CN112621762B - Touch perception acquisition system with temperature compensation function and method thereof - Google Patents
Touch perception acquisition system with temperature compensation function and method thereof Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
- B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
- B25J13/081—Touching devices, e.g. pressure-sensitive
- B25J13/084—Tactile sensors
Abstract
The invention relates to a touch perception acquisition system with a temperature compensation function and a method thereof, belonging to the technical field of touch perception acquisition systems; the technical problem to be solved is as follows: the improvement of the hardware structure of the tactile perception acquisition system with the temperature compensation function is provided; the technical scheme for solving the technical problems is as follows: the temperature compensation system comprises an upper computer, a controller, a display screen, an acquisition module and a temperature compensation module, wherein the acquisition module comprises a first analog switch, a second analog switch and a pressure sensor array, the temperature compensation module comprises a temperature compensation thermistor, the first analog switch is connected with one end of the pressure sensor array through a lead, the other end of the pressure sensor array is connected with one end of the temperature compensation thermistor through a lead, and the other end of the temperature compensation thermistor is connected with the second analog switch through a lead; the invention is applied to mechanical arms.
Description
Technical Field
The invention discloses a touch perception acquisition system with a temperature compensation function and a method thereof, and belongs to the technical field of touch perception acquisition methods and systems thereof.
Background
Whether the mechanical arm can execute various operations of grabbing, moving or fixing objects and the like needs to realize accurate control on operation force through the tactile feedback of the mechanical arm in the operation process, and damage to the operated objects or damage to the mechanical arm is avoided. To achieve accurate control of the operating force, a force sensor is required to collect and analyze the operating force data. However, when the force sensor is used for acquiring data, the force sensor is influenced by factors such as temperature, so that in order to improve the accuracy of the data acquisition of the mechanical arm, a touch perception acquisition system capable of compensating the temperature in real time is provided, and an acquisition method of the acquisition system is provided to realize the temperature compensation of touch perception.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to solve the technical problems that: an improvement of a hardware structure of a tactile sensation acquisition system with a temperature compensation function is provided.
In order to solve the technical problems, the invention adopts the technical scheme that: a touch perception acquisition system with a temperature compensation function comprises an upper computer, a controller, a display screen, an acquisition module and a temperature compensation module, wherein the controller is respectively connected with the upper computer, the display screen and the temperature compensation module through leads;
the acquisition module comprises a first analog switch, a second analog switch and a pressure sensor array, the temperature compensation module comprises a temperature compensation thermistor, the first analog switch is connected with one end of the pressure sensor array through a lead, the other end of the pressure sensor array is connected with one end of the temperature compensation thermistor through a lead, and the other end of the temperature compensation thermistor is connected with the second analog switch through a lead;
the touch perception acquisition system scans the pressure sensor array through the first analog switch, acquires the voltage subjected to temperature compensation through the A/D module, and transmits the voltage to the upper computer for display after Kalman filtering.
The pressure sensor array adopts RP-C resistance type pressure sensitive sensors, and each RP-C resistance type pressure sensitive sensor is connected with a constant value resistor in parallel.
The model adopted by the first analog switch and the second analog switch is CD 4051.
The display screen is an LCD12864 liquid crystal display screen;
the controller adopts an ARM model STM32F103ZET6 as a core chip.
A haptic perception collection method with a temperature compensation function comprises the following steps:
the method comprises the following steps: after the acquisition system is powered on, setting initial reference voltage through a one-key reset function;
step two: the controller controls the first analog switch to perform row-column scanning on the pressure sensor array and determine a collection point;
step three: calculating output voltage according to a formula Vout Vcc Rs2/(Rs2+ Rp + Rt// Rs1), collecting the output voltage Vout by an A/D module in the controller, transmitting the output voltage Vout to an upper computer after Kalman filtering processing, and displaying the output voltage on a display screen;
in the above formula: vout is the output voltage, Vcc is the input voltage, Rs1 is the constant resistor, Rs2 is the divider resistor, Rp is the pressure sensor equivalent variable resistor, Rt is the resistance of the RNT thermistor.
The second step comprises the following specific steps:
step 2.1: the pressure sensor array is equivalent to a variable resistor Rp;
step 2.2: the resistance value of Rp is determined by performing row-column scanning on the pressure sensor array through the first analog switch, the Rp is increased along with the increase of the temperature, the Rp is compensated through a circuit formed by connecting an RNT thermistor Rt and a fixed value resistor Rs1 in parallel, and the parallel resistance value of the Rp is reduced along with the increase of the temperature;
step 2.3: calculating an output voltage according to Vout Vcc Rs2/(Rs2+ Rp + Rt// Rs 1);
step 2.4: the A/D module in the controller collects output voltage Vout, and the output voltage Vout is transmitted to the upper computer and displayed on the display screen after Kalman filtering processing.
The process of performing kalman filtering on the output voltage in the third step includes the following steps:
step 3.1: setting an initial value as a first sensor measurement value, wherein the covariance of the first sensor measurement value is the square Q of the sensor precision errork；
Step 3.2: using a linear random differential equation to describe the measured estimated voltage X at kkThe calculation formula is as follows:
Xk＝AXk-1'+Buk；
in the above formula: xkIs a priori estimate of the pressure value at time k, Xk-1' is an optimal estimation value at the time k-1, i.e., a system state, and represents an estimated state at the time k-1, ukThe control quantity of the system at the moment k is, A and B are system parameters, and A is set to be 1, and B is set to be 1;
step 3.3: calculating a measured estimated voltage XkCovariance P ofkThe calculation formula is as follows:
in the above formula: pkIs the variance of the prior estimate at time k, Pk-1Is' corresponding to Xk-1' covariance, σa 2Is the covariance (about σ) of the system processa 2＝0.1)；
Step 3.4: using the voltage estimate X at time kkAnd the gain K' of the system state at the next moment is calculated by the measured value, which is the product of the measured Gaussian distribution and the empirically estimated Gaussian distribution, and the calculation formula is as follows:
in the above formula: k 'is updated Kalman gain, and the value of K' is 0.82-0.91; r sigmab 2Is the measurement variance, which is the square of the maximum error of the sensor;
the optimal measurement estimation voltage calculation formula of the system at the moment k is as follows:
Xk'＝Xk+K'(Zk-HXk)；
in the above formula: zkFor the measured voltage at time k, H is a system parameter, set H to 1, and the estimated voltage X is estimated for this optimum measurementk' covariance PkThe formula for calculation of' is:
Pk'＝Pk-K'HPk；
step 3.5: carrying out iterative computation according to the prior estimation and the posterior estimation of the pressure value at the k moment;
at time k, the a posteriori estimate X (k-1| k-1) at time k-1 is set equal to the a priori estimate X (k | k-1) at time k, i.e. X (k | k-1) ═ X (k-1| k-1), and the a priori estimate variance P (k | k-1) ═ P (k-1| k-1) + σ ∑ k-1a 2；
In the above formula: sigmaa 2For the process variance of the pressure change at two consecutive moments, P (k | k-1) is the variance of the prior estimation at moment k, and P (k-1| k-1) is the variance of the posterior estimation at moment k-1;
kalman gain kg at time k (k) (P (k | k-1))/(P (k | k-1) + σb 2)，
In the above formula: sigmab 2The measurement variance of the sensor precision and the real temperature difference value is obtained;
the posterior estimate at time k is X (k | k) ═ X (k | k-1) + Kg (k)*(z (k) -X (k | k-1)), where the variance of the a posteriori estimate at time k is P (k | k) ═ 1-kg (k))*P (k | k-1), and at this time, the posterior estimate at the time k and the variance of the posterior estimate are calculated, and the prior estimate, kalman gain, and posterior estimate at the next time (k +1) can be estimated.
Compared with the prior art, the invention has the beneficial effects that: the tactile perception acquisition system with the temperature compensation function can realize acquisition of tactile perception by arranging the acquisition module, realize temperature compensation of acquired data by arranging the temperature compensation module, eliminate noise interference of the sensor by adopting Kalman filtering, optimize the acquired voltage value and improve the accuracy of the acquired data.
Drawings
The invention is further described below with reference to the accompanying drawings:
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a simplified schematic of the circuit of the present invention;
FIG. 3 is a schematic diagram of an acquisition module of the present invention;
FIG. 4 is a schematic diagram of the circuit structure of the present invention;
in the figure: the system comprises an upper computer 1, a controller 2, a display screen 3, an acquisition module 4, a temperature compensation module 5, an A/D module 6, a first analog switch 41, a second analog switch 42, a pressure sensor array 43 and a temperature compensation thermistor 51.
Detailed Description
As shown in fig. 1 to 4, the tactile sensation acquisition system with the temperature compensation function of the invention comprises an upper computer 1, a controller 2, a display screen 3, an acquisition module 4 and a temperature compensation module 5, wherein the controller 2 is respectively connected with the upper computer 1, the display screen 3 and the temperature compensation module 5 through wires, an a/D module 6 is integrated on the controller 2, and the a/D module 6 is connected with the acquisition module 4 through wires;
the acquisition module 4 comprises a first analog switch 41, a second analog switch 42 and a pressure sensor array 43, the temperature compensation module 5 comprises a temperature compensation thermistor 51, the first analog switch 41 is connected with one end of the pressure sensor array 43 through a lead, the other end of the pressure sensor array 43 is connected with one end of the temperature compensation thermistor 51 through a lead, and the other end of the temperature compensation thermistor 51 is connected with the second analog switch 42 through a lead;
the tactile perception acquisition system scans the pressure sensor array 43 through the first analog switch 41, acquires the voltage subjected to temperature compensation through the A/D module 6, and transmits the voltage to the upper computer 1 for display after Kalman filtering.
The pressure sensor array 43 is made of RP-C resistance type pressure sensitive sensors, and each RP-C resistance type pressure sensitive sensor is connected with a constant value resistor in parallel.
The first analog switch 41 and the second analog switch 42 are of a type of CD 4051.
The display screen 3 adopts an LCD12864 liquid crystal display screen;
the controller 2 adopts an ARM model STM32F103ZET6 as a core chip.
A haptic perception collection method with a temperature compensation function comprises the following steps:
the method comprises the following steps: after the acquisition system is powered on, setting initial reference voltage through a one-key reset function;
step two: the controller 2 controls the first analog switch 41 to perform row-column scanning on the pressure sensor array 51 to determine a collection point;
step three: calculating an output voltage according to a formula Vout Vcc Rs2/(Rs2+ Rp + Rt// Rs1), collecting the output voltage Vout by an A/D module 6 in the controller 2, transmitting the output voltage Vout to the upper computer 1 after Kalman filtering processing, and displaying the output voltage on a display screen 3;
in the above formula: vout is the output voltage, Vcc is the input voltage, Rs1 is the constant resistor, Rs2 is the divider resistor, Rp is the pressure sensor equivalent variable resistor, Rt is the resistance of the RNT thermistor.
The second step comprises the following specific steps:
step 2.1: the pressure sensor array 51 is equivalent to a variable resistance Rp;
step 2.2: the resistance value of Rp is determined by the row-column scanning of the pressure sensor array 51 through the first analog switch 41, the Rp is increased along with the increase of the temperature, the Rp is compensated through a circuit formed by connecting an RNT thermistor Rt and a fixed value resistor Rs1 in parallel, and the parallel resistance value of the Rp is reduced along with the increase of the temperature;
step 2.3: calculating an output voltage according to Vout Vcc Rs2/(Rs2+ Rp + Rt// Rs 1);
step 2.4: the A/D module 6 in the controller 2 collects the output voltage Vout, and the output voltage Vout is transmitted to the upper computer 1 after Kalman filtering processing and displayed on the display screen 3.
The process of performing kalman filtering on the output voltage in the third step includes the following steps:
step 3.1: setting an initial value to a first timeThe covariance of the measured values of the sensor is the square Q of the accuracy error of the sensork；
Step 3.2: using a linear random differential equation to describe the measured estimated voltage X at kkThe calculation formula is as follows:
Xk＝AXk-1'+Buk；
in the above formula: xkIs a priori estimate of the pressure value at time k, Xk-1' is an optimal estimation value at the time k-1, i.e., a system state, and represents an estimated state at the time k-1, ukThe control quantity of the system at the moment k is, A and B are system parameters, and A is set to be 1, and B is set to be 1;
step 3.3: calculating a measured estimated voltage XkCovariance P ofkThe calculation formula is as follows:
in the above formula: pkIs the variance of the prior estimate at time k, Pk-1Is' corresponding to Xk-1' covariance, σa 2Is the covariance (about σ) of the system processa 2＝0.1)；
Step 3.4: using the voltage estimate X at time kkAnd the gain K' of the system state at the next moment is calculated by the measured value, which is the product of the measured Gaussian distribution and the empirically estimated Gaussian distribution, and the calculation formula is as follows:
in the above formula: k 'is updated Kalman gain, and the value of K' is 0.82-0.91; r sigmab 2Is the measurement variance, which is the square of the maximum error of the sensor;
the optimal measurement estimation voltage calculation formula of the system at the moment k is as follows:
Xk'＝Xk+K'(Zk-HXk)；
in the above formula：ZkFor the measured voltage at time k, H is a system parameter, set H to 1, and the estimated voltage X is estimated for this optimum measurementk' covariance PkThe formula for calculation of' is:
Pk'＝Pk-K'HPk；
step 3.5: carrying out iterative computation according to the prior estimation and the posterior estimation of the pressure value at the k moment;
at time k, the a posteriori estimate X (k-1| k-1) at time k-1 is set equal to the a priori estimate X (k | k-1) at time k, i.e. X (k | k-1) ═ X (k-1| k-1), and the a priori estimate variance P (k | k-1) ═ P (k-1| k-1) + σ ∑ k-1a 2；
In the above formula: sigmaa 2For the process variance of the pressure change at two consecutive moments, P (k | k-1) is the variance of the prior estimation at moment k, and P (k-1| k-1) is the variance of the posterior estimation at moment k-1;
kalman gain kg at time k (k) (P (k | k-1))/(P (k | k-1) + σb 2)，
In the above formula: sigmab 2The measurement variance of the sensor precision and the real temperature difference value is obtained;
the posterior estimate at time k is X (k | k) ═ X (k | k-1) + Kg (k)*(z (k) -X (k | k-1)), where the variance of the a posteriori estimate at time k is P (k | k) ═ 1-kg (k))*P (k | k-1), and at this time, the posterior estimate at the time k and the variance of the posterior estimate are calculated, and the prior estimate, kalman gain, and posterior estimate at the next time (k +1) can be estimated.
The invention provides a touch perception acquisition system with a temperature compensation function, which mainly comprises an upper computer 1, a controller 2, a pressure sensor array 43, a liquid crystal display screen 3, an analog switch and a temperature compensation thermistor 51; the upper computer 1 is connected with the controller 2; the pressure sensor array 43 is connected with the first analog switch 41 and the temperature compensation thermistor 51, respectively; the temperature-compensated thermistor 51 is connected to the second analog switch 43; the analog switch and the liquid crystal display screen 3 are both connected with the controller 2. The liquid crystal display screen 3 adopts LCD12864 liquid crystal display, the first analog switch 41 and the second analog switch 42 adopt CD4051, the temperature compensation thermistor 51 adopts an RNT thermistor, and the controller 2 adopts an ARM model STM32F103ZET6 as a core chip and is powered by 5V.
The tactile sensing acquisition function consists of a pressure sensor array 43, the pressure sensor of the invention uses an RP-C resistive pressure sensitive sensor, which is a flexible film sensor with a resistance value that decreases as the pressure acting on the sensing area increases. When the pressure sensor array 43 collects signals, the resistance characteristic of the pressure sensor increases with the temperature, so the pressure sensor array is connected with the RNT thermistor to compensate the temperature (the RNT thermistor has the characteristic that the resistance value decreases with the temperature increase). The pressure sensor array 43 is scanned by the CD4051, the a/D module 6 in the controller 2 collects the output voltage (voltage after temperature compensation), and the output voltage is transmitted to the upper computer 1 after kalman filtering and displayed on the liquid crystal.
The collection module is connected with the temperature compensation module, and the resistance of the pressure sensor is influenced by the temperature when the data is collected, and the resistance change caused by the temperature change of the pressure sensor is compensated by adopting a temperature compensation mode. The equivalent resistance value of the pressure sensor is Rp, and the resistance value of the pressure sensor linearly increases along with the rise of the temperature; the resistance value of the RNT thermistor is Rt, the resistance value of the RNT thermistor is nonlinearly reduced along with the temperature rise, according to the characteristics of the RNT thermistor, after the RNT thermistor is connected with a proper fixed value resistor Rs1 in parallel, the parallel resistance value of the RNT thermistor is linearly reduced along with the temperature rise, the resistance value of the pressure sensor changing along with the temperature rise can be just compensated, and the compensated equivalent resistance value is Rp + Rt// Rs 1.
The invention relates to a method for acquiring a touch perception acquisition system with a temperature compensation function, which is provided with a one-key reset function; due to the fact that the initial value of the acquisition is not zero due to different acquisition environments or installation and other factors, after the acquisition system is powered on, a one-key reset function is set, the acquisition system is enabled to have a reference initial value, and therefore the acquisition system can be used in different acquisition environments conveniently.
After the acquisition system is powered on, setting an initial reference value through a one-key reset function; the controller 2 controls the CD4051 to perform row-column scanning on the pressure sensor array 43, and determines an acquisition point, wherein the scanning frequency is 200 Hz; the output voltage is calculated according to the formula Vout Vcc Rs2/(Rs2+ Rp + Rt// Rs1), the output voltage Vout is collected by the a/D module 6 in the controller 2, and is transmitted to the upper computer after kalman filtering processing and displayed on the liquid crystal.
The pressure sensor is equivalent to a variable resistor Rp; the resistance value of Rp is determined by performing row-column scanning on the pressure sensor array 43 through the CD4051, and since the equivalent resistance of the pressure sensor can be increased along with the increase of the temperature, a circuit formed by connecting the RNT thermistor Rt and the fixed value resistor Rs1 in parallel is used for compensation, and the parallel resistance value can be reduced along with the increase of the temperature; calculating an output voltage according to Vout Vcc Rs2/(Rs2+ Rp + Rt// Rs 1); the A/D module 6 in the controller 2 collects the output voltage Vout, the output voltage Vout is transmitted to the upper computer after Kalman filtering processing and is displayed on the liquid crystal, and a circuit simplified diagram is shown in figure 2.
After the upper computer 1 collects data, the actual value fluctuates because the sensor has certain noise when in use, on one hand, the sensor is not an actual value and has certain measurement error on the basis of the actual value, on the other hand, the numerical value of the sensor is a continuous change curve, and the change amplitude of the sensor can be considered to have the maximum value within a measurable time difference.
Kalman filtering is essentially the process of optimally estimating the true value (system state) based on both observed and estimated data. Both the estimated value and the measured value have errors with respect to the true value. Because of the effect of error accumulation, the difference between the estimated value and the true value is larger and larger when the system is simply estimated, and therefore the estimation is corrected by the observation data of the sensor. Meanwhile, the observation data also has the problem of noise interference, such as sensor noise, and the like, and the pure use of the sensor also deviates from the true value. The observed value and the estimated value are in the same state space, but have different probability distributions, and the overlapping part of the two probability distributions approaches the real data of the system more, namely, the confidence coefficient is higher. And continuously correcting the overlapped part of the estimated value and the observed value to obtain a reliable optimal estimated value of the system.
Setting a default system initial value at an initial time (0 th time), and setting the initial value as a first sensor measurement value for facilitating subsequent calculation, wherein the covariance is the square Q of the sensor precision errork。
At time k, the sensor pressure measurement is ZkThe prior estimation of the pressure value at the k moment is XkThe variance P of the prior estimate at time kkThe posterior estimation of the pressure value at the k moment is Xk' k time posterior estimated variance Pk', process variance is σa 2The variance of pressure changes at two continuous time points is reflected, the pressure changes in a tiny continuous time point are small, and the difference value of the pressure changes in a tiny continuous time point does not exceed sigma at mostaThen the process variance is σa 2Measurement error of the sensor is σbI.e. the measured value of the sensor fluctuates a over the true pressure value FbMeasuring the variance σb 2The difference between the sensor precision and the real temperature is the variance, and the Kalman gain at the moment K is K'.
The system can be described by a linear random differential equation, namely measuring the estimated voltage X at the moment kk：
Xk＝AXk-1'+Buk (3-1)
Wherein Xk-1"is an optimal estimated value at time k-1, that is, a system state, and indicates an estimated state at time k-1, and if k is 0, X isk-1Term is 0, ukAnd a and B are system parameters, wherein a is 1 and B is 1. This equation completes the pre-estimation of the state at time k.
Estimating a voltage X corresponding to the measurementkCovariance P ofk：
In the formula Pk-1Is' corresponding to Xk-1' of the covariance of the data,σa 2is the covariance (about σ) of the system processa 2＝0.1)。
Kalman gain is determined by the voltage estimate X at time kkAnd the measured value calculates the gain K' of the system state at the next moment:
wherein K' is the updated Kalman gain; sigmab 2To measure the variance, which is the square of the maximum error of the sensor (sigma of a typical pressure sensor)b 2＝0.000025)。
The optimum measured estimated voltage for the system at time k is Xk’：
Xk'＝Xk+K'(Zk-HXk) (3-4)
In the formula ZkFor the measured voltage at time k, H is the system parameter (H ═ 1), which establishes the measured voltage Z at time kkAnd the optimum measurement estimate voltage Xk' the link between them.
And the estimated voltage X corresponding to the optimal measurementk' covariance is Pk’：
Pk'＝Pk-K'HPk (3-5)
P is obtained by calculation according to the formula (3-5)k' for the next kalman filtering.
The voltage Z can be measured according to the k time through five formulas (3-1) to (3-5)kCalculating the optimal estimated voltage value X of the systemk' to make up for the error problem of the sensor.
At time k, the prior estimate of the pressure value at time k is X (k | k-1), the posterior estimate of the pressure value at time k is X (k | k), and the process variance is σa 2The variance of pressure changes at two continuous time points is reflected, the pressure changes in a tiny continuous time point are small, and the difference value of the pressure changes in a tiny continuous time point does not exceed sigma at mostaThen the process variance is σa 2The measurement error of the sensor isσbI.e. the measured value of the sensor fluctuates a over the true pressure value FbMeasuring the variance σb 2The method is characterized by comprising the steps of obtaining the variance of the difference between the sensor precision and the real temperature, measuring values Z (k) at the k moment, wherein Kalman gain (Kalman gain) at the k moment is Kg (k), the value of the Kalman gain is 0.82-0.91, the k moment is estimated a priori, the variance P (k | k-1) is estimated a priori, and the k moment is estimated a posteriori, the variance P (k | k) is estimated a posteriori.
The iterative calculation method specifically comprises the following steps:
at time k, the a posteriori estimate at time k-1 is considered equal to the a priori estimate at time k, X (k | k-1) ═ X (k-1| k-1), and the a priori estimate variance P (k | k-1) ═ P (k-1| k-1) + σa 2Kalman gain kg at time k (k) (P (k | k-1))/(P (k | k-1) + σb 2) The posterior estimate at time k is X (k | k) ═ X (k | k-1) + kg (k) (z) (k) -X (k | k-1)), and the posterior estimate at time k P (k | k) ═ 1-kg (k) ((k)) (P (k | k-1), and at this time, the variance between the posterior estimate at time k and the posterior estimate is calculated, and the prior estimate, kalman gain, and posterior estimate at next time (k +1) can be estimated. By such iterative calculation, random interference can be eliminated finally, and a reliable estimation value is recurred finally. In practical use, a more reliable voltage acquisition value can be obtained by using one iteration, namely a reliable voltage estimation value is deduced by using two measured voltage values.
It should be noted that, regarding the specific structure of the present invention, the connection relationship between the modules adopted in the present invention is determined and can be realized, except for the specific description in the embodiment, the specific connection relationship can bring the corresponding technical effect, and the technical problem proposed by the present invention is solved on the premise of not depending on the execution of the corresponding software program.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. The utility model provides a tactile sensation collection system with temperature compensation function, includes host computer (1), controller (2), display screen (3), its characterized in that: the device is characterized by further comprising an acquisition module (4) and a temperature compensation module (5), wherein the controller (2) is respectively connected with the upper computer (1), the display screen (3) and the temperature compensation module (5) through leads, an A/D module (6) is integrated on the controller (2), and the A/D module (6) is connected with the acquisition module (4) through leads;
the acquisition module (4) comprises a first analog switch (41), a second analog switch (42) and a pressure sensor array (43), the temperature compensation module (5) comprises a temperature compensation thermistor (51), the first analog switch (41) is connected with one end of the pressure sensor array (43) through a lead, the other end of the pressure sensor array (43) is connected with one end of the temperature compensation thermistor (51) through a lead, and the other end of the temperature compensation thermistor (51) is connected with the second analog switch (42) through a lead;
the touch perception acquisition system scans the pressure sensor array (43) through the first analog switch (41), acquires the voltage subjected to temperature compensation through the A/D module (6), and transmits the voltage to the upper computer (1) for display after Kalman filtering; the Kalman filtering comprises the following specific steps:
setting a default system initial value at the initial moment, wherein the covariance is the square Q of the accuracy error of the sensork；
At time k, the sensor pressure measurement is ZkThe prior estimation of the pressure value at the k moment is XkThe variance P of the prior estimate at time kkThe posterior estimation of the pressure value at the k moment is Xk' k time posterior estimated variance Pk', process variance is σa 2Is the variance of pressure change at two continuous moments, and the measurement error of the sensor is sigmabI.e. the measured value of the sensor fluctuates a over the true pressure value FbMeasuring the variance σb 2The variance of the difference between the sensor precision and the real temperature is obtained, and the Kalman gain at the moment K is K';
the system is described by a linear random differential equation, i.e. measuring the estimated voltage X at time kk：
Xk＝AXk-1′+Buk (3-1)
Wherein Xk-1"is an optimal estimated value at time k-1, that is, a system state, and indicates an estimated state at time k-1, and if k is 0, X isk-1Term is 0, ukThe control quantity of the system at the moment k is, A and B are system parameters, wherein A is 1, and B is 1;
estimating a voltage X corresponding to the measurementkCovariance P ofk：
In the formula Pk-1Is' corresponding to Xk-1' covariance, σa 2Is the covariance of the system process;
kalman gain is determined by the voltage estimate X at time kkAnd the measured value calculates the gain K' of the system state at the next moment:
wherein K' is the updated Kalman gain; sigmab 2Is the measurement variance, which is the square of the maximum error of the sensor;
the optimum measured estimated voltage for the system at time k is Xk’：
Xk′＝Xk+K′(Zk-HXk) (3-4)
In the formula ZkThe measured voltage at the moment k, H is a system parameter;
and the estimated voltage X corresponding to the optimal measurementk' covariance is Pk’：
Pk′＝Pk-K′HPk (3-5)
P is obtained by calculation according to the formula (3-5)k' for the next kalman filtering.
2. A haptic perception acquisition system with temperature compensation function according to claim 1, wherein: the pressure sensor array (43) adopts RP-C resistance type pressure sensitive sensors, and each RP-C resistance type pressure sensitive sensor is connected with a constant value resistor in parallel.
3. A haptic perception acquisition system with temperature compensation function according to claim 1, wherein: the model of the first analog switch (41) and the model of the second analog switch (42) are CD 4051.
4. A haptic perception acquisition system with temperature compensation function according to claim 1, wherein: the display screen (3) adopts a liquid crystal display screen of LCD12864 type;
the controller (2) adopts an ARM model STM32F103ZET6 as a core chip.
5. A haptic perception acquisition method with a temperature compensation function is characterized in that: the method comprises the following steps:
the method comprises the following steps: after the acquisition system is powered on, setting initial reference voltage through a one-key reset function;
step two: the controller (2) controls the first analog switch (41) to carry out row-column scanning on the pressure sensor array (43) and determine an acquisition point;
step three: calculating an output voltage according to a formula Vout Vcc Rs2/(Rs2+ Rp + Rt// Rs1), collecting the output voltage Vout by an A/D module (6) in the controller (2), transmitting the output voltage Vout to an upper computer (1) after Kalman filtering processing, and displaying the output voltage on a display screen (3);
in the above formula: vout is output voltage, Vcc is input voltage, Rs1 is fixed value resistor, Rs2 is divider resistor, Rp is pressure sensor equivalent variable resistor, Rt is resistance value of RNT thermistor;
the process of performing kalman filtering on the output voltage in the third step includes the following steps:
step 3.1: setting an initial value as a first sensor measurement value, wherein the covariance of the first sensor measurement value is the square Q of the sensor precision errork；
Step 3.2: using a linear random differential equation to describe the measured estimated voltage X at kkThe calculation formula is as follows:
Xk＝AXk-1′+Buk；
in the above formula: xkIs a priori estimate of the pressure value at time k, Xk-1' is an optimal estimation value at the time k-1, i.e., a system state, and represents an estimated state at the time k-1, ukThe control quantity of the system at the moment k is, A and B are system parameters, and A is set to be 1, and B is set to be 1;
step 3.3: calculating a measured estimated voltage XkCovariance P ofkThe calculation formula is as follows:
in the above formula: pkIs the variance of the prior estimate at time k, Pk-1Is' corresponding to Xk-1' covariance, σa 2Is the covariance of the system process;
step 3.4: using the voltage estimate X at time kkAnd the gain K' of the system state at the next moment is calculated by the measured value, which is the product of the measured Gaussian distribution and the empirically estimated Gaussian distribution, and the calculation formula is as follows:
in the above formula: k 'is updated Kalman gain, and the value of K' is 0.82-0.91; sigmab 2Is the measurement variance, which is the square of the maximum error of the sensor;
the optimal measurement estimation voltage calculation formula of the system at the moment k is as follows:
Xk′＝Xk+K′(Zk-HXk)；
in the above formula: zkFor the measured voltage at time k, H is a system parameter, set H to 1, and the estimated voltage X is estimated for this optimum measurementk' covariance PkThe formula for calculation of' is:
Pk′＝Pk-K′HPk；
step 3.5: carrying out iterative computation according to the prior estimation and the posterior estimation of the pressure value at the k moment;
at time k, the a posteriori estimate X (k-1| k-1) at time k-1 is set equal to the a priori estimate X (k | k-1) at time k, i.e. X (k | k-1) ═ X (k-1| k-1), and the a priori estimate variance P (k | k-1) ═ P (k-1| k-1) + σ ∑ k-1a 2；
In the above formula: sigmaa 2For the process variance of the pressure change at two consecutive moments, P (k | k-1) is the variance of the prior estimation at moment k, and P (k-1| k-1) is the variance of the posterior estimation at moment k-1;
kalman gain kg at time k (k) (P (k | k-1))/(P (k | k-1) + σb 2)，
In the above formula: sigmab 2The measurement variance of the sensor precision and the real temperature difference value is obtained;
the posterior estimate at time k is X (k | k) ═ X (k | k-1) + kg (k) ((z) (k) -X (k | k-1)), and the variance of the posterior estimate at time k is P (k | k) ═ 1-kg (k) ((k)) P (k | k-1), and in this case, the posterior estimate at time k and the variance of the posterior estimate are calculated, i.e., the prior estimate, kalman gain, and posterior estimate at time k +1 can be estimated.
6. The method for acquiring haptic perception with temperature compensation function according to claim 5, wherein: the second step comprises the following specific steps:
step 2.1: equating the pressure sensor array (43) to a variable resistance Rp;
step 2.2: the resistance value of Rp is determined by row-column scanning of the pressure sensor array (43) through the first analog switch (41), the Rp is increased along with the increase of the temperature, the Rp is compensated through a circuit formed by connecting an RNT thermistor Rt and a fixed value resistor Rs1 in parallel, and the parallel resistance value of the Rp is reduced along with the increase of the temperature;
step 2.3: calculating an output voltage according to Vout Vcc Rs2/(Rs2+ Rp + Rt// Rs 1);
step 2.4: the A/D module (6) in the controller (2) collects output voltage Vout, and the output voltage Vout is transmitted to the upper computer (1) and displayed on the display screen (3) after Kalman filtering processing.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101936791A (en) * | 2010-07-28 | 2011-01-05 | 四川蜀谷仪表科技有限公司 | Digital pressure gauge |
| CN102865951A (en) * | 2012-09-26 | 2013-01-09 | 太原理工大学 | Piezoresistive sensor for internal pressure detection of expressway road bed |
| CN106094963A (en) * | 2016-07-31 | 2016-11-09 | 桂林理工大学 | APD array chip bias voltage Full-automatic temperature compensation system |
| CN106483968A (en) * | 2016-12-13 | 2017-03-08 | 广西师范大学 | A kind of ground surface identifying device automatically landed for unmanned plane |
| CN109620200A (en) * | 2019-01-30 | 2019-04-16 | 深圳市科曼医疗设备有限公司 | A kind of device and method of intracranial pressure, encephalic temperature testing calibration |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110704790A (en) * | 2019-09-06 | 2020-01-17 | 湖北文理学院 | Lithium battery SOC estimation method based on IFA-EKF |
-
2021
- 2021-01-07 CN CN202110018636.9A patent/CN112621762B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101936791A (en) * | 2010-07-28 | 2011-01-05 | 四川蜀谷仪表科技有限公司 | Digital pressure gauge |
| CN102865951A (en) * | 2012-09-26 | 2013-01-09 | 太原理工大学 | Piezoresistive sensor for internal pressure detection of expressway road bed |
| CN106094963A (en) * | 2016-07-31 | 2016-11-09 | 桂林理工大学 | APD array chip bias voltage Full-automatic temperature compensation system |
| CN106483968A (en) * | 2016-12-13 | 2017-03-08 | 广西师范大学 | A kind of ground surface identifying device automatically landed for unmanned plane |
| CN109620200A (en) * | 2019-01-30 | 2019-04-16 | 深圳市科曼医疗设备有限公司 | A kind of device and method of intracranial pressure, encephalic temperature testing calibration |
Non-Patent Citations (2)
| Title |
|---|
| 基于多维尺度法和卡尔曼滤波的机器人;严尔军等;《中国工程机械学报》;20181231;486-491 * |
| 颅内温度和压力传感器校准与补偿算法研究;王音心等;《压电与声光》;20191231;856-860 * |
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