CN114900085B - Robot joint servo motor model prediction parameter optimization method and device - Google Patents

Robot joint servo motor model prediction parameter optimization method and device Download PDF

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CN114900085B
CN114900085B CN202210550079.XA CN202210550079A CN114900085B CN 114900085 B CN114900085 B CN 114900085B CN 202210550079 A CN202210550079 A CN 202210550079A CN 114900085 B CN114900085 B CN 114900085B
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robot joint
prediction
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permanent magnet
current
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CN114900085A (en
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潘月斗
张亚涛
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University of Science and Technology Beijing USTB
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0017—Model reference adaptation, e.g. MRAS or MRAC, useful for control or parameter estimation
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/022—Synchronous motors
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/34—Modelling or simulation for control purposes
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P70/00—Climate change mitigation technologies in the production process for final industrial or consumer products
    • Y02P70/10—Greenhouse gas [GHG] capture, material saving, heat recovery or other energy efficient measures, e.g. motor control, characterised by manufacturing processes, e.g. for rolling metal or metal working

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Ac Motors In General (AREA)
  • Control Of Electric Motors In General (AREA)

Abstract

The invention discloses a robot joint servo motor model prediction parameter optimization method and device, and relates to the technical field of robot joint control. The method comprises the following steps: modeling the permanent magnet synchronous motor to obtain a permanent magnet synchronous motor mathematical model which accords with the robot joint prediction control standard; establishing a model prediction controller of the robot joint permanent magnet synchronous motor according to the mathematical model of the permanent magnet synchronous motor; and predicting the controller according to the model to obtain the optimal voltage vector of the robot joint motor to be predicted. The method can optimize the parameter configuration of the cost function in the model predictive control method on line, solve the problem that the parameter of the cost function is difficult to configure in the model predictive control technology, and further optimize the control performance of the system.

Description

Robot joint servo motor model prediction parameter optimization method and device
Technical Field
The invention relates to the technical field of robot joint control, in particular to a method and a device for optimizing robot joint servo motor model prediction parameters.
Background
An articulated robot is also called an articulated arm robot or an articulated robot arm, is one of the most common forms of industrial robots in the current industrial field, and is suitable for mechanical automation operation in various industrial fields. For example, the joint robot is driven by a motor and realizes high-precision control of the robot joint by using a high-precision permanent magnet synchronous motor in the work of automatic assembly, paint spraying, carrying, welding and the like. The PMSM (permanent magnet synchronous motor) has the advantages of small size, small inertia, high response speed, high efficiency and the like. The traditional control method usually needs a worker to repeatedly debug the parameters of the controller, and the parameters are often inaccurate when being adjusted, so that the satisfactory effect is difficult to achieve.
Disclosure of Invention
The invention provides a method for predicting the weight coefficient of the model, which aims at solving the problem that the weight coefficient is difficult to accurately configure in the traditional model prediction control.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the present invention provides a method for optimizing robot joint servo motor model prediction parameters, where the method is implemented by an electronic device, and the method includes:
s1, modeling is carried out on the permanent magnet synchronous motor, and a permanent magnet synchronous motor mathematical model which accords with a robot joint prediction control standard is obtained.
And S2, establishing a model prediction controller of the robot joint permanent magnet synchronous motor according to the mathematical model of the permanent magnet synchronous motor.
And S3, predicting the controller according to the model to obtain the optimal voltage vector of the robot joint motor to be predicted.
Optionally, the mathematical model of the permanent magnet synchronous motor includes a d-axis and q-axis voltage and current equation of the permanent magnet synchronous motor, a d-axis and q-axis voltage transformation equation of the permanent magnet synchronous motor in a rotating coordinate system, a flux linkage equation, and an electromagnetic torque equation of the permanent magnet synchronous motor.
Optionally, the model predictive controller comprises a decision unit, a current predictive control unit and a parameter optimization unit.
The step 3 of predicting the controller according to the model to obtain the optimal voltage vector of the robot joint motor to be predicted comprises the following steps:
s31, acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor through a judging unit, and judging the motion state of the robot joint motor; the motion state comprises a dynamic regulation stage and a stable regulation stage.
S32, obtaining a value function according to the current prediction control unit; wherein, the cost function comprises a dynamic adjustment stage cost function and a stable adjustment stage cost function.
And S33, performing parameter optimization on the value function through a parameter optimization unit to obtain the optimal voltage vector of the robot joint motor to be predicted.
Optionally, the step S31 of acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor through the determination unit, and determining the motion state of the robot joint motor includes:
acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor under set conditions through a judging unit; wherein the set condition comprises a set cycle period and a set sampling time.
And judging the motion state of the robot joint motor to be a dynamic regulation stage or a stable regulation stage according to the speed feedback data and the current feedback data.
Optionally, the predicting the control unit according to the current in S32, obtaining the cost function includes:
and constructing a current prediction mathematical model, an electromagnetic torque prediction mathematical model and an inverter switching frequency prediction model.
And obtaining a value function according to the current prediction mathematical model, the electromagnetic torque prediction mathematical model and the inverter switching frequency prediction model.
Alternatively, the cost function is as shown in equation (1) below:
g m ＝λ[|i q k+1 -i q k |+|i d k+1 -i d k |]-βT e k+1 +γS w k+1 (1)
wherein λ represents a weighting coefficient of the current error; i all right angle d k+1 、i q k+1 D-axis current and q-axis current at the k +1 moment after compensation prediction; i all right angle d k 、i q k D and q axis currents at the k +1 moment and the k moment after compensation prediction; β represents a weight coefficient of the electromagnetic torque; t is e k +1 Representing the predicted value of the electromagnetic torque at the moment k + 1; gamma represents a weight coefficient of the inverter switching frequency; s. the w k+1 Indicating the number of inverter switching frequency changes at time k + 1.
Optionally, the parameter optimization of the cost function by the parameter optimization unit in S33 includes:
and performing parameter optimization on the weight coefficient of the value function in the dynamic adjustment stage on line through an optimization algorithm of a parameter optimization unit.
And optimizing the parameters of the weight coefficient of the value function in the stable adjusting stage on line through an optimization algorithm of the parameter optimization unit.
Optionally, performing parameter optimization on the weight coefficient of the dynamic adjustment stage cost function on line through an optimization algorithm of a parameter optimization unit includes:
and judging whether the rising time of the dynamic regulation stage exceeds a preset threshold value, if so, reducing the weight coefficient of the current error in the value function of the dynamic regulation stage, and increasing the weight coefficient of the electromagnetic torque in the value function of the dynamic regulation stage.
Optionally, the performing, on-line, parameter optimization on the weight coefficient of the cost function in the stable adjustment stage by using an optimization algorithm of a parameter optimization unit includes:
and judging whether the static error of the stable regulation stage exceeds a preset threshold value, if so, increasing the weight coefficient of the current error in the value function of the stable regulation stage, and reducing the weight coefficient of the electromagnetic torque in the value function of the stable regulation stage.
On the other hand, the invention provides a robot joint servo motor model prediction parameter optimization device, which is applied to a method for realizing the optimization of robot joint servo motor model prediction parameters, and comprises the following steps:
and the mathematical model construction module is used for modeling the permanent magnet synchronous motor to obtain a permanent magnet synchronous motor mathematical model which accords with the robot joint prediction control standard.
And the controller building module is used for building a model prediction controller of the robot joint permanent magnet synchronous motor according to the mathematical model of the permanent magnet synchronous motor.
And the output module is used for predicting the controller according to the model to obtain the optimal voltage vector of the robot joint motor to be predicted.
Optionally, the mathematical model of the permanent magnet synchronous motor includes a d-axis and q-axis voltage and current equation of the permanent magnet synchronous motor, a d-axis and q-axis voltage transformation equation of the permanent magnet synchronous motor in a rotating coordinate system, a flux linkage equation, and an electromagnetic torque equation of the permanent magnet synchronous motor.
Optionally, the model predictive controller comprises a decision unit, a current predictive control unit and a parameter optimization unit.
Optionally, the output module is further configured to:
s31, acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor through a judging unit, and judging the motion state of the robot joint motor; the motion state comprises a dynamic regulation stage and a stable regulation stage.
S32, obtaining a value function according to the current prediction control unit; the cost function comprises a dynamic adjustment stage cost function and a stable adjustment stage cost function.
And S33, carrying out parameter optimization on the value function through a parameter optimization unit to obtain the optimal voltage vector of the robot joint motor to be predicted.
Optionally, the output module is further configured to:
acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor under set conditions through a judging unit; wherein the set condition comprises a set cycle period and a set sampling time.
And judging the motion state of the robot joint motor to be a dynamic regulation stage or a stable regulation stage according to the speed feedback data and the current feedback data.
Optionally, the output module is further configured to:
and constructing a current prediction mathematical model, an electromagnetic torque prediction mathematical model and an inverter switching frequency prediction model.
And obtaining a value function according to the current prediction mathematical model, the electromagnetic torque prediction mathematical model and the inverter switching frequency prediction model.
Alternatively, the cost function is as shown in equation (1) below:
g m ＝λ[|i q k+1 -i q k |+|i d k+1 -i d k |]-βT e k+1 +γS w k+1 (1)
wherein λ represents a weighting coefficient of the current error; i.e. i d k+1 、i q k+1 D-axis current and q-axis current at the k +1 moment after compensation prediction; i.e. i d k 、i q k D and q axis currents at the k +1 moment and the k moment after compensation prediction; β represents a weight coefficient of the electromagnetic torque; t is e k +1 Representing the predicted value of the electromagnetic torque at the moment k + 1; gamma represents a weight coefficient of the switching frequency of the inverter; s. the w k+1 Inverter switching frequency change representing k +1 timeThe number of times.
Optionally, the output module is further configured to:
and performing parameter optimization on the weight coefficient of the value function in the dynamic adjustment stage on line through an optimization algorithm of a parameter optimization unit.
And performing parameter optimization on the weight coefficient of the value function in the stable adjusting stage on line through an optimization algorithm of the parameter optimization unit.
Optionally, the output module is further configured to:
and judging whether the rising time of the dynamic regulation stage exceeds a preset threshold value, if so, reducing the weight coefficient of the current error in the value function of the dynamic regulation stage, and increasing the weight coefficient of the electromagnetic torque in the value function of the dynamic regulation stage.
Optionally, the output module is further configured to:
and judging whether the static error of the stable regulation stage exceeds a preset threshold value, if so, increasing the weight coefficient of the current error in the cost function of the stable regulation stage, and reducing the weight coefficient of the electromagnetic torque in the cost function of the stable regulation stage.
In one aspect, an electronic device is provided, and the electronic device includes a processor and a memory, where the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the above robot joint servo motor model prediction parameter optimization method.
In one aspect, a computer-readable storage medium is provided, where at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the above method for optimizing the prediction parameters of the robot joint servo motor model.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
in the scheme, the parameter setting method is used for traditional model prediction, the method solves the problem that the weight coefficient of the traditional model prediction control method is difficult to configure accurately, and the price function weight coefficient ratio is optimized on line through the algorithm, so that the dynamic performance and the stability of the system can be improved well.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for optimizing a robot joint servo motor model prediction parameter according to an embodiment of the present invention;
FIG. 2 is a control block diagram of a robot joint servo motor model prediction parameter optimization method provided by an embodiment of the invention;
FIG. 3 is a schematic flow chart of an optimization algorithm of a model-based predictive control method according to an embodiment of the present invention;
FIG. 4 is a block diagram of a device for optimizing a prediction parameter of a servo motor model of a robot joint according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, an embodiment of the present invention provides a method for optimizing a robot joint servo motor model prediction parameter, which may be implemented by an electronic device. As shown in fig. 1, a flow chart of a method for optimizing a prediction parameter of a robot joint servo motor model, a processing flow of the method may include the following steps:
s1, modeling is carried out on the permanent magnet synchronous motor to obtain a permanent magnet synchronous motor mathematical model which accords with a robot joint prediction control standard.
Optionally, the mathematical model of the permanent magnet synchronous motor includes a d-axis and q-axis voltage and current equation of the permanent magnet synchronous motor, a d-axis and q-axis voltage transformation equation of the permanent magnet synchronous motor in a rotating coordinate system, a flux linkage equation, and an electromagnetic torque equation of the permanent magnet synchronous motor.
The d-axis and q-axis voltage and current equations of the permanent magnet synchronous motor are shown as the following formula (1):
Figure BDA0003654597380000061
wherein u is d 、u q Respectively represent d-axis voltage, q-axis voltage, R s Represents the stator equivalent resistance, i d 、i q Respectively represent d-axis current, q-axis current, ω t Representing the angular velocity of the motor at time t, L representing the direct and quadrature inductances, # f Is the rotor total flux linkage vector.
The d-axis and q-axis voltages of the permanent magnet synchronous motor are transformed into an equation in a rotating coordinate system, which is shown in the following formula (2):
Figure BDA0003654597380000071
where ω represents the motor angular velocity at time t, u a 、u b 、u c Representing the voltage in a natural coordinate system.
The flux linkage equation is shown in the following formula (3):
Figure BDA0003654597380000072
wherein psi d 、ψ q The components of the stator flux linkage of the motor are the d-axis and the q-axis respectively.
The electromagnetic torque equation of the permanent magnet synchronous motor is shown as the following formula (4):
Figure BDA0003654597380000073
where p is a differential operator, i d 、i q Respectively represent d-axis current, q-axis current, L d 、L q Stator equivalent inductances psi of d-and q-axes, respectively f Is the rotor total flux linkage vector psi d 、ψ q Respectively d-axis and q-axis components of a motor stator flux linkage, J represents the rotational inertia of a motor rotor, and T e Representing electromagnetic torque, p n The number of pole pairs of the motor is shown.
And S2, establishing a model prediction controller of the robot joint permanent magnet synchronous motor according to the mathematical model of the permanent magnet synchronous motor.
And S3, predicting the controller according to the model to obtain the optimal voltage vector of the robot joint motor to be predicted.
Optionally, the model predictive controller comprises a decision unit, a current predictive control unit and a parameter optimization unit.
In the step S3, predicting the controller according to the model, and obtaining the optimal voltage vector of the robot joint motor to be predicted includes:
and S31, acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor through a judging unit, and judging the motion state of the robot joint motor.
The motion state comprises a dynamic regulation stage and a stable regulation stage.
Optionally, the step S31 of acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor through the determination unit, and determining the motion state of the robot joint motor includes:
and acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor under set conditions through a judging unit.
Wherein the set condition comprises a set cycle period and a set sampling time.
And judging the motion state of the robot joint motor to be a dynamic regulation stage or a stable regulation stage according to the speed feedback data and the current feedback data.
In a feasible implementation manner, the step of judging whether the motion state of the robot joint motor is a dynamic regulation stage or a stable regulation stage can be to set a cycle period and sampling time, obtain the rotating speed data of the permanent magnet synchronous motor, set the data acquisition range to be 1000, and judge whether the rise time is long after the acquisition is finished (the data with the static difference value within 10% is less than 3/4 of the total data); and judging whether the static error meets the system requirement (the average value of the static error values of the last 1/5 data is less than 0.5 percent of the given value).
As shown in fig. 2, in which,
Figure BDA0003654597380000081
respectively representing a given value and a feedback value of the rotation speed, I a 、I b 、I c Representing three-phase currents in the natural coordinate system of the motor, I α 、I β Denotes the current in the stationary frame, I d 、I q Indicating the d-q axis current in the synchronous rotating coordinate system. I is q * 、I d * Current reference value, I, representing predictive control model q * Output by a PI controller, I d * Setting I d * And =0, the difference between the reference value of the rotation speed and the angular speed of the motor detected by the rotation speed detector is obtained, the reference current is output through a speed loop PI controller, the rotation speed data is collected through a judging unit of model predictive control, and the state and the performance of the motor are judged.
And S32, obtaining a cost function according to the current prediction control unit.
The cost function comprises a dynamic adjustment stage cost function and a stable adjustment stage cost function.
Optionally, the obtaining the cost function according to the current prediction control unit in S32 includes:
and constructing a current prediction mathematical model, an electromagnetic torque prediction mathematical model and an inverter switching frequency prediction model.
Wherein, the current prediction mathematical model is expressed as the following formula (5) (6):
Figure BDA0003654597380000082
Figure BDA0003654597380000083
wherein i d k 、i q k D and q axis currents at the k moment; i all right angle d k+1 、i q k+1 D-axis current and q-axis current at the k +1 moment after compensation prediction; lambda d k 、λ q k Is a compensation factor; l, L d 、L q Respectively representing d-axis stator equivalent inductance and q-axis stator equivalent inductance; psi f Is the rotor total flux linkage vector; Δ t is the sampling period.
The electromagnetic torque prediction mathematical model is expressed by the following equation (7):
Figure BDA0003654597380000084
wherein, T e k+1 Representing a predicted value of the electromagnetic torque at the next moment; i.e. i d k+1 、i q k+1 D and q axis currents at the k +1 moment after compensation prediction; l is d 、L q Respectively representing d-axis stator equivalent inductance and q-axis stator equivalent inductance; psi f Is the rotor total flux linkage vector.
The model prediction unit outputs 8 candidate basic voltage vectors of the optimal voltage vector, and the method specifically comprises the following steps:
U 0 (000)、U 1 (001)、U 2 (010)、U 3 (011)、U 4 (100)、U 5 (101)、U 6 (110)、U 7 (111) The three-phase voltage source inverter is respectively corresponding to 8 different voltage vectors of the three-phase voltage source inverter.
The inverter switching frequency prediction model is expressed as the following equation (8):
Figure BDA0003654597380000091
wherein, U k+1 、U k Respectively representing the optimal voltage vectors, S, at the next and present time w k+1 The amount of change of the voltage vector at the time of k +1 can be expressed as the number of switching frequencies of the inverter, U 1 ′ i Indicating the amount of change in the ith dimension of the inverter switch at the next time.
Specifically, U' 1i Representing row 1, column i of a 1 row 3 column matrix U'.
And obtaining a value function according to the current prediction mathematical model, the electromagnetic torque prediction mathematical model and the inverter switching frequency prediction model.
Alternatively, the cost function is as shown in equation (9) below:
g m ＝λ[|i q k+1 -i q k |+|i d k+1 -i d k |]-βT e k+1 +γS w k+1 (9)
wherein λ represents a current error weight coefficient; i.e. i d k+1 、i q k+1 D and q axis currents at the k +1 moment after compensation prediction; i.e. i d k 、i q k D and q axis currents at the k +1 moment and the k moment after compensation prediction; β represents an electromagnetic torque weight coefficient; t is e k+1 Representing the predicted value of the electromagnetic torque at the moment k + 1; gamma represents an inverter switching frequency weight coefficient; s w k+1 Indicating the number of inverter switching frequency changes at time k + 1.
And S33, performing parameter optimization on the value function through a parameter optimization unit to obtain the optimal voltage vector of the robot joint motor to be predicted.
Optionally, the parameter optimization of the cost function by the parameter optimization unit in S33 includes:
and S331, performing parameter optimization on the weight coefficient of the dynamic adjustment stage cost function on line through an optimization algorithm of a parameter optimization unit.
Optionally, performing parameter optimization on the weight coefficient of the dynamic adjustment stage cost function on line through an optimization algorithm of a parameter optimization unit includes:
and judging whether the rising time of the dynamic regulation stage exceeds a preset threshold value, if so, reducing the weight coefficient of the current error in the value function of the dynamic regulation stage, and increasing the weight coefficient of the electromagnetic torque in the value function of the dynamic regulation stage.
In one possible embodiment, the rise time t is recorded when the rotational speed first reaches the target value r When the error of the rotating speed is stabilized within +/-5%, the error is recorded as the adjusting time t s At t, at s Selecting a cost function g over time m ＝λ 1 [|i q k+1 -i q k |+|i d k+1 -i d k |]-β 1 T e k+1 +γ 1 S w k+1 Initial value of λ 1 ＝β 1 ，γ 1 ＝0。
t s After a period of time, use g m ＝λ 2 [|i q k+1 -i q k |+|i d k+1 -i d k |]-β 2 T e k+1 +γ 2 S w k+1 Wherein λ is 2 ＝β 2 ，γ 2 ＝0。
S332, performing parameter optimization on the weight coefficient of the value function in the stable adjusting stage on line through an optimization algorithm of the parameter optimization unit.
Optionally, the performing, on-line, parameter optimization on the weight coefficient of the cost function in the stable adjustment stage by using an optimization algorithm of a parameter optimization unit includes:
and judging whether the static error of the stable regulation stage exceeds a preset threshold value, if so, increasing the weight coefficient of the current error in the cost function of the stable regulation stage, and reducing the weight coefficient of the electromagnetic torque in the cost function of the stable regulation stage.
In a possible implementation, as shown in fig. 3, whether the rise time and the static error of the system meet the system requirements is calculated on line according to the obtained feedback data, if not, the weight coefficient of the cost function in each stage is optimized on line through a parameter optimization unit optimization algorithm, if the rise time is long, the weight coefficient of the current error in the cost function in the rise stage is reduced, the weight coefficient of the virtual torque is increased, and the weight coefficient is reduced or increased by 1%, namely λ, every time 1 ′＝λ 1 *99％，β 1 ′＝β 1 *101%; if static error occursIf the difference is large, the weight coefficient of the current error in the cost function in the stable stage is increased, the weight coefficient of the virtual torque is reduced, and the weight coefficient is reduced or increased by 1 percent each time, namely the lambda is increased 2 ′＝λ 2 *101％，β 2 ′＝β 2 *99 percent of the total power supply voltage and the total power supply voltage of the inverter are introduced until the dynamic performance and the stability of the system meet the requirements of the system 1 ＝10*λ 1 ，γ 2 ＝10*λ 2 。
In a feasible implementation mode, the robot joint servo motor model prediction parameter optimization method provided by the embodiment of the invention is used for establishing a permanent magnet synchronous motor mathematical model which accords with a robot joint model prediction control standard; according to the established mathematical model of the permanent magnet synchronous motor, a speed loop PI control unit, a current prediction control unit and a parameter optimization unit are established; acquiring speed feedback data according to a current prediction control unit, judging the state of a robot joint motor, wherein the running state comprises a dynamic regulation stage and a stable regulation stage, acquiring the actual current value of the motor, comparing the actual current value with a given value output by a PI (proportional integral) controller, and setting a staged value function to control the system; and (4) calculating whether the rise time and the static error of the system meet the system requirements or not on line according to the parameter optimization unit, and if not, optimizing the weight coefficient of the cost function at each stage on line through the parameter optimization unit optimization algorithm. By adopting the method, the parameters of the cost function in the model predictive control method can be optimized on line, and the dynamic performance, the steady-state performance and the switching frequency of the inverter of the motor are comprehensively considered, so that the system performance is further optimized.
The embodiment of the invention provides a robot joint servo motor model prediction parameter optimization method, wherein a parameter setting method is used for traditional model prediction, the method solves the problem that the weight coefficient of the traditional model prediction control method is difficult to accurately configure, and the dynamic performance and the stability of a system can be well improved by optimizing the ratio of the weight coefficient of a cost function on line through the algorithm.
As shown in fig. 4, an embodiment of the present invention provides a robot joint servo motor model prediction parameter optimization apparatus 400, where the robot joint servo motor model prediction parameter optimization apparatus 400 is applied to implement a robot joint servo motor model prediction parameter optimization method, and the apparatus 400 includes:
and the mathematical model construction module 410 is used for modeling the permanent magnet synchronous motor to obtain a permanent magnet synchronous motor mathematical model which accords with the robot joint prediction control standard.
And the controller building module 420 is used for building a model prediction controller of the robot joint permanent magnet synchronous motor according to the permanent magnet synchronous motor mathematical model.
And the output module 430 is used for predicting the controller according to the model to obtain the optimal voltage vector of the robot joint motor to be predicted.
Optionally, the mathematical model of the permanent magnet synchronous motor includes a d-axis and q-axis voltage and current equation of the permanent magnet synchronous motor, a d-axis and q-axis voltage transformation equation of the permanent magnet synchronous motor in a rotating coordinate system, a flux linkage equation, and an electromagnetic torque equation of the permanent magnet synchronous motor.
Optionally, the model predictive controller comprises a decision unit, a current predictive control unit and a parameter optimization unit.
Optionally, the output module 430 is further configured to:
s31, acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor through a judging unit, and judging the motion state of the robot joint motor; the motion state comprises a dynamic regulation stage and a stable regulation stage.
S32, obtaining a value function according to the current prediction control unit; wherein, the cost function comprises a dynamic adjustment stage cost function and a stable adjustment stage cost function.
And S33, performing parameter optimization on the value function through a parameter optimization unit to obtain the optimal voltage vector of the robot joint motor to be predicted.
Optionally, the output module 430 is further configured to:
acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor under set conditions through a judging unit; wherein the set condition comprises a set cycle period and a set sampling time.
And judging the motion state of the robot joint motor to be a dynamic regulation stage or a stable regulation stage according to the speed feedback data and the current feedback data.
Optionally, the output module 430 is further configured to:
and constructing a current prediction mathematical model, an electromagnetic torque prediction mathematical model and an inverter switching frequency prediction model.
And obtaining a value function according to the current prediction mathematical model, the electromagnetic torque prediction mathematical model and the inverter switching frequency prediction model.
Alternatively, the cost function is as shown in equation (1) below:
g m ＝λ[|i q k+1 -i q k |+|i d k+1 -i d k |]-βT e k+1 +γS w k+1 (1)
wherein λ represents a weighting coefficient of the current error; i.e. i d k+1 、i q k+1 D-axis current and q-axis current at the k +1 moment after compensation prediction; i all right angle d k 、i q k D and q axis currents at the k +1 moment and the k moment after compensation prediction; β represents a weight coefficient of the electromagnetic torque; t is a unit of e k +1 Representing the predicted value of the electromagnetic torque at the moment k + 1; gamma represents a weight coefficient of the inverter switching frequency; s w k+1 Indicating the number of inverter switching frequency changes at time k + 1.
Optionally, the output module 430 is further configured to:
and performing parameter optimization on the weight coefficient of the value function in the dynamic adjustment stage on line through an optimization algorithm of a parameter optimization unit.
And performing parameter optimization on the weight coefficient of the value function in the stable adjusting stage on line through an optimization algorithm of the parameter optimization unit.
Optionally, the output module 430 is further configured to:
and judging whether the rising time of the dynamic regulation stage exceeds a preset threshold value, if so, reducing the weight coefficient of the current error in the value function of the dynamic regulation stage, and increasing the weight coefficient of the electromagnetic torque in the value function of the dynamic regulation stage.
Optionally, the output module 430 is further configured to:
and judging whether the static error of the stable regulation stage exceeds a preset threshold value, if so, increasing the weight coefficient of the current error in the value function of the stable regulation stage, and reducing the weight coefficient of the electromagnetic torque in the value function of the stable regulation stage.
The embodiment of the invention provides a robot joint servo motor model prediction parameter optimization method, wherein a parameter setting method is used for traditional model prediction, the method solves the problem that the weight coefficient of a traditional model prediction control method is difficult to accurately configure, and the dynamic performance and the stability of a system can be well improved by optimizing the ratio of the weight coefficient of a cost function on line through the algorithm.
Fig. 5 is a schematic structural diagram of an electronic device 500 according to an embodiment of the present invention, where the electronic device 500 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 501 and one or more memories 502, where the memory 502 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 501 to implement the following method for optimizing the prediction parameter of the robot joint servo motor model:
s1, modeling is carried out on the permanent magnet synchronous motor, and a permanent magnet synchronous motor mathematical model which accords with a robot joint prediction control standard is obtained.
And S2, establishing a model prediction controller of the robot joint permanent magnet synchronous motor according to the mathematical model of the permanent magnet synchronous motor.
And S3, predicting the controller according to the model to obtain the optimal voltage vector of the robot joint motor to be predicted.
In an exemplary embodiment, a computer-readable storage medium, such as a memory, including instructions executable by a processor in a terminal to perform the above-described robot joint servo motor model prediction parameter optimization method is also provided. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.

Claims (9)

1. A robot joint servo motor model prediction parameter optimization method is characterized by comprising the following steps:
s1, modeling is carried out on a permanent magnet synchronous motor to obtain a permanent magnet synchronous motor mathematical model meeting a robot joint prediction control standard;
s2, establishing a model prediction controller of the robot joint permanent magnet synchronous motor according to the permanent magnet synchronous motor mathematical model;
s3, obtaining an optimal voltage vector of the robot joint motor to be predicted according to the model prediction controller;
the model predictive controller includes a determination unit, a current predictive control unit, and a parameter optimization unit;
in the S3, obtaining the optimal voltage vector of the robot joint motor to be predicted according to the model prediction controller includes:
s31, acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor through the judging unit, and judging the motion state of the robot joint motor; wherein the motion state comprises a dynamic regulation phase and a stable regulation phase;
s32, obtaining a value function according to the current prediction control unit; wherein the cost function comprises a dynamic adjustment stage cost function and a stable adjustment stage cost function;
and S33, performing parameter optimization on the cost function through the parameter optimization unit to obtain the optimal voltage vector of the robot joint motor to be predicted.
2. The method of claim 1, wherein the PMSM mathematical model comprises a PMSM d-axis and q-axis voltage-current equation, a PMSM d-axis and q-axis voltage-to-rotating coordinate system transformation equation, a flux linkage equation, and a PMSM electromagnetic torque equation.
3. The method according to claim 1, wherein the step S31 of collecting speed feedback data and current feedback data of the permanent magnet synchronous motor by the determination unit and determining the motion state of the robot joint motor comprises:
acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor under set conditions through the judging unit; wherein the set condition comprises a set cycle period and a set sampling time;
and judging the motion state of the robot joint motor to be a dynamic regulation stage or a stable regulation stage according to the speed feedback data and the current feedback data.
4. The method of claim 1, wherein the step of obtaining a cost function according to the current prediction control unit in S32 comprises:
constructing a current prediction mathematical model, an electromagnetic torque prediction mathematical model and an inverter switching frequency prediction model;
and obtaining a value function according to the current prediction mathematical model, the electromagnetic torque prediction mathematical model and the inverter switching frequency prediction model.
5. The method of claim 1, wherein the cost function is represented by the following equation (1):
g m ＝λ[|i q k+1 -i q k |+|i d k+1 -i d k |]-βT e k+1 +γS w k+1 (1)
wherein λ represents a weighting coefficient of the current error; i.e. i d k+1 、i q k+1 D-axis current and q-axis current at the k +1 moment after compensation prediction; i.e. i d k 、i q k D and q axis currents at the k moment; β represents a weight coefficient of the electromagnetic torque; t is e k+1 Representing the predicted value of the electromagnetic torque at the moment k + 1; gamma represents a weight coefficient of the switching frequency of the inverter; s. the w k+1 Indicating the number of inverter switching frequency changes at time k + 1.
6. The method according to claim 1, wherein the parameter optimization of the cost function by the parameter optimization unit in S33 comprises:
the weight coefficient of the dynamic adjustment stage cost function is optimized on line through the optimization algorithm of the parameter optimization unit,
and carrying out parameter optimization on the weight coefficient of the value function in the stable adjusting stage on line through the optimization algorithm of the parameter optimization unit.
7. The method of claim 6, wherein the parameter optimizing weight coefficients of the dynamic adjustment phase cost function on-line by an optimization algorithm of the parameter optimization unit comprises:
and judging whether the rising time of the dynamic regulation stage exceeds a preset threshold value, if so, reducing the weight coefficient of the current error in the value function of the dynamic regulation stage, and increasing the weight coefficient of the electromagnetic torque in the value function of the dynamic regulation stage.
8. The method of claim 6, wherein the parameter optimizing the weight coefficients of the stability adjustment phase cost function on-line by the optimization algorithm of the parameter optimization unit comprises:
and judging whether the static error of the stable regulation stage exceeds a preset threshold value, if so, increasing the weight coefficient of the current error in the cost function of the stable regulation stage, and reducing the weight coefficient of the electromagnetic torque in the cost function of the stable regulation stage.
9. A robot joint servo motor model prediction parameter optimization device is characterized by comprising:
the mathematical model construction module is used for modeling the permanent magnet synchronous motor to obtain a permanent magnet synchronous motor mathematical model which accords with the robot joint prediction control standard;
the controller building module is used for building a model prediction controller of the robot joint permanent magnet synchronous motor according to the permanent magnet synchronous motor mathematical model;
the output module is used for predicting the controller according to the model to obtain the optimal voltage vector of the robot joint motor to be predicted;
the model predictive controller includes a determination unit, a current predictive control unit, and a parameter optimization unit;
the obtaining of the optimal voltage vector of the robot joint motor to be predicted according to the model prediction controller comprises:
s31, acquiring speed feedback data and current feedback data of the permanent magnet synchronous motor through the judging unit, and judging the motion state of the robot joint motor; wherein the motion state comprises a dynamic regulation stage and a stable regulation stage;
s32, obtaining a value function according to the current prediction control unit; wherein the cost function comprises a dynamic adjustment stage cost function and a stable adjustment stage cost function;
and S33, performing parameter optimization on the value function through the parameter optimization unit to obtain the optimal voltage vector of the robot joint motor to be predicted.
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