High-precision hydraulic charging regulation and control method and system based on PID and MPC joint algorithm

Abstract

The invention relates to a high-precision hydraulic charging regulation and control method and system based on a PID and MPC combined algorithm, and the method comprises the steps: collecting a pulse pressure waveform borne by a pressure-bearing part under a target working condition, and collecting a corresponding relation between a control instruction of the opening degree of a regulating valve and the pulse pressure waveform, constructing a pulse pressure waveform library to update a target waveform of the target working condition; creating a state space model to update dynamic characteristics of the hydraulic charging system, dynamically optimizing pre-regulated first-step control input through an MPC algorithm according to the dynamic characteristics, and updating a real waveform tracking target waveform of prediction output in a rolling manner until prediction similarity meets a set threshold value of the target waveform; and in response to the condition that the predicted similarity meets a set threshold value, selecting a PID algorithm to perform feedback adjustment and dynamic compensation on the control input of the real waveform, and outputting target control input simulating the target waveform under the target working condition. The precision, efficiency and flexibility of regulation and control of the hydraulic filling system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic regulation, and particularly relates to a high-precision hydraulic filling regulation method and system based on a combined algorithm of PID and MPC. Background Art

[0002] With the continuous expansion of industrial production scale and the increasing complexity of production requirements, traditional hydraulic filling systems often fail to meet the actual needs when facing pressure test operation scenarios with high precision, high efficiency, and high response speed, severely restricting the high-quality development of industrial production.

[0003] Firstly, the hydraulic filling system uses the existing PID algorithm for pressure regulation. Although the PID algorithm has good steady-state control performance, its regulation accuracy is low, and it takes a long time in high-precision regulation, unable to meet the requirements of high response.

[0004] Secondly, the hydraulic filling system will face multi-condition scenarios in industrial applications. High-precision regulation in this scenario requires simulating dynamic and complex pressure waveforms. The existing regulation methods lack the flexibility to finely solve scenario problems, and the defect of long response time will be even more obvious.

[0005] Therefore, in view of the deficiencies of traditional technologies, it is urgent to solve the pain points of low regulation accuracy, low flexibility, and long response time in the field of hydraulic filling automatic regulation technology. Summary of the Invention

[0006] The primary objective of this application is to solve at least one of the above problems and provide a high-precision hydraulic filling regulation method and system based on a combined algorithm of PID and MPC.

[0007] To meet the various objectives of this application, the following technical solutions are adopted:

[0008] A high-precision hydraulic filling regulation method based on a combined algorithm of PID and MPC provided to meet one of the objectives of this application includes the following steps:

[0009] Collect the pulse pressure waveform received by the pressure-bearing component under the target condition, and collect the corresponding relationship between the control command of the regulating valve opening and the pulse pressure waveform, and construct a pulse pressure waveform library to update the target waveform of the target condition;

[0010] Create a state space model to update the dynamic characteristics of the hydraulic filling system, and dynamically optimize the first-step control input of the pre-regulation through the MPC algorithm, and roll-update the predicted output to track the target waveform until the predicted similarity meets the set threshold of the target waveform;

[0011] In response to the predicted similarity satisfying a set threshold, select the PID algorithm to perform feedback regulation and dynamic compensation on the control input of the real waveform, and output a target control input that simulates the target waveform under the target working condition.

[0012] On the other hand, a high-precision hydraulic filling regulation system based on a combined PID and MPC algorithm provided to meet one of the purposes of the present application includes a waveform library module, a waveform tracking module, and a target control module.

[0013] The waveform library module is used to collect the pulse pressure waveform received by the pressure-bearing component under the target working condition, as well as the corresponding relationship between the control command of the regulating valve opening and the pulse pressure waveform, and construct a pulse pressure waveform library to update the target waveform of the target working condition.

[0014] The waveform tracking module is used to create a state space model to update the dynamic characteristics of the hydraulic filling system, dynamically optimize the first-step control input of the pre-regulation through the MPC algorithm according to the dynamic characteristics, and rollingly update the predicted output of the real waveform to track the target waveform until the predicted similarity satisfies the set threshold of the target waveform.

[0015] The target control module is used to respond to the predicted similarity satisfying the set threshold, select the PID algorithm to perform feedback regulation and dynamic compensation on the control input of the real waveform, and output a target control input that simulates the target waveform under the target working condition.

[0016] On yet another aspect, a high-precision hydraulic filling regulation device based on a combined PID and MPC algorithm provided to meet one of the purposes of the present application includes a central processing unit and a memory. The central processing unit is used to call and run a computer program stored in the memory to execute the steps of the high-precision hydraulic filling regulation method based on the combined PID and MPC algorithm of the present application.

[0017] On yet another aspect, a computer-readable storage medium provided to meet one of the purposes of the present application stores computer-executable instructions, and the computer-executable instructions are used to cause a computer to execute the high-precision hydraulic filling regulation method based on any one of the combinations of the PID and MPC algorithms disclosed in the first aspect of the present invention.

[0018] The technical solution of the present application has many advantages, including but not limited to the following aspects:

[0019] In this application, first, the pulse pressure waveform received by the target device under actual working conditions is collected. The pulse pressure waveforms are classified and sorted according to different working conditions and conditions, and a waveform library that can comprehensively cover the working condition scenarios required for pressure testing is constructed. The typicality, diversity, and expandability of the pressure waveforms in the waveform library can quickly match the target waveforms required for pressure testing, improving the comprehensiveness, accuracy, and refinement of the data required for pressure testing.

[0020] Secondly, by integrating the fast response characteristics of the MPC algorithm and the high-precision adjustment characteristics of the PID algorithm, the MPC algorithm is used to predict the future state of the hydraulic filling system, optimizing the preliminary regulation of the hydraulic filling system within an extremely short reaction time, so that the initial regulation control input can quickly generate a real waveform close to the target waveform. When the similarity between the real waveform and the target waveform reaches the set threshold, the PID algorithm is switched to perform high-precision output for subsequent regulation, achieving seamless connection between fast response and high-precision output.

[0021] Finally, through the comprehensive control of the diversity of the waveform library and the adaptive switching of the fusion algorithm, the hydraulic filling system can meet the pressure pulse test requirements of different types of target devices under various working conditions. The dynamic update of the target waveform by the waveform library, the adaptive adjustment of the fusion algorithm according to the state space model of the hydraulic filling system, and the real-time monitoring and feedback of the regulation can significantly improve the response speed, refinement, and flexibility of the automatic regulation of the hydraulic filling, thus breaking through the constraints of the traditional hydraulic filling system on the high-quality development of industrial production and having high application prospects in industrial production. Description of the Drawings

[0022] The above and / or additional aspects and advantages of this application will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0023] Figure 1 is a schematic diagram of the architecture of the exemplary hydraulic filling system of this application;

[0024] Figure 2 is a flowchart of an embodiment of the high-precision hydraulic filling regulation method based on the combined PID and MPC algorithms of this application;

[0025] Figure 3 is a schematic diagram of the high-precision hydraulic filling regulation system based on the combined PID and MPC algorithms used in this application;

[0026] Figure 4 is a schematic diagram of the simulation process of the exemplary hydraulic filling regulation method of this application in a pressure test operation. Detailed Embodiments

[0027] Before introducing the specific embodiments of the technical solution of this application in detail, the application scenarios suitable for supporting the architecture and control method of the hydraulic filling system in the technical solution of this application are first revealed.

[0028] The technical solution of this application is applicable to the field of automatic control, and is particularly applicable to the scenario of pressure testing operations in industrial production. In this context, the technical solution of this application can be applied in a typical hydraulic filling system, such as Figure 1 shown, the system consists of a hydraulic pump (power element 100), a hydraulic cylinder (actuator 110), a valve group (control element 120), a sensor (detection element 130), a PLC (controller 140), and a pipeline (auxiliary element 150). In a real hydraulic filling system, there should be at least an actuator, a control element, and an auxiliary element, which can generate the pressure required for industrial production. The detection element and the controller are mainly used to perform the regulation, monitoring, and feedback regulation of the hydraulic filling system.

[0029] Among them, the hydraulic pump, as the power element 100, is responsible for providing the pressure and flow required for filling, converting mechanical energy (motor drive) into hydraulic energy. The specific types include gear pumps (suitable for medium and low pressure scenarios such as lubrication systems) and plunger pumps (suitable for high pressure and high precision scenarios, such as servo pumps for electrolyte quantitative filling scenarios and pressure testing scenarios). As the power element 100 in the hydraulic filling system, the hydraulic pump participates in the regulation mainly by adjusting the pump speed through a frequency converter to directly control the flow output.

[0030] Among them, the hydraulic cylinder, as the actuator 110, is responsible for converting hydraulic energy into mechanical motion and driving the filling action (such as the pulsed pressure waveform generated by the filling action in pressure testing). It realizes precise displacement and generates pressure through pressure control in the hydraulic filling system.

[0031] Among them, the valve group, as the control element 120, is responsible for responding to the controller's instructions in real time to adjust the liquid flow parameters (such as pressure, flow, and direction), so as to ensure the accuracy and controllability of the filling process. The types of valve groups include servo valves / proportional valves, overflow valves, and directional valves, etc. In the pressure testing scenario of this application, an electromagnetic proportional regulating directional servo valve is used.

[0032] Among them, the sensor, as the detection element 130, is responsible for monitoring the parameters of the components in the hydraulic filling system, providing real-time data input, and cooperating with the controller 140 to form a closed-loop control. The types of sensors include pressure sensors for monitoring filling pressure, flow meters for measuring real-time flow, temperature sensors for monitoring oil temperature to trigger viscosity compensation, and displacement sensors for feedback of the hydraulic cylinder position.

[0033] Among them, the PLC, as the controller 140, is responsible for running control algorithms (such as PID algorithm and MPC algorithm), generating valve control instructions after processing sensor signals, and can achieve dynamic adjustment when participating in regulation: calculating the control quantity (such as servo valve opening) in real time according to the error (such as pressure deviation) and coordinating multiple parameters: synchronizing pressure, flow, and temperature compensation (such as Smith predictor to offset time delay).

[0034] In some embodiments, the PLC can provide a regulation method that combines the PID algorithm and the MPC algorithm to implement hydraulic filling regulation in pressure test operations. In particular, combined with the real-time feedback provided by sensors in the hydraulic system, it can quickly perform adaptive regulation on the state of the hydraulic system. In addition to the pressure test operation scenario, in other scenarios, for example, the hydraulic equipment in the industrial automation production line simulates complex pressure waveforms required for optimized production to achieve the purpose of improving production efficiency, reducing equipment wear, and extending equipment life; for example, the aircraft hydraulic system simulates the pulse pressure borne during the retraction and extension of the landing gear and the adjustment of flight attitude during flight, thereby ensuring the reliability and safety of the hydraulic system and shortening the test cycle; for example, it is used for the test of the automotive hydraulic braking system, simulating working conditions such as emergency braking and frequent start-stop to evaluate the performance and life of the braking system to improve the reliability of the braking system and optimize the braking performance; for example, it is used for the hydraulic system test of deep-sea equipment (such as hydraulic joints and hydraulic pumps of deep-sea submersibles), simulating the pressure changes in the deep-sea environment to ensure the reliability of the equipment in extreme environments and adapt to the deep-sea high-pressure environment; for example, the joint test of a hydraulic-driven robot simulates complex motion waveforms to optimize the dynamic performance of the joints to achieve the purpose of improving the flexibility and response speed of the robot and extending the joint life; for example, the test of a hydraulic energy storage system simulates the pressure changes during the energy storage process to optimize the energy storage efficiency to achieve the purpose of improving the stability and efficiency of the energy storage system and reducing energy consumption; and for example, it is used for hydraulic-driven medical equipment (such as hydraulic operating tables and hydraulic rehabilitation devices), simulating the pressure changes during the use of medical equipment to ensure the safety and reliability of the equipment to achieve the purpose of improving the use safety of the equipment and optimizing the patient experience; the parameters of hydraulic filling regulation in the PLC control element can be compatibly adjusted in the above scenarios to achieve the same or similar functions.

[0035] Among them, the pipeline, as the auxiliary component 150, is responsible for transporting the hydraulic medium (such as hydraulic oil). Therefore, the connecting pipelines (hoses, rigid pipes) and connecting joints need to be designed to withstand high pressure. The pipelines need to be installed in target equipment (such as automotive brake systems, excavator booms, and aircraft landing gears, etc.) to bear transient and variable pulse pressures and work as pressure-bearing components. Usually, fatigue performance tests need to be carried out on such auxiliary components 150.

[0036] It is understandable that in addition to pipelines in the hydraulic filling system, the auxiliary component 150 may also include an oil tank, a filter, an accumulator, etc., which are used to store and purify hydraulic oil, prevent valve jamming caused by contamination, and absorb pressure pulsations to provide instantaneous large flow rates during the rapid filling stage.

[0037] It should be noted that there are significant differences between the pulsed pressure waveform and the ordinary pressure waveform mentioned in the scenario of the pressure test operation in this application. In hydraulic systems and engineering tests, the pulsed pressure waveform is a periodic or non-periodic pressure signal with steep rising / falling edges, which is used to simulate impact or alternating loads. In pressure tests, the pulsed pressure waveform is used as a dynamic impact simulation tool to apply to the ultimate challenges of pressure tests such as fatigue resistance and impact resistance tests. By exposing potential defects through high-frequency and high-pressure impacts, the test objectives of testing the fatigue life, dynamic sealing performance, and impact resistance of materials can be achieved. For example, in the hydraulic hose test, the pulsed pressure waveform (square wave) simulates the pressure shock generated by the sudden closing of the valve (0 MPa - 35 MPa - 0 MPa, frequency 2 Hz, number of cycles 100,000 times) to detect whether the hose joint cracks due to fatigue. Therefore, compared with the ordinary pressure waveform, the pulsed pressure waveform is more suitable for research in pressure tests. However, the dynamic characteristics of this waveform are relatively complex, and it is impossible to pursue high-precision regulation when processed by the traditional PID algorithm, and there is often a problem that it takes a long time to reach the target value. Especially when the hydraulic system regulates and simulates complex and variable pulsed pressure waveforms, the above defects are particularly obvious.

[0038] Furthermore, to solve the above technical problems, this application proposes a high-precision hydraulic filling regulation method based on the combined algorithm of PID and MPC, aiming to provide a control input with rapid response for the regulation of the hydraulic filling system in pressure tests to achieve the pressure output that meets the pressure test. It is understandable that when providing the control input, multiple influencing factors need to be considered, such as dealing with complex and variable negative pulsed pressure waveforms, quickly responding to the pressure regulation requirements, and real-time, closed-loop, sustainable, high-precision, and highly flexible regulation methods.

[0039] The following uses specific embodiments to elaborate in detail on the technical solution of this application and how the technical solution of this application solves the above technical problems.

[0040] The following specific embodiments can be combined with each other, and the same or similar concepts or processes will not be repeated in some embodiments. The embodiments of this application will be described below in conjunction with the accompanying drawings.

[0041] See Figure 2 , this application discloses a high-precision hydraulic filling regulation method based on the combined algorithm of PID and MPC. In its typical pressure test embodiment, it includes the following steps:

[0042] 101: Collect the pulse pressure waveform received by the pressure-bearing component under the target working condition, as well as the control command of the regulating valve opening and the corresponding relationship between the pulse pressure waveform, and construct a pulse pressure waveform library to update the target waveform of the target working condition.

[0043] The pulse pressure waveform can be collected by a pressure sensor set inside the hydraulic filling system or a pressure sensor outside the system. The main pressure-bearing component of the target equipment to be collected is the connecting pipeline in the auxiliary component 150, or it can also be other components in other target equipment that are responsible for pressure-bearing tasks and require pressure testing.

[0044] When selecting the target working condition, it can be adjusted according to the working state of the hydraulic filling system in the actual pressure test requirements. For example, when conducting a pressure test on the connecting pipelines in the automotive braking system and the boom of an excavator, it is necessary to collect the pulse pressure waveform received by the connecting pipeline in this scenario.

[0045] Specifically, the collection range of the pulse pressure waveform should include but is not limited to the actual working scenarios of the hydraulic filling system such as light load, heavy load, emergency braking, frequent start and stop, etc. The typicality, diversity, and scalability of the waveforms in the pulse pressure waveform data should be considered to facilitate the rapid matching of the target waveform under the target working condition during the regulation process in the pressure test.

[0046] Among them, the pulse pressure waveform is a curve of pressure changing with time, showing a repetitive process of rapid rise, maintaining the peak value, and rapid decline, forming a curve shape similar to a "pulse". The specific parameters involved include: peak pressure (the highest pressure value of the pulse), valley pressure (the lowest pressure value of the pulse), rise time (the time required for the pressure to rise), pulse width (the time when the pressure maintains at the peak value), and frequency (the number of pulse cycles per second). Through the pulse pressure waveform, the working state of the pressure-bearing component can be intuitively expressed, which is convenient for regulating the hydraulic filling system according to the pressure waveform during the pressure test.

[0047] Furthermore, the waveform has a typical morphological distribution, such as a square wave or a rectangular wave (the pressure instantaneously rises to the peak value and maintains, and then suddenly drops to the baseline (steep rising / falling edges)), a trapezoidal wave (the pressure linearly rises to the peak value, briefly maintains, and then linearly drops), and a spike wave (reaches the peak value within a very short time and then rapidly decays to simulate an instantaneous impact). However, in addition to the typical waveform distributions, there are still other non-typical waveform distributions in the actual working conditions of the hydraulic filling system. Therefore, it is necessary to collect and organize the waveforms in combination with the working condition scenarios to ensure the comprehensiveness of the waveform data.

[0048] When constructing the waveform library, in addition to recording the relevant data for saving waveform parameters, it is also necessary to consider the relationship between the control instructions for the opening degree of the control element 120 during working hours and the change of the pulse pressure waveform over time, which is used as a reference basis for providing control instructions to simulate the target waveform in pressure testing. In addition, the process of constructing the waveform library also needs to consider the classification and sorting of waveforms, including but not limited to classifying and filing according to different working conditions, different target equipment types, and different waveform characteristics, which is convenient for data calling in subsequent regulation links, especially for quickly responding to the regulation requirements in pressure testing under new equipment and new working conditions. This also includes calibrating the parameters of the dynamic model of the hydraulic filling system with the waveform library data.

[0049] At the data storage level, the waveform library of this application adopts a unified and structured data storage scheme, which can encapsulate all information such as the working condition scenarios, equipment parameters, and corresponding servo valve opening degrees related to each waveform type as nodes in a standardized node database. Specifically, each node data is sorted according to the waveform type. Each node not only includes the corresponding attributes of the waveform, but also includes the attribute scalars of the associated nodes, which can be one or a group of data and can identify the specific position of the waveform in the waveform classification, such as the waveform ID or waveform category; and the edge connection relationship defines the proximity or similarity between nodes, which is also applicable to the storage and retrieval processing of working condition scenarios, equipment types, and servo valve opening degrees as nodes, providing a solid data foundation for quickly and accurately searching for pressure test data.

[0050] In specific implementation, the characteristic information and system matrix expressing the dynamic characteristics of the hydraulic filling system are obtained in real time. The characteristic information includes pressure, flow rate, hydraulic cylinder output force, servo valve opening degree command, pipeline pressure fluctuation, and the dynamic system matrix associated with the characteristic information. Among them, pressure, flow rate, and hydraulic cylinder output force are used as the response basis for system state modeling.

[0051] Specifically, the dynamic characteristics of the hydraulic filling system need to be collected by the detection element 30 or can be collected by sensors set outside the system. Among them, the system pressure P as a state variable can reflect the storage and transmission of hydraulic energy. The flow rate Q in the state variables can characterize the motion state of the liquid medium. The hydraulic cylinder output force F in the state variables can be obtained by calculating the pressure P and the effective area in the cylinder. The servo valve opening degree, as a relevant parameter of the control element 120, can directly control the regulation of flow rate and pressure. The pipeline pressure fluctuation reflects the interference from external load changes or system leakage, etc. To visualize the dynamic characteristics of the hydraulic filling system changing over time, constructing a state space model through the above characteristics is the basis for further studying the regulation of the hydraulic filling model. Therefore, it is necessary to establish a mathematical framework of the state space model to describe the relationship between the input, output, and internal state of the dynamic system, so as to facilitate the model predictive control of the hydraulic filling system.

[0052] Further, a state - space model is created using characteristic information to express the relationships among inputs, outputs, internal states, and external influences in the hydraulic filling system. The internal - state relationship is the dynamic coupling of energy conversion and the interaction of dynamic characteristics within the hydraulic filling system, and the external influence includes the interference of external pressure fluctuations on the system state.

[0053] It should be noted that before pre - regulating the first - step control of the hydraulic filling system using the MPC algorithm, a state - space model of the hydraulic filling system (hereinafter referred to as the system) needs to be established first. The expression of the state - space model is as follows:

[0054]

[0055] Where, is the derivative of the state - variable function x(t), which is used to represent the dynamic change rate of the system state. x(t) represents the functional relationship of the system's state variables changing with time. The characteristics of the state variables include pressure P, flow rate Q, and the output force F of the hydraulic cylinder. Therefore, the state variable x can be expressed as x = [P, Q, F]. T , T represents matrix transpose, u represents the servo - valve opening command (unit: %), u(t) represents the functional relationship of the opening command changing with time, p represents the pipeline pressure fluctuation (unit: MPa), p(t) represents the functional relationship of the pipeline pressure fluctuation changing with time, y(t) represents the system output (pressure, flow rate, and the output force of the hydraulic cylinder), and A, B, C, D are all dynamic - system matrices. Among them, the system matrix A includes the effective area A of the hydraulic cylinder h = 0.02m 2 , the medium elastic modulus β e = 1.4 GPa and the medium density ρ = 870 kg / m 3 . B represents the input matrix describing the influence of the servo - valve opening on the flow rate and pressure, C represents the perturbation matrix quantifying the interference of the pipeline pressure wave on the system, and D represents the output matrix defining the system output y(t).

[0056] As an example, if the state variables in the state space are associated with the pressure P and the flow rate Q, without considering the medium density, A in the system matrix can be expressed as: If the servo - valve opening u directly affects the change in the flow rate Q (i.e., the changes in pressure and output force are indirectly driven by the flow rate), then B can be expressed as Where K V represents the flow - rate gain, τ v represents the time constant; if the flow rate and output force in the state space are not directly perturbed, but the pipeline pressure fluctuations are directly superimposed on the system pressure, then C can be expressed as: If the pressure P and the flow rate Q are directly associated with the system output y, then D can be expressed as:

[0057] 102: Create a state - space model to update the dynamic characteristics of the hydraulic filling system. According to the dynamic characteristics, dynamically optimize the first - step control input of the pre - regulation through the MPC algorithm, and roll - update the real waveform of the predicted output to track the target waveform until the prediction similarity meets the set threshold of the target waveform;

[0058] In this embodiment, the state - space model is used for data modeling to simulate the pressure test reaching the target waveform. Therefore, before pre - regulating the first - step control input, it is necessary to calibrate the model parameters through experimental data, and then update the dynamic characteristic parameters in the state - space model in real - time to represent the real - time state of the hydraulic filling system.

[0059] In specific implementation, an output tracking term and an input smoothing term are used to define the rolling - horizon optimization objective function, and the real - time optimal control of the system is realized by continuously updating the prediction window and only implementing the first - step control input through the dynamic optimization strategy of the rolling - horizon.

[0060] It should be noted that the rolling - horizon optimization objective function is expressed as:

[0061]

[0062] Among them, k represents the actual step (i.e., the k - th step), N represents the prediction step (usually set as N = 10), M represents the control step (usually set as M = 5), Q represents the weight matrix of the pressure error and the flow error, Q = diag(1, 0.1). To ensure the pressure accuracy and the flow accuracy, the weight of the pressure tracking error in the weight matrix is 1, and the weight of the flow error is 0.1. R represents the weight matrix of the control input change, R = 0.01, Δu represents the constraint condition of the control input change rate of the servo - valve opening, |Δu|≤5%, where u is the range of the servo - valve opening restricted within the physical limit range (i.e., 0≤u≤100%), y(k|t) represents the predicted output of the system corresponding to the actual step (i.e., the k - th step) at time t (used to compare the real waveform generated according to the system predicted output with the target waveform), y ref (k) represents the reference trajectory (used to compare whether the real waveform of the predicted output is close to the target waveform).

[0063] It should be noted that the optimization problem solved by the objective function can be expressed as:

[0064]

[0065] Among them, the first term is the output tracking term, the second term is the control smoothing term, y k represents the predicted output of the system at the actual step (i.e., the k - th step), r k represents the reference trajectory at the actual step (i.e., the k - th step), k represents the sampling time, minu denotes minimizing the predicted output y k and the reference trajectory r k of the sum of squared deviations, λ represents a penalty factor for regulating the change in the control input, Δu k represents the change in the control input between the k-th step and the (k - 1)-th step, N P represents the prediction horizon for the future N P outputs, N C represents the control horizon for optimizing the future N C step control inputs. Specifically, for the control input u k and the system output y k set the upper and lower limits of the minimum and maximum values.

[0066] First, it is necessary to determine the range of the prediction step and the control step within the control period according to the response time of the dynamic optimization. Since there are differences in the response time of each device, it is understandable that the system response time should be added to the determining factors of the prediction step and the control step to avoid the situation where the prediction step and the control step do not match the response time. The prediction step should cover the dynamic response process of the system to ensure that the optimization problem can predict the changes in the system reaching the steady state or the critical state. The control step can be determined according to the empirical rule or dynamic coverage (the control step should cover the fast dynamic stage of the system to ensure that the optimization can adjust the control input in a timely manner).

[0067] Next, based on the output tracking term, minimize the deviation between the true waveform and the target waveform of the predicted output under the current system state within the prediction step of the control period. Determine the priority of the output through the weight matrix for the characteristic information of the system state, that is, minimize the tracking error of the state variables (such as the set values of pressure and flow rate) in the target optimization function. At the same time, sort the priority of the output through the weights of the pressure error and the flow error (give priority to the pressure accuracy in the state variables), and predict the system outputs (such as pressure and flow rate) at the next N time points according to the determined prediction step to achieve the long-term optimization of the system regulation;

[0068] Furthermore, based on the input smoothing term, suppress the mutation of the control input within the control step of the control period. Weigh the tracking performance and control smoothness through the weight λ (if the weight λ is too small, the control is more conservative, the input change is small, but the tracking may lag; if the weight λ is too large, the tracking is faster, but it may cause wear of the actuator or system oscillation); set a feasibility threshold for the control input to limit the input increment, avoid the control input exceeding the physical limit, prevent mechanical shock, and suppress the change in the control input (such as the sudden change in the servo valve opening) through the weight matrix R to ensure that the command is within the feasible range and achieve the purpose of balancing the system response speed and stability.

[0069] Finally, according to the rolling horizon optimization objective function to minimize the tracking error and the change of the control quantity, the first-step control input is generated, and the system output at N time points of the prediction step length is optimized, and the system output at M time points (including pressure control and flow control) is calculated, but only the system output at the first time point within M time points is determined as the first-step output. Compared with the regulation of the traditional PID algorithm, the response time can be greatly shortened and the regulation efficiency can be improved.

[0070] In specific implementation, dynamic model constraints are defined, the actual output corresponding to the first-step control input is associated with the actual output at any time based on the self-evolution describing the inside of the model, and the historical control input is associated with the actual output at any time based on the driving influence of the quantization of the historical control input on the model state;

[0071] It should be noted that the expression of the dynamic model constraint is:

[0072]

[0073] where, y k represents the predicted output at the kth step, A k represents the system matrix corresponding to the actual step length (i.e., the kth step) derived from the state space model, y0 is the initial state of the state space model, A k-i-1 represents each historical system matrix before the kth step, B represents the input matrix describing the influence of the servo valve opening on the flow and pressure, u i represents the ith control input in the control input sequence.

[0074] As an example, through the system matrix A k and the control sequence summation term to describe the self-evolution process inside the model, substitute the output y0 corresponding to the first control input into the dynamic model constraint to calculate the model output of the next step. For example, the hydraulic filling system needs to track the pressure reference value y ref (k)=20 MPa, measure the current pressure P and flow Q at the current moment, predict the pressure P and flow Q within the next N steps, and at the same time calculate the control inputs u(0), u(1), u(2), u(3) and u(4) of the servo valve opening within the next M steps through the objective optimization function. Only apply u(0) as the first-step control input. After each step, the actual situation of pressure and flow also needs to be considered to minimize the pressure tracking error and the change of the control quantity according to the optimization objective, and recalculate the control input of the next step, and optimize the input sequence of the servo valve opening one by one. For example, if it is predicted that the future pressure is lower than the set value, MPC will increase the opening to increase the flow, thereby correcting the pressure.

[0075] Furthermore, for the control input sequence of non-first-step control input, the sequential quadratic programming algorithm is used to accelerate the solution of the real waveform through the dynamic system matrix, and the predicted similarity between the real waveform and the target waveform is compared until the similarity between the real waveform and the target waveform meets the set threshold.

[0076] In this embodiment, in order to improve the response speed of the regulation, an efficient non-linear optimization algorithm - SQP (Sequential Quadratic Programming) algorithm is selected to solve the minimum value of the objective function within 10 ms (including accelerating the solution using system matrices A and B) to generate a safe (|Δu| ≤ 5%) and feasible (0 ≤ u ≤ 100%) control command, where 10 ms is the control period, so that the non-first-step input can match the dynamic response speed of the hydraulic system.

[0077] Specifically, for the real waveform of the predicted output pressure of the MPC algorithm, it still needs to be close to the target waveform required for the pressure test, so as to achieve the effect of accurately simulating the pressure test operation. In this application, the MPC algorithm is mainly used for the regulation of the pressure test operation with fast response. The control input and predicted output of the servo opening are calculated by the MPC algorithm within an extremely short time. During the continuous optimization process, the real waveform is made to approach the target waveform to complete the preliminary regulation of the hydraulic filling system.

[0078] Finally, during the regulation process of the MPC algorithm, it is necessary to determine whether the real waveform reaches near the target waveform. Through the preset prediction similarity threshold (for example, the similarity threshold is 97% - 98% of the target waveform value), it can be determined that the current preliminary regulation of the hydraulic system by the MPC algorithm is close to the target waveform required for the pressure test operation. Further, it is necessary to switch the regulation algorithm to the PID algorithm for steady-state precise control.

[0079] 103. In response to the predicted similarity meeting the set threshold, select the PID algorithm to perform feedback adjustment and dynamic compensation on the control input of the real waveform, and output the target control input that simulates the target waveform under the target working condition.

[0080] It should be noted that compared with the MPC algorithm, the PID algorithm is too redundant for regulation processing, especially in the system initialization to the first-step control stage, which requires long-term optimization. Especially when dealing with massive and diverse data such as waveform libraries, the defect of its long response time will be more obvious. Therefore, after the hydraulic filling system completes the preliminary regulation of the pressure test operation through the MPC algorithm, the PID algorithm is used to perform the regulation process.

[0081] In specific implementation, a discretized equation is defined. Within the sampling period, the system error between the real waveform and the target waveform is corrected by the proportional term, the steady-state error between the real waveform and the target waveform is eliminated by the integral term, and the overshoot and oscillation of the error change are suppressed by the integral term.

[0082] It should be noted that since the digital controller cannot directly process continuous-time signals, it is necessary to adapt to the sampling system through discretization (such as rectangular integration, backward difference). The discretization method can convert the continuous PID control algorithm into a discrete form suitable for implementation by the digital controller, enabling it to calculate the control quantity in real time within a fixed sampling period, and solving the real-time and computational feasibility problems of the digital control system.

[0083] Specifically, the discretization equation is expressed as:

[0084]

[0085] Among them, u(k) represents the control quantity at the sampling moment k, and e(k) represents the error at the k-th step. represents the cumulative error, e(k - 1) represents the error at the (k - 1)-th step, T s represents the sampling period, K p represents the proportional gain of the PID control, K i represents the integral gain of the PID control, K d represents the derivative gain of the PID control.

[0086] First, through the proportional term K p e(k) quickly responds to the current error (such as pressure deviation) and corrects the system output. Secondly, through the integral term accumulates historical errors and eliminates steady-state deviations (such as long-term offset of the charging pressure). Finally, through the derivative term predicts the changing trend of the error and suppresses overshoot (such as the oscillation during sudden flow changes), and outputs the servo valve opening control input corresponding to the k-th step. It can be understood that in the PID algorithm, continuous and high-precision feedback adjustment is still required for the output of each step, so that the waveform of the system output conforms to the target waveform to meet the requirements of pressure testing. Especially in the pressure testing scenario where the number of tests exceeds 100,000 times, for each test waveform output to the target device within each sampling time to conform to the target waveform under the actual working conditions will effectively improve the result of the pressure test.

[0087] Furthermore, in order to determine the servo valve opening control input for each step, it is also necessary to determine the corresponding PID parameters (including the proportional gain K p , the integral gain K i and the derivative gain K d ) in the PID algorithm. In the embodiments of the present application, the above PID parameters are determined based on the Ziegler-Nichols critical ratio method.

[0088] In specific implementation, a parameter self-tuning rule is defined to determine the optimal combination of control parameters in the PID algorithm through the critical proportional gain and the critical oscillation period;

[0089] Specifically, the parameter self-tuning rule is the Ziegler-Nirchols critical ratio method, and the parameter self-tuning rule is expressed as:

[0090]

[0091] where K p represents the proportional gain, K u represents the critical proportional gain, T i represents the integral time constant, T u represents the critical oscillation period, T d represents the derivative time constant.

[0092] In the embodiment of the present application, based on the critical proportional gain K u increase K p gradually through experiments until the system output generates equal-amplitude oscillations. At the same time, based on the critical oscillation period T u record the time of a complete cycle of the oscillation waveform. As an example, when determining the combination of PID parameters, it can be determined through the calculation of the critical ratio method. For example, K p = 0.6K u , K d = K p T d = 0.075K u T u . Determine the PID parameters through the critical oscillation period T u and the critical proportional gain K u , and then substitute them into the discretization formula to further solve the target control input. It can be understood that...

[0093] It should be noted that when the MPC algorithm switches to the PID algorithm, the integral term of the PID needs to be initialized to the current cumulative error to avoid the situation of integral saturation. In this embodiment, defining anti-integral saturation and modifying the constraint reverse integral is adopted to limit the amplitude (ensure that the control output is within the physical limit) and reverse correction (when the output saturates, correct the cumulative amount of the integral term to prevent the integral term from continuously increasing), and add the correction amount to the integral term of the discretization equation to make it match the actual output, avoiding the actuator from jamming or the system from getting out of control.

[0094] In specific implementation, define the anti-integral saturation correction constraint and the reverse integral correction constraint, correct the cumulative error of the integral term according to the constraint, and output the discretized calculated control quantity within the sampling period.

[0095] Specifically, the expression of the anti-integral saturation correction constraint is:

[0096]

[0097] where u sat (k) represents the amplitude limit of the control input (i.e., the control variable) u(k), u(k) represents the control input (i.e., the control variable) at the k-th step (i.e., the sampling time k), u min represents the minimum limit of the control input (i.e., the control variable), u max represents the maximum limit of the control input (i.e., the control variable).

[0098] Specifically, the expression of the reverse integral correction constraint is:

[0099] ΔI = K i T s (u sat (k) - u(k))

[0100] where ΔI represents the correction value of the reverse integral, K i represents the cumulative gain of the PID control, T s represents the sampling period, u sat (k) represents the amplitude limit of the control input (i.e., the control variable), u(k) represents the control input (i.e., the control variable) at the k-th step (i.e., the sampling time k).

[0101] It should be noted that in the above disclosed embodiments, the state variables (pressure P, flow rate Q, and hydraulic cylinder output force F) and the servo valve opening control input u in the state space model of the hydraulic filling system are optimized and regulated. Therefore, it is necessary to further consider the influence of pipeline interference fluctuations on the control input u to solve the problem of lag overshoot in long pipelines or high-frequency control.

[0102] In specific implementation, determine the system gain, input signal, inertia time, time delay time, and time delay link under PID control, predict the pressure response value through the pipeline pressure fluctuation predictor, and perform time delay compensation on the calculated control variable according to the pressure response value;

[0103] It should be noted that the pipeline pressure fluctuation predictor in the dynamic compensation model can be expressed as:

[0104]

[0105] where u comp (t) represents the control variable after compensating and correcting the control input to offset the time delay effect, u(t) represents the original uncompensated control input, represents the compensation term, represents the fluctuation estimate value, K represents the system gain (i.e., the proportional relationship between the input signal U(s) and the steady-state pressure), τ represents the inertial time required for the system pressure to reach the steady state. The inertial time is often used as a time constant and is also related to the medium compressibility and the system volume. s represents the complex variable in the complex frequency domain of the Laplace transform, which is used to convert the differential equation in the time domain (t) into a differential equation in the complex frequency domain (s) for easier solution and design of the controller. U(s) represents the control input after Laplace transform (i.e., the control quantity), θ represents the time delay (for example, θ = 0.1s means that after the valve action, the pressure change takes effect with a delay of 0.1 second), e -θs represents the time delay link that represents the delay effect with a time delay of θ in the frequency domain.

[0106] Further, in the hydraulic system, there is a time delay (θ = 0.1) in the transmission of the control signal (such as the servo valve opening control instruction) to the pressure response. The Smith predictor models the time delay link e -θs and the first-order inertial link to predict future pressure fluctuations reversely superimposes the predicted pressure error onto the control quantity to generate the compensated control instruction u comp (t), which offsets the time delay effect in advance, thereby improving the control response speed of the hydraulic filling system, enabling the actual pressure to track the set value faster (i.e., the output waveform highly fits the actual complex and variable waveform). In addition to time delay compensation, the viscosity of the oil in the pipeline (auxiliary component 150) of the hydraulic filling system will decrease as the temperature rises, and this change in the pipeline will cause a flow deviation. Therefore, it is necessary to solve the influence of temperature disturbance on the stability of the hydraulic filling system.

[0107] In specific implementation, an oil viscosity-temperature compensation model is constructed to determine the reference viscosity and the temperature coefficient, and the flow correction instruction for calculating the control quantity is dynamically output according to the detected real-time temperature and the temperature change amount using the compensation model.

[0108] It should be noted that the oil viscosity-temperature compensation model includes the viscosity-temperature relationship and the corrected flow equation. The viscosity-temperature relationship is expressed as:

[0109] μ(T) = μ0exp(-λ(T - T0)

[0110] where, μ(T) represents the functional relationship between viscosity and temperature change, μ0 represents the reference viscosity at the reference temperature T0 (for example, the viscosity μ0 of mineral oil at 20°C is μ0 = 46 cSt), λ represents the viscosity temperature coefficient (as an example, λ = 0.03 / °C means that for every 1°C increase in temperature, the viscosity decreases by about 3%), T0 represents the reference temperature for viscosity calibration, and T represents the current temperature (i.e., the current temperature can be measured by a temperature sensor).

[0111] Further, the corrected flow equation can be expressed as:

[0112]

[0113] Among them, Q represents the original flow set value, and Q comp represents the corrected flow set value, μ0 represents the reference viscosity, and μ(T) represents the functional relationship between viscosity and temperature change.

[0114] Specifically, by solving the flow compensation value through the temperature compensation model and the corrected flow equation, the hydraulic filling system can automatically reduce the valve opening to maintain the target flow when the viscosity decreases in a high-temperature environment, avoiding overfilling. When facing temperature fluctuations caused by day-night temperature differences or continuous operation, it ensures system control consistency, reduces faults or calibration requirements caused by temperature drift, and improves system reliability.

[0115] Immediately afterwards, according to the time-delay compensation and the flow correction instruction, the calculated control quantity is output as the target control input. The Smith predictor is used to solve the tracking error caused by time delay, and the viscosity compensation model eliminates the flow drift caused by temperature, jointly ensuring the set value accuracy of the filling pressure and flow (such as ±0.5%). It endows the hydraulic filling system with the characteristics of predictability, self-adaptability and reliability, can respond to time delay and temperature changes in advance, rather than correcting errors passively. By automatically adjusting the control strategy to adapt to the dynamic environment, it still maintains high precision and stable output even under disturbances. The above characteristics make the hydraulic filling system more competitive in scenarios such as industrial automation and precision manufacturing.

[0116] In the embodiment of the present application, by collecting the pulse pressure waveform received by the target device under actual working conditions, classifying and sorting the pulse pressure waveform according to different working conditions and conditions, a waveform library that can comprehensively cover the working condition scenarios required for pressure testing is constructed. The typicality, diversity and expandability of the pressure waveforms in the waveform library can quickly match the target waveforms required for pressure testing, improving the comprehensiveness, accuracy and refinement of the data required for pressure testing.

[0117] Secondly, by integrating the fast response characteristics of the MPC algorithm and the high-precision adjustment characteristics of the PID algorithm, the MPC algorithm is used to predict the future state of the hydraulic filling system, and the preliminary regulation of the hydraulic filling system is optimized within an extremely short reaction time, so that the initial regulation control input can quickly generate a real waveform close to the target waveform. When the similarity between the real waveform and the target waveform reaches the set threshold, the PID algorithm is switched to perform high-precision output for subsequent regulation, realizing seamless connection between fast response and high-precision output.

[0118] In an alternative embodiment, after outputting the target control input that simulates the target waveform under the target working condition, the following steps are included:

[0119] Set up an update mechanism for the pulse pressure waveform library to obtain the target waveform under the new target working condition in real time, and obtain the updated equipment parameters and control parameters of the target equipment in the hydraulic filling system in real time. Save the corresponding relationship between the latest target waveform and the target equipment parameters to the waveform pulse pressure waveform database;

[0120] Read the variable information of the pulse pressure waveform database in real time, and adaptively optimize the parameters in the MPC algorithm and the PID algorithm according to the newly added working condition types, target waveforms and the corresponding relationships in the variable information;

[0121] Monitor the dynamic characteristics, system control inputs and outputs of the hydraulic filling system in real time, and judge the selection decision of the control algorithm through the monitoring regulation threshold. The selection decision includes at least one selection of the MPC algorithm for regulation or the PID algorithm for regulation, at least one selection of the joint regulation of the MPC algorithm and the PID algorithm, and no less than two selections of one or several of the regulation of the MPC algorithm and / or the PID algorithm. The number of times of selecting the MPC algorithm or the PID algorithm in the no less than two selections of the regulation of the MPC algorithm and / or the PID algorithm is at least once.

[0122] Finally, the unique technical advantage of this application is that it can realize comprehensive control through the diversity of the waveform library and the adaptive switching of the fusion algorithm. As Figure 4 shown, it can regulate the hydraulic filling system to output a real waveform that is infinitely close to the target pressure test waveform, so that the hydraulic filling system can meet the pressure pulse test requirements of different types of target equipment under various working conditions. The dynamic update of the target waveform by the waveform library, the adaptive adjustment of the fusion algorithm according to the state space model of the hydraulic filling system, and the real-time monitoring and feedback of the regulation can significantly improve the response speed, refinement degree and flexibility of the hydraulic filling automatic regulation, thus breaking through the restriction of the traditional hydraulic filling system on the high-quality development of industrial production and having a high application prospect in industrial production.

[0123] Please refer to Figure 3, A high-precision hydraulic filling control system based on a combined PID and MPC algorithm provided according to an aspect of the present application, the system comprising: a waveform library module for collecting the pulse pressure waveform received by a pressure-bearing member under a target working condition, and collecting the correspondence between the control command of the regulating valve opening and the pulse pressure waveform, constructing a pulse pressure waveform library to update the target waveform of the target working condition; a waveform tracking module for creating a state space model to update the dynamic characteristics of the hydraulic filling system, dynamically optimizing the first-step control input of pre-regulation through the MPC algorithm according to the dynamic characteristics, and rolling updaing the predicted output to track the target waveform until the prediction similarity meets the set threshold of the target waveform; a target control module for, in response to the prediction similarity meeting the set threshold, selecting the PID algorithm to perform feedback regulation and dynamic compensation on the control input of the real waveform, and outputting a target control input that simulates the target waveform under the target working condition.

[0124] Based on any embodiment of the system of the present application, the system of the present application further comprises: a state space construction module configured to obtain in real time the characteristic information and system matrix expressing the dynamic characteristics of the hydraulic filling system, the characteristic information including pressure, flow rate, hydraulic cylinder output force, servo valve opening command, pipeline pressure fluctuation, and the dynamic system matrix associated with the characteristic information, wherein the pressure, flow rate, and hydraulic cylinder output force are used as the response basis for system state modeling; creating a state space model for expressing the relationship between the input, output, internal state, and external influence in the hydraulic filling system, the internal state relationship being the dynamic coupling of energy conversion and interaction between dynamic characteristics within the hydraulic filling system, and the external influence including the interference of external pressure fluctuation on the system state.

[0125] Based on any embodiment of the system of the present application, the system of the present application further comprises: a pre-regulation module configured to define a rolling horizon optimization objective function using an output tracking term and an input smoothing term, and determine the range of the prediction step length and the control step length within the control period according to the response time of dynamic optimization; minimizing the deviation between the real waveform of the predicted output and the target waveform under the current system state within the prediction step length of the control period based on the output tracking term, and determining the output priority through a weight matrix for the characteristic information of the system state; suppressing the mutation of the control input within the control step length of the control period based on the input smoothing term, and setting a feasibility threshold for the control input to limit the input increment; generating a first-step control input by minimizing the tracking error and the change of the control quantity according to the rolling horizon optimization objective function.

[0126] Based on any embodiment of the system of the present application, the system of the present application further includes: a rolling update module, configured to define dynamic model constraints, associate the actual output corresponding to the first-step control input with the actual output at any time based on the self-evolution within the description model, and associate the historical control input with the actual output at any time based on the driving influence of the quantization history control input on the model state; for the control input sequence of non-first-step control inputs, use the sequential quadratic programming algorithm to accelerate the solution of the true waveform through the dynamic system matrix, and compare the prediction similarity between the true waveform and the target waveform until the similarity between the true waveform and the target waveform meets the set threshold.

[0127] Based on any embodiment of the system of the present application, the system of the present application further includes: a feedback regulation module, configured to define a discretization equation, correct the system error between the true waveform and the target waveform through a proportional term within the sampling period, eliminate the steady-state error between the true waveform and the target waveform through an integral term, and suppress the overshoot and oscillation of the error change through an integral term; define a parameter self-tuning rule to determine the optimal combination of control parameters in the PID algorithm through the critical proportional gain and the critical oscillation period; define an anti-integral saturation correction constraint and a reverse integral correction constraint, and correct the cumulative error of the integral term according to the constraints to output the discretized calculated control quantity within the sampling period.

[0128] Based on any embodiment of the system of the present application, the system of the present application further includes: a dynamic compensation module, configured to determine the system gain, input signal, inertia time, time delay time, and time delay link under PID control, predict the pressure response value through a pipeline pressure fluctuation predictor, and perform time delay compensation on the calculated control quantity according to the pressure response value; construct an oil viscosity-temperature compensation model, determine the reference viscosity and the temperature coefficient, and dynamically output a flow correction instruction for the calculated control quantity according to the detected real-time temperature and the temperature change amount. Output the calculated control quantity as the target control input according to the time delay compensation and the flow correction instruction.

[0129] Based on any embodiment of the system of the present application, the system of the present application further includes: a real-time monitoring module, configured to set an update mechanism for the pulse pressure waveform library, obtain the target waveform under the new target working condition in real time, and obtain the updated device parameters and control parameters of the target device in the hydraulic filling system in real time, and save the corresponding relationship between the latest target waveform and the target device parameters to the waveform pulse pressure waveform database; read the variable information of the pulse pressure waveform database in real time, and adaptively optimize the parameters in the MPC algorithm and the PID algorithm according to the newly added working condition type, target waveform and the corresponding relationship in the variable information; monitor the dynamic characteristics, system control inputs and outputs of the hydraulic filling system in real time, and judge the selection decision of the control algorithm through the monitoring regulation threshold, where the selection decision includes at least one selection of the MPC algorithm for regulation or the PID algorithm for regulation, at least one selection of the MPC algorithm and the PID algorithm for joint regulation, and no less than two selections of one or several of the MPC algorithm for regulation and / or the PID algorithm for regulation, and the number of times of selecting the MPC algorithm or the PID algorithm in the no less than two selections of the MPC algorithm for regulation and / or the PID algorithm for regulation is at least one time.

[0130] Another embodiment of the present application further provides a high-precision hydraulic filling control device based on the combined PID and MPC algorithms. The high-precision hydraulic filling control device based on the combined PID and MPC algorithms includes a processor, a computer-readable storage medium, a memory and a network interface connected through a system bus. Among them, the computer-readable non-volatile readable storage medium of the high-precision hydraulic filling control device based on the combined PID and MPC algorithms stores an operating system, a database and computer-readable instructions. Information sequences can be stored in the database. When the computer-readable instructions are executed by the processor, the processor can implement a high-precision hydraulic filling control method based on the combined PID and MPC algorithms.

[0131] The processor of the high-precision hydraulic filling control device based on the combined PID and MPC algorithms is used to provide computing and control capabilities to support the operation of the entire high-precision hydraulic filling control device based on the combined PID and MPC algorithms. Computer-readable instructions can be stored in the memory of the high-precision hydraulic filling control device based on the combined PID and MPC algorithms. When the computer-readable instructions are executed by the processor, the processor can execute the high-precision hydraulic filling control method of the present application. The network interface of the high-precision hydraulic filling control device based on the combined PID and MPC algorithms is used to connect and communicate with the terminal.

[0132] In this embodiment, the processor is used to execute Figure 3For the specific functions of each module, the memory stores the program codes and various types of data required to execute the above modules or sub-modules. The network interface is used to implement data transmission between user terminals or servers.

[0133] In the non-volatile readable storage medium of this embodiment, the program codes and data required to execute all modules in the high-precision hydraulic filling control system based on the combined PID and MPC algorithms of the present application are stored. The server can call the program codes and data of the server to execute the functions of all modules.

[0134] The present application also provides a non-volatile readable storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors are caused to execute the steps of the high-precision hydraulic filling method based on the combined PID and MPC algorithms in any embodiment of the present application.

[0135] The present application also provides a computer program product, including a computer program / instructions. When the computer program / instructions are executed by one or more processors, the steps of the method described in any embodiment of the present application are implemented.

[0136] In summary, the present application provides an efficient, high-precision, and highly flexible control solution for the automatic control of the hydraulic filling system. Through the waveform library construction strategy, fast matching of a large amount of pulse pressure waveform data is achieved. At the same time, by integrating the characteristics of the MPC and PID algorithms, the production requirements of control accuracy and response speed are taken into account, significantly improving the response speed and output accuracy of the hydraulic filling system control, optimizing the defects of low precision, low flexibility, and long response time in the traditional hydraulic filling system, and providing strong technical support for the operation of the hydraulic filling system.

Claims

1. A high-precision hydraulic filling regulation method based on the combined algorithm of PID and MPC, characterized in that It includes the following steps: Collect the pulse pressure waveform received by the pressure-bearing component under the target working condition, and collect the correspondence between the control command of the regulating valve opening and the pulse pressure waveform, and construct a pulse pressure waveform library to update the target waveform of the target working condition; Create a state space model to update the dynamic characteristics of the hydraulic filling system, and dynamically optimize the first-step control input of the pre-regulation according to the dynamic characteristics. Rollingly update the predicted output of the real waveform to track the target waveform until the prediction similarity meets the set threshold of the target waveform; In response to the prediction similarity meeting the set threshold, select the PID algorithm to perform feedback adjustment and dynamic compensation on the control input of the real waveform, and output the target control input that simulates the target waveform under the target working condition.

2. The high-precision hydraulic filling regulation method based on the combined PID and MPC algorithms according to claim 1, wherein The creating a state space model to update the dynamic characteristics of the hydraulic filling system includes the following steps: Obtain in real time the characteristic information and system matrix expressing the dynamic characteristics of the hydraulic filling system. The characteristic information includes pressure, flow rate, hydraulic cylinder output force, servo valve opening command, pipeline pressure fluctuation, and the dynamic system matrix associated with the characteristic information. Among them, pressure, flow rate, and hydraulic cylinder output force are used as the response basis for system state modeling; Use the characteristic information to create a state space model for expressing the relationship between input, output, internal state and external influence in the hydraulic filling system. The internal state relationship is the dynamic coupling of energy conversion and interaction between dynamic characteristics in the hydraulic filling system, and the external influence includes the interference of external pressure fluctuation on the system state.

3. The high-precision hydraulic filling regulation method based on the combined algorithm of PID and MPC according to claim 2, wherein The dynamically optimizing the first-step control input of the pre-regulation according to the dynamic characteristics includes the following steps: Define a rolling horizon optimization objective function using an output tracking term and an input smoothing term, and determine the range of prediction steps and control steps within the control period according to the response time of the dynamic optimization; Based on the output tracking term, minimize the deviation between the real waveform of the predicted output and the target waveform under the current system state within the prediction steps of the control period, and determine the output priority through the weight matrix for the characteristic information of the system state; Based on the input smoothing term, suppress the mutation of the control input within the control steps of the control period, and set a feasibility threshold for the control input to limit the input increment; Generate the first-step control input by minimizing the tracking error and the change of the control quantity according to the rolling horizon optimization objective function.

4. The high-precision hydraulic filling control method based on the combined PID and MPC algorithms according to claim 3, wherein, The rolling update of the real waveform of the predicted output to track the target waveform until the prediction similarity meets the set threshold of the target waveform includes the following steps: Define dynamic model constraints, associate the actual output corresponding to the first-step control input with the actual output at any time based on the self-evolution describing the inside of the model, and associate the historical control input with the actual output at any time based on the driving influence of the quantization of the historical control input on the model state; For the control input sequence of non-first-step control input, use the sequential quadratic programming algorithm to accelerate the solution of the real waveform through the dynamic system matrix, and compare the prediction similarity between the real waveform and the target waveform until the similarity between the real waveform and the target waveform meets the set threshold.

5. The high-precision hydraulic filling regulation method based on the combined PID and MPC algorithms according to claim 2, characterized in that In response to the predicted similarity satisfying a set threshold, selecting a PID algorithm to perform feedback regulation on the control input of the real waveform, including the following steps: Define a discretization equation, correct the system error between the real waveform and the target waveform through a proportional term within a sampling period, eliminate the steady-state error between the real waveform and the target waveform through an integral term, and suppress the overshoot and oscillation of the error change through a derivative term; Define a parameter self-tuning rule, and determine the optimal combination of control parameters in the PID algorithm through the critical proportional gain and the critical oscillation period; Define an anti-integral saturation correction constraint and a reverse integral correction constraint, correct the cumulative error of the integral term according to the constraints, and output the discretized calculated control quantity within a sampling period.

6. The high-precision hydraulic filling regulation method based on the combined PID and MPC algorithms according to claim 5, characterized in that, In response to the predicted similarity satisfying a set threshold, selecting a PID algorithm to perform dynamic compensation on the control input of the real waveform, including the following steps: Determine the system gain, input signal, inertia time, time delay time, and time delay link under PID control, predict the pressure response value through a pipeline pressure fluctuation predictor, and perform time delay compensation on the calculated control quantity according to the pressure response value; Construct an oil viscosity-temperature compensation model, determine the reference viscosity and temperature coefficient, and dynamically output a flow correction instruction for the calculated control quantity according to the detected real-time temperature and temperature change amount by using the compensation model; Output the calculated control quantity as the target control input according to the time delay compensation and the flow correction instruction.

7. The high-precision hydraulic filling regulation method based on the combined PID and MPC algorithms according to claim 1, characterized in that After outputting the target control input that simulates the target waveform under the target working condition, including the following steps: Set an update mechanism for the pulse pressure waveform library, obtain the target waveform under the new target working condition in real time, and obtain the updated device parameters and control parameters of the target device in the hydraulic filling system in real time, and save the corresponding relationship between the latest target waveform and the target device parameters to the pulse pressure waveform database; Read the variable information of the pulse pressure waveform database in real time, and adaptively optimize the parameters in the MPC algorithm and the PID algorithm according to the new working condition type, target waveform, and the corresponding relationship in the variable information; Monitor the dynamic characteristics, system control input and output of the hydraulic filling system in real time, and judge the selection decision of the control algorithm through the monitoring regulation threshold. The selection decision includes at least one selection of the MPC algorithm for regulation or the PID algorithm for regulation, at least one selection of the combined regulation of the MPC algorithm and the PID algorithm, and one or more decisions of selecting the MPC algorithm for regulation and / or the PID algorithm for regulation not less than twice. The number of times of selecting the MPC algorithm or the PID algorithm in the selection of the MPC algorithm for regulation and / or the PID algorithm for regulation not less than twice is at least once.

8. A high-precision hydraulic filling control system based on a combined PID and MPC algorithm, characterized in that The system is used to execute the high-precision hydraulic filling regulation method based on the combined PID and MPC algorithms according to any one of claims 1-7. The system includes: A waveform library module, configured to collect the pulse pressure waveform received by the pressure-bearing member under the target working condition, and collect the corresponding relationship between the control instruction of the regulating valve opening and the pulse pressure waveform, and construct a pulse pressure waveform library to update the target waveform of the target working condition; A waveform tracking module, configured to create a state space model to update the dynamic characteristics of the hydraulic filling system, dynamically optimize the first-step control input of pre-regulation according to the dynamic characteristics, and rollingly update the true waveform of the predicted output to track the target waveform until the predicted similarity meets the set threshold of the target waveform; A target control module, configured to, in response to the predicted similarity meeting the set threshold, select a PID algorithm to perform feedback regulation and dynamic compensation on the control input of the true waveform, and output a target control input that simulates the target waveform under target working conditions.

9. A high-precision hydraulic filling regulation device based on a combined PID and MPC algorithm, comprising: At least one processor, and A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the high-precision hydraulic filling regulation method based on the combined PID and MPC algorithm according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program implemented according to the method according to any one of claims 1 to 7 in the form of computer-readable instructions. When the computer program is called and run by a computer, it executes the steps included in the corresponding method.

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