Water-based paint production and processing equipment control system

Abstract

The present application relates to the technical field of industrial automation control, and discloses a water-based paint production and processing equipment control system, which comprises the following steps: collecting stator current and rotor angular velocity, reconstructing total load torque by using a sliding mode algorithm; extracting low-frequency components of input power to construct a heat dissipation slow manifold, calculating heat drift disturbance components and orthogonal residual components through orthogonal projection; and generating superimposed instruction closed-loop adjustment driving mechanisms based on the two components. The present application uses a double time scale orthogonal projection mechanism to automatically strip non-structural heat drift interference without needing to know the equipment heat transfer parameters, solves the control divergence problem caused by model parameter mismatch, and realizes decoupling control of the real flow state of materials.

Description

Technical Field

[0001] This invention relates to a control system for water-based paint production and processing equipment, belonging to the field of industrial automation control technology. Background Technology

[0002] Currently, in the production process of water-based paints, the drive control systems of core processing equipment such as dispersers and grinders typically employ vector control or PID closed-loop architecture. These systems collect motor current, speed, or torque signals to maintain the equipment's operation according to preset process parameters. This type of control relies on the relative stability of the physical model of the controlled object. It assumes that key parameters such as load damping characteristics, environmental thermal resistance, and equipment mechanical friction coefficient remain constant or experience slow linear drift within the control cycle. The controller calculates instructions and adjusts outputs based on fixed model parameters. While some existing technologies have made progress in enhancing the automation level of equipment, they often neglect the deep perception and refined control of the rheological state of the core process. For example, the utility model patent with authorization announcement number CN212576053U discloses a mixing device for water-based paint production. This solution solves the mechanical implementation problem of automatic addition and basic mixing of multi-component raw materials by designing a flip-type clamping mechanism and a dual-motor drive architecture, improving the feeding efficiency at the physical operation level. However, such equipment essentially still focuses primarily on the execution of mechanical actions and the realization of physical mixing, lacking a real-time feedback mechanism for the complex rheological behavior of materials during the mixing process.

[0003] In actual chemical production scenarios, especially in high-speed shearing or grinding processes involving high-viscosity materials, the energy form of the drive mechanism changes during the work done on the material. As processing time progresses, part of the mechanical energy injected by the motor is converted into shear heat, leading to an increase in the temperature inside the reactor. Scaling in the reactor jacket, fluctuations in cooling circulating water pressure, and diurnal temperature variations introduce nonlinear thermal impedance perturbations. These perturbations, resulting from the combined effects of time-varying energy accumulation and dissipation characteristics, produce a thermal effect that directly alters the rheological properties of the material, causing a non-structural thermally induced decrease in apparent viscosity. Existing control technologies face a fundamental contradiction between model rigidity and the time-varying nature of the operating conditions. Conventional constant speed or constant torque control strategies cannot distinguish between changes in load torque caused by… Whether the evolution of the material's true chemical structure is caused by the arrival of the dispersion endpoint, the completion of emulsification, or thermal thinning due to temperature rise, traditional controllers are prone to misjudging the processing endpoint or load reduction when the material's viscosity decreases due to severe shearing and heating. This can lead to incorrect reduction of output power or premature termination of the process, resulting in insufficient grinding or substandard fineness of the product. If the rotational speed is forcibly increased to maintain torque, it may damage the chemical stability of the material due to overheating. Although the industry has attempted to introduce temperature sensor compensation, it is constrained by sensor response lag, limited installation location, and the inability to obtain accurate dynamic heat transfer coefficients in real time. Compensation methods based on static thermal models are difficult to achieve accurate real-time decoupling in complex and variable industrial environments, and may even introduce new human disturbances due to compensation deviations.

[0004] Therefore, the technical problem to be solved by this invention is how to accurately extract the non-structural drift component caused by thermal effect from the total load torque in real time through signal processing, without relying on external physical sensors and precisely preset thermophysical parameters, under conditions where model parameters are unknown or time-varying. This allows for locking in the true rheological state of the material and achieving adaptive and precise control. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A control system for water-based paint production and processing equipment, comprising:

[0006] The first signal acquisition unit is used to acquire the stator current signal and rotor angular velocity signal of the drive actuator in real time.

[0007] The second dynamic observation unit is connected to the first signal acquisition unit and is used to process the stator current signal and rotor angular velocity signal based on the sliding mode observation algorithm to reconstruct the total load damping torque including thermal drift interference.

[0008] The third manifold construction unit is used to extract the low-frequency component in the input power signal of the drive actuator and establish a quasi-static mapping relationship between the low-frequency component and the reference damping coefficient, thereby generating a heat dissipation slow manifold in the state space that characterizes the trajectory of pure thermal effect.

[0009] The fourth orthogonal projection unit, connected to the second dynamic observation unit and the third manifold construction unit, is used to calculate the projection vector of the total load damping torque in the tangent direction of the heat dissipation slow manifold and define the projection vector as the thermal drift disturbance component, and to calculate the vector difference between the total load damping torque and the projection vector and define the vector difference as the orthogonal residual component characterizing the load structured rheological characteristics.

[0010] The fifth closed-loop control unit, connected to the fourth orthogonal projection unit, is used to generate feedforward compensation commands based on thermal drift disturbance components and feedback adjustment commands based on orthogonal residual components. The feedforward compensation commands and feedback adjustment commands are superimposed and output to the drive actuator.

[0011] Preferably, the third manifold construction unit includes: an adaptive filtering module, used to set a first frequency threshold associated with the thermal inertia time constant of the driving actuator, and filter out frequency band components in the input power signal that are higher than the first frequency threshold to obtain low-frequency components; and a parameter identification module, used to identify the steady-state gain coefficient between the low-frequency components and the reference damping coefficient online using a recursive least squares algorithm, and use the steady-state gain coefficient as a geometric parameter to define the slope of the heat dissipation slow manifold.

[0012] Preferably, the calculation logic of the orthogonal residual components performed by the fourth orthogonal projection unit satisfies the following mathematical relationship: ,in, Defined as orthogonal residual components, Defined as the total load damping torque. Defined as the unit vector of the tangent direction of the heat-dissipating slow manifold at the current operating point, with the symbol... The dot product operation represents vectors, with the symbol... This represents vector norm operations.

[0013] Preferably, the fifth closed-loop control unit includes: a feedforward suppression module, used to generate a feedforward compensation command after inverting the thermal drift disturbance component to offset the non-structural damping drift caused by energy accumulation; and a rheology follower module, used to compare the orthogonal residual component as a controlled variable with a preset process rheology target value, and generate a feedback adjustment command based on the comparison deviation to drive the actuator to respond to the structured physical property changes of the load.

[0014] Preferably, the system further includes: a sixth frequency domain perturbation unit, connected to the drive actuator, for superimposing a perturbation detection signal of a preset frequency into the reference speed command of the drive actuator; a seventh phase analysis unit, connected to the first signal acquisition unit, for extracting the response component of the rotor angular velocity signal with the same frequency as the perturbation detection signal, and calculating the phase lag angle of the response component relative to the perturbation detection signal; and a fifth closed-loop control unit, for generating a forced shutdown command or a load reduction operation command when the phase lag angle exceeds a preset safety threshold.

[0015] Preferably, the system further includes: an eighth health monitoring unit, connected to the fourth orthogonal projection unit, for continuously monitoring the time change rate of the thermal drift disturbance component; the eighth health monitoring unit is used to generate an equipment maintenance early warning signal indicating that the thermal impedance characteristics of the equipment have undergone non-structural changes when the time change rate of the thermal drift disturbance component deviates from the preset reference curve for a long period of time.

[0016] Preferably, the second dynamic observation unit includes: a mechanical loss model storage module for storing the reference mechanical friction torque curve of the drive actuator under no-load conditions; and a net load extraction module for subtracting the inertial torque component derived from the rotor angular velocity signal and the friction component corresponding to the reference mechanical friction torque curve from the electromagnetic torque observation value output by the sliding mode observation algorithm to obtain the total load damping torque.

[0017] Preferably, the adaptive filtering module is also used to monitor the root mean square value of the orthogonal residual components, and automatically reduce the first frequency threshold when the root mean square value is continuously lower than the preset convergence threshold, so as to enhance the smooth suppression capability of the heat dissipation slow manifold for low-frequency thermal noise.

[0018] Preferably, the fifth closed-loop control unit is also used to freeze the current heat dissipation slow manifold parameters when receiving the process switching signal, and to perform initial decoupling of the total load damping torque based on the frozen parameters during the new process start-up phase, until the third manifold building unit completes a new round of parameter convergence.

[0019] Preferably, the system is specifically defined as a variable frequency drive control device for driving a disperser or a grinder, wherein the drive actuator is the main drive motor of the disperser or grinder, and the orthogonal residual component is defined as the real-time equivalent structural viscosity index of the processed material.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. In the control of water-based paint production and processing equipment, a closed loop based on energy flow state observation is constructed to solve the problem of insufficient stability caused by the reliance on preset physical model parameters in traditional control schemes. By utilizing the time scale difference between thermal inertia and load rheological characteristics, the low-frequency component of input power is extracted to construct a reference trajectory characterizing the pure thermal effect. The real-time observed total damping torque is projected and decomposed onto the reference trajectory. Based on the signal frequency domain feature decoupling mechanism, the controller does not need to know the specific physical parameters of the reactor heat transfer coefficient, specific heat capacity, or environmental thermal resistance. It automatically isolates the non-structural parameter drift caused by the heat generated by the equipment, avoids the influence of thermal model mismatch caused by equipment scaling, cooling medium fluctuations, or environmental temperature differences, and ensures that the true load damping state is locked under uncertain parameter conditions.

[0022] 2. A state vector orthogonal residual extraction mechanism is adopted to avoid the blind spot in the control of material structure changes due to thermally induced thinning effect during high-power shearing. The total load torque output by the sliding mode observer is regarded as the state vector. The projection component of the heat dissipation manifold tangent direction is calculated as the thermal disturbance feedforward cancellation, and the residual component perpendicular to the thermal effect direction is locked as the feedback control variable. The signal orthogonality is used to directly filter out the background noise in phase with the thermal effect. Even under the condition of a sharp temperature rise that causes a significant decrease in apparent viscosity, the system can still accurately identify weak material structure rheological signals, avoid misjudging thermal thinning as the reaction endpoint and causing processing deviations, and achieve precise process control of nonlinear time-varying load.

[0023] 3. By mathematically reconstructing the internal electrical state variables of the driver, full-state closed-loop control is achieved without the intervention of external physical sensors. The dynamic torque, including inertia and friction loss, is reconstructed in real time through a sliding mode observer. A virtual thermodynamic state equation is established by combining input energy integration calculation, and the net load torque after removing mechanical friction and thermal drift is directly calculated from the current and speed signals. Based on a pure algorithm soft measurement architecture, the engineering risks of response lag, installation limitations, and susceptibility to chemical corrosion failure of physical sensors are eliminated, reducing hardware complexity and improving the reliability and response bandwidth of the control system in harsh industrial environments. Attached Figure Description

[0024] Figure 1 This is a block diagram illustrating the overall principle of the control system based on the dual-time-scale orthogonal projection mechanism of the present invention.

[0025] Figure 2 This is a comparison curve of the time-varying characteristics of the speed drift rate under different control strategies of the present invention;

[0026] Figure 3 This is a system function interaction use case diagram for the present invention, which includes parameter self-learning and closed-loop control logic. Detailed Implementation

[0027] This specific embodiment is only used to explain the present invention and is not intended to limit the scope of protection of the present invention. Those skilled in the art can make various modifications or equivalent substitutions to the present invention in accordance with the spirit of the present invention, but such modifications or equivalent substitutions should all be covered within the scope of the claims of the present invention.

[0028] This invention proposes a control system for water-based paint production and processing equipment. Based on a dual-time-scale orthogonal projection state separation mechanism, it mainly consists of a first signal acquisition unit, a second dynamic observation unit, a third manifold construction unit, a fourth orthogonal projection unit, and a fifth closed-loop control unit. During system operation, the first signal acquisition unit reads the stator current of the main motor driving the disperser or grinder in real time through a current transformer and encoder interface. With rotor mechanical angular velocity The second dynamic observation unit reconstructs the real-time total load damping torque, including non-structural thermal drift and structural load changes, based on the stator current and rotor mechanical angular velocity using a sliding mode algorithm. The third manifold construction unit extracts the quasi-static component from the input power through low-pass filtering and establishes a heat dissipation slow manifold in the state space to characterize the trajectory of pure thermal effects. The fourth orthogonal projection unit orthogonally decomposes the real-time total load damping torque onto the heat dissipation slow manifold, separating the thermal drift disturbance component and the orthogonal residual component characterizing the true rheological characteristics of the material. The fifth closed-loop control unit generates feedforward compensation commands based on thermal drift disturbance components and feedback adjustment commands based on orthogonal residual components, ultimately driving the frequency converter to precisely adjust the motor speed or torque. In the high-speed dispersion or grinding process of water-based paint production, the drive actuator performs work on high-viscosity materials, generating shear heat, which causes the temperature inside the reactor to rise. Consequently, the apparent viscosity of the material experiences a non-structural thermally induced decrease. To obtain the load state, the second dynamic observation unit adopts a sliding mode observer strategy based on full-order state equations. This unit calls the baseline mechanical loss model pre-stored in non-volatile memory. This model was obtained by running the equipment under no-load conditions, and is based on rotational speed. A lookup table is used as the independent variable and mechanical friction and wind resistance torque as the dependent variables. The processor executes a sliding mode observation algorithm to construct a state equation with the stator current estimation error as the sliding surface. By switching control laws, the estimated current converges to the measured stator current. Thus, the electromagnetic torque observation value can be analyzed from the sliding mode equivalent control value. The processor performs dynamic operations, from Subtract the rotor's mechanical angular velocity from the middle The inertial torque calculated from the differential term and the corresponding benchmark mechanical loss model Output the real-time total load damping torque after removing mechanical and inertial factors. The rated power of the machine is At that time, the system sampling frequency was set to The sliding mode gain coefficient is set to the rated current value. times.

[0029] To address the need to distinguish between the thermally thinned and structural rheological components in the total load damping torque, the third manifold building unit utilizes the difference in frequency domain between the large time-scale characteristics of thermal inertia and the rapid variation characteristics of rheological dynamics. The processor then calculates the real-time input active power of the motor. ,in, The real-time effective value of the line voltage driving the actuator, To measure the real-time effective value of the line current driving the actuator, The phase angle between line voltage and line current (i.e. (This is the power factor), and the power signal is input to a digital infinite impulse response low-pass filter, the cutoff frequency of which is... Set as the thermal inertia time constant of the controlled object The associated first frequency threshold is calculated using the following formula: ,in The safety margin factor is set to a value of [value missing]. For thermal inertia time constant The determination is as of Dispersion vessel, cutoff frequency Set as After filtering, the low-frequency power component characterizing the quasi-static energy injection is extracted. The processor employs a recursive least squares algorithm with a forgetting factor for online identification. Linear mapping coefficients between the steady-state damping reference value output by the sliding mode observer and the linear mapping coefficients between the linear mapping coefficients .... Establish equations The equation defines a time-evolving trajectory of a slow manifold with heat dissipation in the state space with damping and power as coordinate axes. The fourth orthogonal projection unit performs state separation calculations based on the real-time total load damping torque and the slow manifold with heat dissipation. The processor calculates the unit vector of the tangent direction of the slow manifold at the current operating point. Next, the processor performs vector projection calculations to calculate the real-time total load damping torque. exist Projection components in the direction The calculation formula is: The projection component The processor directly characterizes the unstructured thermal drift caused by energy accumulation and calculates the orthogonal residual components. ,Right now ,Should The vector is perpendicular to the thermal drift direction, and changes in its magnitude and direction are caused solely by alterations in the material's internal microstructure. During a single dispersion process, when the material temperature changes from... Rise to lead to decline At that time, after orthogonal decomposition, the projected components Absorbing this decrease, orthogonal residual components It remains stable until the dispersion endpoint is reached, at which point a step change occurs.

[0030] To address nonlinear cooling medium fluctuations or environmental temperature differences in the production environment, the fourth orthogonal projection unit executes vector decomposition logic based on frequency domain separation physics, targeting thermal drift disturbance components. Limited to low-frequency power Define the direction of the slow manifold, with high frequency. Rapid load fluctuations cannot be addressed The direction forms a projection and is incorporated into the orthogonal residual components. Based on the orthogonal mathematical sieving mechanism, without relying on external temperature sensor feedback, the algorithm automatically filters out high-frequency noise and process dynamics that are mismatched with the thermal effect time scale, directly locking in the residual signal characterizing material structure changes; the fifth closed-loop control unit uses the above separation results to execute control, and the feedforward channel reads... The numerical value, after being inverted and amplified, is directly superimposed on the torque setpoint of the current loop to compensate for speed fluctuations caused by thermal drift. The feedback channel will... As the core controlled variable, it is compared with the preset process rheological target value. The rate of change exceeds the preset rheological equilibrium threshold, that is... continued At a certain time, the controller determines that the processing endpoint has been reached and automatically generates a deceleration or stop command. If this is detected... If the root mean square value remains below the preset noise floor threshold during steady-state operation, the processor automatically lowers the cutoff frequency of the low-pass filter in the third manifold building block. To enhance the suppression of low-frequency thermal drift, and to further improve the system's ability to perceive load dynamics, especially for real-time diagnosis of material thixotropy or equipment mechanical connection status, this control system integrates a sixth-frequency-domain perturbation unit and a seventh-phase analysis unit. The sixth-frequency-domain perturbation unit is directly coupled to the control loop of the drive actuator, and its core function is to provide a reference speed command. High-frequency, low-amplitude sinusoidal perturbation signals superimposed on the signal. The perturbation frequency Typically, a specific value is set that is far from the system's mechanical resonance frequency and higher than the dynamic frequency of the main process. The amplitude A is then set to be sufficiently small, such as the reference rotational speed. .

[0031] The seventh phase analysis unit synchronously acquires the real-time rotor angular velocity signal obtained by the first signal acquisition unit. The signal is then bandpass filtered to extract the response component with the same frequency as the injected perturbation signal. This unit employs a phase detection algorithm based on Discrete Fourier Transform (DFT) or orthogonal demodulation to calculate the response components in real time. Compared to the injected perturbation signal phase lag angle The phase lag angle The phase lag angle directly reflects the dynamic damping ratio and stiffness characteristics of the load system. Under normal viscous fluid loads, the phase lag angle remains within a specific range. However, when abnormal conditions such as wall slip, agitator cavitation, or drive belt slippage occur, the system's equivalent damping characteristics will change abruptly, leading to an increase in the phase lag angle. Therefore, the fifth closed-loop control unit continuously monitors the phase lag angle. Once the preset safety threshold is exceeded like The system determines when it is on the verge of nonlinear instability or mechanical failure and generates a forced shutdown command or a load reduction command, thereby achieving proactive safety protection for the equipment and process. Furthermore, to enable predictive maintenance of the equipment's long-term operating status, the system is also equipped with an eighth health monitoring unit, which is connected to the data output of the fourth orthogonal projection unit to continuously record and analyze thermal drift disturbance components. The long-term evolution trend, due to Essentially, it represents the sum of the system's heat dissipation capacity and mechanical friction loss under the current operating conditions, and its rate of change over time. The steady-state amplitude can reflect the slow drift of the equipment's thermal resistance characteristics; the eighth health monitoring unit has an internal reference health curve, which is based on the equipment after new machine commissioning or major overhaul, under standard test conditions. Historical data statistical models are used by this unit to calculate data in real time during daily production. The normalized data is compared with the baseline health curve. If any issues are found... The steady-state value exhibits an irreversible unidirectional increasing trend under the same operating conditions, and the deviation exceeds the preset aging threshold. If the system determines that the equipment has a potential risk of declining heat exchange efficiency or abnormally increased mechanical friction, the eighth health monitoring unit will generate a specific equipment maintenance warning signal to prompt the operator to carry out targeted inspections and maintenance to avoid sudden equipment failures.

[0032] Example 1: In In scenarios involving the production of high-viscosity water-based anti-corrosion paint using high-speed dispersion reactors, the system faces challenges such as initial material viscosity reaching high levels. And it is necessary to Completed at high speed Under conditions of intense shear dispersion lasting several minutes, as the dispersion process continues, a large amount of mechanical energy is converted into heat energy, causing the temperature of the material inside the vessel to rise. Within minutes Rapidly climbed to This temperature rise causes a thermally induced decrease in the apparent viscosity of the material, resulting in a decrease in the total load damping torque. Consequently, it decreased Traditional constant torque control misjudges a reduced load and decreases output power, resulting in pigment agglomerates failing to obtain sufficient shear stress for complete breakage, ultimately leading to substandard product fineness. In the distributed start-up phase, the control system of this invention uses a first signal acquisition unit to monitor the stator current of the drive motor in real time. With rotor mechanical angular velocity The data is then transmitted to the second dynamic observation unit, which uses a sliding mode observation algorithm to subtract the time difference caused by rotor inertia. and reference mechanical loss The resulting torque component outputs a real-time total load damping torque that includes thermal drift and structural rheological information. Meanwhile, the third manifold building unit is set to a cutoff frequency of 0. The low-pass filter, from the input active power Extracting quasi-static components The heat dissipation slow manifold is updated in real time using a recursive least squares algorithm. This manifold defines a damping reference trajectory caused solely by purely thermal effects, where a rapid increase in temperature leads to... During descent, the fourth orthogonal projection element calculates... Tangential direction of slow manifold in heat dissipation Projected components This component accurately captures Thermally induced damping attenuation, orthogonal residual components The values ​​were analyzed and remained stable, indicating that the microstructure of the material had not undergone substantial changes. As dispersion continued, when the pigment particles deagglomerated and the system underwent thixotropic structural recombination, A characteristic step change occurs.

[0033] The fifth closed-loop control unit performs dual-channel regulation based on this separation result, with the feedforward channel utilizing... A reverse torque compensation command is generated to directly offset the speed fluctuations caused by thermal drift, maintain a constant shearing power output from the motor, and ensure that the material still receives sufficient mechanical shearing action at high temperatures. The feedback channel is locked. As a controlled variable, when monitored rate of change Less than And continue At a given second, the system accurately determines when the dispersion endpoint is reached and automatically issues a deceleration command. This control strategy not only eliminates the main interference from thermal effects, but also achieves precise tracking of the material's true rheological state and endpoint judgment by locking the orthogonal residual components, thus avoiding thermal degradation caused by over-processing or insufficient fineness caused by under-processing.

[0034] Example 2: This example aims to verify the effectiveness of the control system of the present invention in dealing with the problem of thermally induced parameter drift under actual working conditions by constructing a systematic test including control experiments; on a test platform simulating an industrial production environment, a unit with a rated power of A variable frequency drive disperser, the controlled object of which is a volume of A jacketed reactor, loaded with a standard high-viscosity simulated material (a mixture of polyvinyl alcohol aqueous solution and silica powder), with an initial apparent viscosity set to [value missing]. The platform is equipped with a high-precision external torque sensor (accuracy...). A thermocouple array is used to collect mechanical torque and material temperature data as a reference true value. However, the signals from these sensors are used for later verification and analysis and do not participate in the closed-loop feedback of the control system. To simulate electromagnetic interference and non-ideal working conditions in real industrial environments, a signal-to-noise ratio of [value missing] is actively injected into the stator current signal. Gaussian white noise is introduced into the control loop. Random communication delay.

[0035] The experimental design included three sets of comparative experiments: Control Group A (traditional constant torque control): only an open-loop constant torque strategy based on voltage-frequency (V / F) control was used, without introducing any temperature compensation mechanism; Control Group B (thermal compensation based on static model): based on constant torque control, traditional open-loop thermal compensation based on the Arrhenius equation was introduced, but the thermodynamic parameters used, such as the heat dissipation coefficient, were set to differ from the actual values. The deviation is used to simulate model mismatch caused by equipment aging or scaling; the sample group of this invention: fully adopts the adaptive control strategy based on dual time-scale orthogonal projection of this invention, including sliding mode observer, heat dissipation slow manifold construction and orthogonal projection decoupling module; the experimental process is as follows: start the disperser, set the target speed to to carry out for a period of time Continuous strong shear dispersion over minutes, in the first Minutes to the During the minute interval, the jacket steam heating is turned on to bring the material temperature to approximately [temperature value missing]. The rate increases linearly, from Rise to Heating was stopped and cooling water was turned on, allowing the temperature to drop slowly. During this process, the material did not undergo any actual chemical structural change; that is, the actual structural viscosity remained constant. The change in total load torque was mainly caused by the thermally induced thinning effect. At minute 1 minute, a small amount of thickener was artificially added, inducing a real, approximately [missing value] event. The structural viscosity step increase was used to test the system's ability to capture real rheological changes. The output torque, speed response, and identification of real viscosity changes of the three systems were recorded throughout the process. Key intermediate characteristic data are shown in Table 1.

[0036] Table 1: Comparison of Key Performance Indicators under Different Control Strategies

[0037]

[0038] Data analysis shows that during the heating phase, control group A, unable to distinguish between thermally induced leaning and load reduction, experienced a positive shift in rotational speed. The process significantly deviated from the constant shear power requirement. Although control group B introduced thermal compensation, the compensation effect was limited due to the deviation between the preset model parameters and the actual working conditions. The speed drift, and the recognition rate for thermally induced torque decay is only The prototype of this invention uses a heat dissipation slow manifold construction module to identify and update thermal impedance characteristics in real time online. Even in the presence of model parameter uncertainties and injected noise interference, it can still accurately identify the torque component caused by thermal effects. Separating it allows the speed drift rate to be suppressed. It exhibits extremely high stability at extremely low levels; in the first... When a real viscosity step is artificially introduced, the feedback signals of control groups A and B are both submerged by strong thermal drift background noise, making it difficult to distinguish weak structural change signals. However, the sample group of this invention is decoupled through orthogonal projection, and its output orthogonal residual components It maintains high baseline stability in the context of thermal drift and after thickener addition. Within seconds, it exhibits a clear step response, capturing the true rheological changes.

[0039] In addition, to verify the cutoff frequency of the low-pass filter The rationality of the setting, and targeted A gradient control experiment showed that when Set at to When within the preferred range, the thermal drift separation degree remains at The above; and when Higher than At that time, some low-frequency rheological signals were mistakenly filtered out, resulting in a prolonged response time. More than a second; when Below At that time, the manifold update lag leads to increased rotational speed fluctuations in the initial stage of heating. This nonlinear trend confirms the effectiveness of the present invention. The range of values ​​is not arbitrarily chosen, but rather determined by the optimal working window based on the physical constraints of the system's thermal inertia characteristics.

[0040] Example 3: This example combines Figures 1 to 3 A description of a control system for a water-based paint production and processing equipment, such as... Figure 1 As shown in the diagram, the signal flow and logical connections of each processing unit are detailed. The first signal acquisition unit is responsible for acquiring stator current and rotor angular velocity signals and transmitting the processed current / speed signals to the second dynamic observation unit. The second dynamic observation unit uses a sliding mode algorithm to reconstruct the total load damping torque and outputs it to the fourth orthogonal projection unit. Simultaneously, the third manifold construction unit receives the input power signal, extracts low-frequency components to construct a heat dissipation slow manifold trajectory, and inputs it to the fourth orthogonal projection unit. The fourth orthogonal projection unit calculates the thermal drift disturbance component and orthogonal residual component based on the above input and transmits these two components to the fifth closed-loop control unit. The fifth closed-loop control unit generates feedforward compensation and feedback adjustment commands accordingly, and finally outputs superimposed adjustment commands to the drive actuators containing the motor and frequency converter. In addition, the eighth health monitoring unit is connected to the fourth orthogonal projection unit and monitors the thermal drift change rate based on the thermal drift trend to issue early warning maintenance signals.

[0041] like Figure 2As shown, the horizontal axis of the graph represents the time range from 0 to 60 minutes, and the vertical axis represents the speed drift rate in percentage. The graph contains three broken lines with different line types: the dashed line represents control group A (constant torque control), showing that the speed drift rate increases over time and eventually exceeds 12%; the dotted line represents control group B (static model thermal compensation), showing that the drift rate increases more slowly; and the solid line represents the sample group of this invention. Figure 3 As shown, the operator on the left interacts with the core functional module of the system, triggering the self-learning function of initialization parameters to identify mechanical losses and thermal inertia constants, entering the core state of closed-loop control of the production process, and generating superimposed adjustment commands to control the drive actuator motor / frequency converter on the right. During this closed-loop control process, the internal logic of the system triggers three sub-functional modules: the function of reconstructing the total load damping torque based on the sliding mode observation algorithm, the function of decoupling the dual time-scale orthogonal projection for separating thermal drift and structural rheology, and the function of automatically determining the process endpoint based on the rate of change of orthogonal residual components. In addition, the operator also interacts with two other independent functional modules: one is the monitoring function of the thermal impedance health of the equipment to analyze the trend of thermal drift components, and the other is the triggering of the phase hysteresis safety protection function to perform forced shutdown when rheological instability is detected.

[0042] Example 4: This example describes an automated initialization procedure used to construct a baseline mechanical loss model and calibrate key control parameters after initial operation or major equipment overhaul. This procedure aims to eliminate control model mismatch caused by mechanical assembly tolerances, lubrication state differences, and uncertainties in environmental thermal resistance, ensuring that the input parameters of the system's core algorithm have a definite physical source. Under the initial conditions of no-load and cold operation, the controller initiates a mechanical loss self-learning scan program, and the processor drives the motor to execute a stepped speed increase command, setting the speed command to start from zero. To gradually increase the incremental step size to the maximum rated speed of the equipment, at each speed step point, the system maintains... The second dynamic observation unit operates at a steady state for seconds to eliminate the influence of dynamic inertial torque. During this steady state, the second dynamic observation unit... The sampling frequency continuously collects the stator current. And calculate the observed electromagnetic torque values. within that time window The data sequence undergoes moving average filtering to remove high-frequency electromagnetic noise and mechanical vibration interference. The processor uses the calculated average torque value as the reference mechanical loss torque at that speed point and compares it with the corresponding speed value. The key-value pairs are composed and written into a lookup table in non-volatile memory, thus completing the baseline mechanical loss model. After the mechanical loss calibration is completed, the system enters the thermal parameter identification stage. A standard test medium, such as water or a basic solvent, is loaded into the reactor to the rated level. The controller drives the motor to operate at a constant rated power, injecting a step-like flow of thermal energy into the system. The third manifold construction unit enters the parameter identification mode, monitoring the total load damping torque output by the sliding mode observer in real time. The time response curve, since there is no structural change in the material at this time. The degradation is caused by temperature rise, and the processor records... The decay amplitude from the initial value to the steady-state decay value The length of time elapsed, which is directly defined as the thermal inertia time constant of the controlled object. This process utilizes the time response characteristics of a first-order inertial element, allowing for accurate acquisition of the system's thermal dynamics without the need for external temperature sensors.

[0043] Based on actual measurements Value, cutoff frequency of the adaptive filtering module The final setting is to address the safety margin factor. The uncertainty in the value of the above thermal parameters affects the processor's calculation process for identification. The system performs the following quantization decision logic regarding the signal-to-noise ratio (SNR) of the signal: if the SNR is lower than... This indicates that the system has strong non-thermal background noise, and the processor will... Set as upper limit value In order to reduce This enhances the ability to suppress high-frequency noise; if the signal-to-noise ratio is higher than... Then Set as lower limit value In order to improve This enhances the system's tracking response speed to real thermal drift; to between, The value is determined by linear interpolation with the signal-to-noise ratio. Ultimately, the processor bases the value on the determined value. and Through formula Calculate and lock the cutoff frequency of the low-pass filter to complete system initialization; before the control system is put into formal operation, perform the thermal inertia parameter calibration procedure to obtain the thermal inertia time constant. The drive motor is controlled to maintain a constant output within 50% to 80% of its rated power. A step heat flux is injected into the reactor. The total load damping torque time response curve is recorded at a sampling frequency of 10Hz to 50Hz. The processor performs first-order inertial element fitting on the response curve and extracts the time length corresponding to the steady-state decay amplitude of 63.2%, which is then locked as the thermal inertial time constant. ,Will Substitute the value into the cutoff frequency calculation formula Safety margin coefficient Determined based on linear interpolation of signal-to-noise ratio during calibration: when signal-to-noise ratio is 10dB lower. When the value is 5, and the value is 30dB higher. Take 2, the middle range The value changes linearly with the signal-to-noise ratio, uniquely determining the cutoff frequency of the low-pass filter.

[0044] Example 5: This example addresses the individual differences in equipment parameters and nonlinear load characteristics that control systems may encounter in actual industrial deployments. It supplements a systematic pre-deployment calibration and debugging procedure to eliminate non-structural errors introduced by mechanical assembly tolerances, sensor zero-point drift, and minor fluctuations in the rheological characteristics between batches of materials. After the system is powered on for the first time or the actuator is replaced, the controller automatically starts the zero-speed impedance identification subroutine, drives the inverter to inject a high-frequency low-voltage probe pulse sequence into the stator winding, and identifies key electrical parameters such as the stator resistance, inductance, and rotor time constant of the motor by analyzing the amplitude and phase characteristics of the pulse response current. Based on this, the internal model of the sliding mode observer is fine-tuned and calibrated. The system executes the no-load inertia scanning process, drives the motor to perform step-by-step acceleration and free stop experiments under no-load conditions, records the back EMF decay curve during deceleration, calculates the dynamic baseline including the transmission chain rotational inertia and the reference mechanical friction coefficient, and stores this baseline as the initial value of the mechanical loss model.

[0045] To address batch-to-batch variations in material rheological properties, the system incorporates a rheological baseline self-learning function. In the initial stage of each new batch of material entering production, the control system temporarily operates in open-loop monitoring mode. During this period, the system frequently collects and analyzes the fluctuation characteristics of the total load damping torque, extracting characteristic fingerprints representing the initial static viscosity and thixotropic recovery rate of the material. The processor compares these characteristic fingerprints with the historical best process curves in the database, automatically calculating the orthogonal residual judgment threshold and feedforward compensation gain coefficient suitable for the current batch of material. This ensures that the control strategy can dynamically adapt to minor fluctuations in the physical properties of the raw materials, avoiding control overshoot or response lag caused by fixed parameters, thereby guaranteeing the stability of the production process and the consistency of product quality.

[0046] Example 6: This example details the online parameter optimization and active detection logic of the system under long-term continuous operation, aiming to eliminate engineering uncertainties in the algorithm's internal hyperparameter settings and perturbation detection signal generation. During the operation of the heat-dissipating slow manifold building unit, the processor uses a recursive least squares algorithm with a forgetting factor for online identification. Strictly set as to Within this range, the value is determined based on the thermal inertia time constant of the controlled object. With control cycle ratio relationship To ensure that parameter updates dynamically match the timescale of the physical thermal process, the algorithm introduces a reset mechanism based on the covariance trace. When the trace of the covariance matrix exceeds a preset threshold... When necessary, it is reset to the initial diagonal matrix to prevent algorithm divergence; during the real-time construction of the heat-dissipating slow manifold, the parameter identification module runs a recursive least squares algorithm with a forgetting factor, and the forgetting factor... Through formula Dynamic calculation, This setting, representing the system sampling period, ensures that the algorithm parameter update memory length is strictly matched to the physical thermal inertia timescale of the controlled object. To prevent the covariance matrix from diverging during long-term steady-state operation, the algorithm incorporates covariance reset logic: monitoring the trace of the covariance matrix; if the value falls below a preset numerical precision lower limit... The covariance matrix is ​​then reset to its initial diagonal form, with the main diagonal elements set. to The range is maintained to ensure the algorithm's continued sensitivity to new heating conditions and to avoid numerical calculation saturation.

[0047] In the active detection phase involving the sixth frequency domain perturbation unit, the perturbation detection signal is defined as the frequency... for to And amplitude Do not exceed the reference speed command The sinusoidal wave sequence, with its frequency range chosen to avoid the first-order mechanical resonance frequency of the equipment and within the sensitive region of the material rheological response, is used in the seventh phase analysis unit, which employs a sliding window discrete Fourier transform algorithm to calculate the rotor angular velocity response in real time. Phase lag angle of frequency component relative to the injected signal ,when Exceed When the system reaches the safety threshold, it is determined that it is on the verge of rheological instability or mechanical slippage. The fifth closed-loop control unit triggers a load reduction protection command. In addition, the eighth health monitoring unit calculates the thermal drift disturbance component. With low-frequency power components The ratio is used to estimate the equivalent thermal impedance in real time, and a length of [missing information] is maintained. The current thermal resistance value deviates from the reference curve by more than [a certain percentage] of the moving average of the production batches. At that time, the system generates a maintenance warning signal indicating that the equipment is scaling or has cooling failure.

[0048] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An aqueous paint production and processing plant control system, characterized by, The system comprises: a first signal acquisition unit for acquiring stator current signals and rotor angular velocity signals of a driving actuator in real time; a second dynamic observation unit connected with the first signal acquisition unit, for processing the stator current signals and the rotor angular velocity signals based on a sliding mode observation algorithm, and reconstructing a total load damping torque containing thermal drift interference; a third flow field construction unit for extracting a low-frequency component in an input power signal of the driving actuator, and establishing a quasi-static mapping relationship between the low-frequency component and a reference damping coefficient, and generating a thermal dissipation slow flow field representing a pure thermal effect trajectory in a state space; a fourth orthogonal projection unit connected with the second dynamic observation unit and the third flow field construction unit, for calculating a projection vector of the total load damping torque in a tangent direction of the thermal dissipation slow flow field and defining the projection vector as a thermal drift disturbance component, and calculating a vector difference between the total load damping torque and the projection vector and defining the vector difference as an orthogonal residual component representing a load structured flow behavior characteristic; a fifth closed-loop control unit connected with the fourth orthogonal projection unit, for generating a feedforward compensation instruction based on the thermal drift disturbance component, and generating a feedback adjustment instruction based on the orthogonal residual component, and superimposing the feedforward compensation instruction and the feedback adjustment instruction and outputting the superimposed result to the driving actuator; wherein the third flow field construction unit comprises: an adaptive filtering module for setting a first frequency threshold associated with a thermal inertia time constant of the driving actuator, and filtering out components in a frequency band higher than the first frequency threshold in the input power signal to obtain the low-frequency component; and a parameter identification module for identifying a steady-state gain coefficient between the low-frequency component and the reference damping coefficient online using a recursive least squares algorithm, and taking the steady-state gain coefficient as a geometric parameter defining a slope of the thermal dissipation slow flow field; The fourth orthogonal projection unit performs a calculation logic of the orthogonal residual component satisfying the following mathematical relationship: wherein, is defined as the orthogonal residual component, is defined as the total load damping torque, is defined as the tangent direction unit vector of the heat dissipation slow manifold at the current operating point, and the symbol represents a vector dot product operation, and the symbol represents a vector norm operation.

2. The water-based paint production and processing equipment control system according to claim 1, characterized in that, the fifth closed-loop control unit comprises: a feedforward suppression module for generating the feedforward compensation instruction by inverting the thermal drift disturbance component, to offset non-structural damping drift caused by energy accumulation; and a flow behavior following module for comparing the orthogonal residual component as a controlled variable with a preset process flow behavior target value, and generating the feedback adjustment instruction according to a comparison deviation, to drive the driving actuator to respond to changes in the structural physical properties of the load.

3. The water-based paint production and processing equipment control system according to claim 1, characterized in that, The system further comprises: a sixth frequency domain perturbation unit connected with the driving actuator, for superimposing a perturbation detection signal of a preset frequency in a reference speed instruction of the driving actuator; a seventh phase analysis unit connected with the first signal acquisition unit, for extracting a response component in the rotor angular velocity signal with the same frequency as the perturbation detection signal, and calculating a phase lag angle of the response component relative to the perturbation detection signal; and the fifth closed-loop control unit is further configured to generate a forced shutdown instruction or a reduced load operation instruction when the phase lag angle exceeds a preset safety threshold.

4. The water-based paint production and processing equipment control system according to claim 1, characterized in that, The system further comprises: an eighth health monitoring unit connected with the fourth orthogonal projection unit, for continuously monitoring a time variation rate of the thermal drift disturbance component; and the eighth health monitoring unit is configured to generate a device maintenance warning signal indicating that a non-structural change has occurred in a device thermal impedance characteristic when the time variation rate of the thermal drift disturbance component deviates from a preset reference curve for a long time.

5. The water-based paint production and processing equipment control system according to claim 1, characterized in that, The second dynamic observation unit comprises: a mechanical loss model storage module for storing a reference mechanical friction torque curve of the driving actuator in an idle state; and a net load extraction module for subtracting an inertia torque component derived from a rotor angular velocity signal and a friction component corresponding to the reference mechanical friction torque curve from an electromagnetic torque observation value output by the sliding mode observation algorithm to obtain a total load damping torque.

6. The water-based paint production and processing equipment control system according to claim 1, characterized in that, The adaptive filtering module is further configured to monitor a root mean square value of the quadrature residual component, and automatically reduce the first frequency threshold when the root mean square value continuously falls below a preset convergence threshold.

7. The water-based paint production and processing equipment control system according to claim 1, characterized in that, The fifth closed-loop control unit is further configured to freeze the current heat dissipation slow manifold parameters upon receiving a process switching signal, and perform initial decoupling of the total load damping torque based on the frozen parameters during a new process startup phase until the third manifold construction unit completes a new round of parameter convergence.

8. The water-based paint production and processing equipment control system according to claim 1, characterized in that, The system is specifically defined as a variable frequency drive control device for driving a disperser or a grinder, the driving actuator is a main drive motor of the disperser or the grinder, and the quadrature residual component is defined as a real-time equivalent structure viscosity index of the processed material.

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