SYSTEM AND METHOD FOR REGULATING MOTOR SPEED AND TORQUE BY ESTIMATING SPEED AND CONTROLLING CURRENT

Abstract

The present disclosure relates to a system for regulating motor speed and torque by estimating speed and controlling current. The system includes processors that receive reference speed signals from sources and feedback current signals from permanent magnet synchronous motor (PMSM) (106), the sources can include manual inputs, higher-level control systems, pre-programmed motion profiles, and communication interfaces. The processors generate dq-axis reference currents based on the reference speed signals and the feedback current signals using pre-stored Field-Oriented Control (FOC) technique. The processors convert the feedback current signals to αβ-axis currents and dq-axis currents using pre-stored Clarke and Park transformation techniques and determine estimated speed of PMSM (106). The processors generate voltage reference commands based on the dq-axis reference currents, the dq-axis currents, and decoupling terms. The processors generate switching pulses based on the voltage reference commands and control the current to regulate speed and torque of the PMSM (106).

Specification

Description:TECHNICAL FIELD
[0001] The present disclosure relates to a field of a control system for Permanent Magnet Synchronous Motors (PMSM). More precisely, the present disclosure relates to a system and method for regulating motor speed and torque by estimating speed and controlling current.

BACKGROUND
[0002] The following description of the related art is intended to provide background information pertaining to the field of the present disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] Vector control is a widely adopted method for speed control of Alternating Current (AC) motors, particularly due to its ability to provide enhanced motor response across a broad speed range and high starting torque. Among various vector control techniques, Field Oriented Control (FOC) stands out as the superior choice, particularly noted for its high motor performance at low speeds. Typically, an indirect FOC is more commonly employed in motor control applications. However, existing systems utilizing this technique necessitate speed estimation, which typically requires feedback from both voltage and current signals.
[0004] In current methodologies, the control of PMSM systems involves distinct subsystems dedicated to current control and speed estimation. The current control process relies on dq-axis current signals or voltage vectors to regulate the motor's performance effectively. Meanwhile, the speed estimation process demands either the acquisition of both current or voltage signals from the motor or the implementation of complex estimation procedures. The studies conducted in this area have revealed a significant gap in the development of a more compact and integrated system that streamlines both current control and speed estimation processes. This deficiency underscores the need for innovations that can simplify the architecture of PMSM control systems while maintaining performance and efficiency.
[0005] There is, therefore, a need in the art to provide a system and method that can overcome the shortcomings of the existing prior arts.

OBJECTS OF THE PRESENT DISCLOSURE
[0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0007] It is an object of the present disclosure to provide a system and method for regulating motor speed and torque by estimating speed and controlling current.
[0008] It is another object of the present disclosure to provide a system and method for regulating motor speed and torque by estimating speed and controlling current, which facilitates a machine learning (ML)-based controller for current control and ML-based speed estimation using motor currents.
[0009] It is another object of the present disclosure to provide a system and method for regulating motor speed and torque by estimating speed and controlling current, which eliminates the need for traditional speed sensors and reduces the complexity and cost of motor control systems.

SUMMARY
[0010] This summary is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0011] An aspect of the present disclosure relates to regulating motor speed and torque by estimating speed and controlling current. The system pertains to a control system for Permanent Magnet Synchronous Motors (PMSM). The system can include processors that can receive reference speed signals from a plurality of sources and feedback current signals from a permanent magnet synchronous motor (PMSM). The plurality of sources can include manual inputs, higher-level control systems, pre-programmed motion profiles, and communication interfaces. The processors can generate dq-axis reference currents based on the reference speed signals and the feedback current signals using a pre-stored Field-Oriented Control (FOC) technique. The processors can convert feedback current signals to αβ-axis currents and dq-axis currents using pre-stored Clarke and Park transformation techniques and can determine the estimated speed of the Permanent Magnet Synchronous Motor (PMSM). The processors can generate voltage reference commands based on the dq-axis reference currents, the dq-axis currents, and decoupling terms. The processors can generate switching pulses based on the voltage reference commands and control the current to regulate the speed and torque of the Permanent Magnet Synchronous Motor (PMSM).
[0012] In an aspect, a method for regulating motor speed and torque by estimating speed and controlling current, the method includes the steps of receiving reference speed signals from a plurality of sources and feedback current signals from a permanent magnet synchronous motor (PMSM), the plurality of sources comprising manual inputs, higher-level control systems, pre-programmed motion profiles, and communication interfaces. The method includes the steps of generating dq-axis reference currents based on the reference speed signals and the feedback current signals using a pre-stored Field-Oriented Control (FOC) technique. The method includes the steps of converting feedback current signals to αβ-axis currents and dq-axis currents using pre-stored Clarke and Park transformation techniques and determining the estimated speed of the Permanent Magnet Synchronous Motor (PMSM). The method includes the steps of generating voltage reference commands based on the dq-axis reference currents, the dq-axis currents, and decoupling terms. The method includes the steps of generating switching pulses based on the voltage reference commands and controlling the current to regulate the speed and torque of the Permanent Magnet Synchronous Motor (PMSM).
[0013] Various objects, features, aspects, and advantages of the present disclosure will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which numerals represent like features.
[0014] Within the scope of this application, it is expressly envisaged that the various aspects, embodiments, examples, and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments, unless such features are incompatible.

BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In the figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
[0016] FIG. 1 illustrates an exemplary representation of the proposed system for regulating motor speed and torque by estimating speed and controlling current, by an embodiment of the present disclosure.
[0017] FIG. 2 illustrates a block diagram of the proposed system, by an embodiment of the present disclosure.
[0018] FIG. 3A illustrates an exemplary representation of the system, in accordance with an embodiment of the present disclosure.
[0019] FIG. 3B illustrates exemplary representations of the controller module, in accordance with an embodiment of the present disclosure.
[0020] FIG. 3C illustrates exemplary representations of the estimator module, in accordance with an embodiment of the present disclosure.
[0021] FIG. 4 illustrates a flow diagram illustrating a method for regulating motor speed and torque by estimating speed and controlling current, in accordance with an embodiment of the present disclosure.
[0022] FIG. 5 illustrates an exemplary computer system to implement the proposed system in accordance with embodiments of the present disclosure.
DETAILED DESCRIPTION
[0023] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
[0024] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0025] The present disclosure relates to a field of a control system for Permanent Magnet Synchronous Motors (PMSM). More precisely, the present disclosure relates to a system and method for regulating motor speed and torque by estimating speed and controlling current.
[0026] An aspect of the present disclosure relates to a system for regulating motor speed and torque by estimating speed and controlling current. The system includes one or more processors; and at least one memory coupled to the one or more processors, said memory having instructions executable by the one or more processors to receive reference speed signals from a plurality of sources and feedback current signals from a permanent magnet synchronous motor (PMSM), where the plurality of sources can include manual inputs, higher-level control systems, pre-programmed motion profiles, and communication interfaces. The processor can generate dq-axis reference currents based on the reference speed signals and the feedback current signals using a pre-stored Field-Oriented Control (FOC) technique. The processor can convert the feedback current signals to αβ-axis currents and dq-axis currents using pre-stored Clarke and Park transformation techniques and determine estimated speed of the Permanent Magnet Synchronous Motor (PMSM). The processor can generate voltage reference commands based on the dq-axis reference currents, the dq-axis currents, and decoupling terms. The processors can generate switching pulses based on the voltage reference commands and control the current to regulate speed and torque of the Permanent Magnet Synchronous Motor (PMSM).
[0027] FIG. 1 illustrates an exemplary architecture (100) of the proposed system for regulating motor speed and torque by estimating speed and controlling current, in accordance with an embodiment of the present disclosure.
[0028] In an embodiment, referring to FIG. 1, the exemplary architecture (100) can include a system (102), an inverter (104), and a motor (106). The system (102) pertains to a control system that may be configured to regulate motor (106) speed and torque by estimating speed and controlling current. The motor (106) may include, but not limited to, a Permanent Magnet Synchronous Motor (PMSM). The system (102) can include a Machine Learning (ML)-based controller in the FOC technique and a ML-based speed estimation for PMSM (permanent magnet synchronous motor). The control system (102) can include a plurality of current sensors configured to detect the feedback current signals of the permanent magnet synchronous motor (PMSM) (106).
[0029] The speed is estimated from the αβ-currents of the motor using ML, thereby eliminating the need for a speed sensor. The current control loop of the vector control is implemented using dq-currents but uses ML-based controller instead of a PI (proportional-integral) controller. Therefore, only current sensors are required to estimate motor speed and rotor position. The system (102) facilitates the reduction of circuitry and simple control configuration. The key benefits of the system (102) can include reduction of sensors, controller complexity, computing complexity, and cost.
[0030] FIG. 2 illustrates a block diagram of the system (102), in accordance with an embodiment of the present disclosure.
[0031] In an aspect, referring to FIG. 2, the system (102) may include one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in the memory (204) of the system (102). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may include any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.
[0032] Referring to FIG. 2, the system (102) may include an interface(s) (206). The interface(s) (206) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) (206) may facilitate communication to/from the system (102). The interface(s) (206) may also provide a communication pathway for one or more components of the system (102). Examples of such components include but are not limited to, processing unit/engine(s) (208) and a local database (210).
[0033] In an embodiment, the processing unit/engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (102) may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (102) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[0034] In an embodiment, the local database (210) may include data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor (202) or the processing engines (208). In an embodiment, the local database (210) may be separate from the system (102).
[0035] In an exemplary embodiment, the processing engine (208) may include one or more engines selected from any of a controller module (212), an estimator module (214), a proportional-integral (PI) controller (216), and a pulse generator (218), and other modules (220) having functions that may include but are not limited to testing, storage, and peripheral functions, such as wireless communication unit for remote operation, audio unit for alerts and the like.
[0036] In an embodiment, the system (102) can include the processors (202) which may be configured to receive reference speed signals from a plurality of sources and feedback current signals from the permanent magnet synchronous motor (PMSM) (106). The plurality of sources can include manual inputs, higher-level control systems, pre-programmed motion profiles, and communication interfaces.
[0037] In an embodiment, the system (102) can include the proportional-integral controller (202) which may be configured to receive the reference speed signals from the plurality of sources and the feedback current signals from the permanent magnet synchronous motor (PMSM) (106). The proportional-integral controller (202) may be configured to generate the dq-axis reference currents based on the reference speed signals and the feedback current signals using the pre-stored Field-Oriented Control (FOC) technique.
[0038] In an embodiment, the processors (202) which can be configured to convert the feedback current signals to αβ-axis currents and dq-axis currents using pre-stored Clarke and Park transformation techniques and determine the estimated speed of the Permanent Magnet Synchronous Motor (PMSM) (106) using a plurality of pre-stored machine learning techniques. The plurality of pre-stored machine learning techniques may include, but not limited to, Linear Regression (LR), Support Vector Machine (SVM), and Neural Networks (NN), and the like.
[0039] In an embodiment, the system (102) can include the estimator module (214) pertains to a machine learning (ML) estimator which can be configured to receive the αβ-axis currents and determine the estimated speed of the Permanent Magnet Synchronous Motor (PMSM) (106) using a plurality of pre-stored machine learning techniques. The machine learning (ML) estimator may be configured to determine the estimated speed of the motor (106) based on the αβ-axis currents and transfer the estimated speed to the PI controller (216) as an input.
[0040] In an embodiment, the system (102) can include the controller module (212) pertains to a machine learning (ML) controller which may be configured to receive the dq-axis reference currents from the proportional-integral controller (216), the dq-axis currents converted using the pre-stored Clarke and Park transformation techniques, and the decoupling terms. The decoupling terms pertain to cross-coupling terms that compensate for the interaction between dq-axis inductances. The controller module (212) may be configured to generate voltage reference commands based on the dq-axis reference currents, the dq-axis currents, and the decoupling terms.
[0041] In an embodiment, the machine learning (ML) controller (212) which may be configured to perform a comparison between the feedback current signals and the reference current signals to compute control actions and regulate the motor's operating parameters to minimize the error between the reference currents signals and the feedback current signals, thereby optimizing the motor's speed, torque, or position. The motor's operating parameters can include voltage and current. The control actions can include the control signals for regulating the motor's d-axis and q-axis currents, adjusting the applied voltage to the motor, and determining the switching signals for the inverter (104). The machine learning (ML) controller (206) can be configured to incorporate the decoupling terms into the control signals to compensate for cross-coupling effects between the dq-axis currents, and to account for back electromotive force (back-EMF), thereby ensuring independent control of the torque and flux-producing components of the permanent magnet synchronous motor (PMSM) (106).
[0042] In an embodiment, the system (102) can include the pulse generator (218) which may be configured to generate switching pulses based on the voltage reference commands and control the current to regulate the speed and torque of the Permanent Magnet Synchronous Motor (PMSM) (106).
[0043] FIG. 3A illustrates exemplary representation (300a) of the system, in accordance with an embodiment of the present disclosure.
[0044] In an embodiment, the reference speed and estimated speed are given to the PI controller (216), and dq-axis reference currents are generated (based on the FOC method) and are given to the ML controller block (212). The feedback current signals are converted to dq-axis currents, and αβ-axis currents using Clarke and Park transformation techniques. The dq-axis currents which are given as inputs to the ML controller block (212) and the αβ-axis currents which are given to the ML estimator (214). The decoupling terms are also given as inputs to the ML controller block (212). From the ML controller block (212), the outputs obtained are voltage reference commands for pulse generation. Based on the voltage reference commands, the pulse generator (218) provides switching pulses needed for the inverter (104). The outputs of ML estimator (214) are the estimated speed, and the estimated rotor position.
[0045] FIG. 3B illustrates an exemplary representation (300b) of the controller module, in accordance with an embodiment of the present disclosure.
[0046] In an embodiment, the ML controller block (212) uses a comparison of the feedback signals and reference signals, and adds in the decoupling terms. The machine learning (ML) controller (212) can be configured to perform a comparison between the feedback current signals and the reference current signals to compute control actions and regulate the motor's operating parameters to minimize the error between the reference current signals and the feedback current signals, thereby optimizing the motor's speed, torque, or position. The motor's operating parameters can include voltage and current. The control actions can include the control signals for regulating the motor's d-axis and q-axis currents, adjusting the applied voltage to the motor, and determining the switching signals for the inverter (104). The machine learning (ML) controller (206) can be configured to incorporate the decoupling terms into the control signals to compensate for cross-coupling effects between the dq-axis currents, and to account for back electromotive force (back-EMF), thereby ensuring independent control of the torque and flux-producing components of the permanent magnet synchronous motor (PMSM) (106).
[0047] FIG. 3C illustrates an exemplary representation (300c) of the estimator module, in accordance with an embodiment of the present disclosure.
[0048] In an embodiment, the ML estimator (214) uses the relation between αβ-axes and trigonometric functions to estimate the speed and rotor position. The trigonometric functions used by the ML estimator (214) can include Sine (sin(θ)) for calculating the projection onto the dq-axis. Cosine (cos(θ)) for calculating the alignment with the rotor's magnetic field. The trigonometric functions help estimate the rotor speed and position by using the αβ currents and voltages and projecting them into the rotating reference frame (dq), where they can be used for control purposes.
[0049] FIG. 4 illustrates a flow diagram illustrating a method for regulating motor speed and torque by estimating speed and controlling current, in accordance with an embodiment of the present disclosure.
[0050] As illustrated, method (400) includes, at block (402), receiving reference speed signals from a plurality of sources and feedback current signals from a permanent magnet synchronous motor (PMSM), where the plurality of sources can include manual inputs, higher-level control systems, pre-programmed motion profiles, and communication interfaces.
[0051] Continuing further, method (400) includes, at block (404), generating dq-axis reference currents based on the reference speed signals and the feedback current signals using a pre-stored Field-Oriented Control (FOC) technique.
[0052] Continuing further, method (400) includes, at block (406), converting feedback current signals to αβ-axis currents and dq-axis currents using pre-stored Clarke and Park transformation techniques and determining the estimated speed of the Permanent Magnet Synchronous Motor (PMSM).
[0053] Continuing further, method (400) includes, at block (408), generating voltage reference commands based on the dq-axis reference currents, the dq-axis currents, and decoupling terms.
[0054] Continuing further, method (400) includes, at block (410), generating switching pulses based on the voltage reference commands and controlling the current to regulate the speed and torque of the Permanent Magnet Synchronous Motor (PMSM).
[0055] FIG. 5 illustrates an exemplary computer system to implement the proposed system in accordance with embodiments of the present disclosure.
[0056] As illustrated in Fig. 5, a computer system (500) can include an external storage device (510), a bus (520), a main memory (530), a read only memory (540), a mass storage device (550), communication port (560), and a processor (570). A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. Examples of processor (570) include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on chip processors or other future processors. Processor (570) may include various modules associated with embodiments of the present disclosure. Communication port (560) can be any of an RS-232 port for use with a modem based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. Communication port (560) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects.
[0057] Memory (530) can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory (540) can be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor (570). Mass storage (550) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda 7102 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[0058] Bus (520) communicatively couple processor(s) (570) with the other memory, storage and communication blocks. Bus 520 can be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor (570) to software system.
[0059] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus (520) to support direct operator interaction with a computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port (560). The external storage device (510) can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc-Re-Writable (CD-RW), Digital Video Disk-Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0060] If the specification states a component or feature "may", "can", "could", or "might" be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0061] As used in the description herein and throughout the claims that follow, the meaning of "a," "an," and "the" includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.
[0062] Moreover, in interpreting the specification, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms "comprises" and "comprising" should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C ….and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
[0063] While the foregoing describes various embodiments of the proposed disclosure, other and further embodiments of the proposed disclosure may be devised without departing from the basic scope thereof. The scope of the proposed disclosure is determined by the claims that follow. The proposed disclosure is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0064] The present disclosure provides a system and method for regulating motor speed and torque by estimating speed and controlling the current
[0065] The present disclosure provides a system and method that controls current control and estimated speed using machine learning techniques based on the current feedback and eliminates the need for speed sensors and voltage sensors.
[0066] The present disclosure provides a system and method that reduces the circuit components and increases the compactness of the overall motor drive.
, Claims:1. A system for regulating motor speed and torque by estimating speed and controlling current, the system (102) comprising:
one or more processors (202); and
at least one memory (204) coupled to the one or more processors (202), said memory (204) having instructions executable by the one or more processors (104) to:
receive reference speed signals from a plurality of sources and feedback current signals from a permanent magnet synchronous motor (PMSM) (106), wherein the plurality of sources comprising manual inputs, higher-level control systems, pre-programmed motion profiles, and communication interfaces;
generate dq-axis reference currents based on the reference speed signals and the feedback current signals using a pre-stored Field-Oriented Control (FOC) technique;
convert the feedback current signals to αβ-axis currents and dq-axis currents using pre-stored Clarke and Park transformation techniques and determine estimated speed of the Permanent Magnet Synchronous Motor (PMSM) (106);
generate voltage reference commands based on the dq-axis reference currents, the dq-axis currents, and decoupling terms; and
generate switching pulses based on the voltage reference commands and control the current to regulate speed and torque of the Permanent Magnet Synchronous Motor (PMSM) (106).

2. The system as claimed in claim 1, wherein the system (102) comprising a proportional-integral controller (216) configured to receive the reference speed signals from the plurality of sources and the feedback current signals from the permanent magnet synchronous motor (PMSM) (106),
wherein the proportional-integral controller (216) configured to generate the dq-axis reference currents based on the reference speed signals and the feedback current signals using the pre-stored Field-Oriented Control (FOC) technique.

3. The system as claimed in claim 1, wherein the system (102) comprising a machine learning (ML) estimator (214) configured to receive the αβ-axis currents and determine the estimated speed of the Permanent Magnet Synchronous Motor (PMSM) (106) using a plurality of pre-stored machine learning techniques.

4. The system as claimed in claim 3, wherein the plurality of pre-stored machine learning techniques comprising Linear Regression (LR), Support Vector Machine (SVM) and Neural Network (NN).

5. The system as claimed in claim 1, wherein the system (102) comprising a machine learning (ML) controller (212) configured to receive the dq-axis reference currents from the proportional-integral controller (216), the dq-axis currents converted using the pre-stored Clarke and Park transformation techniques, and the decoupling terms,
wherein the decoupling terms pertain to cross-coupling terms that compensate for the interaction between dq-axis inductances.

6. The system as claimed in claim 5, wherein the machine learning (ML) controller (212) configured to generate voltage reference commands based on the dq-axis currents, the dq-axis reference currents, and the decoupling terms.

7. The system as claimed in claim 1, wherein the system (102) comprising a plurality of current sensors configured to detect the feedback current signals of the permanent magnet synchronous motor (PMSM) (106).

8. The system as claimed in claim 1, wherein the machine learning (ML) controller (212) configured to perform a comparison between the feedback current signals and the reference current signals to compute control actions and regulate the motor's operating parameters to minimize the error between the reference currents signals and the feedback current signals, thereby optimizing the motor's speed, torque, or position,
wherein the motor's operating parameters comprising voltage and current,
wherein control actions comprising the control signals for regulating the motor's d-axis and q-axis currents, adjusting the applied voltage to the motor, and determining the switching signals for an inverter (104).

9. The system as claimed in claim 1, wherein the machine learning (ML) controller (212) configured to incorporate the decoupling terms into the control signals to compensate for cross-coupling effects between the dq-axis currents, and to account for back electromotive force (back-EMF), thereby ensuring independent control of the torque and flux-producing components of the permanent magnet synchronous motor (PMSM) (106).

10. A method for regulating motor speed and torque by estimating speed and controlling current, the method (400) comprising:
receiving reference speed signals from a plurality of sources and feedback current signals from a permanent magnet synchronous motor (PMSM) (106), wherein the plurality of sources comprising manual inputs, higher-level control systems, pre-programmed motion profiles, and communication interfaces;
generating dq-axis reference currents based on the reference speed signals and the feedback current signals using a pre-stored Field-Oriented Control (FOC) technique;
converting feedback current signals to αβ-axis currents and dq-axis currents using pre-stored Clarke and Park transformation techniques and determining estimated speed of the Permanent Magnet Synchronous Motor (PMSM) (106);
generating voltage reference commands based on the dq-axis reference currents, the dq-axis currents, and decoupling terms; and
generating switching pulses based on the voltage reference commands and controlling the current to regulate the speed and torque of the Permanent Magnet Synchronous Motor (PMSM) (106).

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