Intelligent control method and system for pressurization and flow stabilization of sealing-tape machine pipeline

Abstract

The invention relates to an intelligent control method and system for pressurization and flow stabilization of a sealing-tape machine pipeline. The intelligent control method comprises the following steps that S1, a target pipeline pressure value Pset and a target flow value Qset are determined; the real-time pressure Preal is collected through a pressure sensor, the real-time flow Qreal is collected through a flow sensor, and a pressure error ep and a flow error eq are calculated; s2, double-closed-loop control is adopted, and compensation is conducted according to the flow error; by means of a master-slave control mode, pressure adjustment and flow adjustment are coordinated with each other; s3, feedforward compensation and MPC are introduced, optimization adjustment is carried out in combination with the process dynamic model, and the optimized pump speed and valve control value are obtained; s4, on the basis of the optimized pump speed and valve control value, optimal control is conducted on the sealing-tape machine pipeline; and S5, carrying out abnormal state alarm. Cooperative control over the pump speed and the valve of the sealing-tape machine is achieved, and reliable operation is ensured in combination with abnormal state protection.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control, and in particular to a method and system for intelligent control of pressure-boosting and flow-stabilizing of a belt conveyor pipeline. Background Art

[0002] With the continuous upgrading of industrial manufacturing and the rapid growth of intelligent demand, the tape industry has put forward higher requirements for production equipment, especially in the coating process, the glue supply system is an important link to ensure product quality and production efficiency. In actual operation, the tape machine pipeline system needs to ensure the stability of glue pressure and flow to achieve coating uniformity, consistency and efficient production. However, due to the complex process conditions (such as dynamic changes in flow demand, pressure fluctuations, changes in pipeline resistance, etc.) and the high energy consumption characteristics of the equipment itself, the traditional single pump speed regulation or valve throttling control method is difficult to meet the needs of modern industry.

[0003] At present, the pipeline system of the tape machine mainly faces the following challenges: High difficulty in coordinated control of pressure and flow: The system needs to accurately control the pipeline pressure and flow at the same time. A single regulating mechanism (such as only controlling the pump speed or valve) cannot meet the regulation needs under dynamic conditions, which may lead to uneven coating or increased energy consumption. Insufficient dynamic response speed: The glue supply needs to respond quickly to changes in coating demand (such as sudden changes in pressure and flow), while traditional control methods have a lag in response to sudden disturbances, affecting production continuity and stability. Summary of the invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a method and system for intelligent control of pressure boosting and flow stabilization in a belt conveyor pipeline, so as to realize coordinated control of the belt conveyor pump speed and valve, and ensure reliable operation in combination with abnormal state protection.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A method for intelligently controlling pressure-boosting and flow-stabilizing of a belt conveyor pipeline comprises the following steps: S1: Determine the target pipeline pressure value P set and target flow value Q set ; Collect real-time pressure P through pressure sensor real And flow sensor collects real-time flow Q real , calculate the pressure error e p and flow error e q ; ； S2: Dual closed-loop control is adopted. The outer loop makes a rough adjustment of the booster pump speed based on the pressure error according to the pressure setting value. The inner loop makes real-time adjustment of the opening θ of the regulating valve to compensate for the flow error. The master-slave control mode is used to coordinate the pressure and flow regulation to avoid the shortcomings caused by single control. S3: Introduce feedforward compensation, directly adjust the control variables by estimating the sudden change trend of pressure and flow in real time, and optimize the pump speed and valve control values ​​based on model predictive control MPC combined with process dynamic model optimization adjustment; S4: Based on the optimized pump speed and valve control value, the belt conveyor pipeline is optimized and controlled; S5: abnormal state alarm, high voltage protection: when P real >P max , P max is the maximum pipeline pressure value, reduce the pump speed or turn off the glue supply; flow abnormality: if Q real min , Q min When the flow rate reaches the minimum value, production will be stopped immediately and an alarm will be sounded. According to the recorded set value, the pump speed and valve angle will be quickly adjusted to ensure coating uniformity.

[0006] Furthermore, S2 is specifically: Outer loop control, by real-time control of booster pump speed f pump , control the pressure error within the minimum range, so that P real Tracking P set , pump speed adjustment formula:

[0007] Among them, K p1 ,K i1 ,K d1 is the outer loop PID control parameter, t represents time t; Pressure dynamic weight adjustment: W p =adjust(v line ); Among them, v line is the coating line speed; The inner loop control reduces the flow error e by adjusting the valve opening θ q , and at the same time compensate for pressure fluctuations, flow closed-loop control formula: ; Among them, K p2 ,K i2 ,K d2 is the inner loop PID control parameter; According to the pressure error e p ​, weighted correction for flow error: ; Among them, K f Pressure-flow coupling correction factor; is the corrected valve opening; When the flow demand rises above the threshold, the control priority of the inner loop is increased to ensure feedback adjustment of the pressure when the flow is sufficient.

[0008] Furthermore, S3 is specifically: S31: Introducing feedforward compensation, directly adjusting the control variable by estimating the sudden change trend of pressure and flow in real time, according to the rate of change of the set value and Adjust pump speed and valve opening in real time: , ; in, is the pump speed adjustment value; is the valve opening adjustment value; F p is the pump speed feedforward gain coefficient; F q is the valve opening feedforward gain coefficient; S32: Based on model predictive control MPC, combined with process dynamic model optimization and adjustment, the optimal output of the control variables is calculated, including the pump speed adjustment value and the optimal valve opening; S33: Control the booster pump and valve according to the combined signal of feedforward, MPC and PID.

[0009] Furthermore, based on the model predictive control MPC, the future pressure and flow changes are predicted, and the optimal control signals of the pump speed and valve at the current moment are solved in combination with the optimization objectives, specifically: Pressure dynamic model: ; Traffic dynamics model: ; in, is the system parameter; is the real pressure at the current time t; is the booster pump speed at the current time t; The flow value at the current time t; For the prediction time domain N p Pressure error within e p , flow error e q Comprehensive optimization with minimization of energy consumption: ; Among them, W p ,W qare the pressure dynamic weight and flow dynamic weight, respectively, to control the priority of pressure and flow errors; W u Control variable adjustment cost; Δu=[ , ]N p is the pump speed and valve adjustment amount; a is the index of the prediction time domain; Control variables meet hardware and practical operating constraints: ; in, are the minimum and maximum values ​​of the booster pump speed respectively; are the minimum and maximum flow values ​​respectively; The QP algorithm is used to solve the optimization objective function J in real time to obtain the pump speed adjustment value and the optimal valve opening.

[0010] Furthermore, the QP algorithm is used to solve the optimization objective function J in real time, as follows: Through the above objective function and constraints, a typical quadratic optimization problem is formed: ; Among them, H is the quadratic weight matrix in the objective function, which is composed of weights W p ,W q ,W u and dynamic matrices A and B are generated; F is the linear vector for error partial derivative calculation; ΔU is the control variable increment, including Δf pump ,Δθ; Use the interior point method to iteratively solve and obtain the optimal control sequence, including the optimal value of the pump speed and optimal valve opening ; The constraints will be of the form: ; Among them, C, d are constraint matrix and constraint vector respectively; U is the control input sequence; Integrate into the objective function to form a logarithmic potential objective function: ; in, μ is the obstacle parameter; m is the number of constraints; is the i-th constraint; The logarithmic potential objective function is calculated by Newton's method. (U,μ) Solve and gradually reduce μ so that the solution approaches the optimal solution of the original constraint problem.

[0011] Furthermore, the logarithmic potential objective function is calculated by Newton's method. (U,μ)Solve and gradually reduce μ so that the solution approaches the optimal solution of the original constraint problem, specifically: In each iteration, the solution is updated using Newton's method using the gradient and Hessian matrix. Let the current iteration point be U (k) : For the gradient descent direction, use Cholesky decomposition to solve: ; Determine step size using backtracking line search α ,ensure: ; Among them, β is the step length attenuation factor; Updated solution: ; As the number of iterations k increases, μ is gradually reduced; Stop iteration if one of the following conditions is met: Optimize the norm of the gradient: ; is the preset threshold; μ is small enough to fit the target allowable range.

[0012] Furthermore, the booster pump and valve are controlled according to the combined signal of feedforward, MPC and PID, as follows: PID control for real-time error p ,e q Perform feedback correction to compensate for the part of the error that is not corrected by feedforward and prediction; The feedforward, MPC and PID output signals are superimposed to form optimized signals for pump speed and valve control: Pump speed control value: ; in, is the final pump speed control value; f pump-feedback It is the pump speed adjustment signal of the feedback control part; It is the pump speed regulation signal of the feedforward compensation part; To optimize the pump speed regulation signal of the calculation part; Valve control value: ; in, θ final is the final valve opening control signal; θ feedback It is the valve opening signal of the feedback control part; It is the valve opening signal of the feedforward compensation part; To optimize the calculation part of the valve opening signal.

[0013] A belt conveyor pipeline pressurization and steady flow intelligent control system includes a processor, a memory and a computer program stored in the memory. When the processor executes the computer program, it specifically executes the steps in the above-mentioned belt conveyor pipeline pressurization and steady flow intelligent control method.

[0014] The present invention has the following beneficial effects: 1. The present invention realizes the coordinated control of pump speed and valve through double closed-loop regulation, feedforward compensation and MPC prediction optimization, and combines abnormal state protection to ensure reliable operation; 2. The present invention realizes synchronous regulation of pressure and flow through a dual closed-loop control system (pressure outer loop control and flow inner loop control), avoids the shortcomings of single variable control, and utilizes the master-slave control mode to coordinate the speed adjustment of the booster pump with the opening adjustment of the regulating valve, thereby improving the control accuracy of the system; 3. The present invention detects the trend of pressure and flow rate changes and performs feedforward control to effectively predict system disturbances, adjust the booster pump speed and valve angle in advance, quickly respond to changes in operating conditions, and use process dynamic models to predict future pipeline pressure and flow rate change trends. It combines real-time error optimization to adjust the control quantity of the booster pump and valve, and solves the optimization control problem of the pump speed and valve through the interior point method, ensuring accurate control of pressure and flow under constraints. It also has efficient and stable convergence, effectively improving the quality of control. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0016] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments: refer to Figure 1 In this embodiment, a method for intelligent control of pressure-boosting and flow-stabilizing of a belt conveyor pipeline is provided, comprising the following steps: S1: Determine the target pipeline pressure value P set and target flow value Q set ; Collect real-time pressure P through pressure sensor real And flow sensor collects real-time flow Q real , calculate the pressure error e p and flow error e q ; ； Target pressure value P set :Set according to the characteristics of the glue (such as viscosity) and the coating process requirements, for example, maintaining a constant pressure of 0.8 MPa.

[0017] Target flow value Q set :Set the glue flow rate according to the tape production speed and coating width, for example 10 L / min; S2: Dual closed-loop control (pressure control and flow control) is adopted. The outer loop makes a rough adjustment of the booster pump speed based on the pressure error and the pressure setting value. The inner loop makes real-time adjustment of the opening θ of the regulating valve and compensates for the flow error. The master-slave control mode is used to coordinate the pressure and flow regulation to avoid the shortcomings caused by single control. S3: Introduce feedforward compensation, directly adjust the control variables by estimating the sudden change trend of pressure and flow in real time, and optimize the pump speed and valve control values ​​based on model predictive control MPC combined with process dynamic model optimization adjustment; S4: Based on the optimized pump speed and valve control value, the belt conveyor pipeline is optimized and controlled; S5: abnormal state alarm, high voltage protection: when P real >P max , P max is the maximum pipeline pressure value, reduce the pump speed or turn off the glue supply; flow abnormality: if Q real min , Q min When the flow rate reaches the minimum value, production will be stopped immediately and an alarm will be sounded. According to the recorded set value, the pump speed and valve angle will be quickly adjusted to ensure coating uniformity.

[0018] In this embodiment, S2 is specifically: Outer loop control, by real-time control of booster pump speed f pump , control the pressure error within the minimum range, so that P real Tracking P set , pump speed adjustment formula:

[0019] Among them, K p1 ,K i1 ,K d1 is the outer loop PID control parameter, t represents time t; Pressure dynamic weight adjustment: W p =adjust(v line ); Among them, v line is the coating line speed; The inner loop control reduces the flow error e by adjusting the valve opening θ q , and at the same time compensate for pressure fluctuations, flow closed-loop control formula: ; ​Among them, K p2 ,K i2 ,K d2 is the inner loop PID control parameter; According to the pressure error e p , weighted correction for flow error: ; Among them, K f Pressure-flow coupling correction factor; is the corrected valve opening; When the flow demand rises above the threshold, the control priority of the inner loop is increased to ensure feedback adjustment of the pressure when the flow is sufficient.

[0020] In this embodiment, S3 is specifically: S31: Introducing feedforward compensation, directly adjusting the control variable by estimating the sudden change trend of pressure and flow in real time, according to the rate of change of the set value and Adjust pump speed and valve opening in real time: , ; in, is the pump speed adjustment value; is the valve opening adjustment value; F p is the pump speed feedforward gain coefficient; F q is the valve opening feedforward gain coefficient; S32: Based on model predictive control MPC, combined with process dynamic model optimization and adjustment, the optimal output of the control variables is calculated, including the pump speed adjustment value and the optimal valve opening; S33: Control the booster pump and valve according to the combined signal of feedforward, MPC and PID.

[0021] In this embodiment, based on the model predictive control MPC, the future pressure and flow changes are predicted, and the optimal control signals of the pump speed and valve at the current moment are solved in combination with the optimization target, specifically: Pressure dynamic model: ; Traffic dynamics model: ; in, is the system parameter; is the real pressure at the current time t; is the booster pump speed at the current time t; The flow value at the current time t; For the prediction time domain N p Pressure error within e p, flow error e q Comprehensive optimization with minimization of energy consumption: ; Among them, W p ,W q are the pressure dynamic weight and flow dynamic weight, respectively, to control the priority of pressure and flow errors; W u Control variable adjustment cost (to prevent excessive adjustment from causing system oscillation); Δu=[ , ]N p is the pump speed and valve adjustment amount; a is the index of the prediction time domain; Control variables meet hardware and practical operating constraints: ; in, are the minimum and maximum values ​​of the booster pump speed respectively; are the minimum and maximum flow values ​​respectively; The QP algorithm is used to solve the optimization objective function J in real time to obtain the pump speed adjustment value and the optimal valve opening.

[0022] In this embodiment, the QP algorithm is used to solve the optimization objective function J in real time, as follows: Through the above objective function and constraints, a typical quadratic optimization problem is formed: ; Among them, H is the quadratic weight matrix in the objective function, which is composed of weights W p ,W q ,W u and dynamic matrices A and B are generated; F is the linear vector for error partial derivative calculation; ΔU is the control variable increment, including Δf pump ,Δθ; Use the interior point method to iteratively solve and obtain the optimal control sequence, including the optimal value of the pump speed and optimal valve opening ; The constraints will be of the form: ; Among them, C, d are constraint matrix and constraint vector respectively; U is the control input sequence; Integrate into the objective function to form a logarithmic potential objective function: ; in, μ is the obstacle parameter; m is the number of constraints; is the i-th constraint; The logarithmic potential objective function is calculated by Newton's method. (U,μ) Solve and gradually reduce μ so that the solution approaches the optimal solution of the original constraint problem.

[0023] In this embodiment, the logarithmic potential objective function is calculated by Newton's method. (U,μ) Solve and gradually reduce μ so that the solution approaches the optimal solution of the original constraint problem, specifically: In each iteration, the solution is updated using Newton's method using the gradient and Hessian matrix. Let the current iteration point be U (k) : For the gradient descent direction, use Cholesky decomposition to solve: ; Determine step size using backtracking line search α ,ensure: ; Among them, β is the step length attenuation factor; Updated solution: ; As the number of iterations k increases, μ is gradually reduced; Stop iteration if one of the following conditions is met: Optimize the norm of the gradient: ; is the preset threshold; μ is small enough to fit the target allowable range.

[0024] In this embodiment, the booster pump and valve are controlled according to the combined signal of feedforward, MPC and PID, as follows: PID control for real-time error p ,e q Perform feedback correction to compensate for the part of the error that is not corrected by feedforward and prediction; The feedforward, MPC and PID output signals are superimposed to form optimized signals for pump speed and valve control: Pump speed control value: ; in, is the final pump speed control value; f pump-feedback It is the pump speed adjustment signal of the feedback control part; It is the pump speed regulation signal of the feedforward compensation part; To optimize the pump speed regulation signal of the calculation part; Valve control value: ; in, θ finalis the final valve opening control signal; θ feedback It is the valve opening signal of the feedback control part; It is the valve opening signal of the feedforward compensation part; To optimize the calculation part of the valve opening signal.

[0025] A belt conveyor pipeline pressurization and steady flow intelligent control system comprises a processor, a memory and a computer program stored in the memory. When the processor executes the computer program, it specifically executes the steps in the belt conveyor pipeline pressurization and steady flow intelligent control method as described above.

[0026] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0027] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0028] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0029] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0030] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for intelligent control of pressure-boosting and flow-stabilizing in a belt conveyor pipeline, characterized in that: The following steps are involved: S1: Determine the target pipeline pressure value P set and target flow value Q set ; Collect real-time pressure P through pressure sensor real And flow sensor collects real-time flow Q real , calculate the pressure error e p and flow error e q ; ； S2: Dual closed-loop control is adopted. The outer loop makes a rough adjustment of the booster pump speed based on the pressure error according to the pressure setting value. The inner loop makes real-time adjustment of the opening θ of the regulating valve to compensate for the flow error. The master-slave control mode is used to coordinate the pressure and flow regulation to avoid the shortcomings caused by single control. S3: Introduce feedforward compensation, directly adjust the control variables by estimating the sudden change trend of pressure and flow in real time, and optimize the pump speed and valve control values ​​based on model predictive control MPC combined with process dynamic model optimization adjustment; S4: Based on the optimized pump speed and valve control value, the belt conveyor pipeline is optimized and controlled; S5: abnormal state alarm, high voltage protection: when P real >P max , P max is the maximum pipeline pressure value, reduce the pump speed or turn off the glue supply; flow abnormality: if Q real min , Q min When the flow rate reaches the minimum value, production will be stopped immediately and an alarm will be sounded. According to the recorded set value, the pump speed and valve angle will be quickly adjusted to ensure coating uniformity.​ 2. According to claim 1, a belt conveyor pipeline pressurization and flow stabilization intelligent control method is characterized in that: The S2 is specifically: Outer loop control, by real-time control of booster pump speed f pump , control the pressure error within the minimum range, so that P real Tracking P set , pump speed adjustment formula: Among them, K p1 ,K i1 ,K d1 is the outer loop PID control parameter, t represents time t; Pressure dynamic weight W p Adjustment: W p =adjust(v line ); Among them, v line is the coating line speed; The inner loop control reduces the flow error e by adjusting the valve opening θ q , and at the same time compensate for pressure fluctuations, flow closed-loop control formula: ; Among them, K p2 ,K i2 ,K d2 is the inner loop PID control parameter; According to the pressure error e p , weighted correction for flow error: ； Among them, K f Pressure-flow coupling correction factor; is the corrected valve opening; When the flow demand rises above the threshold, the control priority of the inner loop is increased to ensure feedback adjustment of the pressure when the flow is sufficient.

3. The method for intelligent control of pressure-boosting and flow-stabilizing of a belt conveyor pipeline according to claim 2 is characterized in that: The S3 is specifically: S31: Introducing feedforward compensation, directly adjusting the control variable by estimating the sudden change trend of pressure and flow in real time, according to the rate of change of the set value and Adjust pump speed and valve opening in real time: , ; in, is the pump speed adjustment value; is the valve opening adjustment value; F p is the pump speed feedforward gain coefficient; F q is the valve opening feedforward gain coefficient; S32: Based on model predictive control MPC, combined with process dynamic model optimization and adjustment, the optimal output of the control variables is calculated, including the pump speed adjustment value and the optimal valve opening; S33: Control the booster pump and valve according to the combined signal of feedforward, MPC and PID.

4. The method for intelligent control of pressure-boosting and flow-stabilizing of a belt conveyor pipeline according to claim 3 is characterized in that: The model-based predictive control MPC, combined with the process dynamic model optimization adjustment, calculates the optimal output of the control variable, specifically: Pressure dynamic model: ； Traffic dynamics model: ； in, is the system parameter; is the real pressure at the current time t; is the booster pump speed at the current time t; The flow value at the current time t; For the prediction time domain N p Pressure error within e p , flow error e q Comprehensive optimization with minimization of energy consumption: ； Among them, W p ,W q are the pressure dynamic weight and flow dynamic weight, respectively, to control the priority of pressure and flow errors; W u Control variable adjustment cost; Δu=[ , ]N p is the pump speed and valve adjustment amount; a is the index of the prediction time domain; Control variables meet hardware and practical operating constraints: ； in, are the minimum and maximum values ​​of the booster pump speed respectively; are the minimum and maximum flow values ​​respectively; The QP algorithm is used to solve the optimization objective function J in real time to obtain the pump speed adjustment value and the optimal valve opening.

5. The method for intelligent control of pressure-boosting and flow-stabilizing of a belt conveyor pipeline according to claim 4 is characterized in that: The QP algorithm is used to solve the optimization objective function J in real time, as follows: Through the above objective function and constraints, a typical quadratic optimization problem is formed: ； Among them, H is the quadratic weight matrix in the objective function, which is composed of weights W p ,W q ,W u and dynamic matrices A and B are generated; F is the linear vector for error partial derivative calculation; ΔU is the control variable increment, including Δf pump ,Δθ; Use the interior point method to iteratively solve and obtain the optimal control sequence, including the optimal value of the pump speed and optimal valve opening ; The constraints will be of the form: ； Among them, C, d are constraint matrix and constraint vector respectively; U is the control input sequence; Integrate into the objective function to form a logarithmic potential objective function: ； in, μ is the obstacle parameter; m is the number of constraints; is the i-th constraint; The logarithmic potential objective function is calculated by Newton's method. (U,μ) Solve and gradually reduce μ so that the solution approaches the optimal solution of the original constraint problem.

6. The method for intelligent control of pressure-boosting and flow-stabilizing of a belt conveyor pipeline according to claim 5 is characterized in that: The Newton method is used to calculate the logarithmic potential objective function (U,μ) Solve and gradually reduce μ so that the solution approaches the optimal solution of the original constraint problem, specifically: In each iteration, the solution is updated using Newton's method using the gradient and Hessian matrix. Let the current iteration point be U (k) : For the gradient descent direction, use Cholesky decomposition to solve: ； Determine step size using backtracking line search α ,ensure: ； Among them, β is the step length attenuation factor; Updated solution: ； As the number of iterations k increases, μ is gradually reduced; Stop iteration if one of the following conditions is met: Optimize the norm of the gradient: ; is the preset threshold; μ is small enough to fit the target allowable range.

7. The method for intelligent control of pressure-boosting and flow-stabilizing of a belt conveyor pipeline according to claim 3 is characterized in that: The booster pump and valve are controlled according to the combined signal of feedforward, MPC and PID, as follows: PID control for real-time error p ,e q Perform feedback correction to compensate for the part of the error that is not corrected by feedforward and prediction; The feedforward, MPC and PID output signals are superimposed to form optimized signals for pump speed and valve control: Pump speed control value: ； in, is the final pump speed control value; f pump-feedback It is the pump speed adjustment signal of the feedback control part; It is the pump speed regulation signal of the feedforward compensation part; To optimize the pump speed regulation signal of the calculation part; Valve control value: ； in, θ final is the final valve opening control signal; θ feedback It is the valve opening signal of the feedback control part; It is the valve opening signal of the feedforward compensation part; To optimize the calculation part of the valve opening signal.

8. An intelligent control system for pressurization and flow stabilization of a belt conveyor pipeline, characterized in that: It includes a processor, a memory and a computer program stored in the memory. When the processor executes the computer program, it specifically executes the steps in the intelligent control method for pressurization and stabilization of flow in a belt conveyor pipeline as described in any one of claims 1 to 7.

Images