Surface processing position control method, control system and electronic equipment

Abstract

The present invention discloses a surface processing position control method, control system and electronic equipment, the method comprising: obtaining an input height reference value, a previous predicted output value and a current system state parameter; obtaining the total control input required for the desired position height according to the current system state parameter, the height reference value and the previous output predicted value; obtaining a system input value according to the total control input, wherein the system input value is an input value of a controlled system; obtaining a system predicted output value for maintaining the desired position height according to the system input value and the current system state parameter; adjusting the system input value according to the system predicted output value, and driving the controlled system according to the system input value. When processing some irregular surfaces, the present invention can stably control the controlled system at the desired position height to adapt to the complex and changeable surface processing of the workpiece to be processed.

Description

Technical Field

[0001] The present invention relates to the technical field of control engineering, and in particular to a surface processing position control method, a control system and an electronic device. Background Art

[0002] When processing irregular surface shapes of workpieces, such as glass cutting, metal surface sandblasting, polishing and other processes, PLC control that relies on control model to set parameters cannot process the workpieces with programmed surface shapes. In addition, this kind of time-varying trajectory industrial process is difficult to achieve with a PID parameter or even a fuzzy PID control with an expert fuzzy rule table.

[0003] In this regard, the existing scheme uses optomechanical integration technology to fix the sensor on the actuator to detect the height through the sensor feedback value, and convert the position physical model into analog or digital quantity in the system for calculation. However, due to the excessive reliance on the processing model to set the PID parameters of traditional PID control, if the control model changes beyond the tolerance range of the PID setting parameters, PID control cannot achieve a stable production line, and in severe cases, it will also cause quality risks such as over-processing and incomplete processing. In addition, the vibration of the machine during processing in the assembly line will directly affect the feedback value of the sensor, causing the system to misjudge and shut down. Although the fuzzy PID control in the control of some large lag systems used for temperature control and drone flight altitude control has strong anti-interference ability, and the expert two-dimensional fuzzy rule table in the system can also adjust very good PID control parameters, when facing high-speed surface technology processing, the error caused by system lag will even be much larger than the overshoot of traditional PID control.

[0004] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a surface processing position control method, control system and electronic device to solve the problem that the parameters in the traditional PID control method are overly dependent on the control model and cause system misjudgment and crash or random fluctuation error accumulation when subject to external disturbances.

[0006] The technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention provides a surface processing position control method, which comprises:

[0008] Obtain input altitude reference values, previously predicted output values, and current system state parameters;

[0009] Obtaining a total control input required for the desired position height according to the current system state parameter, the height reference value and the previous output prediction value;

[0010] Obtaining a system input value according to the total control input, wherein the system input value is an input value of a controlled system;

[0011] Obtaining a system predicted output value for maintaining a desired position height according to the system input value and the current system state parameter;

[0012] The system input value is adjusted according to the system predicted output value, and the controlled system is driven according to the system input value.

[0013] According to a further configuration of the present invention, the step of obtaining the current system state parameters comprises:

[0014] Get the real-time position measured by the sensor;

[0015] The real-time position measured by the sensor is optimized into the mean value of the variance of continuous samples by using the mean difference algorithm, and the mean value of the variance is used as the current system state parameter;

[0016] A current steady-state response is obtained according to the current system state parameters.

[0017] According to a further configuration of the present invention, the current steady-state response is positively correlated with a sensor feedback value variable caused by a running stroke within a unit response time of motion planning during equipment operation.

[0018] In a further configuration of the present invention, the step of obtaining the input altitude reference value comprises:

[0019] Get the expected position height;

[0020] The expected position height is converted into an analog quantity or a digital quantity of an equivalent sensor to obtain a height reference value.

[0021] In a further arrangement of the present invention, the step of obtaining the total control input required for the desired position height according to the current system state parameter, the height reference value and the previous output prediction value comprises:

[0022] Obtaining a deviation value according to the altitude reference value and the current steady-state response;

[0023] After the deviation value is fuzzified, the corresponding membership of the deviation value is obtained according to the fuzzy control rule, and a fuzzy decision is made according to the membership and the fuzzy rule;

[0024] Defuzzifying the fuzzy decision to obtain the total control input required for the desired position height;

[0025] The initial value of the previous output prediction value is the steady-state response of the system under a unit pulse, and the steady-state response of the system under the unit pulse offsets the vibration of the system in the steady-state range.

[0026] In a further configuration of the present invention, the step of obtaining a system input value according to the total control input comprises:

[0027] The total control input is planned as a smooth motion curve according to the control cycle of the motion controller through the look-ahead algorithm and point motion control, wherein the input of the next cycle is the system input value.

[0028] In a further configuration of the present invention, the step of adjusting the system input value according to the system predicted output value comprises:

[0029] Obtaining a total control input required for a desired position height according to the current system state parameter, the height reference value and the system predicted output value;

[0030] A system input value is obtained according to the total control input amount.

[0031] The present invention is further configured such that, in the step of obtaining the membership of the corresponding deviation value according to the fuzzy control rule after fuzzifying the deviation value, and making a fuzzy rule decision according to the membership, the fuzzy rule table adopts a one-dimensional fuzzy rule table.

[0032] In a second aspect, the present invention provides a control system based on the surface processing position control method described above, which comprises: an adaptive controller, a fuzzy controller, a motion controller and a predictive controller; wherein:

[0033] The adaptive controller is connected to the fuzzy controller and is used to obtain current system state parameters and input the current system state parameters into the fuzzy controller;

[0034] The fuzzy controller is connected to the motion controller and is used to obtain the total control input required for the desired position height according to the input height reference value, the previous prediction value and the current system state parameter and input it into the motion controller;

[0035] The motion controller is used to obtain a system input value according to the total control input, and is also used to adjust the system input value according to the system predicted output value, and drive the controlled system according to the system input value;

[0036] The prediction controller is used to obtain a system prediction output value for maintaining a desired position height according to the system input value and the current system state parameter.

[0037] In a third aspect, the present invention provides an electronic device comprising the surface processing position control system as described above.

[0038] The surface processing position control method, control system and electronic equipment provided by the present invention have the following beneficial effects:

[0039] The present invention combines adaptive control and fuzzy control to obtain the motion variables that need to be controlled in the next cycle. There is no need to set up a PID controller separately, which avoids excessive dependence on the control model and reduces the situation where the system crashes due to misjudgment when subjected to external disturbances. The present invention adopts a predictive control method to obtain the system predicted output value that maintains the desired position height, and uses the system predicted output value as the gain of the system input value, so that the controlled system can return to a steady state more quickly, thereby avoiding the problem of random fluctuation error accumulation when the controlled system is subjected to external disturbances. In this way, when processing some irregular surfaces, the controlled system can be stably controlled at the desired position height to adapt to the complex and changeable surface processing of the workpiece to be processed. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary personnel in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0041] Figure 1 It is a principle block diagram of the surface processing position control system in the present invention.

[0042] Figure 2 It is a flow chart of the surface processing position control method in the present invention.

[0043] Figure 3 It is a schematic diagram of the spring damping physical model of the adaptive controller in the present invention.

[0044] Figure 4 It is the principle block diagram of the fuzzy controller in the present invention.

[0045] Figure 5 It is a principle diagram of the triangular membership function in the present invention.

[0046] Figure 6 It is a curve diagram of the system following condition under random external disturbance in one embodiment of the present invention.

[0047] Figure 7 It is a simulation diagram of distance maintenance stability under slight vibration interference in one embodiment of the present invention.

[0048] Figure 8 It is a comparison diagram of actual machine operation sampling diagrams of distance maintenance stability under slight vibration interference in one embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention provides a surface processing position control method, control system and electronic device. In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] In the embodiments and the scope of the patent application, unless the text specifically defines the article, "a", "an", "the" and "the" may also include plural forms. If there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0051] It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when an element is said to be "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or there can also be intermediate elements. In addition, the "connection" or "coupling" used herein can include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0052] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as herein.

[0053] In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0054] It should be understood that when processing irregular surface workpieces, traditional PID (proportional-integral-differential) control is overly dependent on the processing model to set the PID parameters, and cannot effectively control the control model that controls unknown changes and disturbances. If the expert two-dimensional fuzzy rule table is used to adjust the PID control parameters, although better PID parameters can be obtained, when facing high-speed surface technology processing, the error caused by system lag may even be much larger than the overshoot of traditional PID control. Therefore, traditional PID control has the problem of over-reliance on the control model and control lag reaching a time-varying trajectory process.

[0055] In view of the problems existing in the traditional PID control when processing irregular surface workpieces, the present invention provides a surface processing position control method, control system and electronic equipment, which combines the control methods of forward adaptive control, fuzzy control and predictive control to realize the processing of the above-mentioned irregular surface workpieces, and does not need to set up a separate PID controller, avoiding the transition dependence on the control model, reducing the situation of system downtime due to misjudgment when subjected to external disturbances, and adopts the predictive control method to obtain the system predicted output value of the desired position height, and uses the system predicted output value as the gain of the system input value, so that the controlled system can return to the steady state faster, thereby avoiding the problem of random fluctuation error accumulation when the controlled system is subjected to external disturbances. In this way, when processing some irregular surfaces, the controlled system can be stably controlled at the desired position height to adapt to the complex and changeable surface processing of the workpiece to be processed, and solve the problem of industrial process of time-varying trajectory with excessive dependence on the control model and large control lag.

[0056] Please also see Figures 1 to 8 , the present invention provides a preferred embodiment of a surface processing position control method.

[0057] like Figure 1 As shown, the present invention provides a surface processing position control method, which is applied to a surface processing position control system. The surface processing position control system includes an adaptive controller, a fuzzy controller, a motion controller and a predictive controller. The adaptive controller is connected to the fuzzy controller and the predictive controller respectively. The fuzzy controller is also connected to the motion controller and the predictive controller respectively. The motion controller is connected to the predictive controller and, as the output end of the system, is connected to the controlled system.

[0058] In some embodiments, Figure 2 As shown, the surface processing position control method includes the steps of:

[0059] S100, obtaining an input altitude reference value, a previously predicted output value, and a current system state parameter;

[0060] Specifically, as a forward-looking control part, the adaptive controller can calculate the current system state parameters by collecting the real-time position of the controlled system measured by the sensor. The height reference value Fs is a reference value input by the user according to the actual situation, and the height reference value is input into the fuzzy controller. The previous predicted output value is the initial predicted value, which is the steady-state response of the system under the unit pulse. The steady-state response of the system under the unit pulse offsets the vibration of the system in the steady-state range.

[0061] S200, obtaining a total control input required for a desired position height according to the current system state parameter, the height reference value and the previous output prediction value;

[0062] Specifically, after receiving the current system state parameters input by the adaptive controller and the height reference value Fs input by the staff, the fuzzy control can calculate the total control input required for the control system to return to the desired position height in combination with the previously predicted output value.

[0063] S300, obtaining a system input value according to the total control input, wherein the system input value is an input value of a controlled system;

[0064] Specifically, because fuzzy control generates high-speed jittering parameters when facing frequent changes in the external environment, the macroscopic perception is that the actuator of the controlled system twitches, shakes, and cannot maintain stability. Based on this, this embodiment reduces the frequency of use of the sensor by the control system. For example, the control system can adjust this parameter every three cycles, and use an algorithm for interpolation of forward-looking control (this forward-looking algorithm is already a relatively mature technology and will not be described in detail here), and combines the point control of the motion controller to plan the total control input into a smooth motion curve according to the control cycle of the motion controller to achieve a smooth speed transition, wherein, in the smooth motion curve, the input of the next cycle is the system input value.

[0065] S400, obtaining a system predicted output value for maintaining a desired position height according to the system input value and the current system state parameter;

[0066] Specifically, while the motion controller outputs the system input value to the controlled system, the system input value is also input to the prediction controller. At the same time, the prediction controller also receives the current system state parameters output by the adaptive controller, and calculates the system predicted output value required to maintain the expected position height by combining the system input parameters with the current system state parameters, and inputs the value into the fuzzy controller.

[0067] S500, adjusting the system input value according to the system predicted output value, and driving the controlled system according to the system input value.

[0068] Specifically, because fuzzy control will cause the arrival time to be extended, the control system needs to spend multiple control cycles to adjust the controlled system to the desired position to maintain the final height. However, when facing the complex and changeable surface processing of the workpiece to be processed, multiple step-change nodes will cause the accumulation of control delay cycles, and occasional height follow-up is prone to occur during the process, which will result in discontinuous processing. For this, the system input value of the feedback is predicted by the predictive controller, and the system predicted output value is used as the gain of the system input. When the fuzzy controller calculates the system input value, the system input value can be adjusted in combination with the system predicted output value, so that the controlled system can return to a steady state faster.

[0069] In the above technical scheme, the present invention combines adaptive control and fuzzy control to obtain the motion variables to be controlled in the next cycle. It is not necessary to set up a PID controller separately, avoiding the transition dependence control model, reducing the situation of system downtime due to misjudgment when subjected to external disturbances, and having strong anti-interference ability. For example, when subjected to large external disturbances (such as machine vibration noise), the high-frequency disturbance will be greatly reduced, and only the change amount generated by the low-frequency industrial time-varying trajectory will be input to the system. The intuitive effect is that the actuator will not oscillate due to the vibration of the machine. And the predictive control method is adopted to obtain the system predicted output value that maintains the expected position height, and the system predicted output value is used as the gain of the system input value. The historical data of its own variables can be used to predict itself, so that the controlled system can return to a steady state faster, thereby avoiding the problem of random fluctuation error accumulation when the controlled system is subjected to external disturbances. For example, when fuzzy control is introduced into the predictive controller in fuzzy predictive PID control, random fluctuation error accumulation will be generated when the predictive controller is subjected to external disturbances. In this way, when processing some irregular surfaces, the controlled system can be stably controlled at the desired position height to adapt to the complex and changeable surface processing of the workpiece to be processed, such as high-speed irregular surface processing.

[0070] In some embodiments, the step of obtaining the current system status parameters includes:

[0071] S110, obtaining a real-time position measured by a sensor;

[0072] S120, optimizing the real-time position measured by the sensor into a variance mean of continuous samples using a mean difference algorithm, and using the variance mean as the current system state parameter;

[0073] S130. Obtain a current steady-state response according to the current system state parameters.

[0074] Specifically, after the sensor detects the real-time position of the controlled system, the adaptive controller reads the real-time position measured by the sensor, reduces the impact of noise and disturbance on the true value through the mean difference method (i.e., weighted variance error processing method), and optimizes the real-time position measured by the sensor to the variance mean of continuous samples, i.e., the current system state parameter.

[0075] The adaptive controller can calculate the current system state parameter q based on the mean square error optimization of the collected samples according to the following formula: RMSE :

[0076]

[0077] Among them, q i is the average value of the samples in this section, B m is the weight of the sample, q(x) is the single sample value, and m is the number of samples. The weight calculation method can be selected according to the model used. The weight calculation method is the variance expectation weighted method, that is, the farther away from the positive and negative variance lines, the lower the weight.

[0078] The weight of this sample is B m The expression is:

[0079]

[0080] Among them, varience is the mean square error of the samples in this section. The purpose of processing the parameters in this way is to reduce the impact of excessive mutation interference on the system parameters, while increasing the small signal and making the small signal closer to the detected sample average value, which can reduce the gain value of the subsequent algorithm, make the subsequent algorithm parameters more uniform, and the control system will be more stable.

[0081] The control effect of the adaptive controller can be seen as Figure 3 The spring damping physical model: In actual work, there will be differences and unknown small disturbances between the system state Y(x) and the environmental variable h(x). The sensor can feed back the mixed signal to the adaptive controller system through the spring structure of the system. However, due to the effect of the damping structure, the instantaneous disturbance will obtain a loss. Only the variable with continuous single change will be output to the next link after multiple cycles. The specific manifestation of this variable is that the trend of multiple consecutive samples is consistent. The result is the steady-state response of the system in the current state, that is, the current steady-state response. The current steady-state response is positively correlated with the sensor feedback value variable caused by the running stroke within the unit response time of the motion planning when the equipment is running. The expression of the current steady-state response is:

[0082]

[0083] Among them, q(x) is the sensor feedback value variable (i.e., single sample value) caused by the running stroke within the unit response time of the motion planning when the equipment is running, is the time first derivative of the variable. k1 and k2 are the stiffness coefficient and damping coefficient of the system response, respectively, which are calculated based on the developer's expectations of the device response.

[0084] In some embodiments, the step of obtaining an input altitude reference value includes:

[0085] S140, obtaining the expected position height;

[0086] S150: Convert the expected position height into an analog value or a digital value of an equivalent sensor to obtain a height reference value.

[0087] Specifically, after the expected position height to be controlled is input, the expected position height is converted into an analog or digital value of an equivalent sensor according to calculation or test, and the analog or digital value is used as the height reference value Fs of the fuzzy controller.

[0088] In some embodiments, please combine Figure 4 , the step of obtaining the total control input required for the desired position height according to the current system state parameter, the height reference value and the previous output prediction value comprises:

[0089] S210, obtaining a deviation value according to the altitude reference value and the current steady-state response;

[0090] S220, after fuzzifying the deviation value, obtain the corresponding membership of the deviation value according to the fuzzy rule, and make a fuzzy decision according to the membership and the fuzzy rule;

[0091] S230, performing inverse fuzzification processing on the fuzzy decision to obtain the total control input required for the desired position height.

[0092] Specifically, the current steady-state response y(t) calculated by the adaptive controller will be input into the fuzzy controller and calculated with the input height reference value Fs to obtain the deviation value. In this embodiment, the controlled system model of the application scenario has a first-order linear relationship with the environment, so the deviation value e(t) can be expressed as:

[0093] e(t)=F s -y(t).

[0094] After calculating the deviation value, a membership function is formulated to achieve fuzzification of the control quantity. The membership function can be expressed as a triangular membership function, such as Figure 5 As shown. Figure 5In the figure, the upper horizontal coordinates NB, NM, NS, Z, PS, PM, and PB are respectively large negative deviation changes, moderate negative deviation changes, small negative deviation changes, stable deviation changes, small positive deviation changes, moderate positive deviation changes, and large positive deviation changes. The left vertical coordinate is the membership degree, ranging from 0 to 1. The lower horizontal coordinate is the reference of the deviation value e(t), indicating that the maximum control range of fuzzy control is -5mm to 5mm. If the range exceeds this range, the actuator is directly controlled to follow the maximum control amount. Only within this range will the P parameter be adjusted. The control model itself is a first-order model, but in the process of the control model changing from large deviation to small deviation, deceleration movement is required, and in the process of changing from small deviation to large deviation, acceleration and deceleration movement is required. Therefore, in the formulation of the membership function, a cross membership function should be set.

[0095] The fuzzy controller calculates the membership of the corresponding deviation value according to the input deviation value and the conditions in the fuzzy rule. The membership can be expressed as e by the first-order determinant of 7x1. -T .

[0096] It should be understood that the sensor senses the external changes and converts the physical quantity of the change into a digital quantity or an analog quantity and inputs it into the adaptive controller, but this input signal is a complex signal mixed with noise, interference, and real signals. If this signal value is directly input into the fuzzy controller for calculation, the error will cause the membership calculation error and ultimately directly lead to unsatisfactory output of the motion control parameters. The existing technology often uses the setting of thresholds to process this change value in stages, but this method responds very slowly in the case of low-speed motion, and the time-varying trajectory following delay is large. In addition, because fuzzy control relies on the fuzzy rule table set by expert experience, the control parameters are adjusted by scanning the table. The PID parameters need to scan three tables to finally adjust the control parameters. When facing complex working conditions, the system response cannot achieve real-time control. In the control object in this embodiment, the relationship between the control object model and the current system state parameters can be expressed by a first-order linear equation, so a one-dimensional fuzzy rule table is used to calculate the current system state parameters. In addition, fuzzy rule control can be implemented based on a one-dimensional fuzzy rule table, and fuzzy decisions can be made according to the corresponding fuzzy rules. Among them, the fuzzy rule table can be represented by a matrix as R T , the expression of membership is:

[0097] Among them, qv represents the degree of membership.

[0098] In some embodiments, the one-dimensional fuzzy rule table is shown in the following table:

[0099] E u PB -3 PM -2 PS -1 Z f(qv) NS 1 NM 2 NB 3

[0100] It should be noted that, because in the position height control industrial model, the feedback value of the sensor and the displacement are in a first-order relationship, a one-dimensional fuzzy rule table can be used. If the present invention is applied to a nonlinear model, such as force control in a cutting process, joint force control in robot motion, etc., a two-dimensional fuzzy rule table can be used.

[0101] After the fuzzy decision is obtained, the fuzzy decision is defuzzified to obtain the total control input U(t), and the total control input is input into the motion controller, which calculates the system input value u(t) according to the point motion function and inputs it into the controlled system. The expression of the system input value u(t) is:

[0102] u(t)=∑(qv×u);

[0103] The matrix expression of the system input value u(t) is:

[0104] u(t)=e·R T .

[0105] In some embodiments, the step of adjusting the system input value according to the system predicted output value comprises:

[0106] S510, obtaining a total control input required for a desired position height according to the current system state parameter, the height reference value and the system predicted output value;

[0107] S520: Obtain a system input value according to the total control input amount.

[0108] Specifically, the system input value u(t) output by the motion controller is input into the prediction controller to obtain the system prediction output value of the next controller cycle The expression of the current steady-state response y(t) is:

[0109]

[0110] Among them, a i and b i are the weight coefficients of the system input value and the current system state parameter respectively. d is the sampling period included in the system delay. By integrating y(t) and u(t), we can get:

[0111] A(q -1 )y(t)=q -d B(q -1 )u(t)=q -d-1 B *-1 (q -1 )u(t);

[0112] in,

[0113]

[0114]

[0115]

[0116]

[0117] In some embodiments, when an autoregressive moving average model (ARMAX) is used, an external disturbance n(t) should be introduced under external disturbance, and the expression of the current steady-state response is as follows:

[0118]

[0119] in,

[0120]

[0121]

[0122] Therefore, the system predicts the output value The calculation formula is:

[0123] The system prediction value can be used as a gain for the system input value u(t), so that the control system can return to a steady state more quickly.

[0124] It should be noted that, in some embodiments, the present invention can also use a single-input single-output SISO model. Unlike the ARMAX model, the SISO model does not need to input current and previous system measurements into the predictive controller, which can make system programming simpler.

[0125] In addition, in the ARMAX system, the martingale theory can also be used to implement the parameter adaptation algorithm in an unknown environment. The adaptive error variance equation can be regarded as a martingale difference sequence defined in the probability space. The expression of the current steady-state response is:

[0126]

[0127] Among them, ω(t+1) is an adaptive error equation. For example, white noise also meets the use conditions of this difference equation.

[0128] Then, according to the system prediction output value in the ARMAX system model The expression of the prediction error v(t+1) can be obtained:

[0129]

[0130] In order to verify the technical effect of the present invention, the present invention is described with a specific application embodiment.

[0131] The following rate was tested on a machine equipped with an infrared sensor, and the cardboard that moved freely and irregularly up and down, forward and backward was used to affect the sensor, simulating a completely unknown external environment in an extreme environment. The control algorithm can still ensure the reliability of the planning parameters. Finally, it was stopped suddenly to test whether it can converge quickly, which proves the advantages of the algorithm: 1. It can ensure fast following response in the face of unknown and rapidly changing external environment; 2. It will not cause overshoot in the face of sudden jump interference; 3. It converges quickly and stably; 4. Compared with traditional PID control, this control algorithm is not affected by the control model.

[0132] like Figure 6 As shown, Figure 6 This is a curve diagram of the system following the random external disturbance. The surface of the sensed external target object makes irregular up and down, forward and backward tilt movements, so the green curve (sensor feedback value) is an irregular curve. The analog value corresponding to the height to be maintained in this embodiment is 11140, that is, the actuator needs to adjust the height by moving up and down so that the sensor feedback value remains around 11140.

[0133] exist Figure 6 In the figure, for the red and blue curves, the left ordinate is the number of motor pulses. The equipment parameters used in this embodiment are as follows: motor parameters: 1r / 10000pulse, lead screw: 75mm / r, that is, the actuator moves 3mm for every 400 pulses. For the green curve, the value on the left is the analog value, and the height corresponding to the analog value of 11140 is 5mm, that is, the analog value changes by 2228 for every 1mm change in distance.

[0134] from Figure 6 It can be seen from the results that the actuator moves within the range of 10mm up and down (the encoder pulse range is 6030-7060), and the height is always maintained at 4.736mm-5.281mm (the analog range is 10551-11764). The following rate is measured by detecting the phase difference between the analog peak and the motor pulse number curve trough. The measured following time is about 44-53ms.

[0135] Figure 7 and Figure 8 Comparison of simulation and actual machine operation sampling diagrams for distance stability under slight vibration interference.

[0136] In the simulation diagram, red is the analog change, and blue is the number of pulses sent in the cycle. The response time for the distance to be in place in the simulation is 26 control cycles (usually one control cycle is 1ms), and then slight vibration will not cause the system response to cause actuator jitter. Figure 8 The actual machine operation sampling diagram shows that after the external environment stops changing, the actuator adjusts to its position after 32ms.

[0137] In some embodiments, the present invention also provides a surface processing position control system, which includes: an adaptive controller, a fuzzy controller, a motion controller and a predictive controller; wherein the adaptive controller is connected to the fuzzy controller, and is used to obtain the current system state parameters, and input the current system state parameters into the fuzzy controller; the fuzzy controller is connected to the motion controller, and is used to obtain the total control input required for the desired position height according to the input height reference value, the previous predicted value and the current system state parameters, and input it into the motion controller; the motion controller is used to obtain the system input value according to the total control input, and is also used to adjust the system input value according to the system predicted output value, and drive the controlled system according to the system input value; the predictive controller is used to obtain the system predicted output value for maintaining the desired position height according to the system input value and the current system state parameters. The details are as described in the above-mentioned embodiment of a surface processing position control method, and will not be repeated here.

[0138] In some embodiments, the present invention further provides an electronic device, which includes the surface processing position control system as described above, specifically as described in an embodiment of a surface processing position control method, which will not be repeated here.

[0139] In summary, the surface processing position control method, control system and electronic device provided by the present invention have the following beneficial effects:

[0140] It has strong anti-interference ability, high model adaptability and strong robustness in the face of large external disturbances, which can improve the response speed of the control system;

[0141] There is no need to set up a separate PID controller, which avoids excessive reliance on the control model, can achieve a stable production line, avoid quality risks such as over-processing and incomplete processing, and reduce the situation of system downtime due to misjudgment when disturbed by the outside world;

[0142] The historical data of its own variables can be used to predict itself, so that the controlled system can return to a steady state faster and avoid the problem of random fluctuation error accumulation when the controlled system is disturbed by the outside world. When processing some irregular surfaces, the controlled system can be stably controlled at the desired position height to adapt to the complex and changeable surface processing of the workpiece to be processed.

[0143] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A surface processing position control method, characterized in that: include: Obtain input altitude reference values, previously predicted output values, and current system state parameters; The step of obtaining the current system status parameters comprises: Get the real-time position measured by the sensor; The real-time position measured by the sensor is optimized into the mean value of the variance of continuous samples by using the mean difference algorithm, and the mean value of the variance is used as the current system state parameter; Obtaining a current steady-state response according to the current system state parameters; Obtaining a total control input required for a desired position height according to the current system state parameter, the height reference value and the previous predicted output value; The step of obtaining the total control input required for the desired position height according to the current system state parameter, the height reference value and the previous predicted output value comprises: Obtaining a deviation value according to the altitude reference value and the current steady-state response; After the deviation value is fuzzified, the corresponding membership of the deviation value is obtained according to the fuzzy rule, and a fuzzy decision is made according to the membership and the fuzzy rule; Defuzzifying the fuzzy decision to obtain the total control input required for the desired position height; Wherein, the initial value of the previously predicted output value is the steady-state response of the system under a unit pulse, and the steady-state response of the system under the unit pulse offsets the vibration of the system in the steady-state range; Obtaining a system input value according to the total control input, wherein the system input value is an input value of a controlled system; Obtaining a system predicted output value for maintaining a desired position height according to the system input value and the current system state parameter; The system input value is adjusted according to the system predicted output value, and the controlled system is driven according to the system input value.

2. The surface processing position control method according to claim 1, characterized in that: The current steady-state response is positively correlated with the sensor feedback value variable caused by the running stroke within the unit response time of the motion planning when the equipment is running.

3. The surface processing position control method according to claim 1, characterized in that: The step of obtaining the input altitude reference value comprises: Get the expected position height; The expected position height is converted into an analog quantity or a digital quantity of an equivalent sensor to obtain a height reference value.

4. The surface processing position control method according to claim 1, characterized in that: The step of obtaining the system input value according to the total control input comprises: The total control input is planned as a smooth motion curve according to the control cycle of the motion controller through the look-ahead algorithm and point motion control, wherein the input of the next cycle is the system input value.

5. The surface processing position control method according to claim 1, characterized in that: The step of adjusting the system input value according to the system predicted output value comprises: Obtaining a total control input required for a desired position height according to the current system state parameter, the height reference value and the system predicted output value; A system input value is obtained according to the total control input amount.

6. The surface processing position control method according to claim 1, characterized in that: In the step of fuzzifying the deviation value, obtaining the corresponding membership of the deviation value according to the fuzzy control rule, and making a decision according to the membership according to the fuzzy rule, the fuzzy rule table adopts a one-dimensional fuzzy rule table.

7. A control system based on the surface processing position control method according to any one of claims 1 to 6, characterized in that: include: Adaptive controller, fuzzy controller, motion controller and predictive controller; among them, The adaptive controller is connected to the fuzzy controller and is used to obtain current system state parameters and input the current system state parameters into the fuzzy controller; The fuzzy controller is connected to the motion controller and is used to obtain the total control input required for the desired position height according to the input height reference value, the previous predicted output value and the current system state parameter and input it into the motion controller; The motion controller is used to obtain a system input value according to the total control input, and is also used to adjust the system input value according to the system predicted output value, and drive the controlled system according to the system input value; The prediction controller is used to obtain a system prediction output value for maintaining a desired position height according to the system input value and the current system state parameter.

8. An electronic device, characterized in that: Includes the surface processing position control system as described in claim 7.

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