Ship six-phase built-in permanent magnet motor model-free prediction control method

Abstract

The invention discloses a ship six-phase built-in permanent magnet motor model-free prediction control method, which comprises the following steps: S1, carrying out vector space decoupling analysis on a six-phase built-in permanent magnet synchronous motor, and extracting voltage vector distribution characteristics of an alpha-beta subspace and an x-y subspace, s2, synthesizing a composite virtual vector control set by adopting a space vector off-line calculation mode based on the pre-synthesized basic virtual voltage vector, S3, constructing a current prediction super-local model in an alpha-beta subspace, and designing an inductance decoupling type expansion state observer, S4, optimizing a current prediction value based on an error function, and obtaining a current prediction result. And obtaining an optimal six-way switching signal corresponding to the target current, and inputting the optimal six-way switching signal into a six-phase two-level inverter to perform driving control of a six-phase built-in permanent magnet synchronous motor in the ship propulsion system. According to the method, high-precision tracking of double-sub-space current can be realized, the robustness under the condition of system parameter mismatching is improved, and the method is suitable for high-performance control of the six-phase IPMSM in a ship propulsion system.

Description

Technical Field

[0001] The present invention relates to the field of motor control technology, and in particular to a model-free predictive control method for a six-phase internal permanent magnet motor of a ship. Background Art

[0002] As the marine industry develops towards high reliability, electrification, intelligence, and a green and low-carbon future, electric propulsion technology has become a key research direction. The six-phase IPMSM, with its high efficiency, strong robustness, multi-degree-of-freedom control capabilities, and suitability for low-voltage, high-power scenarios, shows promising application prospects in marine propulsion systems.

[0003] To achieve smooth and responsive propulsion system power output and reduce energy consumption, the current control loop in the drive system must possess excellent dynamic regulation capabilities and steady-state control accuracy. Traditional model predictive current control (MPCC) methods are widely used for motor speed control due to their simple computation and strong real-time performance. However, six-phase PMSMs have more control degrees of freedom than three-phase systems, placing higher optimization and constraint requirements on the control strategy. Performance needs to be improved, especially under complex operating conditions.

[0004] Existing virtual vector model predictive control (VVMPC) technology primarily constructs basic virtual vectors with fixed direction and amplitude to effectively suppress harmonic currents in the x-y subspace. However, current regulation in the α-β subspace is insufficiently precise and susceptible to parameter fluctuations and cross-coupling dynamics, resulting in degraded control performance. Furthermore, prolonged application of a single virtual vector can trigger current surges, further restricting the system's dynamic response. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention discloses a model-free predictive control method for a six-phase interior permanent magnet motor of a ship, which specifically includes the following steps:

[0006] S1: Perform vector space decoupling analysis on a six-phase interior permanent magnet synchronous motor, extract the voltage vector distribution characteristics of the α-β subspace and the xy subspace, pre-select the basic voltage vector based on the voltage vector distribution characteristics, and pre-synthesize the basic virtual voltage vector by calculating the action time ratio of each real voltage vector with the goal of suppressing the harmonic current in the xy subspace;

[0007] S2: Based on the pre-synthesized basic virtual voltage vector, a composite virtual vector control set is synthesized using a space vector offline calculation method to ensure that each composite virtual vector can generate the desired voltage component in both the α-β subspace and the xy subspace, thereby achieving coordinated regulation of the current in the α-β subspace and the xy subspace. The zero vector space modulation technology is combined to suppress the current surge caused by the prolonged action time of the optimal vector.

[0008] S3: Construct a hyperlocal model for current prediction in the α-β subspace and design an inductance-decoupled extended state observer for real-time estimation and compensation of the unmodeled dynamics and external disturbances of a six-phase interior permanent magnet motor.

[0009] S4: Optimize the current prediction value based on the error function to obtain the optimal six-way switch signals corresponding to the target current, and input the optimal six-way switch signals into the six-phase two-level inverter to drive and control the six-phase built-in permanent magnet synchronous motor in the ship propulsion system.

[0010] Furthermore, an effective composite virtual vector control set is synthesized through discrete space virtual vector modulation technology, which improves the tracking accuracy of the α-β subspace current while suppressing the xy subspace harmonic current, thereby coordinating the dual-subspace currents of the ship's six-phase built-in permanent magnet synchronous motor.

[0011] Furthermore, a current prediction hyperlocal model for a six-phase interior permanent magnet synchronous motor is constructed based on the composite virtual vector control set for the α-β subspace, and the current is predicted and optimized using a model-free predictive control method:

[0012] The following hyperlocal modeling is used to transform the nonlinear system into a time-varying first-order elementary model, which is mathematically expressed as:

[0013] y=G+αu

[0014] Where u and y represent the control input and output variables of the system respectively, α is a non-physical proportional coefficient, and G represents the unmodeled dynamics and external disturbances of the system;

[0015] The model-free prediction current dynamic equation of the ship's six-phase interior permanent magnet motor in the dq coordinate system is expressed as:

[0016]

[0017] α i It is a non-physical constant, and is the inverse of the inductance value 1 / L. d,q ,G dq represents all unknown dynamics and disturbances and is expressed as:

[0018] I dq (k+1)=I dq (k)+T s [G * dq (k)+α i U dq (k)]

[0019] T s is the sampling period, is the disturbance estimate obtained by ESO, Udq is the voltage applied at the current moment;

[0020] In order to make the current track the set value at the next k+2 time Consider continuing the discrete integration based on the prediction at time k+1 to obtain:

[0021]

[0022] The solution is:

[0023]

[0024] Furthermore, an inductive decoupling-type extended state observer is designed using the inductive decoupling gain method to estimate and compensate for the unmodeled dynamics and external disturbances of the six-phase interior permanent magnet synchronous motor in real time. Extended state observers are designed for the d-axis and q-axis respectively, and the extended disturbance state variables are:

[0025]

[0026] Using the gain l d2 With L d The relationship is proportional to the inductance difference, and the adaptive compensation is performed. The estimation equation of the extended state observer after discretization is:

[0027]

[0028] The same method is used to obtain the extended state observer gain corresponding to the q-axis.

[0029] Furthermore, the current prediction value is optimized based on the error function to obtain the optimal predicted current, and the corresponding optimal six-phase permanent magnet synchronous motor six-way switching signals are generated. The error function is:

[0030]

[0031] In the above formula are the α and β components of the target voltage, vv αi , vv βi are the α and β components of the composite virtual voltage vector in the corresponding sector;

[0032] Optimal modulation duty cycle r opt By taking the derivative of the error function and finding that the derivative value is zero, we get:

[0033]

[0034] According to the principle of composite virtual vector synthesis: T L =0.731·r opt For the large vector v l The action time, T M=0.269·r opt is the large vector v ml The action time, T0 = 1-r opt is the zero vector action time, and v is determined by the optimal duty cycle l 、v ml and the action time of the zero vector, thereby completing spatial modulation and suppressing the current shock caused by the continuous action time of the optimal composite virtual voltage vector being too long.

[0035] Furthermore, an experimental platform was constructed, consisting primarily of a 2.2 kW six-phase interior permanent magnet synchronous motor, a six-phase drive circuit based on a TEXAS Instruments TMS320F28379d digital signal processor, a three-phase asynchronous motor controlled by a Siemens S120 to simulate propeller dynamic loads, and other related measurement and data acquisition equipment. This platform was used to verify the effectiveness of the disclosed model-free predictive control method for a six-phase interior permanent magnet motor for ships.

[0036] Due to the adoption of the above technical solution, the present invention provides a model-free predictive control method for a six-phase permanent magnet synchronous motor of a ship based on dual-subspace current control. The method first constructs a composite virtual vector (CVVs) set based on the 12 basic virtual vectors of VVMPC to achieve coordinated current control of the α-β and xy dual-subspaces, and combines zero vector space modulation to effectively suppress the current shock caused by the long action time of the optimal vector. In addition, a current prediction super-local model is constructed in the α-β subspace, and an inductance decoupling extended state observer (ID-ESO) is designed to estimate and compensate for the unmodeled dynamics and external disturbances of the IPMSM in real time, reducing dependence on motor parameters. Experimental verification shows that this method can achieve high-precision tracking of dual-subspace currents, improve the robustness in the case of system parameter mismatch, and is suitable for high-performance control of six-phase IPMSM in ship propulsion systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a control block diagram of a model-free predictive control method for a six-phase IPMSM ship based on dual-space current regulation provided by the present invention;

[0039] Figure 2A six-phase IPMSM spatial voltage vector distribution diagram provided by the present invention;

[0040] Figure 3 A six-phase IPMSM basic virtual vector synthesis principle diagram provided by the present invention;

[0041] Figure 4 A six-phase IPMSM composite virtual vector synthesis principle diagram provided by the present invention;

[0042] Figure 5 Schematic diagram of voltage output of a six-phase IPMSM composite virtual vector in the xy plane provided by the present invention, (a) large vector, (b) BVV, (c) and (d) proposed CVV;

[0043] Figure 6 A composite virtual vector CVVs control set provided by the present invention is projected on the α-β plane;

[0044] Figure 7 Flowchart of execution of the control algorithm provided by the present invention;

[0045] Figure 8 Schematic diagram of the six-phase IPMSM experimental platform for ship propulsion provided by the present invention;

[0046] Figure 9 The steady-state experimental results of the six-phase IPMSM for ship propulsion provided by the present invention under accurate parameters;

[0047] Figure 10 The steady-state experimental results of the six-phase IPMSM for ship propulsion under parameter mismatch provided by the present invention;

[0048] Figure 11 The load regulation experimental results of the six-phase IPMSM for ship propulsion under parameter mismatch provided by the present invention;

[0049] Figure 12 The experimental results of the speed regulation of the six-phase IPMSM for ship propulsion under parameter mismatch provided by the present invention are shown. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0051] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or are inherent to these processes, methods, products or apparatus.

[0052] like Figure 1 The model predictive control method for a six-phase permanent magnet motor for ship propulsion based on virtual voltage vector space modulation is shown, and specifically includes the following steps:

[0053] S1: Perform vector space decoupling analysis on the six-phase IPMSM to extract the voltage vector distribution characteristics of the two effective control subspaces, α-β and xy. Based on the preselected basic voltage vector, a basic virtual voltage vector is pre-synthesized to effectively suppress the harmonic currents in the xy subspace.

[0054] S2: In terms of vector modulation optimization, a composite virtual vector (CVVs) pre-synthesis strategy is proposed. Different from the 12 basic virtual vectors (BVVs) used traditionally, this method is based on the principle of space vector modulation and generates and stores multiple sets of optimized CVVs through offline calculation. The CVVs have the following characteristics: (1) they can generate desired voltage components in both the α-β and xy subspaces, realizing dual-subspace coordinated control; (2) they introduce a zero vector space modulation mechanism to effectively adjust the action time of the optimal vector and suppress the current overshoot problem.

[0055] S3: Construct a hyperlocal model-free predictive control structure to avoid dependence on precise motor parameters. This method introduces an improved hyperlocal model in the α-β subspace, uses current and historical current measurement data for real-time prediction, and designs an inductor-decoupled extended state observer (ID-ESO) for internal permanent magnet synchronous motors. Based on the decoupling gain design principle, the ID-ESO can adaptively adjust the dq-axis observer bandwidth according to the operating state, thereby compensating for unmodeled dynamics and external disturbances, achieving effective decoupling and dynamic compensation of inductor parameters.

[0056] Specifically, Figure 1 The overall control steps shown are:

[0057] (1) Sample the phase current, rotor position and speed at time k, perform VSD and Park transformation on the phase current, and obtain the current i of each subspace at time k d,q and i x,y ;

[0058] (2) Synthesize composite virtual vectors (CVVs) based on 12 basic virtual vectors (BVVs)

[0059] Based on the principle of space vector modulation, multiple sets of optimized CVVs are generated and stored through offline calculation.

[0060] (3) An improved hyperlocal model is introduced in the α-β subspace, and real-time prediction is performed using current and historical current measurement data. At the same time, an inductively decoupled extended state observer (ID-ESO) suitable for six-phase IPMSM is designed.

[0061] (4) Inserting virtual vector modulation coefficient r in model-free prediction opt , reconstruct the prediction model, this paper derives the error function h about r opt function.

[0062] (6) The model-free structure of the inductor decoupling extended state observer is used to predict the current, the predicted current value is substituted into the error function, and the voltage vector when the error function takes the minimum value is input into the inverter.

[0063] S1: Decoupled modeling of a six-phase IPMSM with a Y-shaped phase shift of 30° is performed. Based on the amplitude invariance constraint, the VSD transformation matrix is ​​expressed as:

[0064]

[0065] The relationship between the decoupling plane voltage and the six-phase voltage is:

[0066] [v α v β v x v y v o1 v o2 ] T =T αβ [v a v b v c v d v e v f ] T

[0067] Using T αβ The first two rows of the formula map the 12n±1-order (n=1, 2, 3,...) primitive components and harmonics to the α-β subspace, which controls energy conversion. The middle two rows of Equation (1.1) map the 6n±1-order (n=1, 3, 5,...) harmonics to the xy subspace, which does not generate electromagnetic torque but results in additional power loss. The last two rows of Equation (1.1) represent the zero-sequence harmonic components, which are 3n-order (n=1, 3, 5,...). Because the neutral points of the two sets of three-phase windings in the motor structure are independently connected, the zero-sequence plane current does not flow and is zero, so it is not used as a control plane.

[0068] Perform Park transformation on the machine variables on the α-β plane to obtain the synchronous rotating coordinate system model:

[0069]

[0070] [u d u q ] T =T dq [u α u β ] T

[0071] Using this method, the actual model of the motor in the synchronous rotating coordinate system and xy subspace can be obtained as:

[0072]

[0073] The electromagnetic torque equation of the six-phase permanent magnet synchronous motor is:

[0074] T e =3p n i q [i d (L d -L q )+ψ f ]

[0075] In the above formula, u d and u q is the stator voltage component on the d-axis and q-axis; i d and i q are the components of the stator current on the d-axis and q-axis respectively; R is the stator resistance; L d and L q are the d-axis and q-axis stator inductances respectively; ω r is the rotor angular velocity; P is the time derivative operator; ψ f is the permanent magnetic flux linkage; u x and u y is the stator voltage component in the xy subspace; i x and i y is the stator current component in the xy subspace; L ls is the leakage self-inductance.

[0076] Based on the six-phase IPMSM mathematical model, its motor control algorithm is constructed in the Matlab / Simulink environment.

[0077] Furthermore, S2: In the six-phase IPMSM drive system, only the control variables in the α-β subspace are directly involved in the electromechanical energy conversion, while the control variables in the xy subspace mainly generate losses. The voltage vector generated by each inverter will be mapped in the α-β subspace and the xy subspace at the same time. According to the formula of harmonic current, even a small voltage vector may cause an increase in harmonic current after passing through a low-harmonic inductor, thereby affecting the performance of the system. To this end, based on the distribution characteristics of the spatial voltage vector: in the six-phase motor, the outermost and second outermost voltage vectors in the same direction of the α-β subspace are in opposite directions after being mapped to the xy subspace. Utilizing this characteristic, a virtual voltage vector can be synthesized by calculating the action time of each sub-voltage vector so that the component in the xy subspace is zero, thereby effectively reducing the harmonic current in the system.

[0078] The specific steps of virtual vector synthesis are: First, take two vectors v in the same direction in the α-β subspace 53 and v 36 For example, let v 53 The action ratio is T n , v 36 The action ratio is T m Then, T is the sampling period. According to the basic idea of ​​synthesizing the virtual voltage vector, the relationship between the two vectors in the harmonic xy subspace is as follows:

[0079]

[0080] Substituting the amplitude of each voltage vector in the harmonic xy subspace, we can get T n and T m Value:

[0081]

[0082] Voltage vector v 53 and v 36 The corresponding relationship table between the action time and the switch state:

[0083] In some optional embodiments, referring to Table 1, as follows:

[0084] Table 1. Correspondence between the action time of voltage vectors v53 and v36 and the switch status

[0085]

[0086] The remaining virtual voltage vectors are synthesized in this way.

[0087] Furthermore, S3: In terms of vector optimization, a composite virtual vector (CVVs) pre-synthesis method is proposed. Unlike the direct use of 12 basic virtual vectors (BVVs), this method generates and stores multiple sets of optimized composite virtual vectors (CVVs) through offline calculation. These CVVs have two significant features: 1) Through a carefully designed vector synthesis method, it is ensured that each CVV can generate the desired voltage component in both the α-β and xy subspaces; 2) By introducing zero vector space modulation, the action time of the optimal vector is dynamically adjusted, effectively avoiding the current overshoot problem caused by the long action time of a single vector in the traditional method (the schematic diagram of composite virtual vector synthesis is shown in the figure). Figure 4-Figure 6 Second, a hyperlocal model-free prediction model is designed to eliminate the reliance on precise motor parameters and achieve accurate prediction using only current and historical current measurements.

[0088] Furthermore, S4: Further, the ultra-local model (ULM) and the extended state observer (ESO) are used to construct a model-free predictive current controller. In terms of modeling method, the ultra-local modeling is used to transform the nonlinear system into a time-varying first-order elementary model, and its mathematical expression is:

[0089] y=G+αu

[0090] In the hyperlocal model shown above, u and y represent the control input and output variables of the system, respectively, α is a non-physical proportional coefficient, and G represents the unmodeled dynamics and external disturbances of the system.

[0091] The extended state observer is used to estimate G in real time. In terms of control law design, this paper adopts the following control law form:

[0092] u=α -1 (-G * +y * +K p e+K i ∫e+K d e * )

[0093] In the above formula, y* represents the expected output trajectory of the system, K p,i,d are the proportional, integral, and derivative gain coefficients, respectively, e is the tracking error, and G* represents the unmodeled dynamic estimate obtained through real-time observations of the ESO.

[0094] For the SP-IPMSM, the system current can be converted to an equivalent dq coordinate system through appropriate coordinate transformation (such as the extended Park transformation). The SP-IPMSM current dynamic equation in the dq coordinate system can be approximately written as:

[0095]

[0096] α i It is a non-physical constant, generally taken as the inverse of the inductance value 1 / L d,q ,G dq represents all unknown dynamics and disturbances, which can be written as:

[0097]

[0098] Δ dq They represent the additional disturbances introduced by phase coupling and imbalance in the SP-IPMSM structure. Using forward Euler discretization, we get:

[0099] I dq (k+1)=I dq (k)+T s [G * dq (k)+α i U dq (k)]

[0100] T s is the sampling period, is the disturbance estimate obtained by ESO; U dq is the voltage applied at the current moment.

[0101] In order to make the current track the set value at the next k+2 time Consider continuing the discrete integration based on the prediction at time k+1:

[0102]

[0103] The solution is:

[0104]

[0105] Given that As a time-varying lumped disturbance with continuous dynamic characteristics, this paper designs an inductance decoupled extended state observer (ID-ESO) that can realize online estimation to track and update the unknown disturbance in real time, aiming at the problem of d-q axis inductance asymmetry in IPMSM.

[0106] Aiming at the asymmetry of the d-q axis inductance of IPMSM, a design scheme of inductance-decoupled extended state observer (ID-ESO) is proposed.

[0107] The ESO is designed for the d-axis and q-axis respectively, and the expanded disturbance state variables are:

[0108]

[0109] According to the bandwidth method, the characteristic polynomial of ESO is configured into an ideal form, so that the dynamic performance of the IPMSM observer is determined by a single parameter ω i Decide:

[0110]

[0111] ω i Determines the convergence speed of ESO (ω in ESO design i The parameterization is set as a compromise between convergence speed and noise sensitivity). The pole is s = -ω i (double root), ensure critical damping and avoid overshoot. For d-axis and q-axis ESO, the gain l is designed respectively. d ,l d2 and l q ,l q2 , so that the characteristic polynomials of the ESO of both axes match P(s). Taking the d-axis as an example, the estimated error equation of its ESO is:

[0112]

[0113] The error equation can be written in state space form:

[0114]

[0115] The characteristic polynomial of the system matrix is:

[0116]

[0117] To match the ideal polynomial, let:

[0118]

[0119] l d2 With L d Proportional to the inductance, automatically compensates for the difference in inductance. If R is ignored (usually Rv<L d ω i ), which can be simplified to l d ≈2ω i Therefore, let the bandwidth of ESO on both d-axis and q-axis be ω i ,but:

[0120]

[0121] Gain l d2 With L d Proportional to the inductance difference, adaptive compensation is achieved, and the discretized ESO estimation equation is:

[0122]

[0123] The ESO gain design corresponding to the q-axis can be obtained in the same way.

[0124] Optimal modulation duty cycle r opt is obtained by minimizing the error between the target voltage and CVV. opt , define the error function:

[0125]

[0126] In the above formula are the α and β components of the target voltage, vv αi , vv βi are the α and β components of CVV ​​in the corresponding sector.

[0127] By taking the derivative of the error function and finding it to be zero, we can get the optimal duty cycle:

[0128]

[0129] According to the CVVs synthesis principle, T L =0.731·r opt For the large vector v l The action time, T M =0.269·r opt is the large vector v ml The action time, T0 = 1-r opt is the zero vector action time. Determine v by the optimal duty cycle l , v ml and the action time of the zero vector, thereby completing spatial modulation and suppressing the current shock caused by the optimal CVV lasting too long.

[0130] Under the newly constructed model-free predictive control algorithm of the six-phase IPMSM of ships based on twin-space current regulation, the control algorithm disclosed this time was verified by building an experimental platform. The experimental platform shows the following Figure 8 shown.

[0131] In the experimental verification, the motor is set at a given speed of 100-300 rpm, and the load motor is used to load the six-phase IPMSM under test. dc The voltage is 380V and the sampling frequency is 10kHz.

[0132] Figures 9 to 12 The experimental results of the present invention's model-free predictive control algorithm for a six-phase permanent magnet synchronous motor for ships based on optimal modulation of a composite virtual vector are presented. Specifically, the results include: Figure 9 The steady-state experimental results of the six-phase IPMSM for ship propulsion provided by the present invention under accurate parameters; Figure 10The steady-state experimental results of the six-phase IPMSM for ship propulsion under parameter mismatch provided by the present invention; Figure 11 The load regulation experimental results of the six-phase IPMSM for ship propulsion under parameter mismatch provided by the present invention; Figure 12 The experimental results of the speed regulation of the six-phase IPMSM for ship propulsion under parameter mismatch provided by the present invention are shown.

[0133] From the experimental results, it can be seen that ( Figures 9 to 12 (a) shows the experimental results of VVMPC, and (b) shows the experimental results of MFPC-CVV. Under precise motor parameter conditions, when the motor runs at 300 rpm with rated load, the tracking capability of the main plane current is significantly improved, and the THD of the proposed method is reduced to 6.73%. Because CVVs are synthesized through multiple iterations of virtual vectors, they improve the suppression of xy-plane harmonic currents, the tracking of q-axis currents, and the speed fluctuations.

[0134] In the case of motor parameter mismatch, the motor resistance, inductance, and flux linkage values ​​used in the MBPC method are 200%, 50%, and 50% of their nominal values, respectively; while the gain of the MFPC method is set to 50% of its nominal value. When the motor is running at 300 rpm with rated load, the proposed method reduces the steady-state THD to 6.68%, a 36% improvement compared to VVMPC. MFPC also demonstrates improvements in xy-plane harmonic current suppression, speed fluctuation, and q-axis current tracking. This is because the impact of motor parameter mismatch on control performance is difficult to completely avoid, especially for IPMSMs with large differences in dq-axis inductance, and may even further amplify the parameter asymmetry of the dq-axis inductances.

[0135] Under parameter mismatch conditions, when the motor is running at 300 rpm, a sudden load is applied to the system (from 50% of the rated load to 100% of the rated load). The increased load causes a momentary drop in motor speed, which then recovers to the target speed after approximately 500 ms of regulation. Experimental data shows that the proposed control strategy significantly improves current tracking performance compared to the VVMPC method, with the q-axis current ripple amplitude reduced to 0.2 A, approximately 50% lower than the VVMPC scheme. A 50% rated load is applied to the motor, and the speed is stepped from 200 rpm to 300 rpm. The two control methods exhibit similar dynamic characteristics. However, the proposed method demonstrates significant advantages in current tracking performance. Specifically, 1) the proposed phase current and q-axis current tracking accuracy are significantly superior to the traditional VVMPC method; 2) after stabilization, the traditional VVMPC method suffers from insufficient compensation for speed disturbances, resulting in increased q-axis current ripple.

[0136] Therefore, the feasibility and effectiveness of the model-free predictive control method for a six-phase IPMSM ship based on dual-space current regulation disclosed in the present invention are proved.

[0137] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A model-free predictive control method for a six-phase internal permanent magnet motor for ships, characterized in that include: S1: Perform vector space decoupling analysis on a six-phase interior permanent magnet synchronous motor, extract the voltage vector distribution characteristics of the α-β subspace and the xy subspace, pre-select the basic voltage vector based on the voltage vector distribution characteristics, and pre-synthesize the basic virtual voltage vector by calculating the action time ratio of each real voltage vector with the goal of suppressing the harmonic current in the xy subspace; S2: Based on the pre-synthesized basic virtual voltage vector, a composite virtual vector control set is synthesized using a space vector offline calculation method to ensure that each composite virtual vector can generate the desired voltage component in both the α-β subspace and the xy subspace, thereby achieving coordinated regulation of the current in the α-β subspace and the xy subspace. The zero vector space modulation technology is combined to suppress the current surge caused by the prolonged action time of the optimal vector. S3: Construct a hyperlocal model for current prediction in the α-β subspace and design an inductance-decoupled extended state observer for real-time estimation and compensation of the unmodeled dynamics and external disturbances of a six-phase interior permanent magnet motor. S4: Optimize the current prediction value based on the error function to obtain the optimal six-way switch signals corresponding to the target current, and input the optimal six-way switch signals into the six-phase two-level inverter to drive and control the six-phase built-in permanent magnet synchronous motor in the ship propulsion system.

2. The model-free predictive control method for a six-phase interior permanent magnet motor for a ship according to claim 1, characterized in that: An effective composite virtual vector control set is synthesized through discrete space virtual vector modulation technology, which improves the tracking accuracy of the α-β subspace current while suppressing the xy subspace harmonic current, thereby achieving coordinated regulation of the dual-subspace currents of the ship's six-phase interior permanent magnet synchronous motor.

3. The model-free predictive control method for a six-phase interior permanent magnet motor for a ship according to claim 2, characterized in that: A current prediction hyperlocal model for a six-phase interior permanent magnet synchronous motor is constructed based on a composite virtual vector control set in the α-β subspace. The current is predicted and optimized using a model-free predictive control approach. The following hyperlocal modeling is used to transform the nonlinear system into a time-varying first-order elementary model, which is mathematically expressed as: y=G+αu Where u and y represent the control input and output variables of the system respectively, α is a non-physical proportional coefficient, and G represents the unmodeled dynamics and external disturbances of the system; The model-free prediction current dynamic equation of the ship's six-phase interior permanent magnet motor in the dq coordinate system is expressed as: α i Is a non-physical constant, taking the reciprocal of the inductance value 1 / L d,q ,G dq represents all unknown dynamics and disturbances and is expressed as: I dq (k+1)=I dq (k)+T s [G * dq (k)+α i U dq (k)] T s is the sampling period, is the disturbance estimate obtained by ESO, U dq is the voltage applied at the current moment; In order to make the current track the set value at the next k+2 time Consider continuing the discrete integration based on the prediction at time k+1 to obtain: The solution is:

4. The model-free predictive control method for a six-phase interior permanent magnet motor for a ship according to claim 1, characterized in that: An inductive decoupling extended state observer is designed using the inductive decoupling gain method to estimate and compensate for the unmodeled dynamics and external disturbances of the six-phase interior permanent magnet synchronous motor in real time. Extended state observers are designed for the d-axis and q-axis respectively. The extended disturbance state variables are: Using the gain l d2 With L d The relationship is proportional to the inductance difference, and the adaptive compensation is performed. The estimation equation of the extended state observer after discretization is: The same method is used to obtain the extended state observer gain corresponding to the q-axis.

5. The model-free predictive control method for a six-phase interior permanent magnet motor for a ship according to claim 1, characterized in that: The current prediction value is optimized based on the error function to obtain the optimal predicted current and generate the corresponding optimal six-phase permanent magnet synchronous motor six-way switching signals. The error function is: In the above formula are the α and β components of the target voltage, vv αi , vv βi are the α and β components of the composite virtual voltage vector in the corresponding sector; Optimal modulation duty cycle r opt By taking the derivative of the error function and finding that the derivative value is zero, we get: According to the principle of composite virtual vector synthesis: T L =0.731·r opt For the large vector v l The action time, T M =0.269·r opt is the large vector v ml The action time, T0 = 1-r opt is the zero vector action time, and v is determined by the optimal duty cycle l 、v ml and the action time of the zero vector, thereby completing spatial modulation and suppressing the current shock caused by the continuous action time of the optimal composite virtual voltage vector being too long.

Images