CN110661511B - Matrix type adaptive filter hysteresis control system and method - Google Patents
Matrix type adaptive filter hysteresis control system and method Download PDFInfo
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Abstract
The invention discloses a matrix type self-adaptationA system and method for filter hysteresis control, the system comprising: the device comprises an error calculation module, a self-adaptive algorithm module, a piezoelectric ceramic actuator and a filter; the filter includes: a first adder, a plurality of delay operators and a plurality of filter units FiThe filtering unit includes: the output end of each second adder is connected with the input end of the first adder, the output of the first adder serves as the output of the whole filter, the error calculation module is used for determining an error signal according to two input signals, the adaptive algorithm module is used for adjusting the weight in the adaptive weighting module according to the error signal in real time, the adjusted filter is used for performing hysteresis compensation on the piezoelectric ceramic actuator, and the influence of hysteresis characteristics on the piezoelectric ceramic actuator is reduced through the filter, so that the precision of the piezoelectric ceramic actuator is improved.
Description
Technical Field
The present invention relates to the field of hysteresis control technologies, and in particular, to a hysteresis control system and method for a matrix adaptive filter.
Background
With the development of science and technology, the nano positioning technology and the nano sensing technology are gradually applied to various micro-nano operations, wherein the piezoelectric ceramic actuator is widely applied due to higher precision and speed. In practical application, however, due to the characteristics of nonlinearity, hysteresis, creep and the like of the material, the repeated positioning accuracy of the system is poor. The hysteresis nonlinearity has a significant effect on the accuracy of the system, and is represented by: memory characteristics, output signal dependent on current input signal value and historical input value, multi-value mapping characteristics, the same input signal may correspond to different output signals.
In order to reduce the influence of hysteresis characteristics on the piezoelectric ceramic actuator, at present, three types of direct inverse control, feedback control and feedforward-feedback control are mainly used, and most of controllers are used for performing hysteresis compensation control on the piezoelectric ceramic actuator through establishing an accurate hysteresis nonlinear model and through the hysteresis model. The existing hysteresis nonlinear models comprise Preisach, Prandtl-Ishlinskii, Bouc-Wen, neural network models, support vector machines, segment similarity models and the like based on input and output phenomenon interpretation, and also comprise MAXWELL models based on physical principle interpretation and the like. The Preisach model uses a hysteresis operator, can simulate a hysteresis structure according to input and output, has a simple principle and a good fitting effect, and is widely applied. The PI model replaces a hysteresis operator in the Preisach model with a Backlash hysteresis element model, and the Backlash operator has the characteristic of memorizing historical input, so that the modeling precision is improved, but the model is more complex. Machine learning models such as neural networks and support vector machines use input and output data as training sets and then predict system output, but the problems of local optimization and overfitting are easily caused.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a hysteresis control system and method for a matrix type adaptive filter, which can reduce the influence of hysteresis characteristics on a piezoelectric ceramic actuator through the adaptive filter, so as to improve the accuracy of the piezoelectric ceramic actuator.
In order to achieve the purpose, the invention provides the following technical scheme:
a matrix-type adaptive filter hysteresis control system, the hysteresis control system comprising: the device comprises an error calculation module, a self-adaptive algorithm module, a piezoelectric ceramic actuator and a filter;
the filter includes: a first adder, a plurality of delay operators and a plurality of filter units FiWherein i is 1,2,3.. m;
the input end of the filter is respectively connected with the input end of the ith filtering unit through i-1 delay operators, and the input end of the filter, the i-1 delay operators and the ith filtering unit form a first stage;
the filtering unit includes: the second adder, the multiple adaptive weighting modules and the multiple Backlash operators with the same width form a series structure, the input end of the head Backlash operator is the input end of the filter unit, the input end signal of the head Backlash operator, the output end signal of the tail Backlash operator and the signal between two adjacent Backlash operators are led out and respectively connected to the input ends of the corresponding adaptive weighting modules, and the output end of each adaptive weighting module is connected with the input end of the second adder;
the output end of each second adder is connected with the input end of the first adder, and the output of the first adder serves as the output of the whole filter;
the output of the error calculation module is connected with each self-adaptive weighting module, one input of the error calculation module is an expected output signal of the filter, namely a reference signal, the other input of the error calculation module is an actual output signal, and the actual output signal is a filter output signal or a piezoelectric ceramic actuator output signal; the error calculation module is used for determining an error signal according to two input signals, the self-adaptive algorithm module is used for adjusting the weight in each self-adaptive weighting module in real time according to the error signal, and the adjusted filter is used for performing hysteresis compensation on the piezoelectric ceramic actuator.
Optionally, the output of the entire filter is y (k) sum (w (k)) O ″k) W (k) represents a weight matrix at time k, o (k) represents outputs of respective stages of the filter at time k, and symbol o represents a hadamard product.
Optionally, the calculation error module is configured to calculate the desired output signal yd(k) Error from the actual output signal y (k), i.e. e (k) ═ yd(k)-y(k)。
Optionally, the adaptive algorithm module adjusts the weight in each adaptive weighting module in real time by using an LMS algorithm according to the error.
A hysteresis control method of a matrix type adaptive filter, the control method comprising:
inputting a voltage signal into the piezoelectric ceramic actuator to obtain a first output signal;
inputting the voltage signals into the filter, and obtaining a plurality of second output signals by adjusting the number of filtering units in the filter and/or the number of Backlash operators in the filtering units;
determining a plurality of first signal errors by using an error calculation module according to the first output signal and each second output signal;
determining a second signal error according to each first signal error by adopting a Root Mean Square Error (RMSE) criterion, wherein the second signal error is one of the first signal errors;
and performing hysteresis compensation on the piezoelectric ceramic actuator by adopting a filter corresponding to the second signal error.
Optionally, performing hysteresis compensation on the piezoelectric ceramic actuator by using the filter corresponding to the second signal error specifically includes:
inputting the current moment instruction signal to the input end of the filter corresponding to the second signal error to obtain a current first signal;
inputting the current first signal to an input end of the piezoelectric ceramic actuator to obtain a current second signal;
determining a current error signal by adopting an error calculation module according to the current moment instruction signal and the current second signal;
according to the current error signal, the adaptive algorithm module is adopted to adjust the weight value in each adaptive weighting module of the filter corresponding to the second signal error at the current moment to obtain an adjusted filter;
and inputting a next-time instruction signal to the input end of the adjusted filter to obtain a next-time first signal, and skipping to the step of inputting the current first signal to the input end of the piezoelectric ceramic actuator to obtain a current second signal, so as to perform hysteresis compensation on the piezoelectric ceramic actuator.
Optionally, the second signal error is the smallest of the first signal errors.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a lag control system and method of matrix type adaptive filter, the system includes: the device comprises an error calculation module, a self-adaptive algorithm module, a piezoelectric ceramic actuator and a filter; the filter includes: a first adder, a plurality of delay operators and a plurality of filter units FiThe filtering unit includes: the output end of each second adder is connected with the input end of the first adder, the output of the first adder serves as the output of the whole filter, the error calculation module is used for determining an error signal according to two input signals, the adaptive algorithm module is used for adjusting the weight in the adaptive weighting module according to the error signal in real time, the adjusted filter is used for performing hysteresis compensation on the piezoelectric ceramic actuator, and the influence of hysteresis characteristics on the piezoelectric ceramic actuator is reduced through the filter, so that the precision of the piezoelectric ceramic actuator is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a diagram illustrating the transmission characteristics of the Backlash operator according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an adaptive matrix filter and an error calculation module according to an embodiment of the invention;
FIG. 3 is a block diagram of a modeling of an embodiment of the present invention using an adaptive matrix filter;
fig. 4 is a block diagram of an adaptive matrix filter compensation method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a hysteresis control system and a hysteresis control method of a matrix type adaptive filter, which reduce the influence of hysteresis characteristics on a piezoelectric ceramic actuator through the adaptive filter so as to improve the precision of the piezoelectric ceramic actuator.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a transmission characteristic diagram of a Backlash operator according to an embodiment of the present invention, and fig. 2 is a schematic structural diagram of an adaptive matrix filter and an error calculation module according to an embodiment of the present invention, as shown in fig. 1-2, a hysteresis control system of a matrix adaptive filter, the hysteresis control system including: the device comprises an error calculation module, a self-adaptive algorithm module, a piezoelectric ceramic actuator and a filter; the filter includes: a first adder, a plurality of delay operators and a plurality of filter units FiWherein i is 1,2,3.. m;
the input end of the filter is respectively connected with the input end of the ith filtering unit through i-1 delay operators, and the input end of the filter, the i-1 delay operators and the ith filtering unit form a first stage;
the filtering unit includes: the second adder, the multiple adaptive weighting modules and the multiple Backlash operators with the same width form a series structure, the input end of the head Backlash operator is the input end of the filter unit, the input end signal of the head Backlash operator, the output end signal of the tail Backlash operator and the signal between two adjacent Backlash operators are led out and respectively connected to the input ends of the corresponding adaptive weighting modules, and the output end of each adaptive weighting module is connected with the input end of the second adder;
the output end of each second adder is connected with the input end of the first adder, and the output of the first adder serves as the output of the whole filter;
the output of the error calculation module is connected with each self-adaptive weighting module, one input of the error calculation module is an expected output signal of the filter, namely a reference signal, the other input of the error calculation module is an actual output signal, and the actual output signal is a filter output signal or a piezoelectric ceramic actuator output signal; the error calculation module is used for determining an error signal according to two input signals, the self-adaptive algorithm module is used for adjusting the weight in each self-adaptive weighting module in real time according to the error signal, and the adjusted filter is used for performing hysteresis compensation on the piezoelectric ceramic actuator.
As an embodiment of the present invention, the output of the entire filter is y (k) sum (w (k)) O —k) W (k) represents a weight matrix at time k, o (k) represents outputs of respective stages of the filter at time k, and symbol o represents a hadamard product.
As an embodiment of the invention, the calculation error module of the invention is used for calculating the expected output signal yd(k) Error from the actual output signal y (k), i.e. e (k) ═ yd(k)-y(k)。
As an embodiment of the present invention, the adaptive algorithm module of the present invention adjusts the weight in each adaptive weighting module in real time by using an LMS algorithm according to the error.
A hysteresis control method of a matrix type adaptive filter, the control method comprising:
inputting a voltage signal into the piezoelectric ceramic actuator to obtain a first output signal;
inputting the voltage signals into the filter, and obtaining a plurality of second output signals by adjusting the number of filtering units in the filter and/or the number of Backlash operators in the filtering units;
determining a plurality of first signal errors by using an error calculation module according to the first output signal and each second output signal;
determining a second signal error according to each first signal error by adopting a Root Mean Square Error (RMSE) criterion, wherein the second signal error is one of the first signal errors;
and performing hysteresis compensation on the piezoelectric ceramic actuator by adopting a filter corresponding to the second signal error.
As an embodiment of the present invention, the performing hysteresis compensation on the piezoelectric ceramic actuator by using the filter corresponding to the second signal error specifically includes:
inputting the current moment instruction signal to the input end of the filter corresponding to the second signal error to obtain a current first signal;
inputting the current first signal to an input end of the piezoelectric ceramic actuator to obtain a current second signal;
determining a current error signal by adopting an error calculation module according to the current moment instruction signal and the current second signal;
according to the current error signal, the adaptive algorithm module is adopted to adjust the weight value in each adaptive weighting module of the filter corresponding to the second signal error at the current moment to obtain an adjusted filter;
and inputting a next-time instruction signal to the input end of the adjusted filter to obtain a next-time first signal, and skipping to the step of inputting the current first signal to the input end of the piezoelectric ceramic actuator to obtain a current second signal, so as to perform hysteresis compensation on the piezoelectric ceramic actuator.
As an embodiment of the present invention, the second signal error is the smallest one of the first signal errors.
The following detailed description of each part:
the technical problem solved by the invention is as follows: for the problem of poor repeated positioning accuracy caused by hysteresis characteristics of the piezoelectric ceramic actuator, a hysteresis control system is needed to improve the accuracy of the piezoelectric ceramic actuator. If for an input reference displacement signal ydThe controller can provide a corresponding control signal u such that the output of the piezoceramic actuator is equal to the reference displacement signal, i.e. y ═ ydThereby enabling the system output to accurately track the reference displacement signal.
First, a Backlash operator is explained, as shown in fig. 1, which is a transmission characteristic diagram of the Backlash operator, and an output of the Backlash operator satisfies the following formula:
where u (t) is the input of time t, r is the threshold of the Backlash operator, pr[u(t-)]The output signal representing the Backlash operator is the same as the output signal at the previous instant, and the Backlash operator output can be written in the following discrete form:
where Δ u (k) represents the difference between time k and time k-1. Since the Backlash operator has the characteristic of memorizing input, the Backlash operator can be used for forming an adaptive filter to model the piezoelectric ceramic actuator.
As shown in fig. 2, first n Backlash operators are connected end to obtain a FIR filter based on the Backlash operators, and then a delay operator Z is used-1And connecting corresponding filtering units to improve the modeling capacity of the non-local memory characteristic, wherein u (k) represents an input signal at the time of k, u (k-1) represents an input signal at the time of k-1, namely a signal obtained by adopting one delay operator, and u (k-m) represents an input at the time of k-m, namely a signal obtained by adopting m delay operators.
The output of the entire filter is:
y(k)＝sum(W(k)οOk)
w (k) represents a weight matrix at the time k, o (k) represents outputs of each stage of the filter at the time k, a symbol omicron represents a hadamard product, that is, multiplication of corresponding elements of the two matrices, and sum (omicron) represents addition of all elements of the matrices.
Wherein, wi,j(k) Represents the ith filtering unit, the jth weight, the initial weight is set to zero,
of a preceding column
And if j is 2, the output of u (k-i +1) passing through the Backlash operator.
The systematic error at time k is defined as:
e(k)＝yd(k)-y(k)＝yd(k)-sum(W(k)οO(k))
the weight matrix and filter output matrix can be written in the form of the following vectors:
the error can be written in the form:
FIG. 3 is a block diagram of a modeling method using an adaptive matrix filter according to an embodiment of the present invention, as shown in FIG. 3, comprising the steps of:
and inputting the voltage signal into the piezoelectric ceramic actuator to obtain a first output signal, namely an actual displacement signal.
The obtained actual input and output signals of the piezoelectric ceramic actuator are subjected to hysteresis modeling of the piezoelectric ceramic actuator through an adaptive filter, as shown in fig. 3, the voltage in the experiment is used as the input signal, and then the error between the output signal of the filter and the actual output signal of the piezoelectric ceramic is used for updating the weight. The LMS algorithm is a common algorithm for updating adaptive filter, and its core is to use the instantaneous error value e (k) to obtain the gradient vector of weight
It is expressed as:
the expression for updating the weight is as follows:
and mu is an update step length which determines the convergence rate and stability of the LMS algorithm to the weight, if mu is larger, the update speed is high, but oscillation is easy to occur to cause that convergence cannot be realized, otherwise, if mu is smaller, the convergence speed is lower, so that the system needs to transit for a long time, and therefore, the appropriate update step length needs to be selected by integrating the convergence speed and the stability.
The method comprises the following steps of updating matrix filters with different step lengths to perform hysteresis modeling on a piezoelectric ceramic actuator by using different numbers of filter units and Backlash operators, selecting an optimal structure by integrating modeling effects and complexity of the filters, selecting Root Mean Square Error (RMSE) as a criterion for general modeling and control effects, and adopting the following calculation formula:
wherein l represents the control or modeling time, generally speaking, more filtering units and Backlash operators are used to obtain higher control precision, but if the model is too complicated, the overfitting phenomenon is caused, so that the control effect is poor, and in the invention, the filter with the minimum error and short modeling time is selected as the optimal filter structure.
The optimal hysteresis modeling filter structure obtained by the method is used as an adaptive controller to carry out hysteresis compensation control on the piezoelectric ceramic actuator. FIG. 4 is a block diagram of an adaptive matrix filter compensation method according to an embodiment of the present invention, as shown in FIG. 4, using a reference input displacement signal yd(k) As input signal of the filter, and output signal of the filter as control voltage signal u (k), and reference displacement signal yd(k) And the error of the actual displacement signal y (k) of the piezoelectric ceramics is adapted to the updated weight value.
The Backlash operator and the delay operator are used for forming the matrix type filter, the output weight is dynamically updated through the error signal, and accurate modeling and control are carried out in a self-adaptive mode in the working process of piezoelectric ceramic driving.
Compared with the prior art, the invention has the advantages that: the defect that the existing adaptive FIR filter based on the Backlash operator cannot process non-local memory characteristics is overcome, and the modeling precision and the control effect are improved by connecting a plurality of adaptive FIR filters by using the delay operator.
The parameters of the controller can be adjusted in a self-adaptive mode according to the actual operation condition of the system, and compared with a static controller, the self-adaptive filter has higher adaptability and control performance, and the Backlash operator has the characteristic of memorizing historical input, so that the matrix type self-adaptive filter based on the Backlash operator and the delay operator is used for carrying out high-precision control on the piezoelectric actuator.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (5)
1. A matrix-type adaptive filter hysteresis control system, the hysteresis control system comprising: the device comprises an error calculation module, a self-adaptive algorithm module, a piezoelectric ceramic actuator and a filter;
the filter includes: a first adder, a plurality of delay operators and a plurality of filter units FiWherein i is 1,2,3.. m;
the input end of the filter is respectively connected with the input end of the ith filtering unit through i-1 delay operators, and the input end of the filter, the i-1 delay operators and the ith filtering unit form a first stage;
the filtering unit includes: the second adder, the multiple adaptive weighting modules and the multiple Backlash operators with the same width form a series structure, the input end of the head Backlash operator is the input end of the filter unit, the input end signal of the head Backlash operator, the output end signal of the tail Backlash operator and the signal between two adjacent Backlash operators are led out and respectively connected to the input ends of the corresponding adaptive weighting modules, and the output end of each adaptive weighting module is connected with the input end of the second adder;
the output end of each second adder is connected with the input end of the first adder, and the output of the first adder serves as the output of the whole filter;
the output of the error calculation module is connected with each self-adaptive weighting module, one input of the error calculation module is an expected output signal of the filter, namely a reference signal, the other input of the error calculation module is an actual output signal, and the actual output signal is a filter output signal or a piezoelectric ceramic actuator output signal; the error calculation module is used for determining an error signal according to two input signals, the self-adaptive algorithm module is used for adjusting the weight in each self-adaptive weighting module in real time according to the error signal, and the adjusted filter is used for performing hysteresis compensation on the piezoelectric ceramic actuator;
the output of the entire filter is:
wherein W (k) represents the weight matrix at time k, and O (k) represents the output and sign of each stage of the filter at time k
The product of the hadamard is represented,
represents adding all elements in the matrix;
wherein, wi,j(k) Representing the ith filtering unit, the jth weight, with the initial weight set to zero, Pi,j(k) P of the previous columni,j-1(k) Outputting after the Backlash operator;
the error signal is defined as:
wherein, yd(k) For inputting a reference displacement signal;
and adjusting the weight in each self-adaptive weighting module in real time according to the error signal, wherein the expression for updating the weight is as follows:
wherein
μ is the update step size.
2. The lag control system of claim 1, wherein the adaptive algorithm module adjusts the weights of the adaptive weighting modules in real time according to the error by using an LMS algorithm.
3. A hysteresis control method of a matrix type adaptive filter, applied to a hysteresis control system of a matrix type adaptive filter as claimed in any one of claims 1 to 2, the control method comprising:
inputting a voltage signal into the piezoelectric ceramic actuator to obtain a first output signal;
inputting the voltage signals into the filter, and obtaining a plurality of second output signals by adjusting the number of filtering units in the filter and/or the number of Backlash operators in the filtering units;
determining a plurality of first signal errors by using an error calculation module according to the first output signal and each second output signal;
determining a second signal error according to each first signal error by adopting a Root Mean Square Error (RMSE) criterion, wherein the second signal error is one of the first signal errors;
performing hysteresis compensation on the piezoelectric ceramic actuator by adopting a filter corresponding to the second signal error;
the output of the entire filter is:
wherein W (k) represents the weight matrix at time k, and O (k) represents the output and sign of each stage of the filter at time k
The product of the hadamard is represented,
represents adding all elements in the matrix;
wherein, wi,j(k) Representing the ith filtering unit, the jth weight, with the initial weight set to zero, Pi,j(k) P of the previous columni,j-1(k) Outputting after the Backlash operator;
the error signal is defined as:
wherein, yd(k) For inputting a reference displacement signal;
and adjusting the weight in each self-adaptive weighting module in real time according to the error signal, wherein the expression for updating the weight is as follows:
wherein
μ is the update step size.
4. The hysteresis control method of a matrix-type adaptive filter according to claim 3, wherein the hysteresis compensation of the piezoceramic actuator by using the filter corresponding to the second signal error specifically comprises:
inputting the current moment instruction signal to the input end of the filter corresponding to the second signal error to obtain a current first signal;
inputting the current first signal to an input end of the piezoelectric ceramic actuator to obtain a current second signal;
determining a current error signal by adopting an error calculation module according to the current moment instruction signal and the current second signal;
according to the current error signal, the adaptive algorithm module is adopted to adjust the weight value in each adaptive weighting module of the filter corresponding to the second signal error at the current moment to obtain an adjusted filter;
and inputting a next-time instruction signal to the input end of the adjusted filter to obtain a next-time first signal, and skipping to the step of inputting the current first signal to the input end of the piezoelectric ceramic actuator to obtain a current second signal, so as to perform hysteresis compensation on the piezoelectric ceramic actuator.
5. The hysteresis control method for matrix type adaptive filter as claimed in claim 3, wherein the second signal error is the smallest one of the first signal errors.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101977034A (en) * | 2010-11-08 | 2011-02-16 | 北京理工大学 | Backlash self-adaptive filter and method for modeling and compensating hysteresis thereof |
| CN102394592A (en) * | 2011-10-18 | 2012-03-28 | 北京理工大学 | Adaptive filter based on Backlash operator |
| CN104796111A (en) * | 2015-05-14 | 2015-07-22 | 北京航空航天大学 | Non-linear self-adaptive filter for dynamic hysteretic system modeling and compensation |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101977034A (en) * | 2010-11-08 | 2011-02-16 | 北京理工大学 | Backlash self-adaptive filter and method for modeling and compensating hysteresis thereof |
| CN102394592A (en) * | 2011-10-18 | 2012-03-28 | 北京理工大学 | Adaptive filter based on Backlash operator |
| CN104796111A (en) * | 2015-05-14 | 2015-07-22 | 北京航空航天大学 | Non-linear self-adaptive filter for dynamic hysteretic system modeling and compensation |
Non-Patent Citations (1)
| Title |
|---|
| 压电陶瓷执行器迟滞的滑模逆补偿控制;刘向东等;《光学精密工程》;20110615;第19卷(第6期);第1281-1290页 * |
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