CN110108277B - Aviation inertia stabilized platform friction compensation method based on two-type fuzzy control - Google Patents
Aviation inertia stabilized platform friction compensation method based on two-type fuzzy control Download PDFInfo
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Abstract
本发明公开的一种基于二型模糊控制的航空惯性稳定平台摩擦补偿方法，首先，根据航空惯性稳定平台的机械结构，分析得到平台机械结构摩擦主要来源；其次，采用电流、速率、位置三环控制的逻辑，构造航空惯性稳定平台的三环控制系统；抑制系统多种扰动，补偿摩擦干扰力矩；第三，建立二型模糊控制系统，利用二维模糊器对精确值的输入进行处理得到模糊输入集，然后通过推理机结合设置的规则库、数据库得到模糊输出集，最后利用降型器和解模糊器得到最后的降维集和精确值的输出，实现非线性摩擦扰动补偿。在满足设计的要求同时增强系统应对不确定性干扰的能力和鲁棒性，实现摩擦扰动高精度补偿，提高惯性稳定平台的控制精度。
The invention discloses a friction compensation method for an aviation inertial stable platform based on type II fuzzy control. First, according to the mechanical structure of the aviation inertial stable platform, the main source of friction of the mechanical structure of the platform is analyzed and obtained; secondly, three loops of current, speed and position are used. The logic of control is to construct a three-loop control system of the aviation inertial stabilization platform; to suppress various disturbances of the system and to compensate the frictional disturbance torque; thirdly, to establish a type-2 fuzzy control system, which uses a two-dimensional fuzzer to process the input of precise values to obtain fuzzy The input set is then obtained through the inference engine combined with the set rule base and database to obtain the fuzzy output set. Finally, the reducer and defuzzifier are used to obtain the final dimension reduction set and the output of the precise value to realize nonlinear friction disturbance compensation. While meeting the design requirements, the system can enhance the ability and robustness of the system to deal with uncertain disturbances, realize high-precision compensation of friction disturbances, and improve the control accuracy of the inertial stable platform.
Description
Technical Field
The invention belongs to the technical field of aviation stabilized platform design, and particularly relates to a friction compensation method for an aviation inertia stabilized platform based on two-type fuzzy control, which can enhance the capability of a system for dealing with uncertain interference and enhance the robustness of the system. The method is used for eliminating or inhibiting system disturbance, has great significance for high-precision imaging of the aerial remote sensing system, and is suitable for disturbance inhibition of large, medium and small stable platform systems.
Background
The high-resolution aerial remote sensing system takes aircrafts such as airplanes (air base) and airships (near space) as observation platforms, and becomes the technical field of high-speed development and fierce competition in the world nowadays. The airborne aerial remote sensing system has the advantages of high resolution, flexibility, good real-time performance and the like, and is an important component of the aerial remote sensing system. The camera visual axis shakes seriously due to the influence of airflow, carrier bump and the like, and under the condition of lacking a stable platform, the plane needs to fly for many times in order to obtain a high-definition picture, and the high-definition picture is obtained by adopting a jigsaw mode, so that the labor cost is greatly increased; at the moment, a high-precision inertially stabilized platform is needed to isolate external disturbance, so that the visual axis of the camera is kept stable. The high-precision real-time motion imaging of the aerial remote sensing system requires that the platform maintain ideal motion and stable posture, which presents a serious challenge to the motion of the platform. For an aviation platform, complicated multi-modal non-ideal motion can be formed under the influence of external airflow disturbance, internal engine vibration and the like, and in the imaging principle, no matter synthetic aperture imaging, scanning imaging, staring imaging or interference imaging, the non-ideal motion of the aviation platform can cause imaging blurring, defocusing, deformation and pixel aliasing to cause serious degradation and even no imaging. In order to obtain a high-resolution image, a platform motion error real-time accurate estimation theory and method must be researched, and the imaging precision is improved through motion error compensation.
In 1965, the Zadeh professor fuzzifies the traditional set by using the traditional set elements through a membership function, and provides a one-type fuzzy set theory. In 1975, the teaching of Zadeh proposed a two-type fuzzy set theory, and compared with the previous one-type fuzzy set theory, the two-type fuzzy mainly fuzzifies membership in a set, can represent multiple uncertainty conditions, and has deeper fuzzy characteristics. Ambiguity as an uncertain expression generally exists in the process of communicating with human thinking and languages, but the human brain has the capability of being good at judging and processing the ambiguity phenomenon, and is convenient to solve the ambiguity problem in the real society. If the machine can also have the fuzzy problem processing capability like the human brain, the machine can better cope with the complex situation.
The second type fuzzy belongs to a nonlinear mapping relation, which mainly relies on expert knowledge to establish a rule base and corresponding relation of input and output, and adopts related logic operation (and operation, parallel operation) to obtain fuzzified output, and then obtains final output through type reduction and precision. The two-type fuzzy control system is composed of five parts of a two-type fuzzy inference engine, a rule base, a reduction model and defuzzification.
At present, the existing patents about friction compensation research mainly include two types, the first type is mainly to establish a friction model for the traditional PID control algorithm and then estimate and compensate, for example, chinese patent "a friction compensation method suitable for position control of an airplane active side lever system" (CN201810492409.8), which includes disclosing a friction compensation method suitable for position control of an airplane active side lever system, where the position following is not accurate under follow-up control due to friction factors existing in the airplane active side lever, and the follow-up performance of the active side lever is reduced, and it is difficult to solve the problem by adopting the traditional PID control algorithm. In order to improve the steady-state tracking precision of the system and simultaneously consider the dynamic response process, realize the high-precision control of the airplane active side rod system, a friction model is identified through experiments, the friction compensation is carried out on the system, and the position tracking precision of the system is improved. The method is not only suitable for an airplane active side rod system, but also can be applied to occasions such as an air defense weapon follow-up system, a missile seeker servo system and the like. The second type is mainly to establish an observer for friction parameter prediction, for example, chinese patent "a pneumatic position servo system adaptive inversion friction compensation control method" (CN201810099223.6), and proposes a pneumatic position servo system adaptive friction compensation control method, which comprehensively analyzes nonlinearity and uncertainty of friction in a pneumatic position servo system and combines with a LuGre friction model, and then uses a dual observer to predict friction state factors under the condition that the LuGre friction part parameters are uncertain, so as to perform friction compensation control on the pneumatic position servo system. The dynamic hysteresis phenomenon of the starting stage is improved, the phenomena of crawling under a low-speed working condition and stick-slip oscillation under a high-speed working condition are reduced, and the response speed and the tracking precision of the system are improved.
In conclusion, with the development and popularization of the aerial remote sensing technology, system disturbance is eliminated or suppressed, and a wide prospect is provided for high-precision imaging control of the aerial remote sensing system, and the thesis practice research in the aspect is relatively lacked. The invention starts from the general aspect, the research content relates to a non-model friction compensation research method of an inertia stable platform based on two-type fuzzy control, and the method can provide guidance and reference for the friction compensation control similar to the principle of the aviation inertia stable platform.
Disclosure of Invention
The invention provides a friction compensation method for an aviation inertia stabilized platform based on two-type fuzzy control, which meets the design requirement, enhances the capability and robustness of the system for coping with uncertain interference, is used for eliminating or inhibiting the system disturbance, has great significance for high-precision imaging of an aviation remote sensing system, and is suitable for disturbance inhibition of large, medium and small stabilized platform systems.
The technical scheme of the invention is as follows:
a friction compensation method of an aviation inertia stable platform based on two-type fuzzy control,
firstly, analyzing and obtaining the mechanical structure friction of the platform mainly from gear and belt wheel transmission friction, bearing rotation friction and motor internal friction according to the mechanical structure of the aviation inertia stable platform;
secondly, in order to control the platform more accurately, the aviation inertial stabilization platform adopts the logic of current, speed and position three-loop control to quickly suppress various disturbances of the system, and the compensation of friction interference moment has an important role in improving the precision of the inertial stabilization platform;
thirdly, a conventional PID control method is adopted, a complex parameter adjusting process is needed, in order to solve the complex process, a two-type fuzzy controller is built to replace a traditional PID controller, the process of building a system model and adjusting parameters is omitted by using the two-type fuzzy controller, and the efficiency is greatly improved; the working principle of the controller established based on the two-type fuzzy system is as follows: and finally, a reducer and a deblurring device of the fuzzy system are used for obtaining the final dimension reduction set and the output of the accurate value.
The specific analysis process is as follows:
(1) the design of a three-loop control system of an inertially stabilized platform is established and divided into the following 3 parts:
1) the first loop of the three-loop control is the current loop of the innermost loop, and the response speed is fastest, but the accuracy is lower. The second loop is a speed loop, and the output of the controller in the loop is directly the setting of a current loop by applying negative feedback regulation of the rotation rate of the gyro sensitive frame relative to the inertia space. The third ring is the outermost ring of position rings, typically using either POS or IMU to acquire frame angular position. The two frame angle positions of the accelerometer sensitive roll and pitch can be used without the POS or IMU, the loop being the most accurate but the slowest in response speed.
2) The inner ring is considered as a simplified link when designing the outer ring. The more accurate the simplified approximation model of the inner loop will be if the mid-band of the inner loop is high. The following formula shows the mathematical relationship of bandwidth of each link, wherein omegaRRepresenting the mechanical resonance frequency, omega, of the platform frameB·θRepresenting the desired closed loop bandwidth, ω, of the systemc·θAnd ωc·ωThe open loop cut-off frequencies of the position loop and the velocity loop and the return loop, respectively.
3) In the design of the three-loop control system, the actual mechanical resonance frequency of the inertially stabilized platform is taken as omegaR＝31.4rad/s，ωc·ω＝6.28rad/s，ωc·θ＝1.57rad/s，ωB·θ＝3.14rad/s，ωc·I>90rad/s。
(2) The establishment of the two-type fuzzy control system comprises the following 4 steps:
step 1), designing a fuzzifier:
the precise value input by the system firstly passes through a fuzzifier, and the fuzzifier corresponds the precise value to a corresponding fuzzy set, so that the membership degree of each set can be obtained. The fuzzy controller fuzzifies the error and error change of input by determining input x ═ x1,x2,…xn)TMapping e to set on U
Two of them fuzzy sets
The above. In engineering practice, discrete ranges are defined in multiple stages depending on the resolution of the input quantities.
If an asymmetric type is encountered, we can convert the asymmetric set into a symmetric set by operation, and convert the asymmetric set into a symmetric set by the following formula:
step 2), designing a fuzzy inference machine
(21) Matching
Matching is that the inference engine determines that its input matches with the rules in the rule base and determines the rules used for inference. System n inputs xiI is 1, 2, …, n, and the fuzzy quantity is obtained by fuzzy operation
After the variables are fuzzified, the fuzzy value is generally not more than three, and the general condition is that
The following two phases are the premise of matching.
Stage 1: assembly rule antecedent, x1,x2Is two inputs to the controller, each variable having two fuzzy values
And
then the fuzzy rule antecedents are:
and searching all the rules in the rule base for rules matched with the current element, and if the rules are searched, activating the current rule.
And (2) stage: confidence level of the rule precursor of the activation rule, for the confidence level of the ith rule precursor, we can adopt
In this representation, the "+" indicates both the "minimum" and "multiply" operations.
(22) Reasoning
Each rule activated by matching generates a corresponding fuzzy set, N rules generate N implied fuzzy sets, and the exact output value of the fuzzy controller can be solved by synthesizing all the implied fuzzy sets.
Step 3), design of rule base and database of two-type fuzzy system
The rules are as follows:
step 4), design of shape-reducing device and ambiguity-resolving device
The type-reducing device is an extended deblurring operation module, and the process of converting the two-type fuzzy set into the one-type fuzzy set is called as the union of an infinite number of one-type fuzzy sets of type-reducing operation to form the two-type fuzzy set. The main point of deblurring is to calculate a set of fuzzy models to obtain an accurate value as the system output.
Compared with the prior art, the invention has the advantages that:
(1) the invention is based on the principle of three-ring control, voltage maps current change, current maps torque magnitude, torque magnitude maps change of rotating speed, and the rotating speed maps change of position at the same time.
(2) The invention adopts the principle of fuzzy control, and the fuzzy control can realize better control without a mathematical model of the controlled object because the dynamic characteristic of the controlled object is hidden in the input and output fuzzy sets and the fuzzy rules of the fuzzy controller. Fuzzy control is increasingly being applied to various fields.
(3) Fuzzy control is a rule-based control, which directly adopts language type control rules, and the starting point is the control experience of field operators or the knowledge of relevant experts, and an accurate mathematical model of a controlled object is not required to be established in the design, so that the control mechanism and the strategy are easy to accept and understand, the design is simple, and the application is convenient.
(4) Complex ambiguity situations cannot be fully described since a single ambiguity set can only represent a single piece of uncertainty information. Compared with a one-type fuzzy set, the two-type fuzzy control adopted by the invention can represent multiple uncertain information, and in addition, the two-type fuzzy logic control can enhance the capability of the system for dealing with uncertain interference and the robustness of the system.
Drawings
FIG. 1 is a schematic mechanical structure diagram of an aviation inertia stabilized platform based on two-type fuzzy control according to the present invention.
FIG. 2 is a flow chart of the friction compensation method for the aviation inertia stabilized platform based on the two-type fuzzy control.
FIG. 3 is a block diagram of a three-loop control system of the inertially stabilized platform of the present invention.
FIG. 4 is a block diagram of a two-type fuzzy control system of the present invention.
The reference numbers are listed below:
1-azimuth frame rate gyro, 2-azimuth frame bearing, 3-roll frame rate gyro, 4-roll frame gear pair, 5-roll frame torque motor, 6-roll frame rotary transformer, 7-azimuth frame rotary transformer, 8-azimuth frame torque motor, 9-azimuth frame gear pair, 10-pitch frame torque motor, 11-pitch frame rotary transformer, 12-pitch frame gear pair, 13-pitch frame rate gyro, 14-accelerometer y, 15-accelerometer x, 16-pitch frame, 17-roll frame, 18-azimuth frame, 19-camera, 20 is-lens, 21-POS.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
Fig. 1 is a schematic diagram of a mechanical structure of an aviation inertially stabilized platform based on two-type fuzzy control according to the present invention.
The mechanical structure of the aviation inertial stabilization platform comprises an azimuth frame rate gyroscope 1, an azimuth frame bearing 2, a roll frame rate gyroscope 3, a roll frame gear pair 4, a roll frame torque motor 5, a roll frame rotary transformer 6, an azimuth frame rotary transformer 7, an azimuth frame torque motor 8, an azimuth frame gear pair 9, a pitch frame torque motor 10, a pitch frame rotary transformer 11, a pitch frame gear pair 12, a pitch frame rate gyroscope 13, an accelerometer y 14, an accelerometer x 15, a pitch frame 16, a roll frame 17, an azimuth frame 18, a camera 19, a lens 20 and a POS 21. The azimuth frame rate gyroscope 1 and the azimuth frame torque motor 8 are fixed on an azimuth frame 18, the pitching frame rate gyroscope 13 and the pitching frame torque motor 10 are fixed on a pitching frame 16, and the rolling frame rate gyroscope 3 and the rolling frame torque motor 5 are fixed on a rolling frame 17. In order to increase the motor torque, a direction frame gear pair 9 is added to the direction frame torque motor 8, a pitch frame gear pair 12 is added to the pitch frame torque motor 10, and a roll frame gear pair 4 is added to the roll frame torque motor 5. In order to increase the effectiveness and rapidity of control, an azimuth frame rotary transformer 7, a pitch frame rotary transformer 11 and a roll frame rotary transformer 6 are added. The accelerometer y 14 and the accelerometer x 15 detect the acceleration of the x axis and the y axis and are fixed on the pitching frame 16, the camera 19, the lens 20 and the POS 21 are fixedly connected on the orientation frame, the pitching frame 16 is supported and hung under the rolling frame 17, the rolling frame 17 is supported and hung under the orientation frame 18, and the orientation frame bearing 2 is used for fixing the camera 19 on the orientation frame 18, so that the camera 19 has the characteristic of free rotation.
FIG. 2 is a flow chart of the friction compensation method for the aviation inertia stabilized platform based on the two-type fuzzy control.
Firstly, according to the mechanical structure of the aerial remote sensing inertially stabilized platform shown in FIG. 1, the mechanical structure friction of the platform obtained through analysis mainly comes from gear and belt wheel transmission friction, bearing rotation friction and motor internal friction. In order to control the platform more accurately, the stable platform adopts the three-loop control logic shown in fig. 3 to realize rapid suppression of various system disturbances, and compensation of friction disturbance moment plays an important role in improving the precision of the inertially stabilized platform. In order to solve the complicated process, a two-type fuzzy control system shown in figure 4 is established to replace a traditional PID controller, so that the process of establishing a system model and adjusting parameters is omitted, and the efficiency is greatly improved. The specific process is as follows:
a friction compensation method of an aviation inertia stable platform based on two-type fuzzy control,
firstly, analyzing to obtain a main friction source of a mechanical structure of the aerial remote sensing inertially stabilized platform according to the mechanical structure of the platform;
secondly, a three-loop control system of speed, current and position is constructed by adopting a three-loop control logic; suppressing various disturbances of the system and compensating friction interference torque;
and thirdly, establishing a two-type fuzzy control system, processing the input of an accurate value by using a two-dimensional fuzzy device to obtain a fuzzy input set, then combining a set rule base and a set database by using an inference machine to obtain a fuzzy output set, and finally obtaining the final dimension reduction set and the output of the accurate value by using a degrader and a deblurring processing module to realize nonlinear friction disturbance compensation.
(1) The design of a three-loop control system of an inertially stabilized platform is established and divided into the following 3 parts:
1) the first loop of the three-loop control is the current loop of the innermost loop, and the response speed is fastest, but the accuracy is lower. The second loop is a speed loop, and the output of the controller in the loop is directly the setting of a current loop by applying negative feedback regulation of the rotation rate of the gyro sensitive frame relative to the inertia space. The third ring is the outermost ring of position rings, typically using either POS or IMU to acquire frame angular position. The two frame angle positions of the accelerometer sensitive roll and pitch can be used without the POS or IMU, the loop being the most accurate but the slowest in response speed.
2) The inner ring is considered as a simplified link when designing the outer ring. The more accurate the simplified approximation model of the inner loop will be if the mid-band of the inner loop is high. The following formula shows the mathematical relationship of bandwidth of each link, wherein omegaRRepresenting the mechanical resonance frequency, omega, of the platform frameB·θRepresenting the desired closed loop bandwidth, ω, of the systemc·θAnd ωc·ωThe open loop cut-off frequencies of the position loop and the velocity loop and the return loop, respectively.
3) In the design of the three-loop control system, the actual mechanical resonance frequency of the inertially stabilized platform is taken as omegaR＝31.4rad/s，ωc·ω＝6.28rad/s，ωc·θ＝1.57rad/s，ωB·θ＝3.14rad/s，ωc·I>90rad/s。
Fig. 3 is a block diagram of a three-loop control system of the inertially stabilized platform according to the present invention.
The direct-current torque motors of the inertially stabilized platform are respectively fixed on the azimuth frame, the pitching frame and the roll frame to generate friction torque interference. The three-loop control system for inhibiting the friction torque interference is respectively a current loop, a speed loop and a position loop from inside to outside, wherein the current loop and the speed loop are controlled by adopting a PID control method. The negative feedback quantity of the current loop is the current measured by the Hall sensor, and the deviation between the current loop and the given current quantity can be adjusted in real time, so that the fluctuation of the power supply voltage is inhibited, PWM power driving is carried out on the output voltage of the rate compensator to control the direct current torque motor, the linearity of the output control torque is improved, the dynamic performance is optimized, and the defect of a double-closed-loop PID control structure is overcome. The rate loop negative feedback loop is formed by taking a rate gyroscope as a feedback element, the rate gyroscope senses the interference angular motion of the inertially stabilized platform, and various interferences are quickly compensated through the position compensator, so that the system stability is enhanced. The position loop is the main feedback of the system, and the stability of the control frame is measured through the feedback of the POS and the accelerometer sensor, so that the position loop has an important effect on improving the performance of the system.
(2) The establishment of the two-type fuzzy control system comprises the following 4 steps:
step 1), designing a fuzzifier:
the precise value input by the system firstly passes through a fuzzifier, and the fuzzifier corresponds the precise value to a corresponding fuzzy set, so that the membership degree of each set can be obtained. The fuzzy controller fuzzifies the error and error change of input by determining input x ═ x1,x2,…xn)TMapping e to set on U
Two of them fuzzy sets
The above. In engineering practice, discrete ranges are defined in multiple stages depending on the resolution of the input quantities.
If an asymmetric type is encountered, we can convert the asymmetric set into a symmetric set by operation, and convert the asymmetric set into a symmetric set by the following formula:
step 2), designing a fuzzy inference machine
(21) Matching
Matching is that the inference engine determines that its input matches with the rules in the rule base and determines the rules used for inference. System n inputs xiI is 1, 2, …, n, and the fuzzy quantity is obtained by fuzzy operation
After the variables are fuzzified, the fuzzy value is generally not more than three, and the general condition is that
The following two phases are the premise of matching.
Stage 1: assembly rule antecedent, x1,x2Is two inputs to the controller, each variable having two fuzzy values
And
then the fuzzy rule antecedents are:
and searching all the rules in the rule base for rules matched with the current element, and if the rules are searched, activating the current rule.
And (2) stage: confidence level of the rule precursor of the activation rule, for the confidence level of the ith rule precursor, we can adopt
In this representation, the "+" indicates both the "minimum" and "multiply" operations.
(22) Reasoning
Each rule activated by matching generates a corresponding fuzzy set, N rules generate N implied fuzzy sets, and the exact output value of the fuzzy controller can be solved by synthesizing all the implied fuzzy sets.
Step 3), design of rule base and database of two-type fuzzy system
The rules are as follows:
step 4), design of shape-reducing device and ambiguity-resolving device
The type-reducing device is an extended deblurring operation module, and the process of converting the two-type fuzzy set into the one-type fuzzy set is called as the union of an infinite number of one-type fuzzy sets of type-reducing operation to form the two-type fuzzy set. The main point of deblurring is to calculate a set of fuzzy models to obtain an accurate value as the system output.
FIG. 4 is a schematic diagram of a two-type fuzzy control system according to the present invention. The method comprises a two-dimensional fuzzifier, an inference engine, a rule base, a database, a model reducer and a defuzzifier, wherein the two-dimensional fuzzifier is used for processing an accurate value input by a system to obtain a fuzzy set input set, the inference engine is combined with the set rule base and the set database to obtain a fuzzy output set, and finally the fuzzy output set is input into the model reducer and the defuzzifier to obtain a final fuzzy number model reduction output and an accurate value system output.
Details not described in the present specification belong to the prior art known to those skilled in the art.
Claims (1)
1. A friction compensation method for an aviation inertia stabilized platform based on two-type fuzzy control is characterized by comprising the following steps:
firstly, analyzing to obtain a main friction source of a mechanical structure of the platform according to the mechanical structure of the aviation inertia stable platform;
secondly, a three-loop control system of the aviation inertia stable platform is constructed by adopting the logic of three-loop control of current, speed and position; suppressing various disturbances of the system and compensating friction interference torque;
thirdly, establishing a two-type fuzzy control system, processing the input of an accurate value by using a two-dimensional fuzzifier to obtain a fuzzy input set, then obtaining a fuzzy output set by combining a set rule base and a set database through an inference machine, and finally obtaining the final dimension reduction set and the output of the accurate value by using a degrader and a defuzzifier to realize nonlinear friction disturbance compensation;
the method for constructing the three-loop control system of the aviation inertially stabilized platform comprises the following steps:
1) the first ring of the three-ring control is the current ring of the innermost ring; the second ring is a speed ring, the negative feedback regulation of the rotation rate of the gyro sensitive frame relative to the inertial space is applied, and the output of the controller in the second ring is directly the setting of the current ring; the third ring is the outermost ring of position rings, and the frame angular position is acquired using either POS or IMU; sensing two frame angle positions of roll and pitch using an accelerometer without a POS or IMU;
2) when designing the outer ring, the inner ring is regarded as a simplified link; if the middle frequency band of the inner ring is higher, the simplified approximate model of the inner ring is more accurate; the mathematical relationship of the bandwidth of each link is as follows:
wherein ω isRRepresenting the mechanical resonance frequency, omega, of the platform frameB·θRepresenting desired closed loop of the systemBandwidth, ωc·θAnd ωc·ωThe open loop cut-off frequencies of the position loop and the velocity loop are respectively represented;
3) in the design of the three-loop control system, the actual mechanical resonance frequency of the inertially stabilized platform is omegaR＝31.4rad/s，ωc·ω＝6.28rad/s，ωc·θ＝1.57rad/s，ωB·θ＝3.14rad/s，ωc·I>90rad/s。
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