High-end household dust collector brushless motor high-efficiency driving speed control system

Abstract

The invention discloses an efficient driving speed control system for a brushless motor of a high-end household dust collector, relates to the technical field of motor control, and aims to solve the technical problems of airflow pulsation and discontinuous suction force caused by neglecting of dynamic characteristics of an air path during mode switching of an existing dust collector. The acquisition module is used for acquiring gas path parameters representing a dust collector gas path load state in real time; the flow resistance on-line identification module is connected with the gas path parameter sensing module and is used for identifying and updating the equivalent flow resistance parameter of the current gas path system on line based on the gas path parameter acquired in real time; according to the system, a complete technical closed loop of gas path parameter sensing, flow resistance online identification, pneumatic power smooth trajectory planning and feedforward and feedback compound control is constructed, so that the problems of gas pulsation and suction interruption in the mode switching process are fundamentally solved.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, and more specifically, to a high-efficiency drive speed control system for a high-end household vacuum cleaner brushless motor. Background Technology

[0002] As users' demands for home cleaning quality continue to rise, high-end household vacuum cleaners are evolving towards stronger suction, lower noise, longer battery life, and a more intelligent experience. The performance of the drive control system of its core power source—the brushless motor—is crucial, especially the smoothness of switching between different cleaning modes (such as energy-saving, standard, and high-efficiency modes), which has become an important indicator of product premiumization. Currently, mainstream technologies in this field mostly focus on closed-loop control of the motor's speed, such as using advanced field-oriented control algorithms paired with high-performance microprocessors to achieve rapid speed tracking and stable torque output. However, these solutions share a common, yet unresolved, core problem: during the dynamic process of mode switching, the control objective is usually limited to the motor's own electrical or mechanical variables (such as speed and current), completely ignoring the final output performance of the entire vacuum cleaner system—that is, the continuity and stability of airflow within the duct.

[0003] This "electromechanical separation" control approach leads to a prominent user experience defect: when the user switches working modes, the motor speed may have been adjusted quickly and precisely, but the airflow driven by the impeller often experiences obvious pulsation or even a brief interruption.

[0004] The fundamental reason lies in the fact that the airflow system of a vacuum cleaner is a load with complex dynamic characteristics, and its equivalent flow resistance changes in real time with the type of nozzle, the floor material, and the degree of filter clogging. Step or simple ramp changes in motor speed cannot match the dynamically changing airflow load characteristics, resulting in drastic fluctuations in aerodynamic power (i.e., the effective power driving the airflow). This airflow unevenness directly manifests as instantaneous changes in suction power, affecting cleaning performance and generating unpleasant noise, severely limiting the performance and user experience of high-end vacuum cleaners. Therefore, there is an urgent need for a brushless motor drive speed control system that can directly optimize airflow smoothness and adapt to changes in the airflow path. Based on this, we propose a high-efficiency drive speed control system for brushless motors in high-end household vacuum cleaners. Summary of the Invention

[0005] The purpose of this invention is to provide a high-efficiency drive and speed control system for a brushless motor in a high-end household vacuum cleaner, so as to solve the technical problem of airflow pulsation and discontinuous suction caused by ignoring the dynamic characteristics of the air path when the existing vacuum cleaner switches modes.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a high-efficiency drive speed control system for a brushless motor in a high-end household vacuum cleaner, comprising:

[0007] The air path parameter sensing module is used to acquire air path parameters that characterize the air path load status of the vacuum cleaner in real time;

[0008] The online flow resistance identification module is connected to the gas path parameter sensing module and is used to identify and update the equivalent flow resistance parameters of the current gas path system online based on the gas path parameters acquired in real time.

[0009] The smooth switching trajectory planning module, connected to the online flow resistance identification module, is used to generate a motor desired running trajectory that makes the change of aerodynamic power smooth when a working mode switching command is received, based on the updated equivalent flow resistance parameters and the target aerodynamic power index corresponding to the target working mode. The core optimization target is the smoothness of aerodynamic power during the switching process.

[0010] The feedforward control quantity generation module is connected to the smooth switching trajectory planning module. It is used to analyze and calculate the feedforward control quantity of the brushless motor required to achieve the desired running trajectory of the motor based on the preset aero-electromechanical coupling model.

[0011] The status feedback control module is used to acquire the operating status of the brushless motor in real time, and calculate the feedback control quantity based on the deviation between the operating status and the expected operating trajectory of the motor.

[0012] The drive execution module is connected to the feedforward control quantity generation module and the state feedback control module respectively. It is used to integrate the feedforward control quantity and the feedback control quantity to generate a drive signal and control the operation of the brushless motor, thereby achieving optimized control of the smoothness of pneumatic power during mode switching.

[0013] This invention fundamentally solves the core problem of airflow pulsation and suction interruption during mode switching by constructing a complete technical closed loop of "air path parameter sensing - online flow resistance identification - smooth aerodynamic power trajectory planning - feedforward feedback composite control". Traditional solutions treat the motor as an independent object for control, while this invention considers the motor as an integrated component within the complete aerodynamic system of "motor-impeller-duct". The system identifies the equivalent flow resistance online by sensing or estimating air path parameters in real time, thereby accurately grasping the dynamic characteristics of the air path load. Based on this, the control objective is set as a smooth transition of aerodynamic power, rather than simply a change in motor speed. Specifically, after receiving a mode switching command, the system generates a high-order smooth (e.g., minimizing abrupt changes) desired aerodynamic power trajectory online based on the current flow resistance and the target aerodynamic power. Subsequently, through feedforward control based on differential flatness theory, the aerodynamic power trajectory is precisely mapped to the motor speed and current commands, supplemented by disturbance observation feedback control for compensation. This series of operations ensures a smooth process from control commands to final airflow output, achieving a seamless switching of suction power without being noticed, greatly enhancing the premium feel of the cleaning process and user satisfaction.

[0014] Preferably, the air path parameter sensing module includes at least one of a wind pressure sensor and a flow sensor, or the aerodynamic power is estimated as the air path parameter by the electrical operating parameters of the motor.

[0015] Preferably, the online flow resistance identification module uses any one of the recursive least squares method, Kalman filter, or model reference adaptive algorithm to identify and update the equivalent flow resistance parameters of the gas path system online.

[0016] When using the recursive least squares method, the identification and update process is iteratively implemented through the following formula:

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula, for Estimated values ​​of the equivalent flow resistance parameters of the air path system at any given time; for Estimated equivalent flow resistance parameters at time t; for Gain vector at time step; For regression vectors; for Transpose of the regression vector at time step; for The aerodynamic power value measured or estimated at any time; for The covariance matrix at time t; for The covariance matrix at time t; It is a forgetting factor.

[0021] Preferably, the smooth transition trajectory planning module is specifically used to: plan a motor desired operating trajectory that smoothly transitions from the current aerodynamic power value to the target aerodynamic power value within a space formed by aerodynamic power as a flat output; the motor desired operating trajectory is a polynomial curve that minimizes the integral of the square of the higher-order derivative of the aerodynamic power.

[0022] The desired operating trajectory of the motor is determined by the following: Polynomial description:

[0023] ;

[0024] In the formula, for The expected aerodynamic power value at any given time; For the polynomial of the th The coefficient of the second term, ; For time variables, , This is the preset total time for the mode switching process; Let be the order of the polynomial.

[0025] Preferably, the desired running trajectory of the motor is a fifth-order or seventh-order polynomial curve, the coefficients of which are obtained by solving a quadratic programming problem with linear constraints.

[0026] Taking minimizing the squared integral of the third derivative of aerodynamic power as an example, the objective function of the quadratic programming problem is... The boundary constraints are:

[0027] ;

[0028] The constraints are: , , , ;

[0029] In the formula, The objective function value to be optimized; Let be the third derivative of the desired aerodynamic power with respect to time; To switch the start time ( The expected aerodynamic power value; For the end time of switching ( The expected aerodynamic power value; The first derivative of the aerodynamic power at the start of the switching; The first derivative of the aerodynamic power at the end of the switching process; To switch the initial aerodynamic power boundary value; To switch the target aerodynamic power boundary value; To switch the initial aerodynamic power change rate boundary value; To switch the target aerodynamic power change rate boundary value; the polynomial coefficients This is obtained by solving the optimization problem.

[0030] Preferably, the feedforward control quantity generation module is specifically used to: based on the differential flatness theory, according to the aero-electromechanical coupling model, analyze and map the expected value of aerodynamic power and its derivatives in the expected operating trajectory of the motor into the expected speed trajectory and the expected torque current feedforward quantity of the brushless motor.

[0031] According to aerodynamic power With motor speed Torque Current and equivalent flow resistance The coupling relationship between them is achieved through the following formula:

[0032] ;

[0033] ;

[0034] In the formula, for The expected motor speed at any given time; for The expected torque current feedforward at time t; The mapping function from aerodynamic power to motor speed is determined by the aero-electromechanical coupling model. The mapping function from aerodynamic power and its derivative to torque current is determined by the aerodynamic-electromechanical coupling model. for The expected aerodynamic power value at any given time; for The expected rate of change of aerodynamic power at time t; The equivalent flow resistance parameters of the gas path system are obtained through online identification at the current moment.

[0035] Preferably, the state feedback control module includes a disturbance observer, which is used to estimate system disturbances caused by load abrupt changes or model errors, and generate corresponding disturbance compensation amounts, which are used as part of the feedback control quantities.

[0036] The disturbance observer is a high-order sliding mode observer or an extended state observer.

[0037] Preferably, when an extended state observer is used, it is used to observe the desired rotational speed. Compared with actual speed The total disturbance implied by the dynamic error between them is given by the following formula:

[0038] ;

[0039] ;

[0040] ;

[0041] In the formula, This represents the error between the observed rotational speed and the actual measured speed. This refers to the observed value of the motor speed; The observed value of the total system disturbance is the disturbance compensation amount; This is the actual measured speed of the motor; For feedback control current; , For observer gain parameters; To control the gain coefficient; It is a nonlinear function; , The exponential parameter of the nonlinear function has a range of values. ; This is the threshold value for the linear interval of a nonlinear function.

[0042] Preferably, the drive execution module is a driver based on field-oriented control, which integrates the feedforward control quantity and the feedback control quantity to generate a space vector pulse width modulation signal to drive the brushless motor.

[0043] A method for high-efficiency speed control of a brushless motor in a high-end household vacuum cleaner includes the following steps:

[0044] Real-time acquisition of gas path parameters, and online identification and updating of the equivalent flow resistance parameters of the gas path system;

[0045] When a working mode switching command is received, the desired motor running trajectory is generated based on the updated equivalent flow resistance parameters and target aerodynamic power index, with aerodynamic power smoothness as the optimization objective.

[0046] Based on the aero-electromechanical coupling model, the feedforward control quantity is analytically calculated according to the desired operating trajectory of the motor.

[0047] The motor's operating status is acquired in real time, and its deviation from the motor's desired operating trajectory is calculated to obtain the feedback control quantity.

[0048] By combining feedforward and feedback control signals, the brushless motor is driven to achieve smooth switching.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] 1. This invention fundamentally solves the core problem of airflow pulsation and suction interruption during mode switching by constructing a complete technical closed loop of "air path parameter sensing - online flow resistance identification - smooth aerodynamic power trajectory planning - feedforward feedback composite control". Traditional solutions treat the motor as an independent object for control, while this invention considers the motor as an integrated component within the complete aerodynamic system of "motor-impeller-duct". The system identifies the equivalent flow resistance online by sensing or estimating air path parameters in real time, thereby accurately grasping the dynamic characteristics of the air path load. Based on this, the control objective is set as a smooth transition of aerodynamic power, rather than simply a change in motor speed. Specifically, after receiving a mode switching command, the system generates a high-order smooth (e.g., minimizing abrupt changes) desired aerodynamic power trajectory online based on the current flow resistance and the target aerodynamic power. Subsequently, through feedforward control based on differential flatness theory, the aerodynamic power trajectory is accurately mapped to the motor's speed and current commands, supplemented by disturbance observation feedback control for compensation. This series of operations ensures a smooth process from control commands to final airflow output, achieving a seamless switching of suction power without being noticed, greatly enhancing the premium feel of the cleaning process and user satisfaction.

[0051] 2. This invention also effectively ensures the real-time performance and feasibility of the system on embedded hardware platforms through ingenious algorithm design, resolving the contradiction between high-performance algorithms and limited computing resources. To achieve a smooth transition of aerodynamic power, this invention employs an optimization method that describes the desired trajectory using polynomials and aims to minimize the integral of its higher-order derivatives. Directly performing high-dimensional dynamic optimization would place a computational burden far exceeding the processing capabilities of ordinary motor control MCUs. Therefore, this invention adopts two key designs: First, it uses a polynomial parameterization method to transform the infinite-dimensional trajectory planning problem into a problem of solving a finite number of polynomial coefficients, and constructs it into a standard, fast-solvable quadratic programming problem by setting boundary constraints (starting and ending point power and rate of change). Second, it utilizes the theory of differential flatness to realize the analytical calculation mapping from aerodynamic power trajectory to motor control quantities, avoiding complex numerical integration or iterative solution processes. These two design features significantly reduce the computational load for generating the optimal smooth trajectory and corresponding feedforward control, enabling it to be completed in microseconds. This ensures that the entire control algorithm can run stably and in real time on low-cost, low-power embedded processors, allowing high-end performance to be implemented in mass-produced products.

[0052] 3. This invention also introduces a multi-layered adaptive and observation mechanism. At the slow-varying level, the system continuously updates the gas path flow resistance parameters online using algorithms such as recursive least squares, enabling the model upon which the planning relies to follow the slow time-varying characteristics of the gas path system (such as filter clogging), achieving long-term adaptation. At the fast-varying disturbance level, the system is equipped with advanced disturbance observers such as an extended state observer, treating all uncertainties such as model errors and load steps as a unified "total disturbance" for real-time estimation and compensation. The compensation output of this observer directly acts on the control loop, enabling the system to quickly suppress sudden disturbances and maintain accurate tracking of the planned trajectory. This dual guarantee mechanism, combining "online parameter identification" and "real-time disturbance observation," ensures that the smooth switching experience provided by this invention does not depend on an ideal laboratory environment, but remains stable and reliable in real, complex, and variable user scenarios, significantly improving the overall quality and durability of the product. Attached Figure Description

[0053] Figure 1 This is a diagram showing the overall module connection architecture of the system of the present invention;

[0054] Figure 2 This is a flowchart illustrating the internal architecture of the online flow resistance identification module of the present invention.

[0055] Figure 3 This is a flowchart illustrating the internal architecture of the smooth switching trajectory planning module of the present invention.

[0056] Figure 4 This is the main flowchart of the control method of the present invention. Detailed Implementation

[0057] Example 1: As Figures 1 to 3 As shown, the present invention relates to a high-efficiency drive speed control system for a brushless motor in a high-end household vacuum cleaner, comprising:

[0058] The air path parameter sensing module is used to acquire air path parameters that characterize the air path load status of the vacuum cleaner in real time;

[0059] In an embodiment of the present invention, the air path parameter sensing module includes at least one of a wind pressure sensor and a flow sensor, or the aerodynamic power is estimated as the air path parameter by the electrical operating parameters of the motor.

[0060] The online flow resistance identification module is connected to the gas path parameter sensing module and is used to identify and update the equivalent flow resistance parameters of the current gas path system online based on the gas path parameters acquired in real time.

[0061] In embodiments of the present invention, the online flow resistance identification module uses any one of the following methods: recursive least squares method, Kalman filter, or model reference adaptive algorithm, to identify and update the equivalent flow resistance parameters of the gas path system online.

[0062] When using the recursive least squares method, the identification and update process is iteratively implemented through the following formula:

[0063] ;

[0064] ;

[0065] ;

[0066] In the formula:

[0067] for The estimated value of the equivalent flow resistance parameter of the air path system at any given time, characterizing the degree of obstruction to airflow by the current air path system, in units of... In this invention, the equivalent flow resistance is a lumped parameter used to characterize the overall resistance characteristics of the airflow generated by the entire air path from the vacuum cleaner impeller to the nozzle. Its value is equal to the ratio of air path pressure drop to volumetric flow rate.

[0068] for The estimated equivalent flow resistance parameter at time t is used as the initial reference for the current estimate.

[0069] for The gain vector at time step determines the weighting coefficients of the contribution of new measurement data to parameter updates, and its dimension is the same as that of the regression vector.

[0070] The regression vector is derived from the parameters to be estimated in the system model. The system model satisfies the following conditions: The known variables to be multiplied, such as a combination of functions including motor speed and current. And pressure difference With traffic Flow resistance Satisfying Relationships Then a regression vector can be constructed. , making This ensures consistency of dimensions;

[0071] for The transpose of the regression vector at any given time contains system state variables that affect aerodynamic power, such as motor speed and current.

[0072] for The aerodynamic power value measured or estimated at any time reflects the aerodynamic power that the impeller transmits to the airflow at the current time, and the unit is W.

[0073] for The covariance matrix at time step 1 represents the statistical properties of the parameter estimation error and is used to calculate the gain vector.

[0074] for The covariance matrix at time t.

[0075] Forgetting factor, range of values Used to control the rate at which the influence of historical data decays. The smaller the value, the more sensitive it is to system changes.

[0076] Operational Logic Explanation: This formula employs the recursive least squares method to achieve online identification and dynamic updating of the equivalent flow resistance parameters of the vacuum cleaner's airflow system. The algorithm establishes a linear regression model between aerodynamic power and system state, and uses a recursive calculation method to correct the estimated flow resistance parameters in real time. The core of the algorithm consists of three recursive equations: the first equation updates the estimated flow resistance parameter at the current moment based on the flow resistance estimate from the previous moment and the gain vector at the current moment, combined with the residual between the observed aerodynamic power measurement and the model prediction; the second equation calculates the gain vector at the current moment, which determines the influence weight of new measurement data on the flow resistance estimate update; the third equation updates the covariance matrix, reflecting the uncertainty of the parameter estimation, and gradually reduces the influence weight of historical data through a forgetting factor. Through this recursive least squares identification algorithm, the system can perceive changes in the airflow state in real time during vacuum cleaner operation, such as changes in flow resistance characteristics caused by increased filter clogging or changes in the type of suction head. This dynamic identification mechanism provides an accurate time-varying parameter basis for subsequent trajectory planning, enabling the control algorithm to adapt to the actual working conditions of the vacuum cleaner in different usage stages and cleaning scenarios. This significantly improves the system's adaptability to complex working environments and control accuracy, ensuring optimized switching performance under different air path conditions.

[0077] The smooth switching trajectory planning module, connected to the online flow resistance identification module, is used to generate a motor desired running trajectory that makes the change of aerodynamic power smooth when a working mode switching command is received, based on the updated equivalent flow resistance parameters and the target aerodynamic power index corresponding to the target working mode. The core optimization target is the smoothness of aerodynamic power during the switching process.

[0078] The desired operating trajectory of the motor is specifically defined by a desired aerodynamic power trajectory. The desired speed and torque of the motor are determined by the mapping relationship.

[0079] In an embodiment of the present invention, the core optimization objective of the smooth switching trajectory planning module is to minimize the rate of change of aerodynamic power or the integral value of the higher-order derivative of aerodynamic power.

[0080] In an embodiment of the present invention, the smooth switching trajectory planning module is specifically used for:

[0081] Within a space defined by aerodynamic power as a flat output, plan a desired motor running trajectory that smoothly transitions from the current aerodynamic power value to the target aerodynamic power value;

[0082] The desired operating trajectory of the motor is a polynomial curve that minimizes the integral of the square of the higher-order derivative of the aerodynamic power.

[0083] The process of solving the desired operating trajectory of the motor must satisfy the constraints formed by the equivalent flow resistance parameters, the maximum allowable current of the motor, and the maximum allowable speed.

[0084] The desired operating trajectory of the motor is determined by the following: Polynomial description:

[0085] ;

[0086] In the formula:

[0087] for The expected aerodynamic power value at a given time represents the aerodynamic power target curve obtained from the planning, and the unit is W.

[0088] For the polynomial of the th The coefficient of the second term, These coefficients are determined through optimization calculations, which in turn determine the specific shape of the trajectory.

[0089] For time variables, The start time of mode switching is set as time 0, and the completion time of mode switching is set as time 1. time, The total time for the preset mode switching process is determined based on user experience requirements and system dynamic capabilities, and is expressed in seconds.

[0090] The order of the polynomial determines the flexibility and smoothness of the trajectory curve; it is usually set to 5 or 7 to ensure sufficient high-order continuity.

[0091] Operational Logic Explanation: This formula defines the polynomial mathematical description of the desired aerodynamic power trajectory, providing a parameterized expression basis for subsequent optimization solutions. The polynomial of degree uses a linear combination of time variables. The power terms of the polynomials are used to construct a continuously differentiable aerodynamic power variation curve from the start to the end of the switching process. (Polynomial coefficients) As the variable to be optimized, its specific value is determined through subsequent constraint optimization, thereby obtaining the desired motor running trajectory that meets the smoothness requirements and conforms to the system's dynamic constraints. Polynomial order The choice of polynomial determines the degrees of freedom of the trajectory. Higher-order polynomials can describe more complex curve shapes, but they also increase the complexity of the optimization problem. Using polynomial parameterization to describe the desired motor trajectory provides a structured mathematical framework for smooth aerodynamic power transition. This approach has several significant advantages: First, polynomial functions inherently possess the excellent mathematical property of being continuously differentiable of any order, naturally satisfying the requirements for higher-order smoothness; second, the parameterized form transforms the trajectory planning problem into a finite-dimensional parameter optimization problem, significantly reducing online computational complexity; finally, by adjusting the polynomial order, a flexible trade-off can be achieved between trajectory smoothness and computational burden, providing a feasible basis for real-time implementation in embedded systems and ensuring the generation of high-quality aerodynamic power transition trajectories even with limited computational resources.

[0092] In an embodiment of the present invention, the desired running trajectory of the motor is a fifth-order or seventh-order polynomial curve, the coefficients of which are obtained by solving a quadratic programming problem with linear constraints.

[0093] Taking minimizing the squared integral of the third derivative of aerodynamic power as an example, the objective function of the quadratic programming problem is... The boundary constraints are:

[0094] ;

[0095] The constraints are: , , , ;

[0096] Wherein, the polynomial coefficients This is obtained by solving the optimization problem.

[0097] In the formula:

[0098] The objective function value to be optimized is represented by the time integral of the square of the third derivative of aerodynamic power, which physically represents the total energy of aerodynamic power jerk.

[0099] Let be the third derivative of the desired aerodynamic power with respect to time, which characterizes the rate of change of acceleration of the aerodynamic power, i.e., jerkiness.

[0100] To switch the start time ( The expected aerodynamic power value is equal to the actual aerodynamic power during the current steady-state operation.

[0101] For the end time of switching ( The expected aerodynamic power value is equal to the steady-state aerodynamic power corresponding to the target operating mode.

[0102] To switch the first derivative of aerodynamic power at the start time, it is usually set to zero or the current actual rate of change.

[0103] The first derivative of the aerodynamic power at the end of the switching is usually set to zero to ensure that the rate of change returns to zero when switching to steady state.

[0104] To switch the initial aerodynamic power boundary value, i.e. The specified value.

[0105] To switch the target aerodynamic power boundary value, i.e. The specified value.

[0106] To switch the initial aerodynamic power change rate boundary value, i.e. The specified value.

[0107] To switch the target aerodynamic power change rate boundary value, i.e. The specified value.

[0108] First, generate the optimal smooth trajectory without physical constraints, then check if it exceeds the limits. If it does, extend the total switching time. Or adjust the boundary rate of change Replan until all constraints are met. Add this engineering implementation logic to the description of the "Smooth Switching Trajectory Planning Module".

[0109] Operational Logic Explanation: This formula constructs a constrained optimization problem with the square integral of the third derivative of aerodynamic power as the objective function, aiming to generate a high-order, smooth, desired motor operating trajectory. Optimization Objective Minimizing the cumulative amount of the squared third derivative of the aerodynamic power change over the entire switching time interval is mathematically equivalent to minimizing the jerk energy of the aerodynamic power change, thus ensuring that the aerodynamic power change is not only smooth but also that the rate of change is gradual. Constraints include the aerodynamic power values ​​at the start and end times and their first derivative (rate of change) boundaries. These boundary conditions are determined by the current operating state and the target operating mode, ensuring a smooth transition between the trajectory and the actual system state. By formalizing the smooth aerodynamic power switching problem as a constrained quadratic programming problem, this method achieves a shift from empirical design to mathematical optimization. The optimization objective of minimizing the integral of the squared third derivative inherently guarantees the ultra-smooth characteristics of the aerodynamic power change, ensuring not only a smooth transition of the aerodynamic power value itself but also continuous and abrupt changes in its rate of change and acceleration, thereby suppressing airflow pulsations at their source. Boundary constraints ensure a natural transition between the generated trajectory and the actual system state, avoiding abrupt changes between trajectory planning and actual execution. Compared to traditional linear ramp or S-curve methods, this optimization-based trajectory generation method can obtain the mathematically optimal smooth trajectory under the same boundary conditions, providing a seamless mode switching experience for high-end vacuum cleaners.

[0110] The feedforward control quantity generation module is connected to the smooth switching trajectory planning module. It is used to analyze and calculate the feedforward control quantity of the brushless motor required to achieve the desired running trajectory of the motor based on the preset aero-electromechanical coupling model.

[0111] In an embodiment of the present invention, the feedforward control quantity generation module is specifically used for:

[0112] Based on the differential flatness theory, according to the aero-electromechanical coupling model, the expected value of aerodynamic power and its derivatives in the expected operating trajectory of the motor are analytically mapped to the expected speed trajectory and expected torque current feedforward of the brushless motor.

[0113] According to aerodynamic power With motor speed Torque Current and equivalent flow resistance The coupling relationship between them is achieved through the following formula:

[0114] ;

[0115] ;

[0116] In the formula:

[0117] for The desired motor speed at any given time is calculated based on the aerodynamic power and current flow resistance parameters, and is expressed in rad / s.

[0118] for The desired torque current feedforward at time t is used to generate the required electromagnetic torque, and its unit is A.

[0119] The mapping function from aerodynamic power to motor speed, determined by the aerodynamic-electromechanical coupling model, reflects the physical coupling relationship between aerodynamic power, flow resistance, and speed.

[0120] The mapping function from aerodynamic power and its derivative to torque current, determined by the aerodynamic-electromechanical coupling model, includes the influence of system dynamic characteristics. , The specific form is determined by an aerodynamic-electromechanical coupling analytical model that includes parameters such as impeller mechanical constants and motor torque constants, ensuring dimensional consistency.

[0121] for The expected aerodynamic power value at any given time comes from the output of the trajectory planning module.

[0122] for The expected rate of change of aerodynamic power at any given time is obtained by differentiating the planned trajectory.

[0123] The equivalent flow resistance parameters of the gas path system are obtained through online identification at the current moment.

[0124] Example of an aero-electromechanical coupling model, derived based on the fundamental physical laws of the fan / air circuit:

[0125] pneumatic power (Pressure difference × Flow rate)

[0126] Assuming the characteristics of the fan: (Common simplified model), and (Ohm's Law for the air path)

[0127] Solving the above equations simultaneously, we can eliminate and Finally obtained and the required motor torque (and thus with) Related, Torque is related to the rate of change of power (), while torque is related to the rate of change of power (). Considering the moment of inertia ), thus deriving .

[0128] Operational Logic Explanation: This formula is based on the theory of differential flatness to achieve an analytical mapping from the planned aerodynamic power trajectory to the motor control quantity. The theory of differential flatness states that for a dynamic system satisfying specific conditions, there exists a set of flat output variables such that all state variables and control inputs of the system can be expressed as algebraic functions of these flat outputs and their finite-order derivatives. In this system, aerodynamic power is proven to be a flat output; therefore, the desired motor speed and desired torque current feedforward can be analytically calculated using the desired aerodynamic power value and its derivative, without the need for complex numerical integration or iterative solutions. Mapping Function and The specific form is determined by the aero-electromechanical coupling model, which establishes the physical relationship between aerodynamic power and the electrical and mechanical variables of the motor. The analytical mapping method based on differential flatness theory provides an accurate and efficient mathematical tool for calculating the feedforward control quantity. Compared with traditional methods based on numerical integration or state observation, analytical mapping has significant advantages such as low computational cost, no accumulated error, and high real-time performance. By directly converting the planned aerodynamic power trajectory into motor control commands, feedforward control can pre-compensate for the dynamic characteristics of the system, significantly reducing the adjustment burden on the feedback controller and improving the system's tracking accuracy and response speed. Especially during rapid mode switching, feedforward control dominates the dynamic response process, while feedback control is only used to compensate for model errors and external disturbances. This feedforward-feedback composite control structure fully leverages the advantages of both, enabling the system to achieve excellent dynamic performance while ensuring stability. Furthermore, the reversibility of analytical mapping also facilitates system monitoring and fault diagnosis, realizing an integrated design of control and monitoring.

[0129] The status feedback control module is used to acquire the operating status of the brushless motor in real time, and calculate the feedback control quantity based on the deviation between the operating status and the expected operating trajectory of the motor.

[0130] In an embodiment of the present invention, the state feedback control module includes a disturbance observer, which is used to estimate system disturbances caused by load abrupt changes or model errors and generate corresponding disturbance compensation amounts, which are part of the feedback control quantities.

[0131] In embodiments of the present invention, the disturbance observer is a high-order sliding mode observer or an extended state observer.

[0132] When an extended state observer is used, it is used to observe the desired rotational speed. Compared with actual speed The total disturbance inherent in the dynamic error between the two is given by the observer formula as follows:

[0133] ;

[0134] ;

[0135] ;

[0136] In the formula:

[0137] This is the error between the observed and actual measured values ​​of the rotational speed, i.e. The unit is rad / s.

[0138] The observed motor speed is estimated using the observer's dynamic equations to track the actual speed. .

[0139] The observed value of the total system disturbance, i.e., the disturbance compensation, includes the combined effects of unmodeled dynamics, parameter uncertainties, and external load disturbances, and has angular acceleration ( ) is a unit of measurement.

[0140] The actual measured speed of the motor, obtained from a position sensor or estimation algorithm.

[0141] The feedback control current comes from the output of the state feedback controller.

[0142] , The observer gain parameter determines the convergence rate of the observation error and must satisfy the stability condition.

[0143] The control gain coefficient reflects the intensity of the influence of the control input on the system dynamics.

[0144] It is a nonlinear function, specifically in the form: when hour, ;when hour, .

[0145] , The exponential parameter of the nonlinear function has a range of values. This determines the degree of nonlinearity of the function.

[0146] The threshold of the linear interval of the nonlinear function. When the value is less than this, the function exhibits linear properties.

[0147] Operational Logic Explanation: This formula describes the dynamic equations of the extended state observer, used for real-time estimation and compensation of the total system disturbance. The extended state observer is an advanced state and disturbance estimation technique that unifies the estimation of unmodeled system dynamics, parameter uncertainties, and external disturbances into a new state variable. The observer contains two main equations: the first equation uses a nonlinear function... Address speed tracking errors and update speed observations. It also includes disturbance compensation items. And control input terms; the second equation estimates the total disturbance. This perturbation incorporates the effects of all unmodeled dynamics and external disturbances. Nonlinear function The design allows the observer to have high gain to ensure estimation accuracy when the error is small, while limiting the gain to avoid over-adjustment when the error is large, thus enhancing the robustness and adaptability of the observer. The introduction of the extended state observer significantly improves the system's robustness to uncertainties and external disturbances. Compared with traditional disturbance observers, the extended state observer has two major advantages: first, it treats the unmodeled dynamics of the system and external disturbances as a unified total disturbance for estimation, eliminating the need to precisely distinguish the source of the disturbance and reducing modeling complexity; second, it uses a nonlinear function... By addressing observation errors, the observer maintains linearity within a small error range to ensure estimation accuracy, while limiting gain growth in a large error range to prevent estimation divergence, thus enhancing its adaptability to sudden disturbances. In practical applications, this observer can effectively estimate and compensate for various disturbances commonly encountered during vacuum cleaner operation, such as load surges caused by garbage suction, power supply voltage fluctuations, and motor parameter temperature drift, ensuring stable tracking performance of the control system even under complex operating conditions. The disturbance compensation output from the observer is directly fed forward to the control loop, combining with model-based feedforward control to form a composite control structure of model feedforward + disturbance compensation + error feedback. This improves the system's control quality from multiple dimensions, meeting the comprehensive requirements of high-end vacuum cleaners for stability, speed, and accuracy.

[0148] The drive execution module is connected to the feedforward control quantity generation module and the state feedback control module respectively. It is used to integrate the feedforward control quantity and the feedback control quantity to generate a drive signal and control the operation of the brushless motor, thereby achieving optimized control of the smoothness of pneumatic power during mode switching.

[0149] In an embodiment of the present invention, the drive execution module is a driver based on field-oriented control, which integrates the feedforward control quantity and the feedback control quantity to generate a space vector pulse width modulation signal to drive the brushless motor.

[0150] Example 2: Figure 4As shown, a high-efficiency drive speed control method for a brushless motor in a high-end household vacuum cleaner includes the following steps:

[0151] Real-time acquisition of gas path parameters, and online identification and updating of the equivalent flow resistance parameters of the gas path system;

[0152] When a working mode switching command is received, the desired motor running trajectory is generated based on the updated equivalent flow resistance parameters and target aerodynamic power index, with aerodynamic power smoothness as the optimization objective.

[0153] In another embodiment of the present invention, the step of generating the desired running trajectory of the motor specifically includes:

[0154] Within the flat output space of aerodynamic power, construct a polynomial trajectory that minimizes the square integral of the higher-order derivatives of the aerodynamic power variation.

[0155] The constraints are determined based on the currently identified equivalent flow resistance parameters, and the coefficients of the polynomial trajectory are solved.

[0156] Based on the aero-electromechanical coupling model, the feedforward control quantity is analytically calculated according to the desired operating trajectory of the motor.

[0157] The motor's operating status is acquired in real time, and its deviation from the motor's desired operating trajectory is calculated to obtain the feedback control quantity.

[0158] In another embodiment of the present invention, the step of calculating the feedback control quantity further includes:

[0159] The system disturbance is estimated using a disturbance observer, and a compensation amount is generated. This compensation amount is then incorporated into the feedback control quantity.

[0160] By combining feedforward and feedback control signals, the brushless motor is driven to achieve smooth switching.

[0161] In another embodiment of the present invention, the method further includes a collaborative processing step:

[0162] When a step change in external load is detected and a mode switching command is received at the same time, the load change and mode switching are identified as a unified composite event.

[0163] Based on the initial state and final target state of this composite event, an integrated motor desired running trajectory is generated in response.

[0164] The generation of the integrated trajectory uses the stable aerodynamic power before the occurrence of the composite event and the target aerodynamic power after the end of the event as the boundary conditions of the optimization problem. and Solve the problem.

[0165] In another embodiment of the present invention, the method further includes a parameter adaptation step:

[0166] During the long-term operation of the vacuum cleaner, the changes in air path characteristics are continuously monitored by the air path parameter sensing module and the flow resistance online identification module;

[0167] The parameters in the aero-electromechanical coupling model are dynamically adjusted to make the smooth switching control adapt to the time-varying characteristics of the pneumatic system.

[0168] The dynamic adjustment is specifically manifested in: updating the equivalent flow resistance parameters online. This updated value is then used in real time to resolve the mapping function. and middle.

[0169] Example 3: A high-end household vacuum cleaner includes a vacuum cleaner body, an impeller, a brushless motor, and a high-efficiency drive and speed control system for the brushless motor. The high-efficiency drive and speed control system is used to drive and control the brushless motor, thereby driving the impeller to generate suction.

[0170] As a third embodiment of the present invention, the high-end household vacuum cleaner operating mode includes at least two modes with different target pneumatic power indicators, wherein the target pneumatic power indicators correspond to different suction levels or energy consumption levels.

[0171] As a third embodiment of the present invention, the speed control system responds to the user's switching operation between different working modes and executes a high-efficiency drive speed control method for a brushless motor of a high-end household vacuum cleaner to achieve a smooth transition of suction power during mode switching.

[0172] As a third embodiment of the present invention, the high-end household vacuum cleaner brushless motor high-efficiency drive speed control system further includes a user interface module for receiving user settings of optimization target weights during the switching process. The optimization targets include at least smoothness, switching speed, and energy consumption. The smooth switching trajectory planning module adjusts the trajectory planning strategy according to the weights set by the user.

[0173] As a third embodiment of the present invention, the user interface module is communicatively connected to a mobile terminal application, and the user selects a preset scenario-based switching strategy or a custom strategy weight through the application.

[0174] Example 4: In order to verify the actual effect of solving the airflow pulsation problem during mode switching, this example sets up a composite working condition commonly used in high-end vacuum cleaners, which is "switching from hard floor energy-saving mode to carpet high-efficiency mode", and incorporates the time-varying factor of gradual filter clogging.

[0175] I. Test Scenario and Parameter Settings

[0176] Test platform: A high-end cordless vacuum cleaner prototype equipped with the control system of this invention.

[0177] Control core: ARM Cortex-M4 microprocessor, running the algorithm described in this invention.

[0178] Initial operating condition: The vacuum cleaner is running in "energy-saving mode", corresponding to the target pneumatic power. Place the suction head on a smooth, hard floor.

[0179] Switching instructions and load steps: In At the same time, the system simultaneously receives a switch to "high-efficiency mode" (target aerodynamic power). The instruction simulates the suction head instantly moving into and pressing down on a solid, long-pile carpet, introducing an equivalent of adding an extra layer of protection. Load step of flow resistance.

[0180] Time-varying parameters: The entire test simulates the filter slowly clogging during the cleaning process, with the total flow resistance approximately... The rate increases linearly.

[0181] Algorithm parameters:

[0182] Flow resistance identification forgetting factor .

[0183] Trajectory planning uses a fifth-order polynomial ( ), switch total time The optimization objective is to minimize the square integral of the third derivative of aerodynamic power.

[0184] Extended State Observer Parameters: .

[0185] parameter , The bandwidth can be configured according to the desired bandwidth of the observer, typically set to 3-5 times the bandwidth of the control system. Nonlinear parameters. , The range is usually chosen to be within the (0,1) interval. To ensure convergence, The appropriate setting can be selected based on the noise level measured by the rotation speed.

[0186] II. Key Process Data and Results

[0187] Table 1: Comparison of key parameters during mode switching (traditional linear ramp switching vs. smooth switching of this invention)

[0188] Performance indicators Traditional linear ramp switching scheme The smooth switching scheme of the present invention Improvement / Enhancement of Effect pneumatic power overshoot Approximately 35% (peak value reaches 405W) ＜2% Overshoot basically eliminated aerodynamic power settling time Approximately 0.6s Approximately 0.4 seconds (preset time) More precise response Airflow pulsation rate (standard deviation) 28.5% 4.1% Reduced by approximately 85.6% Maximum motor current during switching process 22.5A 18.2A A decrease of approximately 19.1%. Subjective auditory evaluation (annoyance level) A clearly audible "whoosh-whoosh" wave sound A nearly imperceptible smooth pitch rise Significantly improved experience

[0189] Table 2: Data from the online flow resistance identification module (segment) (Sampling time)

[0190] k Time (s) True total flow resistance Identify flow resistance absolute error 1 0.00 2.50 2.50 (initial value) 0.00 50 0.10 3.00 (After load step) 2.98 0.02 150 0.30 3.10 3.09 0.01 200 0.40 3.20 3.19 0.01

[0191] Data analysis: The identification algorithm can quickly track the load step caused by the suction head pressing into the carpet within about 0.1s, and maintain high-precision tracking in the subsequent slow time-varying process, with a maximum steady-state error of <1%, providing accurate time-varying parameters for trajectory planning.

[0192] III. Smooth Track Switching and Tracking Effect

[0193] Trajectory Generation: Based on the starting power (120W), the target power (300W), and the identified time-varying flow resistance, the trajectory planning module is smoothly switched online to solve the quadratic programming problem, generating a fifth-order polynomial aerodynamic power expectation trajectory. .

[0194] Tracking performance: The feedforward control quantity generation module and the state feedback control module work together to achieve actual aerodynamic power. The root mean square error (RMSE) of the tracking error for the desired trajectory is less than 5W throughout the entire 0.4s switching process. The extended state observer... At the moment of load step change, the disturbance was successfully estimated and the compensation amount was output, so that the actual speed could resume tracking within 5ms after the disturbance occurred, thus avoiding the interruption of airflow.

[0195] IV. Performance Verification of the Disturbance Observer

[0196] exist At this time, an additional instantaneous torque disturbance simulating the inhalation of large particles of debris is injected (pulse width 10ms, amplitude approximately 30% of rated torque). The expanded state observer output... The response data is as follows:

[0197] Disturbance estimation delay: <2ms.

[0198] Peak error of disturbance estimation: <12%.

[0199] Impact on airflow pulsation: Under this sudden disturbance, after the observation compensation was used, the amplitude of airflow pulsation was reduced by about 70% compared with the uncompensated case.

[0200] The data in this embodiment fully demonstrates that the system described in this invention can effectively solve the problem of airflow pulsation during switching caused by neglecting the dynamic characteristics of the air path, as pointed out in the background art. Through a core approach combining online flow resistance identification and smooth aerodynamic power trajectory planning, a seamless and smooth transition of suction is achieved (airflow pulsation reduced by 85.6%). Simultaneously, efficient feedforward mapping based on differential flatness ensures the real-time performance of complex trajectories, while the extended state observer provides strong robustness against actual uncertainties. Together, they constitute a complete drive speed control solution that can stably provide a high-end switching experience under real and complex operating conditions.

[0201] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A high-efficiency drive and speed control system for a brushless motor in a high-end household vacuum cleaner, characterized in that, include: The air path parameter sensing module is used to acquire air path parameters that characterize the air path load status of the vacuum cleaner in real time; The online flow resistance identification module is connected to the gas path parameter sensing module and is used to identify and update the equivalent flow resistance parameters of the current gas path system online based on the gas path parameters acquired in real time. The smooth switching trajectory planning module, connected to the online flow resistance identification module, is used to generate a motor desired running trajectory that makes the change of aerodynamic power smooth when a working mode switching command is received, based on the updated equivalent flow resistance parameters and the target aerodynamic power index corresponding to the target working mode. The core optimization target is the smoothness of aerodynamic power during the switching process. The feedforward control quantity generation module is connected to the smooth switching trajectory planning module. It is used to analyze and calculate the feedforward control quantity of the brushless motor required to achieve the desired running trajectory of the motor based on the preset aero-electromechanical coupling model. The status feedback control module is used to acquire the operating status of the brushless motor in real time, and calculate the feedback control quantity based on the deviation between the operating status and the expected operating trajectory of the motor. The drive execution module is connected to the feedforward control quantity generation module and the state feedback control module respectively. It is used to integrate the feedforward control quantity and the feedback control quantity to generate a drive signal and control the operation of the brushless motor, thereby achieving optimized control of the smoothness of pneumatic power during mode switching.

2. The high-efficiency drive and speed control system for a brushless motor in a high-end household vacuum cleaner according to claim 1, characterized in that, The air path parameter sensing module includes at least one of a wind pressure sensor and a flow sensor, or the aerodynamic power is estimated as the air path parameter by the electrical operating parameters of the motor.

3. The high-efficiency drive speed control system for a brushless motor in a high-end household vacuum cleaner according to claim 1, characterized in that, The online flow resistance identification module uses any one of the recursive least squares method, Kalman filter or model reference adaptive algorithm to identify and update the equivalent flow resistance parameters of the gas path system online. When using the recursive least squares method, the identification and update process is iteratively implemented through the following formula: ； ； ； In the formula, for Estimated values ​​of the equivalent flow resistance parameters of the air path system at any given time; for Estimated equivalent flow resistance parameters at time t; for Gain vector at time step; For regression vectors; for Transpose of the regression vector at time step; for The aerodynamic power value measured or estimated at any time; for The covariance matrix at time t; for The covariance matrix at time t; It is a forgetting factor.

4. The high-efficiency drive speed control system for a brushless motor in a high-end household vacuum cleaner according to claim 1, characterized in that, The smooth transition trajectory planning module is specifically used to: plan a motor's desired operating trajectory that smoothly transitions from the current aerodynamic power value to the target aerodynamic power value within a space formed by aerodynamic power as a flat output; the motor's desired operating trajectory is a polynomial curve that minimizes the integral of the square of the higher-order derivative of the aerodynamic power. The desired operating trajectory of the motor is determined by the following: Polynomial description: ； In the formula, for The expected aerodynamic power value at any given time; For the polynomial of the th The coefficient of the second term, ; For time variables, , This is the preset total time for the mode switching process; Let be the order of the polynomial.

5. The high-efficiency drive and speed control system for a brushless motor in a high-end household vacuum cleaner according to claim 4, characterized in that, The desired operating trajectory of the motor is a fifth or seventh degree polynomial curve, the coefficients of which are obtained by solving a quadratic programming problem with linear constraints. Taking minimizing the squared integral of the third derivative of aerodynamic power as an example, the objective function of the quadratic programming problem is... The boundary constraints are: ； The constraints are: , , , ; In the formula, The objective function value to be optimized; Let be the third derivative of the desired aerodynamic power with respect to time; To switch the start time ( The expected aerodynamic power value; For the end time of switching ( The expected aerodynamic power value; The first derivative of the aerodynamic power at the start of the switching; The first derivative of the aerodynamic power at the end of the switching process; To switch the initial aerodynamic power boundary value; To switch the target aerodynamic power boundary value; To switch the initial aerodynamic power change rate boundary value; To switch the target aerodynamic power change rate boundary value; the polynomial coefficients This is obtained by solving the optimization problem.

6. The high-efficiency drive speed control system for a brushless motor in a high-end household vacuum cleaner according to claim 1, characterized in that, The feedforward control quantity generation module is specifically used to: based on the differential flatness theory, according to the aero-electromechanical coupling model, analyze and map the expected value of aerodynamic power and its derivatives in the expected operating trajectory of the motor into the expected speed trajectory and expected torque current feedforward quantity of the brushless motor. According to aerodynamic power With motor speed Torque Current and equivalent flow resistance The coupling relationship between them is achieved through the following formula: ； ； In the formula, for The expected motor speed at any given time; for The expected torque current feedforward at time t; The mapping function from aerodynamic power to motor speed is determined by the aero-electromechanical coupling model. The mapping function from aerodynamic power and its derivative to torque current is determined by the aerodynamic-electromechanical coupling model. for The expected aerodynamic power value at any given time; for The expected rate of change of aerodynamic power at time t; The equivalent flow resistance parameters of the gas path system are obtained through online identification at the current moment.

7. The high-efficiency drive speed control system for a brushless motor in a high-end household vacuum cleaner according to claim 1, characterized in that, The state feedback control module includes a disturbance observer, which is used to estimate system disturbances caused by load abrupt changes or model errors and generate corresponding disturbance compensation amounts, which are part of the feedback control quantities. The disturbance observer is a high-order sliding mode observer or an extended state observer.

8. The high-efficiency drive speed control system for a brushless motor in a high-end household vacuum cleaner according to claim 7, characterized in that, When an extended state observer is used, it is used to observe the desired rotational speed. Compared with actual speed The total disturbance implied by the dynamic error between them is given by the following formula: ； ； ； In the formula, This represents the error between the observed rotational speed and the actual measured speed. This refers to the observed value of the motor speed; The observed value of the total system disturbance is the disturbance compensation amount; This is the actual measured speed of the motor; For feedback control current; , For observer gain parameters; To control the gain coefficient; It is a nonlinear function; , The exponential parameter of the nonlinear function has a range of values. ; This is the threshold value for the linear interval of a nonlinear function.

9. The high-efficiency drive speed control system for a brushless motor in a high-end household vacuum cleaner according to claim 1, characterized in that, The drive execution module is a driver based on field-oriented control. It integrates the feedforward control quantity and the feedback control quantity to generate a space vector pulse width modulation signal to drive the brushless motor.

10. A control method for high-efficiency drive speed control of a brushless motor in a high-end household vacuum cleaner, applied to the high-efficiency drive speed control system for a brushless motor in a high-end household vacuum cleaner as described in any one of claims 1-9, characterized in that, Includes the following steps: Real-time acquisition of gas path parameters, and online identification and updating of the equivalent flow resistance parameters of the gas path system; When a working mode switching command is received, the desired motor running trajectory is generated based on the updated equivalent flow resistance parameters and target aerodynamic power index, with aerodynamic power smoothness as the optimization objective. Based on the aero-electromechanical coupling model, the feedforward control quantity is analytically calculated according to the desired operating trajectory of the motor. The motor's operating status is acquired in real time, and its deviation from the motor's desired operating trajectory is calculated to obtain the feedback control quantity. By combining feedforward and feedback control signals, the brushless motor is driven to achieve smooth switching.

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