A method for collaborative control of a hydraulic system for batching a multi-media pavement material

Abstract

The present application relates to a kind of multi-medium pavement material's batching hydraulic system collaborative control method, the method includes IPSO optimization hybrid PID control method and mean deviation composite control method.For the problem that single valve control motor metering accuracy is not high and stability is poor, IPSO optimization hybrid PID control method, response time is short, accelerates system to enter steady process, for the tracking performance and collaborative performance of multiple valve control motor are difficult to take into account, the problem that traditional deviation coupling control compensation difference is difficult, mean deviation composite control method reduces the collaborative error and following error in metering process, improves the metering accuracy, further guarantee the quality of entire project.

Description

Technical Field

[0001] This invention belongs to the field of engineering machinery technology, and in particular relates to a collaborative control method for a hydraulic system for batching multi-media road materials. Technical Background

[0002] Multi-media pavement materials contain various raw materials. During the batching process, the sequence of each raw material entering the continuous mixing device is transport, metering, and transport. The batching process of multi-media pavement materials mainly involves three parts: aggregate weighing, powder weighing, reinforcing fiber weighing, and liquid weighing. However, in the automated batching process, after the discharge valve is closed, the material remaining in the pipeline after the solid material is weighed will still enter the metering device, causing a certain deviation between the amount of each solid material used and the theoretical batching amount, thus affecting the final batching accuracy. In addition, if there are too many impurities in the aggregate, it will distort the weighing of the aggregate and affect the metering accuracy of the pavement raw materials.

[0003] On the other hand, while traditional PID control has a certain adjustment capability for the batching hydraulic system, the valve-controlled motor system within the hydraulic system is a complex nonlinear time-varying system. Traditional PID control exhibits low robustness and control efficiency when handling complex nonlinear and time-varying systems, failing to meet the control accuracy requirements under harsh environments. Furthermore, in the process of metering raw materials for multi-media road construction, material metering is a dynamic and continuous process, making mathematical models difficult to establish. When changes occur or disturbances occur within the hydraulic system, traditional PID control exhibits poor stability and response performance for single valve-controlled motors. Simultaneously, traditional PID control has poor coordinated control effects with multiple valve-controlled motors. Multi-media road construction materials are batched in a specific proportion, and the equipment is in motion during operation, making it more susceptible to disturbances than a typical stationary device. Any change in the control accuracy of a subsystem significantly impacts the overall work quality.

[0004] The essence of a batching hydraulic system is the coordinated control of multiple valve-controlled motors, which in turn relies on the optimal metering of each individual valve-controlled motor. Only when the metering accuracy and response of each individual valve-controlled motor are high can optimal coordinated control of multiple valve-controlled motors be achieved, resulting in better mixing uniformity. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention discloses a collaborative control method for a hydraulic system for batching multi-media road materials. To address the issues of low metering accuracy and poor stability of single-valve-controlled motors, an improved particle swarm optimization (IPSO)-optimized hybrid PID (PID-fuzzy PID) control method is proposed, which has a short response time and accelerates the system's entry into steady-state. To address the difficulty in simultaneously achieving tracking and collaborative performance of multiple valve-controlled motors and the poor compensation of traditional deviation coupling control, a mean-deviation composite control strategy is proposed, which reduces collaborative and following errors in the metering process, improves metering accuracy, and further ensures the overall quality of the project.

[0006] To achieve the above objectives, this invention proposes a collaborative control method for a batching hydraulic system of multi-media road materials. The method is characterized by including an IPSO optimized hybrid PID control method and a mean deviation composite control method.

[0007] Preferably, the IPSO optimized hybrid PID control method is used to control a single valve-controlled motor in a batching hydraulic system, and the mean deviation composite control method is used to control multiple valve-controlled motors in a hydraulic system.

[0008] Preferably, the IPSO-optimized hybrid PID control method includes:

[0009] Step 1: When the system is in a dynamic process, the single valve-controlled motor is sequentially optimized by particle swarm optimization using the IPSO-PID control method to obtain the dynamic optimal PID parameters.

[0010] Step 2: When the system transitions from the dynamic process to the steady-state process, the IPSO-FPID control method is switched to the IPSO-FPID control method via a switching factor.

[0011] Step 3: During the steady-state process of the system, the single valve-controlled motor is controlled by the IPSO-FPID control method. The IPSO-FPID control method first tunes the dynamic optimal PID parameters through IPSO, and then combines fuzzy control to obtain the optimal PID parameters for online modification by fuzzy control.

[0012] Preferably, the specific process of obtaining the optimal PID parameters for the single valve-controlled motor by particle swarm optimization using the IPSO-PID control method in step 1 is as follows:

[0013] (1a) Using a single valve-controlled motor as the fitness function, determine the particle swarm parameters;

[0014] (1b) Run the IPSO-PID control simulation model in MATLAB, and simultaneously apply the integral coefficients K to the particle swarm after the parameters are determined. I proportionality coefficient K P and differential coefficient KD Assignment;

[0015] (1c) Compare the output performance metrics with the termination condition. If the output performance meets the termination condition, the simulation stops; if the output performance does not meet the termination condition, the simulation continues to iterate until the output performance metrics meet the termination condition or the number of iterations reaches 90. The simulation then stops. The K value corresponding to the simulation stopping is... I K P K D These are the optimal PID parameters.

[0016] Preferably, the learning factor in the particle swarm in (1a) is dynamic, and the method for adjusting the learning factor is as follows:

[0017] c1=cstart1+(cend1-cstart1)×(k / k max )

[0018] c2=cstart2+(cend2-cstart2)×(k / k max )

[0019] Where c1 is the local extremum learning factor; cstart1 is the minimum value of c1; cend2 is the maximum value of c1; c2 is the global extremum learning factor; cstart2 is the minimum value of c2; cend2 is the maximum value of c2; and k max It represents the maximum number of iterations or generation, and k is the current iteration number.

[0020] Preferably, the weighting coefficients in the particle swarm described in (1a) are adjusted using nonlinear dynamic inertia weights:

[0021]

[0022] Where w is the inertia weight; w max and w min The maximum and minimum inertia weights are represented by , and C is the control factor that controls the smoothness of the w curve.

[0023] Preferably, the conversion factor in step 2 is 0.

[0024] Preferably, step 3 specifically includes:

[0025] (3a) The PID parameters are tuned based on IPSO;

[0026] (3b) In fuzzy control, the fuzzy universe of discourse for the speed deviation e is set to [-3, 3], and the speed deviation change rate ec and ΔK are set to [-3, 3]. P ΔK I and ΔK D The fuzzy universe of discourse is also set to [-3, 3], where ΔKP For the online modification of K in the fuzzy control P Additional value, ΔK I For the online modification of K in the fuzzy control I Additional value, ΔK D For the online modification of K in the fuzzy control D The additional value is that the fuzzy universe of discourse combines Gaussian membership functions and triangular membership functions with respect to e, ec, and ΔK. P ΔK I and ΔK D Perform fuzzification and output e, ec, and ΔK in MATLAB. P ΔK I and ΔK D Membership function;

[0027] (3c) Add quantization and scaling factors to the fuzzy control, so that ec and ΔK P ΔK I and ΔK D The fuzzy universe of discourse is the same as the fuzzy universe of discourse of the rotational speed deviation e;

[0028] (3d) After generating a fuzzy rule base based on the rotational speed deviation e and the rate of change of rotational speed deviation ec, the centroid method is used for defuzzification to obtain ΔK. P ΔK I and ΔK D The optimal value, the fuzzy rule base includes ΔK P Modular fuzzy rules, ΔK I Fuzzy rules, ΔK D There are 49 fuzzy rules in each category.

[0029] Preferably, the fuzzy domain adopts seven fuzzy languages: NB, NM, NS, Z, PS, PM, and PB.

[0030] Preferably, the mean deviation composite control method is as follows: based on the deviation coupling controller, an IPSO optimized hybrid PID control method is introduced to build a composite controller. The composite controller includes a single valve-controlled hydraulic motor controller and a cooperative controller. The cooperative controller synchronously inputs the error between the speed of the single valve-controlled hydraulic motor and the average speed of the other hydraulic motors to the single valve-controlled hydraulic motor controller.

[0031] The beneficial effects of this invention are:

[0032] Compared with traditional IPSO-PID and IPSO-FPID control, the IPSO-optimized hybrid PID control proposed in this invention has a shorter control response time for single valve-controlled motors, accelerates the system's entry into steady-state process, and offers high metering accuracy and good stability. The mean-deviation composite control method proposed in this invention transforms the synchronous control of multiple valve-controlled motors into the problem of single-valve-controlled motor speed regulation, avoiding the phenomenon of traditional deviation-coupled control controllers calculating the speeds of all hydraulic motors. This solves the problems of complex structure, large online calculation load, and excessive coupling, improves metering accuracy, reduces tracking and coordination errors in the batching hydraulic system, and further ensures the quality of the entire project. Attached Figure Description

[0033] Figure 1 This is a flowchart of the present invention;

[0034] Figure 2 A schematic diagram of the structure of the hybrid PID control method optimized for IPSO;

[0035] Figure 3 This is a framework diagram of the IPSO-PID control method;

[0036] Figure 4 This is a schematic diagram of the IPSO-FPID control method.

[0037] Figure 5 For the output of e, ec, and ΔK in MATLAB P ΔK I and ΔK D Membership function;

[0038] Figure 6 It is a bias-coupled controller structure;

[0039] Figure 7 This is a composite controller structure constructed using the first method in deviation coupling control;

[0040] Figure 8 It is a mean-deviation composite controller structure;

[0041] Figure 9 The step response curves of the metering system under different conversion factors;

[0042] Figure 10 This is a composite controller constructed using the second method in deviation coupling control;

[0043] Figure 11 Model the batching hydraulic system;

[0044] Figure 12 A control system built in MATLAB;

[0045] Figure 13 Output response curve for the batching hydraulic system;

[0046] Figure 14 The error response curve of the batching hydraulic system is tracked.

[0047] Figure 15 This is a structural diagram of a single valve-controlled motor control model.

[0048] Figure 16 This is the step response curve of the metering system under normal operating conditions;

[0049] Figure 17 The step response curve of the metering system under disturbance;

[0050] Figure 18 Comparison of tracking errors under three different deviation coupling control methods;

[0051] Figure 19 The cooperative error of the composite controller constructed using the first method in deviation coupling control;

[0052] Figure 20 The cooperative error of the composite controller constructed using the second method in deviation coupling control;

[0053] Figure 21 This refers to the collaborative error in the mean deviation composite control. Specific Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] refer to Figure 1 This invention proposes a collaborative control method for the batching hydraulic system of multi-media pavement materials. The multi-media pavement materials are composed of aggregates, water, fibers, cement, and emulsified asphalt. The method includes an IPSO optimized hybrid PID control method and a mean deviation composite control method.

[0056] The IPSO optimized hybrid PID control method is used to control a single valve-controlled motor in a batching hydraulic system, and the mean deviation composite control method is used to control multiple valve-controlled motors in a hydraulic system.

[0057] refer to Figures 2 to 5 IPSO-optimized hybrid PID control method is used to control a single valve-controlled motor in a batching hydraulic system, including:

[0058] Step 1: When the system is in a dynamic process, the IPSO-PID control method has a fast response speed. The IPSO-PID control method is used to sequentially perform particle swarm optimization on the single valve-controlled motor to obtain the dynamically optimal PID parameters.

[0059] (1a) Using a single valve-controlled motor as the fitness function, determine the particle swarm parameters;

[0060] (1b) Run the IPSO-PID control simulation model in MATLAB, and simultaneously apply the integral coefficients K to the particle swarm after the parameters are determined. I proportionality coefficient K P and differential coefficient K D Assignment;

[0061] (1c) Compare the output performance metrics with the termination condition. If the output performance meets the termination condition, the simulation stops; if the output performance does not meet the termination condition, the simulation continues to iterate until the output performance metrics meet the termination condition or the number of iterations reaches 90. The simulation then stops. The K value corresponding to the simulation stopping is... I K P K D These are the dynamically optimal PID parameters.

[0062] In the particle swarm described in (1a), the learning factor is dynamic, and the method for adjusting the learning factor is as follows:

[0063] c1=cstart1+(cend1-cstart1)×(k / k max )

[0064] c2=cstart2+(cend2-cstart2)×(k / k max )

[0065] Where c1 is the local extremum learning factor; cstart1 is the minimum value of c1; cend2 is the maximum value of c1; c2 is the global extremum learning factor; cstart2 is the minimum value of c2; cend2 is the maximum value of c2; and k max It represents the maximum number of iterations or generation, and k is the current iteration number.

[0066] In (1a), the weighting coefficients in the particle swarm are adjusted using nonlinear dynamic inertia weights.

[0067]

[0068] Where w is the inertia weight; w max and w min The maximum and minimum inertia weights are represented by , and C is the control factor that controls the smoothness of the w curve.

[0069] Step 2: When the system transitions from the dynamic process to the steady-state process, the IPSO-FPID control method is switched to the IPSO-FPID control method via a switching factor of 0.

[0070] Step 3: During the system's steady-state process, the IPSO-FPID control method demonstrates good steady-state performance. The single-valve-controlled motor is controlled using the IPSO-FPID method. This method first tunes the dynamically optimal PID parameters using IPSO, and then combines this with fuzzy control to derive the optimal PID parameters for online modification under fuzzy control.

[0071] (3a) The PID parameters were tuned based on IPSO, and the results are shown in Table 1.

[0072] Table 1. Tuning of PID parameters for valve-controlled motors in batching hydraulic systems based on IPSO.

[0073]

[0074] (3b) To improve the applicability and robustness of the controller, the deviation needs to be kept small or within a reasonable range. Based on the actual aggregate metering situation, in fuzzy control, the fuzzy domain of the speed deviation e is set to [-3, 3], and the speed deviation change rate ec and ΔK are set to [-3, 3]. P ΔK I and ΔK D The fuzzy universe of discourse is also set to [-3, 3], where ΔK P For the online modification of K in the fuzzy control P Additional value, ΔK I For the online modification of K in the fuzzy control I Additional value, ΔK D For the online modification of K in the fuzzy control D The added value.

[0075] In fuzzy control, the fuzzy domain employs seven fuzzy languages ​​(NB, NM, NS, Z, PS, PM, PB) combined with Gaussian and triangular membership functions to represent e, ec, and ΔK. P ΔK I and ΔK D After fuzzification, MATLAB outputs e, ec, and ΔK. P ΔK I and ΔK D Membership function;

[0076] (3c) Add quantization and scaling factors to fuzzy control to make ec and ΔK P ΔK I and ΔK D The fuzzy universe of discourse is the same as the fuzzy universe of discourse of the rotational speed deviation e;

[0077] (3d) After generating a fuzzy rule base based on the rotational speed deviation e and the rate of change of rotational speed deviation ec, the centroid method is used for defuzzification to obtain ΔK. P ΔK I and ΔK D The optimal value, the fuzzy rule base includes ΔK P Fuzzy rules, ΔK I Fuzzy rules, ΔK D There are 49 fuzzy rules in each category, as shown in Tables 2-4:

[0078] Table 2. ΔK in the fuzzy PID controller P rule

[0079]

[0080] Table 3. ΔK in the fuzzy PID controller I rule

[0081]

[0082] Table 4. ΔK in the fuzzy PID controller D rule

[0083]

[0084] refer to Figure 6 , Figure 7 , Figure 8 Based on the deviation coupling controller, an IPSO optimized hybrid PID control method is introduced to build a composite controller. The composite controller includes a single valve-controlled hydraulic motor controller and a cooperative controller. The cooperative controller synchronously inputs the error between the speed of the single valve-controlled hydraulic motor and the average speed of the other hydraulic motors to the single valve-controlled hydraulic motor controller.

[0085] The present invention will be further described in detail below through specific embodiments.

[0086] I. Selection of Transition Factors for IPSO-Optimized Hybrid PID

[0087] Nine sets of conversion factors (-15, -10, -5, 3, 0, 3, 5, 10, 15) are selected to simulate and measure the response changes of the hydraulic system. Figure 9 The optimal conversion factor can be determined to be 0.

[0088] II. Selection of the Construction Method for the Mean Deviation Composite Controller

[0089] refer to Figure 6 , Figure 7 , Figure 8 , Figure 10In the deviation coupling controller, there are two ways to construct the composite controller. The first method uses the output of the coordinating controller as an input to the single valve-controlled motor controller. This method better balances coordinating and tracking performance, improving the system's metering accuracy. The second method uses the coordinating controller after the single valve-controlled motor controller. However, the coordinating controller affects not only the coordinating performance of the batching hydraulic system but also its tracking performance, making it difficult to simultaneously achieve both. The single valve-controlled motor controller has a more significant impact on the metering accuracy of the batching hydraulic system. Therefore, this invention improves upon the first method by using the coordinating controller to compensate for the error between the single valve-controlled hydraulic motor's speed and the average speed of the other hydraulic motors. Upon receiving feedback, the hydraulic motor adjusts its speed, transforming the synchronization of multiple valve-controlled motors into the speed regulation of a single valve-controlled motor.

[0090] III. Step Response of Batching Hydraulic System Without Controller

[0091] refer to Figure 11 , Figure 12 The batching hydraulic system model built with Amesim software was jointly simulated with the control system model built with MATLAB to obtain the output curve of the step response and the tracking error.

[0092] refer to Figure 13 , Figure 14 The response curves under controller-free and interference-free conditions are analyzed, and the response of a single valve-controlled motor and the coordinated control of multiple valve-controlled motors are analyzed.

[0093] IV. Reference Figures 15 to 17 The batching hydraulic system model built with Amesim software was jointly simulated with the uncontrolled, IPSO-PID controlled, IPSO-FPID controlled, and IPSO optimized hybrid PID control system models built with MATLAB. The response performance of the uncontrolled, IPSO-PID controlled, IPSO-FPID controlled, and IPSO optimized hybrid PID controlled systems under step response and added disturbance conditions was compared.

[0094] (1) Step response of a single valve-controlled motor

[0095] To achieve higher metering accuracy in the aggregate metering hydraulic system, a step signal was used as the input signal for the control system. The initial value of the step signal was 0 r / min, and the final value was 393 r / min. The sampling time was 0.02 seconds, and the simulation time was 5 seconds. (Reference) Figure 15 The results are shown in Table 5.

[0096] Table 5 Evaluation Indicators for Metering and Control Systems

[0097]

[0098] According to reference 15:

[0099] ①Under no control, the response time to reach the steady state is 1.16s. Although it can respond quickly, there are large fluctuations in the steady state around 376r / min, and it is quite different from the final value of 393r / min.

[0100] ② Under IPSO-PID control, the response curve reaches the steady state in 3.5s. Although the cyclic fluctuations in the steady state are reduced, the response time is longer and there is overshoot around 1.3s.

[0101] ③ Under IPSO-FPID control, the response curve enters the steady state stage in 1.32s, the cyclic fluctuations in the steady state stage are further reduced, and there is no overshoot in the response stage; however, the response curve fluctuates between 1.4s and 1.6s.

[0102] ④ The response curve of the IPSO-optimized hybrid PID control is similar to that of the IPSO-FPID control, but its steady-state entry time is 1.21s, the rise time is shortened by 0.11s, and there are no fluctuations in the steady-state stage. Based on the above comparative analysis, the aggregate metering hydraulic system under IPSO-optimized hybrid PID control exhibits faster response and stability, ensuring the accuracy of aggregate metering.

[0103] (2) Analysis of the anti-interference performance of a single valve-controlled motor

[0104] To verify the anti-interference performance of the proposed IPSO-optimized hybrid PID controller, a step signal was used as the input signal of the control system. The initial value of the step signal was 0 r / min, and the final value was 393 r / min. The sampling time was 0.02 seconds, and the simulation time was 10 seconds. At the same time, the interference signal was set to occur between 6 seconds and 9 seconds. At 6 seconds, the output signal was reduced by 50 r / min, and the original value was restored at 8 seconds.

[0105] Response results reference Figure 16 It can be seen that during the period of disturbance, the metering hydraulic system under IPSO-PID control exhibits larger fluctuations in the steady state; the metering hydraulic system under IPSO-FPID control shows some oscillations, and overshoot occurs when the disturbance signal disappears, before re-entering the steady state at 9.3s. Therefore, under disturbance, the aggregate metering hydraulic system under IPSO-optimized hybrid PID control shows the best response curve stability and superior static and dynamic performance. The anti-interference capabilities of no control, IPSO-PID control, IPSO-FPID control, and IPSO-optimized hybrid PID control gradually increase under disturbance.

[0106] 5. The batching hydraulic system model built with Amesim software is jointly simulated with the composite controller models built with the first method, the second method, and the mean deviation composite controller models built with MATLAB. The tracking error and coordination error of the simulation results of the composite controller built with the first method, the second method, and the mean deviation composite controller are compared.

[0107] (1) Tracking error

[0108] refer to Figure 18 It can be seen that the tracking error of the semi-flexible pavement material metering in the batching hydraulic system under the three deviation coupling control methods is as follows:

[0109] The similarities are: after the input signal is applied, the tracking error reaches its maximum at 1 second and then decreases rapidly.

[0110] The differences are as follows: The tracking error changes of the composite controller constructed using the first method are quite similar to those of the mean-deviation composite controller, showing no significant change. The tracking errors for aggregate, water, and cement metering tend to stabilize around 1.1s, while those for fiber and emulsified asphalt stabilize around 1.3s, approaching zero after entering the steady-state stage. For the composite controller constructed using the second method, the metering of semi-flexible pavement raw materials tends to stabilize around 1.5s, but remains slightly higher than zero after entering the steady-state stage. In summary, the composite controller constructed using the first method and the mean-deviation composite controller are superior to the composite controller constructed using the second method.

[0111] (2) Cooperative error

[0112] Under the composite controller constructed in the first method, refer to Figure 19 As can be seen in (a), the synergistic error between aggregate and cement is -170 r / min; Reference Figure 19 As can be seen in (b), the synergistic error between cement and water, and between cement and emulsified asphalt, is 215 r / min, and the sum of the two is 430 r / min.

[0113] Under the composite controller constructed in the second method, refer to Figure 20 As can be seen in (a), the synergistic error between cement and fiber is -155 r / min; Reference Figure 20 As can be seen in (b), the synergistic error between fiber and emulsified asphalt is 85 r / min, and the maximum sum of the two is 240 r / min.

[0114] Under the mean deviation composite controller, reference Figure 21 As can be seen in (b), the co-occurrence error between the water level and the mean is -88 r / min; Reference Figure 21(d) It can be seen that the error between cement and the mean is 115 r / min, and the sum of the two is 203 r / min.

[0115] In conclusion, the mean deviation composite control method is the most effective.

[0116] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for coordinated control of a hydraulic system for batching multi-media road materials, characterized in that, This method includes an IPSO-optimized hybrid PID control method and a mean-deviation composite control method; The IPSO optimized hybrid PID control method is used to control a single valve-controlled motor in a batching hydraulic system, and the mean deviation composite control method is used to control a multi-valve-controlled motor in a hydraulic system. The IPSO-optimized hybrid PID control method includes: Step 1: When the system is in a dynamic process, the single valve-controlled motor is sequentially optimized by particle swarm optimization using the IPSO-PID control method to obtain the dynamic optimal PID parameters. Step 2: When the system transitions from the dynamic process to the steady-state process, the IPSO-PID control method is switched to the IPSO-FPID control method via a switching factor. Step 3: During the steady-state process of the system, the single valve-controlled motor is controlled by the IPSO-FPID control method. The IPSO-FPID control method first tunes the dynamic optimal PID parameters through IPSO, and then combines fuzzy control to obtain the optimal PID parameters for online modification by fuzzy control.

2. The method for coordinated control of a hydraulic system for batching multi-media road materials according to claim 1, characterized in that, The specific process of obtaining the optimal PID parameters for the single valve-controlled motor by performing particle swarm optimization using the IPSO-PID control method in step 1 is as follows: (1a) Using a single valve-controlled motor as the fitness function, determine the particle swarm parameters; (1b) Run the IPSO-PID control simulation model in MATLAB, through the particle swarm after parameter determination respectively to the integral coefficient K I , the proportional coefficient K P And the differential coefficient K D Assign value; (1c) Compare the output performance indicators with the termination conditions. When the output performance meets the termination conditions, the simulation stops. When the output performance does not meet the termination condition, the simulation continues to iteratively update until the output performance index meets the termination condition or the number of iterations reaches 90, the simulation stops, and K I , K P , K D are the optimal PID parameters.

3. The method for coordinated control of a hydraulic system for batching multi-media road materials according to claim 2, characterized in that, In the particle swarm described in (1a), the learning factor is dynamic, and the method for adjusting the learning factor is as follows: c1 = cstart1 + (cend1 - cstart1) x (k / k max ) c2 = cstart2 + (cend2 - cstart2) x (k / k max ) wherein cl is a local extremum learning factor; cstartl is a minimum value of cl; cendl is a maximum value of cl, c2 is a global extremum learning factor; cstart2 is a minimum value of c2; cend2 is a maximum value of c2, k max is the maximum number of iterations or generations, k is the current iteration number.

4. The method for coordinated control of a hydraulic system for batching multi-media road materials according to claim 2, characterized in that, The weighting coefficients in the particle swarm described in (1a) are adjusted using nonlinear dynamic inertia weights: Where w is the inertia weight; w max and w min Represents the maximum and minimum inertia weights; C is the control factor controlling the smoothness of the w curve; k is the current iteration number; k max It represents the maximum number of iterations or generation.

5. The method for coordinated control of a hydraulic system for batching multi-media road materials according to claim 1, characterized in that, The conversion factor mentioned in step 2 is 0.

6. The method for coordinated control of a hydraulic system for batching multi-media road materials according to claim 1, characterized in that, Step 3 specifically includes: (3a) The PID parameters are tuned based on IPSO; (3b) In fuzzy control, the fuzzy universe of discourse for the speed deviation e is set to [-3, 3], and the speed deviation change rate ec and ΔK are set to [-3, 3]. P ΔK I and ΔK D The fuzzy universe of discourse is also set to [-3, 3], where ΔK P For the online modification of K in the fuzzy control P Additional value, ΔK I For the online modification of K in the fuzzy control I Additional value, ΔK D For the online modification of K in the fuzzy control D The additional value is that the fuzzy universe of discourse combines Gaussian membership functions and triangular membership functions with respect to e, ec, and ΔK. P ΔK I and ΔK D Perform fuzzification and output e, ec, and ΔK in MATLAB. P ΔK I and ΔK D Membership function; (3c) Add a quantization factor and a scaling factor to the fuzzy control, so that ec and ΔK P ΔK I and ΔK D The fuzzy universe of discourse is the same as the fuzzy universe of discourse of the rotational speed deviation e; (3d) After generating a fuzzy rule base based on the rotational speed deviation e and the rate of change of rotational speed deviation ec, the centroid method is used for defuzzification to obtain ΔK. P ΔK I and ΔK D The optimal value, the fuzzy rule base includes ΔK P Fuzzy rules, ΔK I Fuzzy rules, ΔK D There are 49 fuzzy rules in each category.

7. The method for coordinated control of a hydraulic system for batching multi-media road materials according to claim 6, characterized in that, The fuzzy domain adopts seven fuzzy languages: NB, NM, NS, Z, PS, PM, and PB.

8. The method for coordinated control of a hydraulic system for batching multi-media road materials according to claim 1, characterized in that, The mean deviation composite control method is as follows: based on the deviation coupling controller, an IPSO optimized hybrid PID control method is introduced to build a composite controller. The composite controller includes a single valve-controlled hydraulic motor controller and a cooperative controller. The cooperative controller synchronously inputs the error between the speed of the single valve-controlled hydraulic motor and the average speed of the other hydraulic motors to the single valve-controlled hydraulic motor controller.

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