, so that It is convenient to realize the on-demand optimization of the performance of the servo system.

In the above step (2), the servo parameter optimization variables include position loop gain, speed loop gain, current loop gain and integration time.

In the above step (3), the expression of the scalable dynamic performance evaluation function SOEF for feed system servo optimization is as follows:

In the formula, the feed system performance evaluation index SPI is the response characteristic index evaluation function, n is the number of system performance evaluation index SPI, and w _i is the scalability coefficient of a single performance evaluation index in the scalable dynamic performance evaluation function , the value of w _i is 0 or 1, and f _i is the influence coefficient of the system performance evaluation index SPI _i .

Specifically, when a certain performance evaluation index SPI has no influence on the scalable dynamic performance evaluation function SOEF, w _i =0, that is, the performance evaluation index is not considered in the process of optimizing the response characteristics of the feed system; when a certain When the performance evaluation index SPI has an impact on the scalable dynamic performance evaluation function SOEF, w _i =1, that is, the performance evaluation index is considered in the process of optimizing the response characteristics of the feed system; when w _i =1, through the f _i The size of the value is used to further control the degree of influence of SPI _i on the overall evaluation function. The value of f _i can be any positive number, usually a positive integer can be selected for the convenience of calculation; the larger the value of f _i is, the greater the influence of the performance evaluation index SPI _i on the overall evaluation function is, so when the overall evaluation When function or dynamic performance optimization requires more emphasis on a certain performance evaluation index SPI _i , the corresponding system performance evaluation index influence coefficient f _i can take a larger value.

In step (3), the dynamic response performance of the feed system may include overshoot and transient oscillation response performance, and the corresponding response characteristic index evaluation function is preferably:

In the above formula, φ _s is the set value of displacement, φ _a is the actual value of displacement; a represents the overshoot trade-off coefficient, b represents the transient shock trade-off coefficient, and the values of a and b are arbitrary positive numbers p ₀ , in the form of p ₁ A linear function of t with respect to the time variable t or a quadratic function of the form p ₂ t ² with respect to the time variable t, where p ₁ represents the coefficient of the primary time function, and p ₂ represents the coefficient of the quadratic time function; p ₀ , The values of p ₁ and p ₂ are positive numbers, and usually they can be selected as positive integers for the convenience of calculation.

Among them, the values of the overshoot trade-off coefficient a and the transient oscillation trade-off coefficient b have the following priorities: a positive number p ₀ is preferred, a primary time function of the form p ₁ t is selected second, and a linear time function of the form p ₂ t ² is finally selected Quadratic time function. When the optimization requirements for overshoot and transient oscillation response performance pay more attention to the overshoot phenomenon, the priority of the corresponding overshoot trade-off coefficient a is higher than the transient oscillation trade-off coefficient b, and the values of a and b satisfy p ₀ ＞p ₁ ＞p ₂ ; for example, if the value of a is a positive number p ₀ at this time, the value of b should be a primary time function of the form p ₁ t or a quadratic time function of the form p ₂ t ² , corresponding p ₀ ＞p ₁ or p ₀ ＞p ₂ ; if a takes the value of a primary time function in the shape of p ₁ t at this time, the value of b is a quadratic time function in the shape of p ₂ t ² , and at the same time p ₁ > p ₂ . Conversely, when the optimization requirements for overshoot and transient oscillation response performance pay more attention to the transient oscillation phenomenon, the priority of the corresponding transient oscillation trade-off coefficient b is higher than the overshoot trade-off coefficient a, and the selection of a and b The value satisfies p ₀ >p ₁ >p ₂ . After determining the value types of a and b (positive number, linear function or quadratic function) according to the optimization requirements and value priority, the corresponding values of p ₀ , p ₁ or p ₂ can also be adjusted according to the actual optimization situation, The larger the value of p ₀ , p ₁ or p ₂ is, the larger the value of the corresponding coefficient a or b is, indicating that the overshoot phenomenon corresponding to coefficient a or the transient oscillation phenomenon corresponding to coefficient b under the selected priority The greater the impact of dynamic performance optimization results, and vice versa.

When optimizing the response performance of overshoot and transient oscillation, when the overshoot phenomenon and the optimization requirements of transient oscillation phenomenon have the same or similar importance, the corresponding overshoot trade-off coefficient a and transient oscillation trade-off coefficient b have the same value priority. For example, at this time, a and b can simultaneously take the quadratic time function in the form of p ₂ t ² , or take the primary time function in the form of p ₁ t or the positive number p ₀ at the same time.

Beneficial effect: Compared with the previous feeding system servo optimization method, the beneficial effect of the present invention is: the feeding system servo optimization method of the present invention constructs a scalable dynamic performance evaluation function for servo optimization, according to actual optimization requirements, Based on the scalability of the objective function, the control effect optimization of various aspects of the system can be realized at the same time, and the real-time adjustment and further integration of the objective function of the feed system servo optimization can be realized conveniently, so as to facilitate the realization of the on-demand performance of the servo system optimization.

Description of drawings

Figure 1 is a block diagram of the current loop transfer function;

Figure 2 is the simulation modeling of feed drive system based on MATLAB;

Fig. 3 is the optimal iteration curve of the scalable objective evaluation function SOEF in the servo optimization process in the embodiment;

FIG. 4 is a step response curve corresponding to the optimized servo parameters in the embodiment.

Detailed ways

The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

The feed system servo optimization method based on the scalable dynamic performance evaluation function of the present invention, by constructing the servo optimization scalable target evaluation function, based on the scalability of the response characteristic evaluation index target function, according to the actual optimization requirements, The real-time adjustment and further integration of the servo optimization objective function can be realized conveniently, so as to facilitate the on-demand optimization of the performance of the servo system.

The method specifically includes the following steps:

(1) Establish the servo simulation model of the feed system based on MATLAB;

(2) Determine the servo parameter optimization variable of the feed drive;

(3) Construct a scalable dynamic performance evaluation function for servo optimization of the feed system, select the corresponding response characteristic index evaluation function according to certain dynamic response performances to be considered comprehensively in the feed system, and combine the actual optimization of the feed system Requirements are substituted into the scalable dynamic performance evaluation function to obtain the target evaluation function of servo optimization;

(4) Based on the intelligent optimization algorithm, the performance optimization of the feed system is realized through the optimization of the servo parameters for the constructed target evaluation function.

Taking the feed drive system of a machine tool as an example, the optimization method of the present invention is further described in conjunction with the drawings and embodiments.

Step 1: Establish a servo simulation model of the feed system based on MATLAB

Usually, the servo control method of the feed system often adopts the classic cascade control method, which includes a current loop, a speed loop and a position loop. At present, the transfer function method is often used to establish the servo simulation model of the feed system, because the current loop is included in the speed loop, and the speed loop is included in the position loop. Therefore, when using the transfer function method to model the servo control simulation of the feed system, the current loop is firstly modeled, and on this basis, the speed loop is further modeled, and finally the position loop is modeled. The following modeling process is described according to this step.

The current loop is mainly composed of a current regulator, a servo motor, etc., and its current controller is a PI controller, whose function is to enable the armature winding current of the servo motor to track the current command accurately in real time. The transfer function of the current loop controller can be expressed as K _i (1+1/(T _i s)), where K _i is the current loop gain, and T _i is the current loop integration time. According to the relevant theory of control, the transfer function model of the armature voltage and armature current of the armature circuit of the servo motor can be obtained as

In the formula, e ₀ is the back electromotive force, and its size is K _e ω (K _e is the back electromotive force coefficient, ω is the output speed); i _q is the armature current, u _q is the armature voltage, L _a is the inductance coefficient, and R _a is the resistivity, and s is the Laplacian operator. It can be seen from formula (1) that the servo motor armature circuit can be regarded as a first-order inertia link whose time constant is L _a /R _a .

The torque equation of the servo motor is

M _T = K _T i _q (2)

Where K _T is the torque coefficient, M _T is the output torque.

The current loop is a servo system, and its current adjustment is realized according to the feedback error between the given current and the actual current. The structure diagram of the current loop transfer function can be obtained by synthesizing the transfer functions and equations of each link, as shown in Figure 1, where i _set is the current setting Fixed value, Δi current feedback error.

The speed loop also adopts PI controller, and its controller is expressed as K _p (1+1/(T _n s)) in the form of transfer function, where K _p is the gain of the speed loop, and T _n is the integral time of the speed loop. The speed regulation loop is mainly composed of speed loop controller, current closed loop, mechanical transmission and other links.

The position loop regulator adopts P regulation, expressed as K _v s with the transfer function, K _v is the gain of the position loop.

The simulation model of the feed drive system is built based on MATLAB. The schematic diagram of the model is shown in Figure 2. The modeling of the mechanical transmission link can be modeled by simplifying the mechanical transmission link to an equivalent rotating rigid body, or it can be built by the state space method. In this embodiment, the state space method is used to model it. In Fig. 2, φ _s is the set value of displacement, and φ _a is the actual value of displacement.

According to the servo control system and servo motor model corresponding to the researched machine tool, the technical parameters used in the modeling process are: position loop gain K _v ＝25s ^-1 , speed loop gain K _p ＝27.3N·m·s/rad, speed loop Integral time T _n =60ms, current loop gain K _i =12.157V/A, current loop integral time T _i =2ms, inductance coefficient L _a =3.1mH, resistivity R _a =0.075Ω, back EMF coefficient K _e =1.67 V/(rad/s), torque coefficient K _T =2.72N·m/A.

Step 2: Determine the servo parameter optimization variable of the feed drive;

Usually, cascaded PID control is used in the servo control system to optimize the gain and integral time of the position loop, speed loop and current loop, but because the current loop control parameters can be obtained by the motor manufacturer, and do not need to go through a complicated servo simulation model Therefore, in this embodiment, the three parameters of speed loop gain, integral time, and position loop gain are selected as servo parameter optimization variables for optimization.

Step 3: Construct a scalable dynamic performance evaluation function for servo optimization of the feed system;

Define the response characteristic evaluation index function (such as IAEI, ISE, ITAE mentioned in the background technology) as the system performance evaluation index (System Performance Index, SPI), build the expansion and contraction type target evaluation function ( Scalable Objective Evaluation Function, SOEF) is:

In formula (3), n is the number of system performance evaluation indicators SPI, w _i is the scalability coefficient of a single performance evaluation index in the overall evaluation function, w _i =0 or 1, it can be seen from formula (3) , if the scalability coefficient of w _i is set to 0, the corresponding performance evaluation index SPI will not have any impact on the overall evaluation function SOEF, that is, the performance evaluation index will not be considered in the process of optimizing the response characteristics of the feed system; and if w If _{the i} scalability coefficient is set to 1, the corresponding performance evaluation index SPI will have an impact on the overall evaluation function, that is, the performance evaluation index is considered in the process of optimizing the response characteristics of the feed system. Therefore, the scalability coefficient can be used to select To achieve the scalability selection of the system optimization objective function.

f _i is the influence coefficient of the system performance evaluation index SPI _i . When w _i =1, the degree of influence of SPI _i on the overall evaluation function can be further controlled by the value of f _i . The value of f _i can be selected as any positive number. Usually, it can be selected as a positive integer for the convenience of calculation. The larger the value of f _i is, the greater the influence of the performance evaluation index SPI _i on the overall evaluation function is. Therefore, when the overall evaluation When function or dynamic performance optimization requires more emphasis on a certain performance evaluation index, the corresponding system performance evaluation index influence coefficient f _i can take a larger value.

This example intends to comprehensively consider the dynamic response performance of the feed system, such as response speed, overshoot and transient oscillation. Therefore, the ITSE standard is selected to evaluate the response speed of the system, and it is regarded as SPI ₁ ; in addition, formula (4) is selected as SPI ₂ to evaluate the overshoot and transient oscillation of the system:

In formula (4), φ _s is the set value of displacement, φ _a is the actual value of displacement; a represents the overshoot trade-off coefficient, b represents the transient oscillation trade-off coefficient, and the values of a and b are arbitrary positive numbers p ₀ , in the form A linear function of p ₁ t with respect to the time variable t or a quadratic function of the shape of p ₂ t ² with respect to the time variable t, where p ₁ represents the coefficient of the primary time function, and p ₂ represents the coefficient of the quadratic time function; p The values of ₀ , p ₁ and p ₂ are positive numbers, and usually they can be selected as positive integers for the convenience of calculation.

Among them, the values of the overshoot trade-off coefficient a and the transient oscillation trade-off coefficient b have the following priorities: a positive number p ₀ is preferred, a primary time function of the form p ₁ t is selected second, and a linear time function of the form p ₂ t ² is finally selected Quadratic time function. When the optimization requirements for overshoot and transient oscillation response performance pay more attention to the overshoot phenomenon, the priority of the value of the corresponding overshoot trade-off coefficient a is higher than that of the transient oscillation trade-off coefficient b, and the values of a and b satisfy p ₀ > p ₁ ＞p ₂ ; for example, if the value of a is a positive number p ₀ at this time, the value of b should be a primary time function shaped like p ₁ t or a quadratic time function shaped like p ₂ t ² , correspondingly,

p ₀ ＞p ₁ or p ₀ ＞p ₂ ; if a takes the value of a primary time function in the shape of p ₁ t at this time, the value of b is a quadratic time function in the shape of p ₂ t ² , and at the same time p ₁ > _p2 . Conversely, when the optimization requirements for overshoot and transient oscillation response performance pay more attention to the transient oscillation phenomenon, the priority of the corresponding transient oscillation trade-off coefficient b is higher than the overshoot trade-off coefficient a, and the selection of a and b The value satisfies p ₀ >p ₁ >p ₂ . After determining the value types of a and b (positive number, linear function or quadratic function) according to the optimization requirements and value priority, the corresponding values of p ₀ , p ₁ or p ₂ can also be adjusted according to the actual optimization situation, The larger the value of p ₀ , p ₁ or p ₂ is, the larger the value of the corresponding coefficient a or b is, indicating that the overshoot phenomenon corresponding to coefficient a or the transient oscillation phenomenon corresponding to coefficient b under the selected priority The greater the impact of dynamic performance optimization results, and vice versa.

When optimizing the response performance of overshoot and transient oscillation, when the overshoot phenomenon and the optimization requirements of transient oscillation phenomenon have the same or similar importance, the corresponding overshoot trade-off coefficient a and transient oscillation trade-off coefficient b have the same value priority. For example, at this time, a and b can simultaneously take the quadratic time function in the form of p ₂ t ² , or take the primary time function in the form of p ₁ t or the positive number p ₀ at the same time.

According to the actual optimization requirements corresponding to the feed system studied in this example, that is, the dynamic response performance of the feed system, such as the response speed, overshoot, and transient oscillations mentioned above, is to be considered comprehensively, and w ₁ = w ₂ = 1 (where Indicates that the system response speed, overshoot and transient oscillation are considered at the same time), and the dynamic performance optimization places more emphasis on the system response speed, so f ₁ = 100, f ₂ = 1 (f ₁ takes a larger value than f ₂ , indicating When optimizing dynamic performance, more emphasis is placed on the response speed of the system). When constructing the overshoot and transient oscillation of the SPI ₂ evaluation system, because this example pays more attention to the overshoot phenomenon, it is preferred to select the overshoot trade-off coefficient a as a positive integer p ₀ , and select the transient oscillation trade-off coefficient b to be in the form of p ₂ The quadratic function of t ² , in this example select p ₀ =100, p ₂ =25, so the obtained SPI ₂ =∫[100e _o +25t ² e _s ]dt,

Substitute SPI ₁ and SPI ₂ into formula (3) to get the objective evaluation function of servo optimization:

Step 4: Based on the intelligent optimization algorithm, the performance optimization of the feed system is realized through the optimization of the servo parameters for the constructed target evaluation function.

Based on the intelligent optimization algorithm, the position loop gain K _v , the speed loop gain K _p , and the speed loop integral time T _n are selected as optimization variables, and formula (5) is used as the optimization objective function to comprehensively evaluate the response speed, overshoot and transient oscillation of the system , for the optimization of servo control parameters, the iterative process of the evolution of the objective function is shown in Figure 3, and finally the optimal parameters of the servo controller are K _v =38s ^-1 , K _p =24Nm·s/rad, T _n =19ms, and the step response before and after optimization The curve is shown in Figure 4. It can be seen from the figure that the optimized servo control parameters are adopted, the dynamic response performance of the overall servo control system is good, the response speed of the system is fast (rise time 0.038s) and there is no overshoot, and the adjustment time is 0.092s , the adjustment time is short and the stability of the system is good, that is, there is no long-term shock. These are consistent with preset dynamic performance optimization requirements.

On the basis of the optimization of this embodiment, if it is intended to further increase the energy consumption in the system response process as the evaluation index SPI ₃ , then formula (3) can be used to define w ₁ =w ₂ =w ₃ =1, and according to the actual It is required to control the degree of influence of SPI ₃ on the overall dynamic response evaluation function SOEF through f ₃ , and substitute it into formula (3), so that the selected response characteristic evaluation index objective function can be conveniently adjusted in real time and further integrated to achieve servo system On-demand optimization of performance.

It can be seen that the dynamic performance after optimization using the method of the present invention is significantly improved compared with that before optimization. The optimization process is a process of continuous improvement. In actual operation, the value of each parameter in the evaluation function can be further adjusted according to the feedback of the optimization results, so as to obtain better or even optimal dynamic performance optimization results. For example, according to the f _i value rule mentioned above (the larger the value of f _i is, the greater the influence of the performance evaluation index SPI _i on the overall evaluation function is, when the overall evaluation function or dynamic performance optimization requires more attention to a certain performance evaluation index When the value of p 0 , p 1 , p 2 is greater, the corresponding system performance evaluation index influence coefficient f _i can take a larger value), and the value rules of p ₀ , p ₁ , p ₂ (the larger the value of p ₀ , p ₁ , p ₂ is, the corresponding coefficient a or The larger b is, it indicates that under the selected priority, the overshoot phenomenon corresponding to coefficient a, or the transient oscillation phenomenon corresponding to coefficient b has a greater impact on the dynamic performance optimization results, and vice versa), readjust p ₀ , p ₁ , p ₂ , and f _i can be optimized.

For example: if you want to further improve the response speed of the system in this embodiment, you can further increase the value of _f1 ; if you feel that the response speed and overshoot have met the optimization requirements, but you want to further control the transient oscillation phenomenon, you can further increase _p2 value.

Claims (5)

1. based on the feed system servo optimization method of scalable dynamic performance evaluation function, it is characterized in that, comprising the steps: (1) Establish the servo simulation model of the feed system based on MATLAB; (2) Determine the servo parameter optimization variable of the feed drive; (3) Construct a scalable dynamic performance evaluation function for servo optimization of the feed system, select the corresponding response characteristic index evaluation function according to certain dynamic response performances to be considered comprehensively in the feed system, and combine the actual optimization of the feed system Requirements are substituted into the scalable dynamic performance evaluation function to obtain the target evaluation function of servo optimization; (4) Based on the intelligent optimization algorithm, the performance optimization of the feed system is realized through the optimization of the servo parameters for the constructed target evaluation function; In step (3), the expression of the scalable dynamic performance evaluation function SOEF of described feeding system servo optimization is as follows:

In the above formula, the feed system performance evaluation index SPI is the response characteristic index evaluation function, n is the number of system performance evaluation index SPI; w _i is the scalability of a single performance evaluation index in the scalable dynamic performance evaluation function Coefficient, the value of w _i is 0 or 1; f _i is the influence coefficient of system performance evaluation index SPI _i ; The dynamic response performance of the feed system includes overshoot and transient oscillation response performance, and its corresponding response characteristic index evaluation function is:

In the above formula, φ _s is the set value of displacement, φ _a is the actual value of displacement; a represents the overshoot trade-off coefficient, b represents the transient shock trade-off coefficient, and the values of a and b are arbitrary positive numbers p ₀ , in the form of p ₁ A linear function of t with respect to the time variable t or a quadratic function of the form p ₂ t ² with respect to the time variable t, where p ₁ represents the coefficient of the primary time function, p ₂ represents the coefficient of the quadratic time function, p ₀ , Both p ₁ and p ₂ are positive numbers. 2. the feed system servo optimization method based on scalable dynamic performance evaluation function according to claim 1, is characterized in that, in step (2), described servo parameter optimization variable comprises position loop gain, velocity loop gain , current loop gain and integration time. 3. the feed system servo optimization method based on the scalable dynamic performance evaluation function according to claim 1, wherein when a certain performance evaluation index SPI does not have an impact on the scalable dynamic performance evaluation function SOEF , w _i =0, that is, the performance evaluation index is not considered in the process of optimizing the response characteristics of the feed system; when a certain performance evaluation index SPI has an impact on the scalable dynamic performance evaluation function SOEF, w _i =1, That is, the performance evaluation index is considered in the optimization process of the feed system response characteristics; when w _{i =1, the influence degree of SPI i on} the overall evaluation function is further controlled by the value of fi. 4. The feed system servo optimization method based on scalable dynamic performance evaluation function according to claim 1, wherein the values of overshoot trade-off coefficient a and transient shock trade-off coefficient b have the following priorities: The positive number p ₀ is preferred, followed by the primary time function with the shape of p ₁ t, and finally the quadratic time function with the shape of p ₂ t ² ; when optimizing the performance of overshoot and transient oscillation response, more attention should be paid to overshoot phenomenon, the priority of the value of the corresponding overshoot trade-off coefficient a is higher than that of the transient shock trade-off coefficient b, and the values of a and b satisfy p ₀ >p ₁ >p ₂ ; otherwise, when the overshoot and transient When the optimization requirement of oscillation response performance pays more attention to the phenomenon of transient oscillation, the priority of the value of the corresponding transient oscillation trade-off coefficient b is higher than that of the overshoot trade-off coefficient a, and the values of a and b satisfy p ₀ ＞p ₁ ＞p ₂ . 5. the feed system servo optimization method based on scalable dynamic performance evaluation function according to claim 4, is characterized in that, when overshoot and transient oscillation response performance are optimized, overshoot phenomenon and transient oscillation When the importance of optimization requirements of phenomena is the same or similar, the values of the corresponding overshoot trade-off coefficient a and transient oscillation trade-off coefficient b have the same priority.

Images