CN110022094B - Synchronous reluctance motor cooperative control system and method based on motor cloud - Google Patents
Synchronous reluctance motor cooperative control system and method based on motor cloud Download PDFInfo
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- CN110022094B CN110022094B CN201910388779.1A CN201910388779A CN110022094B CN 110022094 B CN110022094 B CN 110022094B CN 201910388779 A CN201910388779 A CN 201910388779A CN 110022094 B CN110022094 B CN 110022094B
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- 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
- H02P6/04—Arrangements for controlling or regulating the speed or torque of more than one motor
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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
- 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
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Abstract
The invention discloses a synchronous reluctance motor cooperative control system and method based on a motor cloud, wherein the method comprises the following steps: and the design end completes the specific design of the motor. The control end controls the motor to operate. The cloud end collects and stores design parameters and control parameters of the motor, obtains design commonality knowledge and rules and control commonality design and rules according to the design parameters and the control parameters, guides the motor design and control, and completes the fusion and cooperation of the design and control. According to the synchronous reluctance motor cooperative control system and method based on the motor cloud, the design commonality knowledge and rule and the control commonality knowledge and rule are summarized through the individual knowledge induction of motor design and control, so that the motor design and motor control are guided, the motor design and motor control difficulty is reduced, and the design and control effect is improved. And the motor design information and the motor control information can be interacted at the cloud end, and a collaborative coupling mechanism of design and control is established.
Description
Technical Field
The invention relates to the technical field of motors, in particular to a synchronous reluctance motor cooperative control system and method based on a motor cloud.
Background
Synchronous reluctance machines (SynRM) do not have permanent magnets and are receiving more and more extensive attention from industry people because of the advantages of low cost, firm rotor structure, no demagnetization problem, no short-circuit fault current and the like. The synchronous reluctance motor is expected to become a potential substitute for an induction motor and a permanent magnet motor with huge market share due to excellent structural characteristics (no permanent magnet and no rotor winding) and unique control characteristics (low cost, high reliability, high overload capacity and high efficiency of a full-load interval).
The nonlinear problems of high saturation of a magnetic circuit, cross coupling and the like of the synchronous reluctance motor are serious, and the shape, the material, the processing technology, the assembly technology and the like of a rotor core can cause the rotor core to have distinct personalized characteristics. Therefore, the efficient lean driving of the synchronous reluctance motor system depends on the customized motor optimization design based on application requirements, the accurate establishment of a motor magnetic model and the intelligent control of the motor. In the prior art, each link of motor design, control and the like is respectively a law, the relationship between the links is weak, and the most direct and effective integrated driving system with complete information components cannot be obtained from each other, so that the motor design and control difficulty is high, and the effect is poor.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides a synchronous reluctance motor cooperative control system and method based on a motor cloud. The specific technical scheme is as follows:
in a first aspect, a synchronous reluctance motor cooperative control system based on a motor cloud is provided, the system includes: the system comprises a design end, a control end and a cloud end, wherein the design end and the control end are respectively connected with the cloud end; the design end is used for completing the specific design of the motor; the control end is used for controlling the motor to operate; and the cloud end is used for collecting and storing the design information and the control information of the motor, acquiring the design commonality knowledge and rule and the control commonality knowledge and rule according to the design information and the control information, guiding the design and the control of the motor, and finishing the fusion and the cooperation of the design and the control.
In one possible design, the control terminal includes: the control loop and the optimization loop are respectively connected with the cloud end; the control loop is used for controlling the motor to operate according to preset control logic; the optimization loop is used for detecting the motor running condition and sending the motor running parameters to the cloud end; and the cloud end is used for collecting and analyzing the relation between the parameters of the control loop and the motor running parameters, determining better parameters with better motor running effect, and updating the better parameters into the control loop.
In one possible design, the cloud comprises a cloud design module, a cloud control module and a cloud knowledge base; the design end comprises a plurality of motors with the same structure, the design information of each motor is stored in the cloud design module, and the design information comprises the corresponding relation between design parameters and performance indexes; the control end comprises a plurality of controllers which correspond to the motors one by one, each controller controls the corresponding motor to operate, control information of each controller is stored in the cloud control module, and the control information comprises a corresponding relation between control parameters and performance indexes; the cloud knowledge base stores design common knowledge and rules and control common knowledge and rules; the design commonality knowledge and the rules are obtained according to the design information, and the control commonality knowledge and the rules are obtained according to the control information; the design end is used for designing the motor according to the design commonality knowledge and the rules issued by the cloud knowledge base; and the control end is used for controlling the motor according to the control commonality knowledge and the rules issued by the cloud knowledge base.
In one possible design, the control end is an artificial intelligence algorithm controller with heuristic thinking.
In one possible design, the control loop includes: the system comprises a brain emotion intelligent controller, a current follower and a power electronic converter, wherein the output torque of the brain emotion intelligent controller is subjected to current distribution through a current distribution function, and the distributed current controls a motor to operate through the current follower and the power electronic converter; the brain emotion intelligent controller determines an orbit cortex weight and a sensory cortex weight inside the brain emotion intelligent controller according to a mathematical expression relation between a sensory input function and an emotion rewarding function, and a self-learning system is constructed.
In a second aspect, a synchronous reluctance motor cooperative control method based on a motor cloud is provided, and the method includes:
the design end completes the specific design of the motor;
the control end controls the motor to operate;
the cloud end collects and stores design parameters and control parameters of the motor, obtains design commonality knowledge and rules and control commonality knowledge and rules according to the design parameters and the control parameters, guides the motor to be designed and controlled, and completes the fusion and cooperation of the design and the control.
In one possible design, the method includes:
analyzing a core technology of motor design, splitting a design function, and acquiring design commonality knowledge and rules;
storing design commonality knowledge and rules to the cloud;
the design end obtains design commonality knowledge and rules from the cloud end, optimizes the motor design parameters in the range of the design commonality knowledge and rules, and uploads the optimized data to the cloud end.
In one possible design, the method includes:
the control loop controls the motor to operate according to preset control logic;
the optimization loop detects the motor running condition and sends the motor running parameters to the cloud end;
and the cloud end collects and analyzes the relationship between the parameters of the control loop and the motor running parameters, determines better parameters with better motor running effect, and updates the better parameters into the control loop.
In one possible design, the method includes:
carrying out normalization processing on data of the individual knowledge of motor design in the cloud design module and the individual knowledge of motor control in the cloud control module;
according to the individual knowledge of motor design, obtaining the mapping relation between the structural parameters and the performance indexes by using an equivalent magnetic circuit method, and storing the mapping relation as design commonality knowledge and rules to a cloud knowledge base;
according to the individual knowledge of motor control, determining a mapping relation between a control parameter and a performance index by using a motor model, and storing the mapping relation as control common knowledge and rules to a cloud knowledge base;
and designing the motor according to the design commonality knowledge and the rules, and/or controlling the motor according to the control commonality knowledge and the rules.
In one possible design, the method includes: the motor control according to the control commonality knowledge and the rule comprises the following steps: the initial value and the range interval of the control quantity are given through control commonality knowledge and rules, an artificial intelligence algorithm with heuristic thinking in the control end determines a convergence condition according to the initial value and the range interval of the control quantity, determines a fast search area according to the convergence condition, and obtains motor control parameters.
The technical scheme of the invention has the following main advantages:
the synchronous reluctance motor cooperative control system and method based on the motor cloud construct the motor cloud through the connection among the design end, the control end and the cloud end. The design end and the control end are respectively connected with the cloud end to carry out data interaction and storage, the design commonality knowledge and rules and the control commonality knowledge and rules are summarized through the individual knowledge of motor design and control, and then the design and motor control are guided through the design commonality knowledge and rules and the control commonality knowledge and rules, so that the motor design and motor control difficulty is reduced, and the design and control effect is improved. And the motor design information and the motor control information can be interacted at the cloud end to form an open system, so that the integration and cooperation of design and control are completed, and a cooperative coupling mechanism of design and control is established.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic view of an overall structure of a synchronous reluctance motor cooperative control system based on a motor cloud according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a synchronous reluctance motor cooperative control system based on a motor cloud according to an embodiment of the present invention for performing motor control;
fig. 3 is a flowchart of a synchronous reluctance motor cooperative control method based on a motor cloud according to an embodiment of the present invention;
FIG. 4 is a schematic view of a streamlined rotor structure provided by an embodiment of the present invention;
fig. 5 is a parameter diagram of a streamlined rotor structure according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The technical scheme provided by the embodiment of the invention is described in detail below with reference to the accompanying drawings.
The networking and the intellectualization of the existing motor are in a lagging stage compared with other fields, the networking of a motor system is mainly embodied on the group control and synchronous coordination control of the motor, the intellectualization of the motor system is mainly embodied in that a motor designer is dedicated to the multi-objective optimization problem of an intelligent algorithm on the design of a motor body, and if the motor system cannot be effectively combined with a controller, the advantage of the design performance of the motor cannot be embodied. The motor controller is dedicated to realizing automatic identification of motor parameters and improvement of self-learning capability of the controller in the motor driver, while the realization of the intelligent algorithm is limited by the computing capability and the storage space of the core processor, so that the control model and the algorithm have to be simplified as much as possible, the operation process is simplified, and the feasibility is realized at the expense of performance indexes. Such data splitting and information isolation are based on the rapidity (up to μ s level) of motor control information, and if transmission and storage cannot be performed with the same resolution and the smallest possible delay, effective control of internal state variables and control variables of the motor cannot be realized. The rapid development of a core processor (DSP) integrating an ARM core and a control core provides possibility for the construction of a motor cloud, the control core is used for driving control, the ARM is used for high-speed communication of the Ethernet, and the mu s-level transmission and the ms-level delay of control data can be realized. The physical foundation required by the motor micro-cloud and even the motor cloud is provided, on the basis, the control mode and the design mode of the traditional motor are broken through, the motor cloud-based cooperative mechanism is established, the nonlinear problem is solved, the method has extremely important significance for the high-efficiency lean driving system of the nonmagnetic synchronous reluctance motor, provides good reference significance for other types of motor systems, and provides a feasible technical scheme for the follow-up research work of state monitoring, perception and feedback, diagnosis, fault tolerance and the like of the motor systems.
Based on the above, the synchronous reluctance motor cooperative control system and method based on the motor cloud provided by the embodiment of the invention are suitable for synchronous reluctance motor systems with the same motor structure and controller structure and different parameters.
In a first aspect, an embodiment of the present invention provides a synchronous reluctance motor cooperative control system based on a motor cloud, as shown in fig. 1, the system includes: design end, control end and high in the clouds. The design end and the control end are respectively connected with the cloud end. And the design end is used for finishing the specific design of the motor. And the control end is used for controlling the motor to operate. And the cloud end is used for collecting and storing the design information and the control information of the motor, acquiring design commonality knowledge and rules and control commonality design and rules according to the design information and the control information, guiding the design and the control of the motor, and finishing the fusion and the cooperation of the design and the control.
The following describes beneficial effects of the synchronous reluctance motor cooperative control system based on the motor cloud provided by the embodiment of the invention:
the synchronous reluctance motor cooperative control system based on the motor cloud provided by the embodiment of the invention constructs the motor cloud through the connection among the design end, the control end and the cloud end. The design end and the control end are respectively connected with the cloud end to carry out data interaction and storage, the design commonality knowledge and rules and the control commonality knowledge and rules are summarized through the individual knowledge of motor design and control, and then the design and motor control are guided through the design commonality knowledge and rules and the control commonality knowledge and rules, so that the motor design and motor control difficulty is reduced, and the design and control effect is improved. And the motor design information and the motor control information can be interacted at the cloud end to form an open system, so that the integration and cooperation of design and control are completed, and a cooperative coupling mechanism of design and control is established.
The following describes components and working principles of the synchronous reluctance motor cooperative control system based on the motor cloud according to the embodiment of the present invention:
optionally, the control end includes: the control circuit and the optimization circuit are respectively connected with the cloud end. And the control loop is used for controlling the motor to operate according to preset control logic. And the optimization loop is used for detecting the motor running condition and sending the motor running parameters to the cloud. And the cloud end is used for collecting and analyzing the relation between the parameters of the control loop and the motor running parameters, determining better parameters with better motor running effect, and updating the better parameters into the control loop.
The control end is divided into a control loop and an optimization loop to carry out hierarchical management and control, the control loop is used as a main control loop, and the motor is controlled to operate according to preset control logic. The optimization loop is connected with the motor, detects the motor running conditions (for example, running parameters such as current, voltage, position and speed), and sends the motor running parameters to the cloud. The cloud end is respectively connected with the control loop and the optimization loop, parameters of the control loop and motor running parameters can be respectively obtained, the relation between the parameters of the control loop and the motor running parameters is analyzed, the mapping relation between the control parameters and the motor running parameters (performance indexes) is established through a large number of data samples, better parameters corresponding to better running conditions (the better running conditions are determined according to user requirements so as to achieve the expected running conditions as the better running conditions) are determined, the better parameters are updated into the control loop, the control logic of the control loop is continuously updated, and various changes in the running process of the motor are self-adapted. Through the hierarchical control system, the adaptability and the robustness of motor control are high.
Further, the control loop comprises: the system comprises a brain emotion intelligent controller (BELBIC emotion controller), a current follower and a power electronic converter, wherein the output torque of the brain emotion intelligent controller is subjected to current distribution through a current distribution function, and the distributed current controls the motor to operate through the current follower and the power electronic converter.
The brain emotion intelligent controller determines the weight of the orbital cortex and the sensory cortex inside the brain emotion intelligent controller according to the mathematical expression relation between the sensory input function and the emotion rewarding function, and a self-learning system is constructed. Therefore, the intelligence and the adaptability of the control process are improved.
Specifically, the coefficients of the sensory input functions are preset values or given by a cloud; and/or the coefficient of the emotion reward function is a preset value or is given by the cloud. Coefficients of the sensory input function and the emotion reward function are preset values at the initial control stage to form preset control logic, and motor control is independently completed at the initial control stage. The optimization loop continuously collects motor running parameters and feeds the motor running parameters back to the cloud end along with the operation of the motor, and after the cloud end obtains better parameters, the cloud end gives coefficients of a sensory input function and an emotion reward function according to the better parameters, so that the self-adaptive updating of control logic is completed.
In an embodiment of the invention, optionally, the current distribution function is obtained from a magnetic model of the electric machine. The problem of magnetic saturation and cross coupling nonlinearity of the synchronous reluctance motor directly influences the control effect of the motor. To improve control accuracy, the distribution of the output torque to the current of the brain emotion intelligent controller depends on an accurate magnetic model.
Specifically, different magnetic model fitting expressions are preset in the brain emotion intelligent controller, and the magnetic model fitting expressions comprise corresponding relations of the magnetic models and the current distribution functions, so that the corresponding current distribution functions are selected according to different magnetic models. In the running process of the motor, the coefficients are determined and updated at the cloud end according to parameters such as voltage and current in actual running, so that the nonlinearity of a magnetic model, the nonlinear change of temperature rise and the like are overcome, the optimal current trajectory control is obtained, and the quick response of the high dynamic performance of the motor and the lean driving of a stable state are realized.
The control process of the control end can be seen in fig. 2.
Optionally, as shown in fig. 1, the cloud includes a cloud design module, a cloud control module, and a cloud repository. The design end comprises a plurality of motors with the same structure, the design information of each motor is stored in the cloud design module, and the design information comprises the corresponding relation between the design parameters and the performance indexes. The control end comprises a plurality of controllers in one-to-one correspondence with the motors, each controller controls the corresponding motor to operate, control information of each controller is stored in the cloud control module, and the control information comprises a corresponding relation between control parameters and performance indexes. The cloud knowledge base stores design common knowledge and rules and control common knowledge and rules; the design commonality knowledge and rules are obtained from the design information, and the control commonality knowledge and rules are obtained from the control information. The design end is used for designing the motor according to the design commonality knowledge and the rules issued by the cloud knowledge base; and the control end is used for controlling the motor according to the control common knowledge and the rules issued by the cloud knowledge base.
The design information of each motor in the synchronous reluctance motor system is stored in the cloud design module, the control information of each controller is stored in the cloud control module, and a data fusion shared information platform is built. The information interaction is not only the one-to-one correspondence relationship between the specific motor and the specific controller, but also an open system is formed, which is beneficial to the information interaction. The cloud knowledge base stores design commonality knowledge and rules, the design commonality knowledge and the rules are obtained through design information (namely, the corresponding relation between the structural parameters and the performance indexes of each motor) of the motors, the design of the motors is guided through the design commonality knowledge and the rules, the design difficulty can be reduced, the design effect is improved, and the motors meeting the use requirements can be conveniently designed. The cloud knowledge base stores control common knowledge and rules, the control common knowledge and the rules are obtained through control information (namely, corresponding relations between control parameters and performance indexes of each controller) of the controllers, the control of the motor is guided through the control common knowledge and the rules, when a new motor is connected into the system or the motor is required to be adjusted to different running states according to use requirements, adaptive control parameters can be obtained quickly and accurately, and the control precision and the effect are good. In addition, motor design information and motor control information can be interacted at the cloud end to form an open system. When the control is carried out, the control parameters can be adjusted according to the design information of the motor, and a collaborative coupling mechanism of design and control is realized. According to the arrangement, motor design and motor control which are mutually divided in the prior art are organically combined together, on the basis of known design information, accurate control parameters are favorably given, the mapping relation among structural parameters, the control parameters and performance indexes is established, the motor design and the motor control form an organic whole, and a cooperative coupling mechanism of the motor design and the motor control is established on the basis of cloud.
Further, the cloud design module and the cloud control module are mutually independent and are respectively connected with the cloud knowledge base, data interaction is carried out through the cloud knowledge base, and therefore the design information and the control information are respectively stored in independent areas, data mixing is avoided, data interaction is carried out through the cloud knowledge base, and the design information of the motor can provide reference for the control information.
Optionally, in the embodiment of the present invention, the number of the cloud design modules is one or more; and/or the number of the cloud control modules is one or more. The number of the cloud design modules and the number of the cloud control modules can be set according to the total data amount (the total data amount is positively correlated with the number of the motors and the controllers), and the data storage requirement can be met.
In a second aspect, an embodiment of the present invention provides a synchronous reluctance motor cooperative control method based on a motor cloud, as shown in fig. 3, the method includes: and the design end completes the specific design of the motor. The control end controls the motor to operate. The cloud end collects and stores design parameters and control parameters of the motor, obtains design commonality knowledge and rules and control commonality design and rules according to the design parameters and the control parameters, guides the motor design and control, and completes the fusion and cooperation of the design and control.
According to the synchronous reluctance motor cooperative control method based on the motor cloud, which is provided by the embodiment of the invention, the motor cloud is constructed through the connection among the design end, the control end and the cloud end. The design end and the control end are respectively connected with the cloud end to carry out data interaction and storage, the design commonality knowledge and rules and the control commonality knowledge and rules are summarized through the individual knowledge of motor design and control, and then the design and motor control are guided through the design commonality knowledge and rules and the control commonality knowledge and rules, so that the motor design and motor control difficulty is reduced, and the design and control effect is improved. And the motor design information and the motor control information can be interacted at the cloud end to form an open system, so that the integration and cooperation of design and control are completed, and a cooperative coupling mechanism of design and control is established.
For how to perform motor optimization design based on the motor cloud, the following example illustrates:
as an example, the motor optimization design method includes:
and analyzing the core technology of the motor design, splitting the design function and acquiring the common knowledge and rules of the design.
And storing the design commonality knowledge and the rules to the cloud.
The design end obtains design commonality knowledge and rules from the cloud end, optimizes the design parameters of the motor in the range of the design commonality knowledge and rules, and uploads the optimized data to the cloud end.
The core technology of the motor design is analyzed, and the function with the universal reference value for the motor design is separated out and used as the common knowledge and rule of the design. Wherein, the design commonality knowledge and rules are obtained by collecting, analyzing and summarizing parameters of a plurality of motors. The design commonality knowledge and the rules are stored in the cloud, when the motor is designed, the design end acquires the design commonality knowledge and the rules from the cloud and serves as a design reference, the motor design is optimized in the reference, the motor meeting the use requirement is designed, the final design data is uploaded to the cloud, and a cloud database is enriched.
Further, when the design function is split, the design function decomposition principle is as follows: the common technology, the technology needing dynamic adjustment and the technology with portability are decomposed to the cloud end to serve as design common knowledge and rules. That is, design commonality knowledge and rules includes: common technology, dynamic adjustment technology and technology with portability are needed.
Specifically, the design commonality knowledge and rules can be obtained by a certain number of motor samples with the same structure and different parameters, and the mapping relation between the structural parameters and the performance indexes is determined according to the design data of each motor in the samples and the equivalent magnetic circuit method to serve as the design commonality knowledge and rules.
By way of example, techniques that have portability include: the method comprises the steps of multi-target integration, constraint condition design, an initial motor structure sample and a structure parameter sample interval.
The design parameters of the motor comprise: rotor structural parameters, control parameters, and strength parameters. The structural parameters of the rotor comprise the thickness of a magnetic barrier, the angular position of an air gap and offset; the control parameter comprises a current angle; the strength parameter includes a radial rib width. That is, in designing the motor, the rotor structure parameters (the magnetic barrier thickness, the air gap angular position, the offset, and the like), the control parameters (the current angle), and the strength parameters (the radial rib width) can be determined by the design common knowledge and rules.
By way of example, the following is an example of rotor structure parameters, and how to determine motor design parameters according to design commonality knowledge and rules:
the design end optimizes the structural parameters of the rotor, and the design end comprises the following steps:
and receiving a rotor structure sample, a parameter interval, an optimization condition and a constraint condition sent by a cloud end, wherein the rotor structure sample, the parameter interval, the optimization condition and the constraint condition are contained in the design commonality knowledge and rule.
A genetic algorithm is adopted to carry out local search optimization design, and a motor meeting the requirements of a user is designed;
and uploading the designed result structure parameters, ideal model parameters and ideal flux linkage of the motor to a cloud terminal.
And in the range of rotor structure samples, parameter intervals, optimization conditions and constraint conditions sent by the cloud, a genetic algorithm is adopted to carry out local search optimization design, an optimal solution is output, and a motor meeting the use requirement is designed. And uploading the design data (result structure parameters, ideal model parameters and ideal flux linkage) to the cloud end, and enriching the cloud end database.
For example, a streamline rotor structure designed in the above manner can be seen in fig. 4, and the structural parameters can be seen in fig. 5.
Further, the cloud updates the design commonality knowledge and rules according to the result structure parameters, the ideal model parameters and the ideal flux linkage. After the design data (structural parameters, ideal model parameters and ideal flux linkage) are uploaded to the cloud, the design data are used as new individual motor design data, so that the number of motor samples is increased, new design commonality knowledge and rules are summarized and summarized through the expanded motor samples, and the design commonality knowledge and rules are updated.
As another example, the motor optimization design and the motor control optimization method are similar, and are performed by the following methods:
and carrying out normalization processing on data of the motor design personalized knowledge in the cloud design module and the motor control personalized knowledge in the cloud control module. The motor control system comprises a controller, a motor controller and a motor controller, wherein the motor controller is used for controlling the motor, the controller is used for controlling the motor, and the motor controller is used for controlling the motor. Through normalization processing, the consistency of data is guaranteed, and subsequent data processing and inductive summarization are facilitated.
According to the individual knowledge of the motor design, the mapping relation between the structural parameters and the performance indexes is obtained by using an equivalent magnetic circuit method and is stored to a cloud knowledge base as design commonality knowledge and rules. The individual knowledge of the motor design (i.e. the design information of each motor) includes the structural parameters and performance indexes of each motor, and the mapping relation between the structural parameters and the performance indexes can be summarized and summarized from a certain amount of individual knowledge by using an equal magnetic circuit method, and the mapping relation is used as the common knowledge and rule of the design.
And according to the individual knowledge of motor control, determining the mapping relation between the control parameters and the performance indexes by using the motor model, and storing the mapping relation as control common knowledge and rules to a cloud knowledge base. The motor control individual knowledge (i.e., the control parameters of each controller) includes the control parameters and performance indexes of each controller, and the mapping relationship between the control parameters and the performance indexes can be summarized and summarized from a certain amount of individual knowledge by using a motor model as control common knowledge and rules.
And designing the motor according to the design commonality knowledge and the rules, and/or controlling the motor according to the control commonality knowledge and the rules.
Further, in this example, the motor control according to the control commonality knowledge and the rule includes: the initial value and the range interval of the control quantity are given through control commonality knowledge and rules, an artificial intelligence algorithm with heuristic thinking in the control end determines a convergence condition according to the initial value and the range interval of the control quantity, determines a fast search area according to the convergence condition, and obtains motor control parameters.
Based on the above, after the motor control parameters are acquired according to the use requirements (performance indexes), the acquired motor control parameters are stored in the cloud control module as the individual knowledge of motor control. So set up, supply new motor control parameter and performance index's corresponding relation to the high in the clouds, enlarge sample quantity, update control commonality knowledge and rule, improve accurate degree.
Based on the control end comprising a loop and an optimization loop, the method provided by the embodiment of the invention further comprises the following steps:
the control loop controls the motor to operate according to preset control logic.
The optimization loop detects the motor running condition and sends the motor running parameters to the cloud.
The cloud end collects and analyzes the relation between the parameters of the control loop and the motor running parameters, determines better parameters with better motor running effect, and updates the better parameters into the control loop.
The control end is divided into a control loop and an optimization loop, the control loop is used as a main control loop, and the motor is controlled to operate according to preset control logic. The optimization loop is connected with the motor, detects the motor running conditions (for example, running parameters such as current, voltage, position and speed), and sends the motor running parameters to the cloud. The cloud end is respectively connected with the control loop and the optimization loop, parameters of the control loop and motor running parameters can be respectively obtained, the relation between the parameters of the control loop and the motor running parameters is analyzed, the mapping relation between the control parameters and the motor running parameters (performance indexes) is established through a large number of data samples, better parameters corresponding to better running conditions (the better running conditions are determined according to user requirements so as to achieve the expected running conditions as the better running conditions) are determined, the better parameters are updated into the control loop, the control logic of the control loop is continuously updated, and various changes in the running process of the motor are self-adapted. And the adaptability and robustness of the control process are improved through a hierarchical control system.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. In addition, "front", "rear", "left", "right", "upper" and "lower" in this document are referred to the placement states shown in the drawings.
Finally, it should be noted that: the above examples are only for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A synchronous reluctance machine cooperative control system based on a machine cloud, the system comprising: the system comprises a design end, a control end and a cloud end, wherein the design end and the control end are respectively connected with the cloud end; the design end is used for completing the specific design of the motor; the control end is used for controlling the motor to operate; the high in the clouds is used for collecting and storing design information, the control information of motor to obtain design commonality knowledge and rule and control commonality knowledge and rule according to design information and control information, guide motor design and control, accomplish the integration and the cooperation of design and control, its characterized in that:
the cloud comprises a cloud design module, a cloud control module and a cloud knowledge base;
the design end comprises a plurality of motors with the same structure, the design information of each motor is stored in the cloud design module, and the design information comprises the corresponding relation between design parameters and performance indexes;
the control end comprises a plurality of controllers which correspond to the motors one by one, each controller controls the corresponding motor to operate, control information of each controller is stored in the cloud control module, and the control information comprises a corresponding relation between control parameters and performance indexes;
the cloud knowledge base stores design commonality knowledge and rules and control commonality knowledge and rules, the design commonality knowledge and rules are obtained according to design information, and the control commonality knowledge and rules are obtained according to control information;
the design end is used for designing the motor according to the design commonality knowledge and the rules issued by the cloud knowledge base, and the control end is used for controlling the motor according to the control commonality knowledge and the rules issued by the cloud knowledge base.
2. The synchronous reluctance motor cooperative control system based on the motor cloud as claimed in claim 1, wherein the control end comprises a control loop and an optimization loop, and the control loop and the optimization loop are respectively connected to the cloud end;
the control loop is used for controlling the motor to operate according to preset control logic;
the optimization loop is used for detecting the motor running condition and sending the motor running parameters to the cloud end;
the cloud end is used for collecting and analyzing the relation between the parameters of the control loop and the motor running parameters, determining better parameters with better motor running effect, and updating the better parameters into the control loop.
3. The synchronous reluctance motor cooperative control system based on the motor cloud as claimed in claim 1 or 2, wherein the control terminal is an artificial intelligence algorithm controller with heuristic thinking.
4. The motor cloud based synchronous reluctance motor cooperative control system according to claim 2, wherein the control loop comprises: the system comprises a brain emotion intelligent controller, a current follower and a power electronic converter, wherein the output torque of the brain emotion intelligent controller is subjected to current distribution through a current distribution function, and the distributed current controls a motor to operate through the current follower and the power electronic converter;
the brain emotion intelligent controller determines an orbit cortex weight and a sensory cortex weight inside the brain emotion intelligent controller according to a mathematical expression relation between a sensory input function and an emotion rewarding function, and a self-learning system is constructed.
5. A synchronous reluctance motor cooperative control method based on a motor cloud comprises the following steps:
the design end completes the specific design of the motor;
the control end controls the motor to operate;
the cloud end collects and stores the design parameters and the control parameters of the motor, acquires the design commonality knowledge and rules and the control commonality knowledge and rules according to the design parameters and the control parameters, guides the motor design and control, completes the fusion and the cooperation of the design and the control,
characterized in that the method comprises:
analyzing a core technology of motor design, splitting a design function, and acquiring design commonality knowledge and rules;
storing design commonality knowledge and rules to the cloud;
the design end obtains design commonality knowledge and rules from the cloud end, optimizes the motor design parameters in the range of the design commonality knowledge and rules, and uploads the optimized data to the cloud end.
6. The motor cloud-based synchronous reluctance motor cooperative control method according to claim 5, wherein the method comprises:
the control loop controls the motor to operate according to preset control logic;
the optimization loop detects the motor running condition and sends the motor running parameters to the cloud end;
and the cloud end collects and analyzes the relationship between the parameters of the control loop and the motor running parameters, determines better parameters with better motor running effect, and updates the better parameters into the control loop.
7. The motor cloud-based synchronous reluctance motor cooperative control method according to claim 5, wherein the method comprises:
carrying out normalization processing on data of the individual knowledge of motor design in the cloud design module and the individual knowledge of motor control in the cloud control module;
according to the individual knowledge of motor design, obtaining the mapping relation between the structural parameters and the performance indexes by using an equivalent magnetic circuit method, and storing the mapping relation as design commonality knowledge and rules to a cloud knowledge base;
according to the individual knowledge of motor control, determining a mapping relation between a control parameter and a performance index by using a motor model, and storing the mapping relation as control common knowledge and rules to a cloud knowledge base;
and designing the motor according to the design commonality knowledge and the rules, and/or controlling the motor according to the control commonality knowledge and the rules.
8. The motor cloud-based synchronous reluctance motor cooperative control method according to claim 7, wherein the method comprises: the motor control according to the control commonality knowledge and the rule comprises the following steps: the initial value and the range interval of the control quantity are given through control commonality knowledge and rules, an artificial intelligence algorithm with heuristic thinking in the control end determines a convergence condition according to the initial value and the range interval of the control quantity, determines a fast search area according to the convergence condition, and obtains motor control parameters.
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