Hydraulic support multi-objective optimization control method for temporary support of mine roadway tunneling

Abstract

The invention belongs to the technical field of mining machinery automation control, and relates to a hydraulic support multi-objective optimization control method for temporary support of mine roadway tunneling, which comprises the following steps: constructing a state vector and a continuous state equation of a hydraulic support system and discretizing to obtain a discrete state space model; displacement and pressure synchronous tracking of the hydraulic support system is taken as a target, a model prediction controller is adopted to carry out rolling optimization search of a control quantity under a variable constraint condition, an expansion state observer is combined to estimate total disturbance of the hydraulic support system in real time, the total disturbance is fed forward and compensated into the model prediction controller, and a final control quantity is generated; and constructing an asymmetric coupling synchronous network by taking the master cylinder as a benchmark, and forming cooperative control of the master cylinder and the slave cylinder by adopting a synchronous deviation mechanism and a final control quantity to finish multi-objective optimization control of the hydraulic support. According to the invention, safe, efficient, stable and energy-saving accurate control of a single hydraulic column and multi-target cooperative control of pump-controlled multiple hydraulic columns can be realized.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for mining machinery, and in particular to a multi-objective optimization control method for hydraulic supports used for temporary support in mine roadway excavation. Background Technology

[0002] The main function of traditional hydraulic supports for temporary tunneling is as follows: After the tunneling equipment has excavated a certain distance (usually 80cm), the hydraulic supports are moved to the open roof area and placed in a suitable support position. Operators use the hydraulic control system to supply high-pressure emulsion to the column jacks to raise the column, causing the top beam of the support to rise and make close contact with the roof, thus providing temporary roof support. At this stage, operators typically raise the column until it reaches the initial support force set by the system (the initial support force of the support on the roof). At this point, the support is in the initial support stage, and the roof begins to be under pressure. As the roof sinks or the pressure increases, the liquid pressure inside the column continues to rise, and the support force provided by the support also increases (resistance increase stage) to balance the roof pressure and maintain roof stability. When the support reaches its rated working resistance (maximum support force), the safety valve will open to release fluid. After the anchoring workers complete the permanent anchoring work in this area, the temporary hydraulic support set is removed and transported to the front end of the tunneling face to complete a new round of support.

[0003] This process requires flexible adjustment of the displacement and pressure of the column push rod, which is currently mostly done manually by workers, making it time-consuming and dangerous. Aiming for rapid and precise operation with minimal or even no human intervention, this paper proposes to achieve multi-objective synchronous control of displacement and pressure in the hydraulic support control system to limit time costs. Each column raising involves two sets of four-cylinder supports, requiring a centralized pump control + distribution valve power supply method (a single high-power servo pump + electro-hydraulic proportional flow distribution valve controlling the flow rate of each column). This method is low-cost but suffers from coupling interference, and the need for synchronous control of displacement and pressure parameters must be addressed. Therefore, an accurate, rapid, and robust control method and strategy are required. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-objective optimization control method for hydraulic supports used in temporary support for mine roadway excavation, which can achieve precise control of a single hydraulic column in a safe, efficient, stable and energy-saving manner, as well as multi-objective collaborative control of multiple hydraulic columns controlled by a pump.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A multi-objective optimization control method for hydraulic supports used in temporary support during mine roadway excavation includes:

[0007] Construct the state vector and continuous state equation of the hydraulic support system, and discretize them to obtain a discrete state space model;

[0008] Based on the discrete state-space model, with the goal of synchronously tracking the displacement and pressure of the hydraulic support system, a model predictive controller is used to perform rolling optimization search of the control quantity under variable constraints. At the same time, an extended state observer is combined to estimate the total disturbance of the hydraulic support system in real time, and the total disturbance is fed forward into the model predictive controller to generate the final control quantity.

[0009] An asymmetric coupled synchronous network is constructed based on the master cylinder of the hydraulic support system. A synchronous deviation mechanism is adopted to form the collaborative control of the master and slave cylinders with the final control quantity, thereby completing the multi-objective optimization control of the hydraulic support system.

[0010] Optionally, the state vector is:

[0011] ;

[0012] in, For state vectors, For piston displacement, Piston speed; For rodless chamber pressure, For rod chamber pressure; For pump output flow rate, For volumetric pressure, Indicates transpose;

[0013] The continuity state equation is:

[0014] ;

[0015] in, It is a continuous state vector. For piston mass, The effective working area of ​​the rodless cavity. The effective working area of ​​the rod cavity, For frictional resistance, The elastic modulus of hydraulic oil. These are the volumes of the rodless cavity and the rod cavity, respectively. The meaning is the equivalent total volume of the hydraulic system. These represent the inflow and outflow rates from the chamber, respectively. This refers to the pump-valve linkage control coefficient. This is the input signal for pump and valve control.

[0016] Optionally, the discrete state-space model is:

[0017] ;

[0018] in, For discrete time step index, The sampling period is To control the input, Here are the system state equations.

[0019] Optionally, the objective function of the model prediction controller is:

[0020] ;

[0021] The constraints of the model prediction controller are:

[0022] ;

[0023] in, The objective function value, For discrete time step index, For the output vector, For reference trajectory, For the tracking error weight matrix, To control the incremental smoothing weights, To predict the time-domain step size index, To predict the time-domain step size, To control the time-domain step size index, To control the time-domain step size, To control the input increment, For rodless chamber pressure, For rod chamber pressure, This represents the piston displacement.

[0024] Optionally, the extended state observer is:

[0025] ;

[0026] Nonlinear functions: ;

[0027] in, For the observer state, For observer gain, For observation error, To control the gain coefficient, To control the input, For powers, For linear interval thresholds, For piston displacement, For discrete time step index, The sampling period.

[0028] Optionally, the total disturbance is fed forward to the model predictive controller to generate the final control quantity, including:

[0029] ;

[0030] ;

[0031] in, For state vectors, For discrete time step index, The sampling period is The system state equations are as follows: For the observer state, For piston mass, To control the gain coefficient, Indicates transpose. For the final control quantity, The control quantity calculated for the model predictive controller.

[0032] Optionally, employing a synchronization deviation mechanism to form coordinated control of the master and slave cylinders with the final control quantity includes:

[0033] The final control quantity is input into the asymmetric coupling synchronization network to calculate the synchronization deviation between hydraulic cylinders. The synchronization deviation is then embedded into the objective function of the model predictive controller. Through adaptive cooperative gain, force balance constraint, and attitude stability optimization, cooperative control of the master and slave cylinders is formed.

[0034] Optionally, calculating the synchronization deviation between hydraulic cylinders includes:

[0035] ;

[0036] in, For the first Displacement of each hydraulic cylinder Reference displacement for the master cylinder; The coefficients are asymmetric coupling coefficients. For the first Displacement of each hydraulic cylinder This refers to the synchronization deviation between hydraulic cylinders.

[0037] Optionally, the adaptive cooperative gain is:

[0038] ;

[0039] in, For the first The hydraulic cylinder and the first The adaptive cooperative gain between hydraulic cylinders, i.e., the asymmetric coupling coefficient; This is the reference value for the synergistic gain; This is the stiffness sensitivity coefficient; This represents the change in pressure in the rodless chamber. This represents the change in piston displacement. The equivalent stiffness of the top plate;

[0040] The force balance constraint is:

[0041] ;

[0042] in, The tilt angle of the hydraulic support; The supporting force generated for the column; To balance the balancing force generated by the jack; External loads applied to the top plate; For the rodless cavity pressure of the column; The effective working area of ​​the rodless cavity of the column; To balance the pressure in the rodless chamber of the jack; To balance the effective working area of ​​the rodless cavity of the jack;

[0043] The attitude stability is optimized as follows:

[0044] ;

[0045] in, These are the attitude stability weighting coefficients; The base weight value.

[0046] The beneficial effects of this invention are as follows:

[0047] (1) Adopting a disturbance rejection-optimization dual-loop collaborative architecture: By combining ESO disturbance feedforward compensation with MPC rolling optimization, the limitations of traditional single-loop control in balancing optimization and disturbance rejection can be overcome, effectively suppressing pressure fluctuations, improving displacement tracking accuracy, and solving control instability problems under complex working conditions.

[0048] (2) An adaptive coordination mechanism is proposed: the coupling gain between cylinders is adjusted based on the real-time index of the roof stiffness. Compared with fixed gain control, it can dynamically enhance the coordination of multiple cylinders under working conditions such as soft rock, reduce the displacement deviation between cylinders, break through the bottleneck of multi-cylinder synchronization, and reduce the risk of roof accidents. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of a multi-objective optimization control method for hydraulic supports used for temporary support in mine roadway excavation, according to an embodiment of the present invention.

[0051] Figure 2This is a schematic diagram of the process layering structure according to an embodiment of the present invention. Detailed Implementation

[0052] 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.

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] This embodiment provides a multi-objective optimization control method for hydraulic supports used in temporary support for mine roadway excavation, including:

[0055] Construct the state vector and continuous state equation of the hydraulic support system, and discretize them to obtain a discrete state space model;

[0056] Based on the discrete state-space model, with the goal of synchronously tracking the displacement and pressure of the hydraulic support system, a model predictive controller is used to perform rolling optimization search of the control quantity under variable constraints. At the same time, an extended state observer is combined to estimate the total disturbance of the hydraulic support system in real time, and the total disturbance is fed forward into the model predictive controller to generate the final control quantity.

[0057] An asymmetric coupled synchronous network is constructed based on the master cylinder of the hydraulic support system. A synchronous deviation mechanism is adopted to form the collaborative control of the master and slave cylinders with the final control quantity, thereby completing the multi-objective optimization control of the hydraulic support system.

[0058] Specifically, such as Figure 1As shown, this embodiment first establishes a quantitative relationship between multiple objectives such as support displacement and pressure and global performance based on historical working condition data and sensor feedback, thus clarifying the control objectives. Secondly, a pump-controlled multi-hydraulic cylinder system model is established, involving aspects such as hydraulic pump speed control, flow distribution and disturbance of the multi-hydraulic cylinder system with proportional flow valve control, and dynamic balance of single hydraulic cylinder lifting and support. Then, based on real-time roof pressure, coal and rock hardness, and equipment status, the priorities and constraint boundaries of key parameters among the multiple objectives are determined. A lightweight multi-objective optimization algorithm is used, employing model predictive control to solve for the optimal control instruction set (control pump voltage, proportional valve flow distribution coefficient, etc.) that satisfies the dynamic constraints. Finally, a state observer is designed, using the core component of the active disturbance rejection control algorithm—a full-dimensional observer—to estimate the actual values ​​of state variables and correct the reference control variables. This is executed cyclically to achieve automated multi-objective tracking and control of the entire hydraulic system. The method also considers how to detect the system's execution effect through real-time data from attitude sensors and pressure gauges in practical applications, and further correct the flow weight allocation parameters and key parameters of the control model based on the target deviation.

[0059] like Figure 2 The first layer of this embodiment constructs a continuous electro-hydraulic coupling model of a single strut, which is discretized into a real-time calculation model using the forward Euler method, laying the foundation for physical modeling. The second layer innovatively designs a "optimization-disturbance resistance" dual-loop parallel control: MPC (Multi-Objective Calculation) solves the multi-objective optimization problem to generate a reference control quantity, while ESO (Electronic Stability Optimization) estimates the total disturbance in real time and performs feedforward compensation; the fusion of these two approaches achieves high-precision tracking and anti-interference capabilities. The third layer constructs an asymmetric coupled synchronization network based on the master cylinder, embedding the synchronization error penalty term into the MPC objective function, enabling each cylinder to actively achieve force balance and attitude stability while optimizing its own control quantity. This process, through layered decoupling, dual-loop fusion, and collaborative endogenous design, achieves full closed-loop optimization from individual modeling to group collaboration, improving multi-objective control performance under complex operating conditions. Specifically, it includes the following:

[0060] Step 1: System Modeling and Discretization

[0061] A system control model of a single support column and its hydraulic cylinder is constructed. To address the problem of insufficient accuracy of traditional empirical models, a three-stage physical-driven modeling approach is proposed: six key physical quantities are selected to form a state vector, covering the entire chain of mechanical motion (displacement / velocity), hydraulic transmission (chamber pressure), and pump and valve control (flow rate / volume pressure).

[0062] (1) Based on the advanced support hydraulic support structure, the state vector is defined as:

[0063] ;

[0064] in, Let be the piston displacement (m). Piston speed (m / s); The pressure in the rodless chamber is (Pa). The pressure in the rod chamber (Pa); This is the pump output flow rate (m³ / s). Volumetric pressure (Pa); Indicates transpose; control input is (Servo valve voltage, V).

[0065] (2) Based on Newton's laws of motion, piston dynamics are described, and the pressure-flow relationship is constructed by combining the fluid continuity equation. A unified mechanical-electrical-hydraulic equation is established, namely, the continuous-time state equation:

[0066] ;

[0067] in, For piston mass, The effective working area of ​​the rodless cavity. The effective working area of ​​the rod cavity, For frictional resistance, The elastic modulus of hydraulic oil. These are the volumes of the rodless cavity and the rod cavity, respectively. The meaning is the equivalent total volume of the hydraulic system. These represent the inflow and outflow rates from the chamber, respectively. This refers to the pump-valve linkage control coefficient. This is the input signal for pump and valve control.

[0068] (3) By using an explicit numerical discretization method, the complex differential equation is transformed into an iterative calculation model suitable for embedded controllers. The state variables are discretized (forward Euler method) to obtain:

[0069] ;

[0070] in, For discrete time step index; The sampling period is Here are the system state equations.

[0071] The forward Euler method was chosen based on the following criteria: computational complexity of O(n) meets the 1kHz control frequency requirement; and the sampling period is optimized. =0.001s, balancing discrete error (<0.5%) with real-time performance; the state update function fully preserves the physical meaning of the continuity equation. It integrates multi-source parameters, including the friction coefficient. The elastic modulus in this step Twelve parameters are used to achieve physically interpretable calibration.

[0072] Step 2: MPC-ADRC Fusion Control:

[0073] (1) MPC controller design: Based on the discrete state equation in step one, the prediction step number is taken as ;

[0074] 1) Based on the discrete model, a rolling solution of a multi-objective optimization problem is used to generate a baseline control quantity under state and input constraints. The objective function is as follows:

[0075] ;

[0076] in, The objective function value, For discrete time step index, For the output vector, For reference trajectory, For the tracking error weight matrix, To control the incremental smoothing weights, To predict the time-domain step size index, To predict the time-domain step size, To control the time-domain step size index, To control the time-domain step size, To control the input increment, To control the input increment.

[0077] 2) Tracking item design: This reflects the support safety principle of "pressure control taking precedence over displacement"; smoothing phase. Suppressing frequent servo valve commutation and extending component life; constraining safety boundaries, explosion-proof pipe self-unloading mechanical limit protection, and smooth electro-hydraulic conversion:

[0078] ;

[0079] Specifically, pressure constraints: Displacement constraints: Control constraints: ;in, To control the input increment, To control the input.

[0080] 3) The unmodeled dynamics (top plate impact + oil temperature change + frictional abrupt change) are estimated in real time through a nonlinear extended state observer. The extended state observer (ESO) is the core of the ADRC (Active Disturbance Rejection Control) design in this embodiment, and the specific design is as follows:

[0081] ;

[0082] in, The observer state; This is the observation error; For observer gain ( ); This is to control the gain coefficient.

[0083] Nonlinear functions: ;

[0084] in, For powers, This is the threshold for the linear interval.

[0085] (2) The MPC optimized output and ESO feedforward compensation are superimposed in parallel to inject the total disturbance feedforward into the prediction model and correct the output control, forming a closed-loop disturbance rejection mechanism, i.e., a dual-loop fusion strategy:

[0086] 1) Disturbance compensation:

[0087] ;

[0088] 2) Final control quantity:

[0089] ;

[0090] in, For the final control quantity, The control quantity calculated for the model predictive controller.

[0091] This embodiment has significant advantages in industrial applications: co-simulation results show that when facing pressure impact disturbances from the top plate, it can quickly and effectively suppress the pressure tracking deviation caused by the disturbance from ±15% to within ±5%, greatly improving the anti-disturbance capability; through the efficient combination of MPC rolling optimization and ESO observation, the single-step optimization time of the control quantity is within 1ms, which can meet the real-time control requirements and ensure the timeliness of control response; at the same time, by embedding a hard constraint mechanism to prevent pressure over-limit, it effectively avoids hydraulic system failure and provides reliable safety redundancy for equipment operation.

[0092] Step 3: The spatial synchronization layer constructs a master-slave cylinder deviation coupling network to eliminate position dispersion. A distributed synchronization mechanism is adopted, including master-slave tracking terms and inter-cylinder coordination terms. The multi-cylinder cooperative control mechanism is as follows:

[0093] (1) Deviation coupling synchronization setting: master cylinder (front beam cylinder), slave cylinder (column cylinder / balance cylinder);

[0094] (2) Synchronization deviation:

[0095] ;

[0096] in, For the first Displacement of each hydraulic cylinder Reference displacement for the master cylinder (front beam cylinder); For the first Displacement of each hydraulic cylinder This refers to the synchronization deviation between hydraulic cylinders; The coefficients are asymmetric coupling coefficients. The coupled topology employs an asymmetric design and incorporates an optimization objective, namely the coefficient matrix. Asymmetric design is adopted. Simulates the asymmetry of hydraulic pipelines.

[0097] Embed synchronization deviation into the system optimization objective:

[0098] ;

[0099] in, For synchronization error weights; This represents the total number of hydraulic cylinders.

[0100] (3) Weights of the MPC objective function ;

[0101] (4) Adaptive cooperative gain:

[0102] ;

[0103] in, For the first The hydraulic cylinder and the first The adaptive cooperative gain between hydraulic cylinders, i.e., the asymmetric coupling coefficient; The reference value for the synergistic gain ( ); Stiffness sensitivity coefficient ( (Unit: MPa·s² / m) This represents the change in pressure in the rodless chamber. This represents the change in piston displacement. This is the equivalent stiffness of the top plate.

[0104] according to Analysis shows that the softer the top plate (the lower the stiffness), the better. The smaller, The larger (enhancing synergy).

[0105] (5) Force balance constraint (tilt angle) ):

[0106] ;

[0107] in, The tilt angle of the hydraulic support; The supporting force generated for the column; To balance the balancing force generated by the jack; External loads applied to the top plate; For the rodless cavity pressure of the column; The effective working area of ​​the rodless cavity of the column; To balance the pressure in the rodless chamber of the jack; To balance the effective working area of ​​the rodless cavity of the jack.

[0108] (6) Attitude stability optimization (when ):

[0109] ;

[0110] in, These are the attitude stability weighting coefficients; The base weight value.

[0111] Meanwhile, based on the state observation information (specifically including support pressure, attitude, etc.) in the multi-objective optimization control method of the hydraulic support in this embodiment, two types of dynamic adjustment strategies can be established: an environmental adaptation layer and a mechanical stability layer. The environmental adaptation layer dynamically adjusts the collaborative strength based on online identification of roof stiffness, implementing a geological adaptive strategy: lithology (hard rock > 5GN / m, soft rock < 1GN / m) is identified in real time through the slope of the support reaction-displacement curve to achieve roof stiffness perception; collaboration is enhanced in soft rock conditions to prevent local roof collapse, and interference is reduced in hard rock conditions to improve response speed, forming a gain adjustment algorithm.

[0112] The mechanical stabilization layer ensures the safety of the support posture through tilt angle sensing and weight adjustment, constructing a structural mechanical constraint that includes column support force, balance cylinder anti-tilting force, and top plate load; it uses tilt angle sensors with a range of ±30° to achieve real-time tilt angle compensation, transforming the support structure equation into a solvable linear constraint to complete mechanical decoupling; it implements graded attitude stabilization control, using basic weights to maintain conventional control in normal mode, and using exponential gain to achieve rapid weight increase in emergency mode, triggering an anti-tilting control sequence of balance cylinder pressurization and column cylinder pressure adjustment.

[0113] The multi-objective control optimization method for hydraulic supports provided in this embodiment can establish a multi-objective predictive control model for support pressure, displacement, and attitude based on electromechanical-hydraulic coupling mechanism and working condition data. It can suppress external disturbances such as roof impact and frictional abrupt changes in real time through an active disturbance rejection compensation mechanism; it can eliminate conflicts in the actions of multiple hydraulic cylinders and ensure the overall attitude coordination of the support through deviation coupling synchronous control; and it can continuously correct the predictive model using online parameter tuning to match the time-varying characteristics of the system. This embodiment achieves the goal of improving the safety, operational efficiency, and equipment reliability of hydraulic supports through multi-objective dynamic optimization and closed-loop disturbance rejection control.

[0114] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A multi-objective optimization control method for hydraulic supports used in temporary support during mine roadway excavation, characterized in that, include: Construct the state vector and continuous state equation of the hydraulic support system, and discretize them to obtain a discrete state space model; Based on the discrete state-space model, with the goal of synchronously tracking the displacement and pressure of the hydraulic support system, a model predictive controller is used to perform rolling optimization search of the control quantity under variable constraints. At the same time, an extended state observer is combined to estimate the total disturbance of the hydraulic support system in real time, and the total disturbance is fed forward into the model predictive controller to generate the final control quantity. An asymmetric coupled synchronous network is constructed based on the master cylinder of the hydraulic support system. A synchronous deviation mechanism is adopted to form the collaborative control of the master and slave cylinders with the final control quantity, thereby completing the multi-objective optimization control of the hydraulic support system.

2. The multi-objective optimization control method for hydraulic supports used for temporary support in mine roadway excavation according to claim 1, characterized in that, The state vector is: ； in, For state vectors, For piston displacement, Piston speed; For rodless chamber pressure, For rod chamber pressure; For pump output flow rate, For volumetric pressure, Indicates transpose; The continuity state equation is: ； in, It is a continuous state vector. For piston mass, The effective working area of ​​the rodless cavity. The effective working area of ​​the rod cavity, For frictional resistance, The elastic modulus of hydraulic oil. These are the volumes of the rodless cavity and the rod cavity, respectively. The meaning is the equivalent total volume of the hydraulic system. These represent the inflow and outflow rates from the chamber, respectively. This refers to the pump-valve linkage control coefficient. This is the input signal for pump and valve control.

3. The multi-objective optimization control method for hydraulic supports used for temporary support in mine roadway excavation according to claim 2, characterized in that, The discrete state-space model is as follows: ； in, For discrete time step index, The sampling period is To control the input, Here are the system state equations.

4. The multi-objective optimization control method for hydraulic supports used for temporary support in mine roadway excavation according to claim 1, characterized in that, The objective function of the model predictive controller is: ； The constraints of the model prediction controller are: ； in, The objective function value, For discrete time step index, For the output vector, For reference trajectory, For the tracking error weight matrix, To control the incremental smoothing weights, To predict the time-domain step size index, To predict the time-domain step size, To control the time-domain step size index, To control the time-domain step size, To control the input increment, For rodless chamber pressure, For rod chamber pressure, This represents the piston displacement.

5. The multi-objective optimization control method for hydraulic supports used for temporary support in mine roadway excavation according to claim 1, characterized in that, The extended state observer is: ； Nonlinear functions: ; in, For the observer state, For observer gain, For observation error, To control the gain coefficient, To control the input, For powers, For linear interval thresholds, For piston displacement, For discrete time step index, The sampling period.

6. The multi-objective optimization control method for hydraulic supports used for temporary support in mine roadway excavation according to claim 1, characterized in that, The total disturbance is fed forward into the model predictive controller to generate the final control quantity, which includes: ； ； in, For state vectors, For discrete time step index, The sampling period is The system state equations are as follows: For the observer state, For piston mass, To control the gain coefficient, Indicates transpose. For the final control quantity, The control quantity calculated for the model predictive controller.

7. The multi-objective optimization control method for hydraulic supports used for temporary support in mine roadway excavation according to claim 1, characterized in that, The use of a synchronization deviation mechanism to form coordinated control of the master and slave cylinders in the final control quantity includes: The final control quantity is input into the asymmetric coupling synchronization network to calculate the synchronization deviation between hydraulic cylinders. The synchronization deviation is then embedded into the objective function of the model predictive controller. Through adaptive cooperative gain, force balance constraint, and attitude stability optimization, cooperative control of the master and slave cylinders is formed.

8. The multi-objective optimization control method for hydraulic supports used for temporary support in mine roadway excavation according to claim 7, characterized in that, The calculation of synchronization deviation between hydraulic cylinders includes: ； in, For the first Displacement of each hydraulic cylinder Reference displacement for the master cylinder; The coefficients are asymmetric coupling coefficients. For the first Displacement of each hydraulic cylinder This refers to the synchronization deviation between hydraulic cylinders.

9. The multi-objective optimization control method for hydraulic supports used for temporary support in mine roadway excavation according to claim 8, characterized in that, The adaptive cooperative gain is: ； in, For the first The hydraulic cylinder and the first The adaptive cooperative gain between hydraulic cylinders, i.e., the asymmetric coupling coefficient; This is the reference value for the synergistic gain; This is the stiffness sensitivity coefficient; This represents the change in pressure in the rodless chamber. This represents the change in piston displacement. The equivalent stiffness of the top plate; The force balance constraint is: ； in, The tilt angle of the hydraulic support; The supporting force generated for the column; To balance the balancing force generated by the jack; External loads applied to the top plate; For the rodless cavity pressure of the column; The effective working area of ​​the rodless cavity of the column; To balance the pressure in the rodless chamber of the jack; To balance the effective working area of ​​the rodless cavity of the jack; The attitude stability is optimized as follows: ； in, These are the attitude stability weighting coefficients; The base weight value.

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