PID (Proportion Integration Differentiation)-based hydraulic jumbolter rotating speed iterative learning control method

Abstract

The invention discloses a PID (Proportion Integration Differentiation)-based hydraulic jumbolter rotating speed iterative learning control method, which belongs to the technical field of mining jumbolter rotating system control, and comprises the following steps: S1, considering internal and external disturbances of a system, and establishing a dynamical model of a jumbolter system; s2, performing state conversion according to the established mathematical model to obtain a state space expression of the system; s3, the optimal rotating speed of the jumbolter is estimated according to the hardness coefficient of the rock stratum characteristics; and S4, designing a PID iterative learning control law. According to the method, a system mechanism model does not need to be accurately known, the rotating speed can be self-adjusted according to the change of the rock hardness coefficient in the drilling process, and the controller is continuously corrected through feedback tracking errors by utilizing iterative learning, so that the purpose of rapid tracking is achieved. Compared with the traditional PID, the controller has the advantages that the response speed, the adjustment time, the tracking precision and other performances are remarkably improved, the excessive phenomenon is almost avoided, and the robustness is high.

Description

Technical Field

[0001] The invention belongs to the technical field of hydraulic anchor drilling rig control, and in particular is a PID-based hydraulic anchor drilling rig speed iterative learning control method. Background Art

[0002] The quality of hydraulic anchor drills plays a decisive role in the support of tunnels. In traditional support operations, operators mainly use handheld anchor drills to perform drilling and anchoring operations to achieve tunnel support. This operation mode completely relies on the workers' operating experience, with limited working space and time and high labor intensity, which seriously affects the progress and efficiency of tunnel support. In view of this, in order to improve the efficiency of tunnel support, speed up the speed of tunnel construction and reduce the labor intensity of workers, scholars have begun to pay attention to the control method of the drilling speed of anchor drills.

[0003] The speed system of hydraulic anchor drilling rig is essentially an electro-hydraulic servo system. Traditional PID occupies a dominant position in the field of industrial control due to its advantages of easy operation, simple structure and good reliability. However, its parameter setting is completely adjusted based on field experience to ensure the control performance of the system. When the system is subject to uncertain disturbances, this control method often cannot obtain good control performance. Therefore, in order to improve the control performance of the system, some control methods combined with intelligent algorithm PID have been proposed, such as PID based on ant lion optimization, PID based on particle swarm optimization, PID based on fuzzy theory, etc. These methods can realize adaptive adjustment of parameters and improve the response speed and tracking accuracy of the system. However, when dealing with the control problem of hydraulic anchor turning machine, PID still encounters some challenges, such as the contradiction between fast response and overshoot, weak anti-interference ability, and the influence of system element characteristics. In the rotation system of hydraulic anchor drilling rig, the control of the drilling rig speed is mainly achieved by manually adjusting the mechanical device and the opening degree of the multi-position valve according to experience to change the motor oil flow. Since the surrounding rock characteristics of underground tunnels are generally variable, the drilling speed of the drilling rig cannot be self-adjusted according to the surrounding rock characteristics of the tunnel. An unreasonable speed of hydraulic anchor drill will not only cause the rod to break, but also affect the drilling quality and efficiency. In addition, the system will be affected by disturbances, which greatly increases the difficulty of workers' operation. How to achieve adaptive control of the drilling speed of the drill is an urgent problem to be solved. Nowadays, iterative learning control algorithms are constantly developing. They do not require precise information of mathematical models and can effectively track the expected signal in a limited time and interval. Therefore, it is very necessary to design an effective anchor drill speed control method based on PID combined with iterative learning. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a PID-based hydraulic anchor drill speed iterative learning control method.

[0005] The present invention adopts the following technical scheme: a PID-based hydraulic anchor drilling rig speed iterative learning control method, comprising: S1: Establish the dynamic model of the hydraulic anchor drilling rig rotation system; S2: According to the dynamic model, the state transformation is performed to obtain the state space expression of the hydraulic anchor drilling rig speed system, and the output of the hydraulic anchor drilling rig speed system is obtained according to the state space expression; S3: Estimate the optimal rotation speed of the anchor drilling rig based on the hardness coefficient of the measured rock formation characteristics; S4: Design a PID iterative learning control law and continuously correct the controller by feedback tracking error.

[0006] The kinetic model in step S1 includes: Proportional valve spool opening equation: in, is the gain coefficient of the electro-hydraulic proportional valve; is the displacement under the action of current; It is the driving current of the proportional valve; The proportional valve flow and pressure corresponding characteristic equation: in, , Respectively represent the hydraulic oil flow rate of the inlet and return oil chambers; is the valve port flow coefficient; is the valve opening area gradient; is the hydraulic oil density; , They are supply and return oil pressure respectively; , They are respectively the pressure values ​​of the oil inlet and return chambers of the hydraulic motor; Differential equation of the anchor drilling rig rotation system: in, is the hydraulic motor displacement coefficient; Output rotation angle of hydraulic motor; is the output speed of the hydraulic motor; , They are respectively the pressure value of the oil supply chamber and the pressure value of the oil return chamber of the hydraulic motor; , is the internal and external leakage coefficient value of the hydraulic motor; is the bulk elastic modulus of hydraulic oil; , They are the controllable volumes of the hydraulic motor oil inlet and return chambers, , ,in , are the initial volumes of the two chambers respectively; The output shaft of the hydraulic motor and the corresponding load torque it bears: in, is the total moment of inertia of the hydraulic motor output shaft; is the viscous damping coefficient of the oil; is the equivalent stiffness coefficient of the hydraulic motor output shaft; is the load torque of the hydraulic motor output shaft; Unmodeled dynamics and internal and external disturbances of the system.

[0007] In step S2: according to the dynamic model, the output rotation angle, angular velocity and angular acceleration of the hydraulic motor are used as system state variables , , ,definition , , , the state space expression is: in, is the rotation angle of the hydraulic motor output, is the angular velocity of the hydraulic motor output, is the angular acceleration of the hydraulic motor output, and the angular velocity variable of the hydraulic motor output shaft Output of the hydraulic anchor drilling rig speed system, driving current of the electro-hydraulic proportional valve is the control input, , , , , , , is the system disturbance.

[0008] System disturbance is bounded, and there exists an unknown positive constant So that: , , where the parameters is the cutting amount of the drill bit; and is the inner and outer diameter of the drill bit; is the hardness coefficient of rock.

[0009] The optimal speed in step S3 is: in, is the optimal speed, c is the cutting speed of the drill bit; is the hardness coefficient of rock; D is the effective rotation diameter of the drill bit.

[0010] Step S4 includes: S41: Define tracking error: , in, is the optimal speed, which is equivalent to the expected signal given by the system and is continuous and bounded. k is the number of iterative cycles. is the output of the hydraulic anchor drilling rig speed system in the kth cycle; S42: Setting the actual control rate of the PID iterative learning state feedback controller; S43: By controlling the learning rate and continuously iteratively adjusting the control signal, the output of the hydraulic anchor drilling rig speed system is infinitely close to the given expected signal, that is, the optimal speed. .

[0011] In step S42, the actual control rate is the driving current of the motor for: in, is the control input of the hydraulic anchor drilling rig speed system in the k+1th cycle, is the control input of the hydraulic anchor drilling rig speed system in the kth cycle, , , are the controller proportional, integral and derivative gain parameters.

[0012] Compared with the prior art, the controller designed by the present invention for the rotation speed of the hydraulic anchor drilling rig: through iterative learning control law, the controller is continuously corrected by using feedback error, so that the output of the system can quickly track the given optimal rotation speed; the tracking error can be adjusted by design parameters to improve the stability of the system and facilitate the actual operation of the system; the controller has a simple structure and does not require precise knowledge of the system mechanism model, can adapt to the changes in rock formations and rapidity requirements during the drilling process, can better adapt to the actual system control needs, and improve the universality of the system; compared with traditional PID, the controller has significantly improved the performance in response speed, adjustment time and tracking accuracy, almost no overshoot phenomenon occurs, and has strong robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of the PID-based iterative learning control method for the bolter drilling rig speed of the present invention; Figure 2It is a structural block diagram of the bolter rig speed iterative learning control based on PID of the present invention; Figure 3 is the system speed tracking response curve during the iteration process in the example of the present invention; Figure 4 is a system speed tracking response curve under a disturbance load in an example of the present invention; Figure 5 It is the system speed tracking error response curve under the disturbance load in the example of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0015] The technical solution adopted by the present invention comprises the following steps (see Figure 1 ): S1. Considering the influence of internal and external disturbances on the system, the dynamic model of the hydraulic anchor drilling rig rotation system is established; S2, obtain the state space expression of the hydraulic anchor drilling rig speed system by performing state transformation according to the dynamic model; S3. Estimate the optimal rotation speed of the anchor drilling rig based on the hardness coefficient of the measured rock formation characteristics; S4. Design a PID iterative learning control law and continuously correct the controller by feedback tracking error.

[0016] In S1, the proportional valve is the actuator of the hydraulic anchor drilling rig rotation system. Under the action of the controller, the flow rate of the hydraulic oil is changed by adjusting the valve core displacement to control the rotation speed of the anchor drilling rig. The expression of the proportional valve core opening is: in, is the gain coefficient of the electro-hydraulic proportional valve; is the displacement under the action of current; It is the driving current of the proportional valve. The proportional valve flow and pressure corresponding characteristic equation can be expressed as: in, , Respectively represent the hydraulic oil flow rate of the inlet and return oil chambers; is the valve port flow coefficient; is the valve opening area gradient; is the hydraulic oil density; , They are supply and return oil pressure respectively; , They are the pressure values ​​of the oil inlet and return chambers of the hydraulic motor respectively.

[0017] The differential equation of the anchor drilling rig rotation system is established based on the liquid flow rate: in, is the hydraulic motor displacement coefficient; Output rotation angle of hydraulic motor; is the output speed of the hydraulic motor; , They are respectively the pressure value of the oil supply chamber and the pressure value of the oil return chamber of the hydraulic motor; , is the internal and external leakage coefficient value of the hydraulic motor; is the bulk elastic modulus of hydraulic oil; , They are the controllable volumes of the hydraulic motor oil inlet and return chambers, , ,in , are the initial volumes of the two chambers respectively.

[0018] The output shaft of the hydraulic motor and the corresponding load torque it bears can be expressed as: in, is the total moment of inertia of the hydraulic motor output shaft; is the viscous damping coefficient of the oil; is the equivalent stiffness coefficient of the hydraulic motor output shaft; is the load torque of the hydraulic motor output shaft; Unmodeled dynamics and internal and external disturbances of the system.

[0019] In S2, according to the dynamic model, the output rotation angle, angular velocity and angular acceleration of the hydraulic motor are used as system state variables. , , ,definition , , , then the state space expression of the dynamic system model after state transformation is as follows: The angular velocity variable of the hydraulic motor output shaft is the output of the system, the driving current of the electro-hydraulic proportional valve is the control input, where , , , , , , for system disturbance is bounded, and there exists an unknown positive constant So that: , , where the parameters is the cutting amount of the drill bit; and is the inner and outer diameter of the drill bit; is the hardness coefficient of rock.

[0020] In S3, according to the principle of rock cutting by the drill bit, different rock hardness coefficients often cause the drill rod to receive different load torques, and the rock characteristics can be approximately obtained by the load change. For each specific type of rock, its optimal speed is inversely proportional to the rock hardness coefficient and the square root of the difference between the inner and outer diameters of the drill bit, and its expression is: Where c is the cutting speed of the drill bit; is the hardness coefficient of rock; D is the effective rotation diameter of the drill bit.

[0021] In S4, the PID iterative learning control law is designed to continuously iteratively adjust the control signal by controlling the learning rate, so that the output of the system infinitely approaches the given expected signal, that is, the optimal speed. .

[0022] Define tracking error: , in, Given the expected signal for the hydraulic anchor drilling rig rotation system, it is continuous and bounded, k is the number of iteration cycles, For the The actual output of the hydraulic anchor drilling rig rotation system in one cycle.

[0023] The actual control rate of the designed PID iterative learning state feedback controller, that is, the motor drive current u is: in, is the system control input for the k+1th period, is the k-th period system control input, , , are the controller proportional, integral and derivative gain parameters.

[0024] In order to verify the effectiveness of the control method proposed in this invention, a simulation analysis is carried out based on the actual system parameters of a hydraulic anchor drill in a mine. The change of the rock hardness coefficient in the underground geological environment is simulated by the change of the step signal, which is set as: Time is: , ; Cutting speed of drill bit ;Effective rotation diameter of the drill bit . System state initial conditions: , , , ; The parameters of the hydraulic anchor drilling rig rotation system are shown in Table 1: Table 1 System parameters like Figure 3 As shown in the figure, the speed tracking response curve of the iteration process shows that: during the iteration process, the tracking performance is gradually improved and the tracking error gradually converges.

[0025] like Figure 4 As shown in the speed tracking response curve, it can be seen that: compared with the traditional PID, the PID iterative learning control can quickly and smoothly track the optimal speed at the starting point or the rock formation mutation turning point, the system is more stable, and there is almost no overshoot.

[0026] like Figure 5 As shown in the figure, the speed tracking error response curve shows that the PID iterative learning control has a smaller tracking error and a shorter adjustment time. When the system is subject to external disturbances, the steady-state performance is better than that of the traditional PID controller, and the influence of disturbances on the system is effectively suppressed, indicating that the algorithm has strong robustness.

[0027] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A PID-based iterative learning control method for hydraulic anchor drilling rig speed, characterized in that: include: S1: Establish the dynamic model of the hydraulic anchor drilling rig rotation system; S2: According to the dynamic model, the state transformation is performed to obtain the state space expression of the hydraulic anchor drilling rig speed system, and the output of the hydraulic anchor drilling rig speed system is obtained according to the state space expression; S3: Estimate the optimal rotation speed of the anchor drilling rig based on the hardness coefficient of the measured rock formation characteristics; S4: Design a PID iterative learning control law and continuously correct the controller by feedback tracking error.

2. The PID-based iterative learning control method for hydraulic anchor drilling rig speed according to claim 1, characterized in that: The kinetic model in step S1 includes: Proportional valve spool opening equation: in, is the gain coefficient of the electro-hydraulic proportional valve; is the displacement under the action of current; Proportional valve drive current The proportional valve flow and pressure corresponding characteristic equation: in, , Respectively represent the hydraulic oil flow rate of the inlet and return oil chambers; is the valve port flow coefficient; is the valve opening area gradient; is the hydraulic oil density; , They are supply and return oil pressure respectively; , They are respectively the pressure values ​​of the hydraulic motor inlet and return oil chambers; Differential equation of the anchor drilling rig rotation system: in, is the hydraulic motor displacement coefficient; Output rotation angle of hydraulic motor; is the output speed of the hydraulic motor; , They are respectively the pressure value of the oil supply chamber and the pressure value of the oil return chamber of the hydraulic motor; , is the internal and external leakage coefficient value of the hydraulic motor; is the bulk elastic modulus of hydraulic oil; , They are the controllable volumes of the hydraulic motor oil inlet and return chambers, , ,in , are the initial volumes of the two chambers respectively; The output shaft of the hydraulic motor and the corresponding load torque it bears: in, is the total moment of inertia of the hydraulic motor output shaft; is the viscous damping coefficient of the oil; is the equivalent stiffness coefficient of the hydraulic motor output shaft; is the load torque of the hydraulic motor output shaft; The unmodeled dynamics and internal and external disturbances of the system.

3. The PID-based iterative learning control method for hydraulic anchor drilling rig speed according to claim 2 is characterized in that: In step S2: according to the dynamic model, the output rotation angle, angular velocity and angular acceleration of the hydraulic motor are used as system state variables. , , ,definition , , , the state space expression is: in, is the rotation angle of the hydraulic motor output, is the angular velocity of the hydraulic motor output, is the angular acceleration of the hydraulic motor output, and the angular velocity variable of the hydraulic motor output shaft Output of the hydraulic anchor drilling rig speed system, driving current of the electro-hydraulic proportional valve is the control input, , , , , , , is the system disturbance.

4. The PID-based iterative learning control method for hydraulic anchor drilling rig speed according to claim 3 is characterized in that: System disturbance is bounded, and there exists an unknown positive constant So that: , , where the parameters is the cutting amount of the drill bit; and is the inner and outer diameter of the drill bit; is the hardness coefficient of rock.

5. The PID-based iterative learning control method for hydraulic anchor drilling rig speed according to claim 1, characterized in that: The optimal speed in step S3 is: in, is the optimal speed, c is the cutting speed of the drill bit; is the hardness coefficient of rock; D is the effective rotation diameter of the drill bit.

6. The PID-based iterative learning control method for hydraulic anchor drilling rig speed according to claim 1, characterized in that: The step S4 comprises: S41: Define tracking error: ， in, is the optimal speed, which is equivalent to the expected signal given by the rotary system of the hydraulic anchor drilling rig and is continuous and bounded. k is the number of iterative cycles. is the output of the hydraulic anchor drilling rig speed system in the kth cycle; S42: Setting the actual control rate of the PID iterative learning state feedback controller; S43: By controlling the learning rate and continuously iteratively adjusting the control signal, the output of the hydraulic anchor drilling rig rotation system is infinitely close to the given expected signal, that is, the optimal speed. .

7. The PID-based iterative learning control method for hydraulic anchor drilling rig speed according to claim 6, characterized in that: In step S42, the actual control rate is the driving current of the motor for: in, is the system control input for the k+1th period, is the k-th period system control input, , , are the controller proportional, integral and derivative gain parameters.

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