CN113093542B - Motor torque optimization finite set prediction control parallel computing method - Google Patents
Motor torque optimization finite set prediction control parallel computing method Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
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Abstract
The invention relates to a motor torque optimization finite set predictive control parallel computing method, which comprises the following steps: s1: according to the target of motor torque optimization control, giving a description form of an optimization problem and obtaining a target function; s2: performing Euler discretization according to a continuous time model of the motor driving system to construct a prediction model of the system; s3: and performing parallel computing solution optimization on the prediction model and the objective function by adopting a trigger type parallel computing method to realize the optimal prediction control of the motor torque. Compared with the prior art, the method has the advantages that the algorithm is accelerated through a production line and a parallel computing method, and the real-time control of the motor torque optimization is realized.
Description
Technical Field
The invention relates to the field of motor control, in particular to a motor torque optimization finite set predictive control parallel computing method.
Background
The motor driving system is one of three core technologies of an electric automobile, and as a bottom layer execution unit, the motor driving system directly influences the yaw stability of the whole automobile and the execution of an energy management strategy, and the effective control of an inverter as a motor core component is the key of the motor driving control. Considering actual system constraints such as discrete switch states of the inverter, maximum stator current and the like, and a plurality of control requirements such as motor torque tracking, energy conservation and the like, the motor torque optimization control is a discontinuous multi-objective constraint optimization problem. The finite control set predictive control can directly optimize inverter discrete switch variables by considering system constraints, so as to realize multi-objective optimization of motor torque control, and inverter switch devices generally adopt high-frequency (dozens to hundreds of kHz) IGBTs or MOSFETs, and the switching frequency has high real-time requirements, but the finite set predictive control on-line traversal solving has large computational burden, so how to improve the computational performance of the finite set predictive control is a main bottleneck restricting the finite set predictive control application in motor control.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a motor torque optimization finite set predictive control parallel computing method.
The purpose of the invention can be realized by the following technical scheme:
a motor torque optimization finite set predictive control parallel computing method comprises the following steps:
s1: according to the target of motor torque optimization control, giving a description form of an optimization problem and obtaining a target function;
s2: performing Euler discretization according to a continuous time model of the motor driving system to construct a prediction model of the system;
s3: and performing parallel computing solution optimization on the prediction model and the objective function by adopting a trigger type parallel computing method to realize the optimal prediction control of the motor torque.
Preferably, the objective of the motor torque optimization control in step S1 is to control the motor stator d-q axis current to track the expected current value quickly, so as to realize the torque tracking and meet the dynamic requirement, and reduce the copper loss E inside the motor m 。
Preferably, the objective function is:
s.t. x (k+i|k) ＝f(x (k+i-1|k) ,u (k+i-1) )，
u (k+i-1) ∈{u 0 ,u 1 ,…,u 7 },i＝1,2,…p
wherein x (k + I | k) ═ I is defined d (k+i|k),I q (k+i|k)] T In order to predict the control sequence for the state,
for reference to the input sequence, Ts is a discrete step size, R m Is the resistance of the electrical winding or windings,
x (k) is a state quantity, u (k) To control the quantity, I d(k+i|k) Is the d-axis current of the ith step at time k,
is a d-axis current desired value, I, at the k + I time q(k+i|k) Is the q-axis current of the ith step in the kth time,
is a desired value of the q-axis current at the k + I time (k+p) At the k + p th timeCurrent, I (k+p) And the current expectation value at the k + P moment, P is a prediction step length, Q is a positive definite weighting matrix, P is a positive definite terminal punishment matrix, and R is a positive definite control quantity punishment matrix.
Preferably, the calculation of the objective function is divided into two layers of nodes, the first layer of nodes includes two nodes K 1 And two nodes K 2 The second layer node comprises a node J i+1 (1)、J i+1 (2)、J i+1 (3)、J i+1 (4) The calculation formula of each node is as follows:
wherein, I d (k + i +1| k) is the d-axis current of step i +1 at time k,
d-axis current expectation value of the (I + 1) th step in the k time q (k + i +1| k) is the desired value of the q-axis current at step i +1 at time k,
is the q-axis current expected value m of the (i + 1) th step in the k-th time 1 Is the motor rotor mass.
Preferably, in step S2, the future dynamic prediction is performed according to the system dynamic model, euler discretization is performed according to the continuous time model of the motor driving system, and Ts is a discrete step size, so as to obtain the following discrete model:
wherein x is (k+i|k) Is the state quantity, x, of the ith step at the kth time (k+i-1|k) Is the state quantity of the step i-1 at the kth time, u (k+i-1|k) The control quantity of the step i-1 at the kth time,
are system matrix parameters.
Preferably, the system matrix parameters are respectively:
wherein L is d ,L q Equivalent inductances of d-and q-axes, p, respectively m Is the magnetic pole pair of the motor, phi m For the magnetic flux generated in the permanent magnet re-stator phase, d is the motor speed omega r And integrated to obtain theta e ，V dc Is a DC supply voltage, R m Being the resistance of the electronic winding, T s In discrete steps.
Preference is given toWhen the prediction model is calculated, the prediction model comprises a plurality of intermediate layer nodes: d (1,i) 、D (2,i) 、E (1,i) 、 E (2,i) The calculation formula is as follows:
D (1,i) ＝a 11 I d (k+i)+a 12 (ω r )I q (k+i)
D (2,i) ＝a 21 (ω r )I d (k+i)+a 12 I q (k+i)
E (1,i) ＝b 11 (θ e )S a +b 12 (θ e )S b +b 13 (θ e )S c
E (2,i) ＝b 11 (θ e )S a +b 22 (θ e )S b +b 23 (θ e )S c
wherein S is a ,S b ,S c The system input at the time k + i, a 11 ,a 12 (ω r ),a 21 (ω r ),a 22 Is a matrix
Corresponding element, b 11 (θ e ),b 12 (θ e ),b 13 (θ e ),b 21 (θ e ),b 22 (θ e ),b 23 (θ e ) Is a matrix
Corresponding element, h 21 Is a matrix
Corresponding element, ω r And theta e Is a time-varying parameter of the system,
the prediction model obtains a future state formula of the system current as follows:
I d (k+i+1)＝D (1,i) +D (2,i)
I q (k+i+1)＝E (1,i) +E (2,i) +ω r h 21 。
preferably, the calculation formula of the objective function and the prediction model in step S3 is as follows:
J 0 ＝0
wherein J is an objective function, x (k+i|k) Is the state quantity of the ith step at the kth time, r k+i|k And Q is a positive definite weighting matrix, P is a positive definite terminal punishment matrix, and R is a positive definite control quantity punishment matrix.
Preferably, in the triggered parallel computing method in step S3, a flag indicating that both the solution of the prediction model and the solution of the objective function in the current computing step are completed is used as a flag indicating that the next step computing starts, so as to implement the parallel computing of the prediction model and the objective function.
Preferably, in step S3, an FPGA chip is used to perform parallel computation solution.
Compared with the prior art, the method accelerates the solving process of the motor torque optimization finite set predictive control based on the trigger type parallel computation, namely, the computation of each level is completely finished, and then the node solving of the next level is started. Then, according to the characteristic that mutually independent nodes exist between the prediction model and the objective function, on the basis of simplifying the optimization algorithm, a parallel computing structure of the prediction model and the objective function in each prediction time domain is provided, so that the computing efficiency and the computing accuracy are effectively improved, the real-time control of the motor torque optimization is realized, and the real-time control effect is further improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of the parallel computation of the motor drive system control of the present invention;
FIG. 3 is a diagram of a motor drive system predictive model calculation according to the present invention;
FIG. 4 is a future p-step dynamic prediction calculation chart of the motor driving system of the present invention;
FIG. 5 is a diagram of objective function J of the motor driving system of the present invention k+i+1 Calculating a graph;
FIG. 6 is a parallel calculation chart of motor torque optimization finite set predictive control realized based on FPGA;
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
A parallel computing method for motor torque optimization finite set predictive control, as shown in FIG. 1, comprises the following steps:
s1: and according to the target of the motor torque optimization control, giving a description form of an optimization problem and obtaining an objective function.
According to the expected torque Te of the motor, the expected current value under an expected d-q reference frame can be calculated
And
therefore, the motor torque control target is to control the d-q axis current of the motor stator to track the expected current value quickly, realize that the torque tracks quickly to meet the dynamic requirement, reduce the copper loss Em inside the motor and meet the economic requirement. And (3) giving a specific description form of the optimization problem according to the control target:
s.t. x (k+i|k) ＝f(x (k+i-1|k) ,u (k+i-1) )，
u (k+i-1) ∈{u 0 ,u 1 ,…,u 7 },i＝1,2,…p
wherein x (k + I | k) ═ I is defined d (k+i|k),I q (k+i|k)] T In order to predict the control sequence for the state,
for reference to the input sequence, Ts is a discrete step size, R m Is the resistance of the electrical winding or windings,
x (k) is a state quantity, u (k) To control the amount, I d(k+i|k) Is the d-axis current of the ith step at time k,
is a d-axis current desired value, I, at the k + I time q(k+i|k) Is the q-axis current of the ith step in the kth time,
is a desired value of the q-axis current at the k + I time (k+p) Is the current at time k + p, I (k+p) The current expectation value at the k + P moment is shown, P is the prediction step length, Q is a positive definite weighting matrix, P is a positive definite terminal punishment matrix, and R is a positive definite control quantity punishment matrix.
S2: and carrying out Euler discretization according to a continuous time model of the motor driving system to construct a prediction model of the system.
According to the optimization problem, the finite set prediction control needs to perform future dynamic prediction according to a system dynamic model, perform Euler discretization according to a continuous time model of a motor driving system, and obtain the following discrete model with Ts as a discrete step length:
wherein x is (k+i|k) Is the state quantity, x, of the ith step at the kth time (k+i-1|k) The shape of the step i-1 at the kth timeAmount of state, u (k+i-1|k) The control quantity of the step (i-1) at the k time,
are system matrix parameters.
The system matrix parameters are respectively:
wherein L is d ,L q Equivalent inductances of d-and q-axes, p, respectively m Is the magnetic pole pair of the motor, phi m For the magnetic flux generated in the permanent magnet re-stator phase, d is the motor speed omega r And integrated to obtain theta e ，V dc Is a DC supply voltage, R m Being the resistance of the electronic winding, T s In discrete steps.
In this embodiment, the calculation process is represented by a data structure diagram by using a calculation graph, and parallel calculation steps in the algorithm can be conveniently analyzed by representing through a plurality of nodes and edges. In order to fully excavate parallel computing steps in the algorithm, each prediction time model is subjected to node division according to the idea of a computation graph, and a computed value of each node is stored in a storage unit of a microprocessor. Taking the prediction of the k + i +1 moment at the k + i moment as an example, a motor system dynamic equation calculation chart is shown in fig. 3. The input u (k + i) ═ S at the time k + i a ,S b ,S c ) The value is {0,1 }. a is 11 ,a 12 (ω r ),a 21 (ω r ),a 22 Is a matrix in formula (3)
Corresponding element, b 11 (θ e ),b 12 (θ e ),b 13 (θ e ),b 21 (θ e ),b 22 (θ e ),b 23 (θ e ) Is a matrix in formula (3)
Corresponding element, h 21 Is a matrix in formula (3)
Corresponding element, ω r And theta e Is a time-varying parameter of the system. The calculation formula of the 4 nodes defined therein is as follows:
D (1,i) ＝a 11 I d (k+i)+a 12 (ω r )I q (k+i)
D (2,i) ＝a 21 (ω r )I d (k+i)+a 12 I q (k+i)
E (1,i) ＝b 11 (θ e )S a +b 12 (θ e )S b +b 13 (θ e )S c
E (2,i) ＝b 11 (θ e )S a +b 22 (θ e )S b +b 23 (θ e )S c
wherein S is a ,S b ,S c The system input at the time k + i, a 11 ,a 12 (ω r ),a 21 (ω r ),a 22 Is a matrix
Corresponding element, b 11 (θ e ),b 12 (θ e ),b 13 (θ e ),b 21 (θ e ),b 22 (θ e ),b 23 (θ e ) Is a matrix
Corresponding element, h 21 Is a matrix
Corresponding element, ω r And theta e Is a time-varying parameter of the system,
the prediction model obtains a future state formula of the system current as follows:
I d (k+i+1)＝D (1,i) +D (2,i)
I q (k+i+1)＝E (1,i) +E (2,i) +ω r h 21 。
s3: and performing parallel computing solution optimization on the prediction model and the objective function by adopting a trigger type parallel computing method to realize the optimal prediction control of the motor torque. The triggered parallel computing method in step S3 is to use the sign that the solution of the prediction model and the objective function in the current computing step is completed as the sign that the next step computing starts, so as to implement the parallel computing of the prediction model and the objective function.
And (3) predicting the state of the system in the future p steps according to a system dynamic model f shown in FIG. 2 by knowing the state value at the moment k and the initial value of the control quantity, wherein the predicting process is shown in FIG. 4. Because of the coupling relationship between the state predictions, the prediction of each step is performed sequentially.
Aiming at solving the optimization problem of the finite set prediction control, a dynamic programming algorithm is adopted for traversal optimization solution, and in order to reduce the calculated amount in the multi-step prediction process, a simplified optimization algorithm is adopted to reduce the calculated amount. Although the computational burden is reduced by the refinement and optimization, when each node in the search tree is optimized, the future dynamics of the system needs to be calculated first, and then an objective function needs to be calculated, which is a time-consuming key calculation step of the algorithm. According to the system future state calculation and the calculation chart of the objective function, x is calculated for each time domain system state in the process of forward recursion k+i+1|k And J i The objective function and the calculation formula of the prediction model in step S3 can be calculated in parallel as follows:
J 0 ＝0
wherein J is an objective function, x (k+i|k) Is the state quantity of the ith step at the kth time, r k+i|k And Q is a positive definite weighting matrix, P is a positive definite terminal punishment matrix, and R is a positive definite control quantity punishment matrix.
In the calculation of the objective function, as shown in fig. 5, in order to fully utilize parallel calculation, the objective function in the P-step prediction time domain can be rewritten as:
the calculation of the objective function is divided into two layers of nodes, wherein the first layer of nodes comprises two nodes K 1 And two nodes K 2 The second layer node comprises a node J i+1 (1)、J i+1 (2)、J i+1 (3)、J i+1 (4) The calculation formula of each node is as follows:
wherein, I d (k + i +1| k) is the d-axis current of step i +1 at time k,
d-axis current expectation value of the (I + 1) th step in the k time q (k + i +1| k) is the desired value of the q-axis current at step i +1 at time k,
is the q-axis current expected value m of the (i + 1) th step in the k-th time 1 Is the motor rotor mass.
As shown in fig. 6, because the FPGA has the advantages of low power consumption, programmability, parallel computing hardware structure, and the like, the parallel computing method for the motor torque optimization finite set predictive control proposed above is implemented by performing hardware parallel acceleration on an FPGA chip, as shown in fig. 5.
The calculation steps with coupling relation in the algorithm are accelerated by adopting a production line, and the mutually independent calculation steps in the algorithm are accelerated by adopting hardware parallel triggering calculation. The relation between resources and performance is fully considered for the data storage mode, and the register storage is adopted to accelerate the data access speed under the condition that the resources allow.
For the prediction process of each step, data are independent, and because the two are different in computational complexity, parallel computation cannot be completed simultaneously, so that a triggered parallel computation mode is combined, namely a mark that forward derivation in the step P (namely parallel computation content one) and solving of an objective function (namely parallel computation content two) are completed is used as a mark for starting prediction computation in the step P +1, so as to ensure the sequence of data computation numbers.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.
Claims (4)
1. A motor torque optimization finite set predictive control parallel computing method is characterized by comprising the following steps:
s1: according to the target of motor torque optimization control, giving a description form of an optimization problem and obtaining a target function;
s2: performing Euler discretization according to a continuous time model of the motor driving system to construct a prediction model of the system;
s3: based on the adoption of a triggering type parallel computing method to carry out parallel computing solution optimization on the prediction model and the objective function, the optimized prediction control of the motor torque is realized,
the objective of the motor torque optimization control in the step S1 is to control the motor stator d-q axis current to track the expected current value quickly, so as to realize the quick tracking of the torque and meet the dynamic requirement, and reduce the copper loss E in the motor m ，
The objective function is as follows:
s.t.x (k+i|k) ＝f(x (k+i-1|k) ,u (k+i-1) )，
u (k+i-1) ∈{u 0 ,u 1 ,L,u 7 },i＝1,2,L p
wherein x (k + I | k) ═ I is defined d (k+i|k),I q (k+i|k)] T In order to predict the control sequence for the state,
for reference to the input sequence, Ts is a discrete step size, R m Is the resistance of the electronic winding, m 1 As motor rotor mass, x (k) Is a state quantity, u (k) To control the quantity, I d(k+i|k) D-axis current of i-th step in k-th time，
Is a d-axis current desired value, I, at the k + I time q(k+i|k) Is the q-axis current of the ith step in the kth time,
is a desired value of the q-axis current at the k + I time (k+p) Is the current at time k + p, I (k+p) Is the current expectation value in the k + P moment, P is the prediction step length, Q is a positive definite weighting matrix, P is a positive definite terminal punishment matrix, R is a positive definite control quantity punishment matrix,
the calculation of the objective function is divided into two layers of nodes, wherein the first layer of nodes comprises two nodes K 1 And two nodes K 2 The second layer node comprises a node J i+1 (1)、J i+1 (2)、J i+1 (3)、J i+1 (4) The calculation formula of each node is as follows:
wherein, I d (k + i +1| k) is the d-axis current of step i +1 at time k,
d-axis current expectation value of the (I + 1) th step in the k time q (k + i +1| k) is the desired value of the q-axis current at step i +1 at time k,
expected for the q-axis current at step i +1 at time k,
in the step S2, future dynamic prediction is performed according to the system dynamic model, euler discretization is performed according to the continuous time model of the motor driving system, Ts is a discrete step length, and the following discrete model is obtained:
wherein x is (k+i|k) Is the state quantity of the ith step at the kth time, x (k+i-1|k) Is the state quantity of the step i-1 at the kth time, u (k+i-1|k) The control quantity of the step (i-1) at the k time,
as the parameters of the system matrix, are,
the system matrix parameters are respectively as follows:
wherein L is d ,L q Equivalent inductances of d-and q-axes, p, respectively m Is the magnetic pole pair of the motor, phi m Is the magnetic flux generated in the permanent magnet re-stator phase, d is the motor speed, and theta is obtained after integration e ，V dc Is a DC supply voltage, R m Being the resistance of the electronic winding, T s In the form of discrete steps of the size,
the calculation formulas of the objective function and the prediction model in step S3 are as follows:
J 0 ＝0
wherein J is an objective function, x (k+i|k) Is the state quantity of the ith step at the kth time, r k+i|k And Q is a positive definite weighting matrix, P is a positive definite terminal punishment matrix, and R is a positive definite control quantity punishment matrix.
2. The method of claim 1, wherein the predictive model is calculated to include a plurality of intermediate nodes: d (1,i) 、D (2,i) 、E (1,i) 、E (2,i) The calculation formula is as follows:
D (1,i) ＝a 11 I d (k+i)+a 12 (ω r )I q (k+i)
D (2,i) ＝a 21 (ω r )I d (k+i)+a 12 I q (k+i)
E (1,i) ＝b 11 (θ e )S a +b 12 (θ e )S b +b 13 (θ e )S c
E (2,i) ＝b 11 (θ e )S a +b 22 (θ e )S b +b 23 (θ e )S c
wherein S is a ,S b ,S c The system input at the time k + i, a 11 ,a 12 (ω r ),a 21 (ω r ),a 22 Is a matrix
Corresponding element, b 11 (θ e ),b 12 (θ e ),b 13 (θ e ),b 21 (θ e ),b 22 (θ e ),b 23 (θ e ) Is a matrix
Corresponding element, h 21 Is a matrix
Corresponding element, ω r And theta e Is a time-varying parameter of the system,
the prediction model obtains a future state formula of the system current as follows:
I d (k+i+1)＝D (1,i) +D (2,i)
I q (k+i+1)＝E (1,i) +E (2,i) +ω r h 21 。
3. the method of claim 1, wherein the triggered parallel computing method in step S3 is implemented by using a flag indicating that the solution of the prediction model and the objective function in the current computing step is completed as a flag indicating the start of the next-step computing, so as to implement the parallel computing of the prediction model and the objective function.
4. The method for parallel computation of predictive control over a limited set of motor torque optimization according to claim 1, wherein in step S3, the FPGA chip is used to perform parallel computation solution.
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