Hydraulic loom control system and method

Abstract

The invention relates to the technical field of automation control, and discloses a water jet loom control system comprising a physical modeling module used for establishing a loom dynamic model, a nozzle fluid dynamic model and temperature field modeling according to the operation process of a loom; the optimization control module is used for constructing a multi-objective optimization problem based on the model output by the physical modeling module and solving an optimal control parameter through an optimization algorithm, and the invention further provides a water jet loom control method which comprises the following steps: S1, constructing a physical model according to the actual operation condition of the loom, comprising a loom dynamic model, a nozzle fluid dynamic model and a temperature field model; and S2, based on the physical model. According to the invention, through multi-physics field coupling modeling, optimization of a control algorithm and extended Kalman filtering, the control precision of the loom is obviously improved, the production efficiency and the fabric quality are optimized, the energy efficiency is improved, and the stability and adaptability of the system are effectively enhanced.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, in particular to a water jet loom control system and method. Background Art

[0002] Against the backdrop of rapid developments in automated control technology, intelligent control systems are increasingly being used across various fields of industrial production. In particular, achieving precise adjustment and dynamic response to key process parameters in the operation and control of textile equipment has become crucial for improving production stability and product quality. With the continued advancement of multi-sensor fusion, physical modeling, optimization algorithms, and real-time feedback mechanisms, intelligent control systems integrating mechatronics, data processing, and control strategies are becoming a key area of focus for upgrading weaving equipment.

[0003] Existing loom control systems generally employ traditional control methods based on fixed parameters and simple models. These control systems typically rely on mechanical kinematics and empirical rules to control the loom by adjusting the loom's speed, tension, and water jet pressure. Most systems offer a certain degree of stability, ensuring both efficient and basic fabric production quality.

[0004] Although existing technologies have achieved results in ensuring the basic operating performance of looms, there are still some shortcomings. First, traditional control methods mainly rely on linear models and empirical formulas, which cannot accurately reflect the complex dynamics of the loom and the relationship between the nozzle fluid dynamics, which leads to low control accuracy, especially when there is a large mutual influence between parameters such as loom speed, tension and water spray pressure. Second, existing systems usually only focus on a single optimization goal and fail to take into account the multi-dimensional optimization of production efficiency, fabric quality and energy efficiency, resulting in low energy efficiency and energy waste. Finally, traditional real-time data feedback mechanisms often suffer from response lag and sensor errors, making it difficult for the control system to adjust in real time and unable to adapt to changes in the production environment. Summary of the Invention

[0005] In response to the deficiencies of the prior art, the present invention provides a water jet loom control system and method, which solves the problems of the existing loom control system in multi-physical field coupling modeling, energy efficiency optimization and real-time feedback accuracy.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A water jet loom control system, comprising: Physical modeling module, used to establish loom dynamics model, nozzle fluid dynamics model and temperature field modeling according to the operation process of the loom; An optimization control module is used to construct a multi-objective optimization problem based on the model output by the physical modeling module and solve the optimal control parameters through an optimization algorithm; Real-time data feedback module, used to collect the operating status of the loom through sensors and estimate the system status using extended Kalman filter; The adaptive control module is used to dynamically adjust the control strategy of the loom according to the output of the real-time data feedback module and the optimal solution of the optimization control module.

[0007] Preferably, the physical modeling module includes: Loom dynamics modeling unit, used to describe the mechanical kinematics and dynamics of the loom through Lagrange equations and establish the dynamic equations of the loom; Nozzle fluid dynamics modeling unit, used to simulate the airflow and water spray pressure in the nozzle through the Navier-Stokes equations, and coupled with the speed, tension and water spray volume of the loom; The temperature field modeling unit is used to describe the temperature field of the loom through the heat conduction equation, calculate the temperature distribution of the equipment, and provide temperature constraints for the operation of the loom.

[0008] Preferably, the optimization control module includes: An objective function construction unit is used to construct a multi-objective optimization problem based on production efficiency, fabric quality and energy efficiency goals, and define the constraints of the control variables; The optimization algorithm solving unit is used to solve the multi-objective optimization problem by using a genetic algorithm or a particle swarm optimization algorithm to obtain the optimal control variable value.

[0009] Preferably, the optimization algorithm solving unit adopts a particle swarm optimization algorithm to calculate the optimal control variables through the following steps: Initialize the particle population, where each particle represents a possible combination of control variables; Calculate the fitness function of each particle and select the better particle based on the fitness; Update the speed and position of each particle until the predetermined optimization effect is achieved.

[0010] Preferably, the real-time data feedback module includes: A data acquisition unit is used to monitor the speed, tension, water spray pressure and temperature status of the loom in real time through a speed sensor, a tension sensor, a water spray pressure sensor and a temperature sensor; The extended Kalman filter unit is used to estimate the operating status of the loom in real time based on the collected sensor data and perform error correction.

[0011] Preferably, the selection of sensors in the data acquisition unit includes: The speed sensor uses a photoelectric encoder or a magnetic encoder; The tension sensor uses a strain gauge tension sensor; The water spray pressure sensor uses a piezoelectric or strain gauge pressure sensor; Temperature sensors use thermocouples or thermistors.

[0012] Preferably, the extended Kalman filter unit realizes system state estimation by the following steps: Predict the system state and covariance at the current moment based on the loom's dynamics model; The state estimate is updated based on the sensor data, and the filter gains are adjusted to reduce the effects of system noise and sensor errors.

[0013] Preferably, the adaptive control module includes: A control strategy generation unit, configured to generate a control strategy for the loom through an adaptive control algorithm based on real-time data and system state estimation results; The control signal transmission unit is used to convert the generated control strategy into a control signal and transmit it to the execution system.

[0014] Preferably, the adaptive control module realizes the generation of the control strategy through the following steps: Obtain real-time feedback data and analyze the current operating status of the loom; The speed, tension and water spray pressure of the loom are adjusted through adaptive algorithms to achieve the optimal working state.

[0015] The present invention also provides a water jet loom control method, comprising the following steps: S1. Construct a physical model based on the actual operation of the loom, including a loom dynamics model, a nozzle fluid dynamics model, and a temperature field model; S2. Based on the physical model, construct a multi-objective optimization problem and solve the optimal control parameters through an optimization algorithm; S3, collect the loom operation data in real time through sensors, and estimate the system state using extended Kalman filter; S4. Based on the real-time feedback data and the optimal solution output by the optimization algorithm, the control strategy of the loom is adjusted through the adaptive control algorithm so that it always operates in the optimal state.

[0016] The present invention provides a water jet loom control system and method, which has the following beneficial effects: 1. This invention achieves precise modeling of loom dynamics, nozzle fluid dynamics, and temperature fields by incorporating a multi-physics coupling model, including the Lagrange equations, the Navier-Stokes equations, and the heat conduction equation. This approach ensures precision in the control process, overcomes the error issues inherent in existing technologies that rely solely on linear models, and enables the system to operate stably in complex production environments.

[0017] 2. This invention utilizes genetic algorithms and particle swarm optimization algorithms to comprehensively consider production efficiency, fabric quality, and energy efficiency, achieving a balance among the three. This multi-objective optimization strategy enables optimal operation of the loom across multiple dimensions, avoiding the single-objective limitations of traditional optimization methods. This approach improves production efficiency while ensuring fabric quality and energy savings.

[0018] 3. The present invention's real-time data feedback module, combined with extended Kalman filtering (EKF) technology, accurately estimates the loom's real-time status and seamlessly integrates with the adaptive control algorithm to adjust the control strategy in real time. This innovation addresses the poor adaptability of traditional technologies to changing environments, enabling the loom to respond quickly and maintain stable operation, ensuring high efficiency, especially under conditions of large fluctuations in production demand.

[0019] 4. This invention utilizes an adaptive gain adjustment mechanism and gain scheduling technology to ensure system stability in a changing production environment while optimizing energy efficiency. Compared to the fixed gain and simpler control methods used in existing technologies, this invention adjusts gain based on real-time feedback, reducing energy waste and improving overall system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a system structure diagram of the present invention; Figure 2 This is a module architecture diagram of the physical modeling module of the present invention; Figure 3 The module architecture diagram of the optimized control module of the present invention; Figure 4 This is a module architecture diagram of the real-time data feedback module of the present invention; Figure 5 This is a module architecture diagram of the adaptive control module of the present invention; Figure 6 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] Please see the attached Figure 1 -Attached Figure 5 , an embodiment of the present invention provides a water jet loom control system, comprising: Physical modeling module, used to establish loom dynamics model, nozzle fluid dynamics model and temperature field modeling according to the operation process of the loom; The physical modeling module establishes a comprehensive physical model of the loom through the close collaboration of three submodules: loom dynamics modeling, nozzle fluid dynamics modeling, and temperature field modeling. These models not only provide theoretical support for the subsequent optimization and control modules but also ensure the stability and efficiency of the system under different operating conditions.

[0023] Next, we will continue to supplement the technical details and specific formulas of each sub-module to ensure the complete disclosure of the technical content and provide sufficient implementation details for technical personnel in this field.

[0024] The dynamic modeling of the loom is an important part of the present invention. Lagrange equations are used to describe the kinematics and dynamics of the loom. By establishing a dynamic model of the loom, the interrelationships between key variables such as the loom's motion, tension, and water jet pressure can be clarified.

[0025] The form of the Lagrange equation has been given in the previous section. The following will further explain the definition and meaning of the parameters in the formula: ; in: is the state vector of the loom, including the speed of the loom ,tension , water spray pressure etc. dynamic variables; is the mass matrix of the loom, describing the mass distribution of each part of the loom; is the damping matrix; is the stiffness matrix; It is the external force vector, including the water pressure applied by the nozzle, the driving force of the loom motor, the friction of the fabric, etc.

[0026] By describing the loom dynamics using the Lagrange equation, the response behavior of the loom under different working conditions can be simulated mathematically and accurately, providing a basis for subsequent control system optimization.

[0027] The Nozzle Fluid Dynamics Modeling module uses the Navier-Stokes equations to describe the flow characteristics of the fluid within the loom nozzle. This modeling ensures precise control of the loom's water jet effect and can be coupled with variables such as loom speed and tension to achieve optimized water jet control.

[0028] The Navier-Stokes equations are in the following form: ; in: is the velocity field of the fluid, describing the velocity change of the fluid in the nozzle; The density of the fluid is closely related to the type of fluid and the working environment in the nozzle; is the pressure of the fluid, which is related to factors such as the water spray pressure and the resistance of the airflow inside the nozzle; Represents time, used to describe the evolution of fluid over time; The dynamic viscosity of the fluid affects the viscosity of the airflow in the nozzle and the smoothness of the water flow ejected from the nozzle; is the external force term, which represents the influence of external factors such as nozzle force and airflow disturbance on the fluid; is the Laplace operator of the velocity field, which describes the velocity diffusion characteristics of the fluid.

[0029] The introduction of this equation provides a scientific basis for the water jet effect of a loom. The fluid dynamics of the nozzle directly affects the water output of the loom. Through precise control, it is possible to ensure the ideal water jet effect during loom operation, effectively improving fabric quality and production efficiency.

[0030] The heat generated by a loom during operation may affect its stability and the quality of the fabric. To ensure that the loom operates within a stable temperature range, temperature field modeling is an integral part of the physical modeling module.

[0031] The heat conduction equation is used to describe the temperature change of the loom. The equation is: ; in: is the thermal diffusivity, which indicates how quickly heat spreads through the loom components; is the Laplace operator of the temperature field, which describes the diffusion characteristics of the temperature field and reflects how heat flows between the loom components; It is the internal heat source, which refers to the heat generated inside the loom due to the operation of the motor, drive device, etc.

[0032] By modeling the temperature field, the control system can monitor loom temperature changes in real time and make corresponding adjustments. When the loom temperature approaches a critical value, the control system can proactively adjust operating parameters to prevent overheating, which could cause equipment failure or reduced production efficiency.

[0033] The physical modeling module systematically models various physical phenomena of the loom, ensuring that the control system accurately simulates the loom's performance under different operating conditions. The loom dynamics model, nozzle fluid dynamics model, and temperature field model provide accurate input data for optimized control and provide a theoretical basis for the real-time feedback module.

[0034] This physical modeling module not only provides the foundational data for optimal control but also, through collaboration with other modules, ensures that the entire control system can dynamically respond to changes in the loom's production environment. During the optimal control process, the accurate data provided by the physical modeling module ensures that the control parameters derived from the optimization algorithm will achieve optimal results in actual operation.

[0035] The physical modeling module accurately describes the loom's movement, water spraying effects, and heat changes by introducing loom dynamics, nozzle fluid dynamics, and temperature field modeling, providing a reliable theoretical basis for the entire control system.

[0036] The optimization control module is used to construct a multi-objective optimization problem based on the model output by the physical modeling module and solve the optimal control parameters through the optimization algorithm; The core task of the optimization control module is to formulate a multi-objective optimization problem and solve the optimal control parameters using a suitable optimization algorithm (such as a genetic algorithm or particle swarm optimization algorithm). This ensures that the loom maintains optimal operating conditions under various production environments. The module's precision and real-time performance significantly improves loom production efficiency, fabric quality, and energy efficiency.

[0037] The design of this module relies on optimization theory. By accurately constructing objective functions and solving them with optimization algorithms, we ensure that the loom control system performs well under multi-dimensional objectives. Next, we will describe the specific implementation of this module in detail, covering technical details such as objective function construction, optimization algorithm application, and parameter definition.

[0038] The core of the optimization control module is the construction of an objective function. During loom operation, the system needs to simultaneously optimize multiple objectives, including production efficiency, fabric quality, and energy efficiency. Therefore, it is crucial to construct a reasonable objective function that comprehensively considers these three objectives and uses an optimization algorithm to find the optimal control parameters.

[0039] Objective function It can be expressed as: ; in: The objective function representing production efficiency depends on the loom's speed, tension, and water jet pressure. This part is used to measure the loom's production output per unit time. The objective function representing fabric quality depends on the water spraying amount, tension and fabric stability index; It represents the energy efficiency objective function, represents the energy consumption of the loom, and one of the goals of the optimization system is to minimize the energy consumption by adjusting the control parameters; , , is the weight coefficient, which is used to balance the importance of the three objectives; is the speed of the loom, indicating the working rate of the loom; is the tension of the loom, which indicates the stretching force of the fabric during the weaving process; The water spray pressure refers to the water pressure applied by the nozzle. The water spray pressure directly affects the wetting effect and quality of the fabric. The water spray volume indicates the volume of water sprayed by the loom per second; The stability index of the fabric indicates the stability of the fabric during the production process. A higher stability index indicates better fabric quality. is energy consumption, which means the power consumed by the loom per unit time.

[0040] By constructing such a multi-objective function, the optimization control module can simultaneously optimize production efficiency, fabric quality and energy efficiency to solve the optimal control parameters, thereby improving the overall operating efficiency and quality of the loom.

[0041] In order to solve the objective function To determine the optimal control parameters in the control module, the optimization control module uses two commonly used optimization algorithms: genetic algorithm and particle swarm optimization. Both algorithms have strong global optimization capabilities and are suitable for handling complex high-dimensional, nonlinear optimization problems.

[0042] Genetic algorithms simulate natural evolutionary processes to search for optimal solutions. Their core operations include population initialization, fitness evaluation, selection, crossover, mutation, and iterative updates. While genetic algorithms offer strong global search capabilities, they may require significant computing resources and iterations to reach the optimal solution.

[0043] Particle swarm optimization (PSO) is an optimization algorithm that mimics the foraging behavior of bird flocks. Particles move through a solution space, sharing information with other particles to find the global optimal solution. PSO typically converges quickly and is suitable for large-scale, multi-objective optimization problems.

[0044] Specific implementation steps: Initialize the population: Generate a set of initial control parameters (such as loom speed, tension, water jet pressure, etc.) as individuals in the population, each individual represents a possible combination of control parameters.

[0045] Evaluate fitness: According to the objective function Calculate the fitness value of each individual. The higher the fitness value, the better the control parameter combination corresponding to the individual.

[0046] Selection operation: Select excellent individuals based on fitness, perform crossover and mutation operations, and generate a new generation of individuals. The selection operation can retain excellent individuals and generate new solutions through crossover and mutation.

[0047] Crossover and mutation: The crossover operation generates new offspring individuals by exchanging the genes of parent individuals, while the mutation operation introduces randomness into the solution space, thereby increasing the diversity of solutions.

[0048] Update operation: In particle swarm optimization, each particle updates its speed and position based on its historical best position and the global best position to search for the optimal solution. The updated particle re-evaluates its fitness and further approaches the optimal solution in the next iteration.

[0049] Termination condition: When the predetermined optimization accuracy or number of iterations is reached, the optimization algorithm terminates and the optimal control parameters are output.

[0050] The optimal control parameters (such as loom speed, tension, and water jet pressure) obtained through the optimization algorithm serve as the output of the optimization control module. These optimized control parameters are then passed to the adaptive control module, which further adjusts the loom's control strategy based on real-time feedback and optimization results, ensuring that the loom maintains optimal operating conditions during actual production.

[0051] In some embodiments, the system will flexibly adjust the weighting coefficients in the objective function according to the needs of different production tasks. , , For example, in production tasks with high quality requirements, the The weight of the fabric is emphasized, while in the case of pursuing efficient production, the The weight of the system is determined, focusing on optimizing production efficiency.

[0052] The control parameters output by the optimization control module influence the loom's operating status in real time. Through multi-objective optimization, the optimization control module not only improves production efficiency but also ensures stable fabric quality and system energy efficiency. Specifically, during loom operation, the control system adjusts the loom's speed, tension, and water jet pressure based on the control parameters output by the optimization algorithm, achieving efficient and stable production.

[0053] Through optimized control, the loom can maintain optimal operating conditions under varying production conditions, maximizing production efficiency, ensuring fabric quality, and reducing energy consumption. The successful implementation of this module ensures the accuracy and efficiency of the loom control system and provides an intelligent operation solution for the entire water jet loom system.

[0054] Real-time data feedback module, used to collect the operating status of the loom through sensors and estimate the system status using extended Kalman filter; The real-time data feedback module collects loom operating data and estimates the system state using an extended Kalman filter (EKF). This module enables real-time monitoring of key loom operating parameters, such as speed, tension, water pressure, and temperature. This ensures that the optimization and adaptive control modules make adjustments based on accurate data, maintaining the loom in optimal operating condition.

[0055] The real-time data feedback module works closely with the physical modeling module, the optimization control module, and the adaptive control module to ensure efficient and stable operation of the loom under various production conditions. This module not only improves the system's ability to perceive the loom's status but also reduces the impact of sensor noise and errors, further enhancing the accuracy and robustness of the entire control system.

[0056] In this embodiment, the real-time data feedback module first relies on multiple sensors for data collection. The data acquisition unit obtains loom operating status data from speed sensors, tension sensors, water pressure sensors, and temperature sensors. The following is a detailed description of each sensor: Speed sensor: Use photoelectric encoder or magnetic encoder to measure the movement speed of the loom Speed is an important parameter for controlling loom movement and directly affects production efficiency.

[0057] Tension sensor: Use strain gauge tension sensor to monitor fabric tension in real time Tension is an important factor in fabric quality control and has a direct impact on the weaving effect and quality stability of the fabric.

[0058] Water spray pressure sensor: Use piezoelectric or strain gauge pressure sensor to measure the water spray pressure of the nozzle Accurate monitoring of water spray pressure ensures the wetting effect of the fabric, affecting the uniformity and overall quality of the fabric.

[0059] Temperature Sensors: Temperature sensors (such as thermocouples or thermistors) are used to monitor the temperature of various components of the loom. , in degrees Celsius (°C). Excessive temperatures can cause equipment failure or reduce fabric quality, so temperature monitoring is crucial.

[0060] To accurately estimate the loom's system state, this embodiment employs an extended Kalman filter (EKF). Because the loom system's control process involves multiple nonlinear factors, traditional Kalman filters are unsuitable for this problem. Therefore, an EKF is introduced to estimate the system's state.

[0061] The extended Kalman filter consists of two main stages: the prediction stage and the update stage.

[0062] In this test phase, the extended Kalman filter predicts the system state at the next moment through the nonlinear dynamic model of the system. Specifically, based on the state estimate at the previous moment and control inputs , predict the current system state through the following state transition function: ; in: For the current moment The predicted state depends on the control variables such as the speed, tension and water jet pressure of the loom; is the nonlinear state transfer function of the system, which describes the change of the system state from the previous moment to the current moment. for control inputs (e.g., adjusted water spray pressure, loom speed, etc.); is the system state estimate at the previous moment; is the control input at the previous moment.

[0063] In the update phase, the extended Kalman filter uses sensor measurements and predicted status To update the system state estimate. Specifically, the state update formula is: ; in: is the updated system state estimate, which represents the current state of each key parameter of the loom; is the actual measured value at the current moment, in units of loom speed, tension, water jet pressure or temperature, etc. is the measurement function, which represents the process of mapping the predicted state to the measurement space; is the Kalman gain, which represents the weighted ratio between the predicted value and the measured value. The Kalman gain is calculated by the system error covariance matrix and the measurement noise covariance matrix.

[0064] The extended Kalman filter also involves updating the covariance matrix to ensure that the system's error estimate is constantly corrected. The updated covariance matrix formula is: ; in: is the updated covariance matrix, which indicates the size of the estimation error; is the identity matrix; is the Kalman gain; is the measurement matrix, which represents the relationship between measurement and state; is the predicted covariance matrix, which represents the uncertainty of the predicted state.

[0065] The real-time data feedback module combines loom status data collected by sensors with the estimated results of the extended Kalman filter to evaluate the loom's operating status in real time. This module ensures that the control system can adjust based on the latest, filtered data to address dynamic changes in the loom during production.

[0066] Real-time Adjustment: The real-time data feedback module provides the adaptive control module with precise information about the loom's status. When a loom control variable (such as speed, tension, or water pressure) deviates from its ideal value, the control system can detect and adjust the parameter immediately.

[0067] Reduce errors: By estimating data through extended Kalman filtering, the system can effectively remove sensor noise and measurement errors, enhancing the accuracy of the control system.

[0068] Optimized feedback: The real-time feedback module provides the necessary feedback information throughout the control process, ensuring that the loom can quickly respond to changes in production demand at any given moment.

[0069] The real-time data feedback module uses a multi-sensor system and extended Kalman filtering technology to ensure that the loom's status can be accurately estimated and monitored in real time. Through this module, the system can extract precise control inputs from sensor data, eliminate noise and errors, and improve the response speed and accuracy of the entire control system.

[0070] Adaptive control module, used to dynamically adjust the loom control strategy based on the output of the real-time data feedback module and the optimal solution of the optimization control module; The adaptive control module dynamically adjusts the loom's control strategy based on the outputs of the real-time data feedback module and the optimization control module. This module ensures the loom maintains optimal operating conditions under varying production environments. By dynamically adjusting key loom control parameters, the system adapts to varying production needs, ensuring fabric quality and improving production efficiency.

[0071] In the adaptive control module, by measuring the state of the loom in real time and calculating the control error, the system can automatically adjust the control input to achieve optimal control. The key to this process is the control error , which represents the target value and estimated values Based on these errors, the control system adjusts the control parameters in real time according to the adaptive algorithm, so that the loom can always maintain the optimal operating state under various working conditions.

[0072] In this embodiment, control strategy generation is one of the core functions of the adaptive control module. First, the system obtains the state estimation of the loom through the real-time data feedback module. and target value , and calculate the control error The specific error calculation formula is as follows: ; in: is the control error, the unit is consistent with the control variable, and the error represents the current moment The deviation between the system target value and the actual measured value reflects the adjustment needs of the system; The target value, whose unit is consistent with the measured value (such as loom speed, tension, water spray pressure, etc.), is the ideal value that the control system hopes to achieve; For the current moment The system state estimate is in the same unit as the system state. This value comes from the real-time data feedback module and is the actual state of the loom obtained after the extended Kalman filter.

[0073] Calculated control error It will be used as input to generate a control strategy to ensure that the error between the actual operating state of the loom and the target value is minimized.

[0074] When calculating the control error After that, the system adjusts the control parameters of the loom in real time through the following formula : ; in: For the current moment The control input, the unit is consistent with the control variable, which means the adaptive control algorithm is based on the calculated error Output after adjusting the control parameters of the loom; For the previous moment The control input is in the same unit as the control variable; is the adaptive gain, which determines the sensitivity of the system to error adjustments. A larger gain results in a faster system response, while a smaller gain results in a smoother response. It is the control error, and its unit is consistent with the control variable. The larger the error value, the greater the deviation between the control target and the current state, and thus the greater the system adjustment range.

[0075] Through this formula, the control system can adjust the control input of the loom based on real-time feedback and calculated control error to maintain the optimal operating state of the system.

[0076] In order to improve the stability and robustness of the system, a gain scheduling mechanism is usually introduced. The gain scheduling mechanism can adjust the system’s Or the system's working environment automatically adjusts the adaptive gain Specifically, the system can dynamically adjust the gain based on the size of the control error or the intensity of the external disturbance. For larger errors, the gain is increased to speed up the system adjustment, while for smaller errors, the gain is reduced to ensure a smooth and non-drastic system response.

[0077] Furthermore, in some embodiments, when the system state undergoes drastic changes or is subject to large disturbances, a soft limit strategy can be employed to prevent excessive gain from causing system instability. Through these adjustment methods, the system can adaptively cope with various uncertainties during the production process and maintain stability.

[0078] The adaptive control module works closely with the physical modeling module, the real-time data feedback module and the optimization control module to achieve efficient and stable operation of the loom.

[0079] Physical Modeling Module: Provides the dynamic model and control equations of the loom to ensure that the adaptive control algorithm can be properly adjusted according to the physical characteristics of the system.

[0080] Real-time data feedback module: provides real-time status data of the loom, and after processing by extended Kalman filter, provides accurate current state estimation value for adaptive control module , thus ensuring the accuracy of the control strategy.

[0081] Optimized control module: Provides optimal control parameters as a reference for the adaptive control module, ensuring that the system can adaptively adjust in various production environments and achieve efficient and stable operation.

[0082] In some cases, if the system response is too intense or fluctuates greatly, it may affect the stability and efficiency of the loom. In this case, the system can adjust the adaptive gain Further adjustments can be made to improve control performance. For example, under complex or highly variable production conditions, the gain may need to be increased to quickly respond to changes in the loom's state; under stable production conditions, the gain should be appropriately reduced to avoid system instability caused by over-adjustment.

[0083] By controlling the error and adaptive gain The system automatically adjusts loom control parameters under various operating conditions, ensuring the loom remains in optimal condition. This module, in conjunction with the physical modeling module, the optimization control module, and the real-time data feedback module, ensures efficient and stable operation of the water jet loom throughout the production process, providing the system with strong flexibility and adaptability.

[0084] The water jet loom control method described below and the water jet loom control system described above can refer to each other.

[0085] Please see the attached Figure 6 The present invention also provides a water jet loom control method, comprising the following steps: S1. Construct a physical model based on the actual operation of the loom, including a loom dynamics model, a nozzle fluid dynamics model, and a temperature field model; S2. Based on the physical model, construct a multi-objective optimization problem and solve the optimal control parameters through the optimization algorithm; S3, collect the loom operation data in real time through sensors, and estimate the system state using extended Kalman filter; S4. Based on the real-time feedback data and the optimal solution output by the optimization algorithm, the control strategy of the loom is adjusted through the adaptive control algorithm so that it always operates in the optimal state.

[0086] The method of this embodiment can be used to execute the above system embodiment. Its principles and technical effects are similar and will not be described in detail here.

[0087] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A water jet loom control system, characterized in that: include: Physical modeling module, used to establish loom dynamics model, nozzle fluid dynamics model and temperature field modeling according to the operation process of the loom; An optimization control module, configured to construct a multi-objective optimization problem based on the model output by the physical modeling module, and solve the optimal control parameters through an optimization algorithm; Real-time data feedback module, used to collect the operating status of the loom through sensors and estimate the system status using extended Kalman filter; The adaptive control module is used to dynamically adjust the control strategy of the loom according to the output of the real-time data feedback module and the optimal solution of the optimization control module.

2. A water jet loom control system according to claim 1, characterized in that: The physical modeling module includes: Loom dynamics modeling unit, used to describe the mechanical kinematics and dynamics of the loom through Lagrange equations and establish the dynamic equations of the loom; Nozzle fluid dynamics modeling unit, used to simulate the airflow and water spray pressure in the nozzle through the Navier-Stokes equations, and coupled with the speed, tension and water spray volume of the loom; The temperature field modeling unit is used to describe the temperature field of the loom through the heat conduction equation, calculate the temperature distribution of the equipment, and provide temperature constraints for the operation of the loom.

3. A water jet loom control system according to claim 1, characterized in that: The optimization control module includes: An objective function construction unit is used to construct a multi-objective optimization problem based on production efficiency, fabric quality and energy efficiency goals, and define the constraints of the control variables; The optimization algorithm solving unit is used to solve the multi-objective optimization problem by using a genetic algorithm or a particle swarm optimization algorithm to obtain the optimal control variable value.

4. A water jet loom control system according to claim 1, characterized in that: The optimization algorithm solving unit adopts the particle swarm optimization algorithm to calculate the optimal control variables through the following steps: Initialize the particle population, where each particle represents a possible combination of control variables; Calculate the fitness function of each particle and select the better particle based on the fitness; Update the speed and position of each particle until the predetermined optimization effect is achieved.

5. A water jet loom control system according to claim 1, characterized in that: The real-time data feedback module includes: A data acquisition unit is used to monitor the speed, tension, water spray pressure and temperature status of the loom in real time through a speed sensor, a tension sensor, a water spray pressure sensor and a temperature sensor; The extended Kalman filter unit is used to estimate the operating status of the loom in real time based on the collected sensor data and perform error correction.

6. A water jet loom control system according to claim 5, characterized in that: The selection of sensors in the data acquisition unit includes: The speed sensor uses a photoelectric encoder or a magnetic encoder; The tension sensor uses a strain gauge tension sensor; The water spray pressure sensor uses a piezoelectric or strain gauge pressure sensor; Temperature sensors use thermocouples or thermistors.

7. A water jet loom control system according to claim 1, characterized in that: The extended Kalman filter unit realizes system state estimation by the following steps: Predict the system state and covariance at the current moment based on the loom's dynamics model; The state estimate is updated based on the sensor data, and the filter gains are adjusted to reduce the effects of system noise and sensor errors.

8. A water jet loom control system according to claim 1, characterized in that: The adaptive control module includes: A control strategy generation unit, configured to generate a control strategy for the loom through an adaptive control algorithm based on real-time data and system state estimation results; The control signal transmission unit is used to convert the generated control strategy into a control signal and transmit it to the execution system.

9. A water jet loom control system according to claim 1, characterized in that: The adaptive control module generates a control strategy through the following steps: Obtain real-time feedback data and analyze the current operating status of the loom; The speed, tension and water spray pressure of the loom are adjusted through adaptive algorithms to achieve the optimal working state.

10. A water jet loom control method, characterized in that: A water jet loom control system according to any one of claims 1 to 9 comprises the following steps: S1. Construct a physical model based on the actual operation of the loom, including a loom dynamics model, a nozzle fluid dynamics model, and a temperature field model; S2. Based on the physical model, construct a multi-objective optimization problem and solve the optimal control parameters through an optimization algorithm; S3, collect the loom operation data in real time through sensors, and estimate the system state using extended Kalman filter; S4. Based on the real-time feedback data and the optimal solution output by the optimization algorithm, the control strategy of the loom is adjusted through the adaptive control algorithm so that it always operates in the optimal state.

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