Control method of percussion drill semi-active buffer device based on magnetorheological effect

Abstract

The invention discloses a control method of a percussion drill semi-active buffer device based on a magneto-rheological effect, and the method comprises the steps: building a magneto-rheological damper forward model and a buffer system overall dynamic model, and analyzing and determining a forward impact speed and a front impact point as key factors affecting the buffer performance; on the basis, a multi-mode control strategy is designed, a single motion period is divided into a backward stage and a counter-recoil stage, and an H-infinity controller and an LQR controller are adopted respectively; aiming at system mode change caused by forward energy in different impact periods, configuring a corresponding gain matrix for each mode by utilizing an optimization algorithm so as to realize self-adaptive adjustment; and finally, according to the calculated target damping force, solving and outputting a control current through an inverse model. According to the method, self-adaptive adjustment of the whole impact process is achieved, the forward impact energy consumption efficiency is remarkably improved, meanwhile, the impedance force peak value is effectively restrained, and the dynamic stability of the system under continuous impact is guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of impact buffering control, and particularly relates to a control method of a semi-active buffering device for an impact drill based on a magneto-rheological effect. BACKGROUND

[0002] In the operation process of the impact drill, continuous and intensive impact load not only causes instantaneous overload threat to the equipment structure, but also causes time-varying cumulative effect of the buffering resistance force due to the rapid cycle characteristics, thereby causing a series of serious consequences such as the peak force exceeding the safety threshold and system dynamic instability, which directly affects the shooting accuracy, operation reliability and service life of the equipment. The traditional buffering system generally adopts a pre-impact technical route, which uses the partial energy of the previous impact to consume the energy of the next impact (i.e., realizes "pre-impact") through floating machines, locking and other precise mechanical mechanisms, so as to reduce the peak value of the resistance force after the next impact. However, such a mechanical pre-impact mechanism has problems such as complex structure, high machining precision requirement and inability to adaptively adjust according to the impact working condition changes.

[0003] The semi-active intelligent buffering device based on the magneto-rheological intelligent material has advantages such as controllable damping, wide dynamic range, simple structure and low energy consumption. The speed during system resetting and the dynamic balance point position are controlled by adjusting the magnetic field to change the output damping force, so as to replace the mechanical mechanism such as the floating machine to realize the pre-impact energy consumption effect. However, the existing control methods for the magneto-rheological damper are mostly single control strategies, such as PID control or Skyhook control, which do not fully consider the difference in the dynamics characteristics of the different stages of the single cycle in the buffering process, and also do not solve the dynamic coupling influence between different buffering cycles. SUMMARY

[0004] The application provides a control method of a semi-active buffering device for an impact drill based on a magneto-rheological effect, which divides a motion cycle into a retreat stage and a recovery stage by using a multi-modal control strategy, automatically identifies and switches system modes according to impact energy, matches H-infinity and LQR controllers for different stages, realizes adaptive and refined adjustment of the whole impact process, thereby significantly improving the pre-impact energy consumption efficiency, effectively suppressing the peak value of the resistance force and guaranteeing the dynamic stability of the system under continuous impact.

[0005] The technical solution of the application is as follows:

[0006] S1, a forward dynamics model of a magneto-rheological damper is established to obtain a mapping relationship of damping force-velocity-current.

[0007] S2, the impact drill buffer system is constituted by using the above-mentioned magneto-rheological damper, the overall dynamics model of the buffer system is established, and each physical parameter of the overall dynamics model is determined, in the Simulink simulation environment, the overall dynamics simulation model is built in combination with each physical parameter of the overall dynamics model.

[0008] S3, according to the overall dynamics simulation model, the key influencing factors affecting the pre-impact energy consumption are pre-impact speed and pre-impact firing point, the specific influence of pre-impact speed and pre-impact firing point is theoretically analyzed, and the numerical simulation of MATLAB is used for verification.

[0009] S4, according to the dynamics characteristics of the overall dynamics model, a multi-modal control strategy is designed, the multi-modal control strategy divides a single motion cycle into two stages of retreat and advance, H-infinity controller and LQR controller are used for the two stages respectively, different controller parameters are set for different system modes due to the change of dynamic balance point caused by pre-impact in different cycles, and target damping force is obtained.

[0010] S5, according to the target damping force, the required control current is solved through the inverse model of the magneto-rheological damper.

[0011] Compared with the prior art, the present application has the following advantages: through the multi-modal control strategy combined with the output adjustable characteristics of the magneto-rheological damper, the design concept of the traditional pre-impact mechanism is fundamentally innovated, that is, the output adjustable characteristics of the magneto-rheological damper are matched with the optimization algorithm and control algorithm to replace the traditional complex mechanical structure. The variable working condition adaptability problem is systematically solved, the pre-impact energy consumption efficiency is significantly improved and the impact load is greatly reduced under the premise of ensuring the system floating stability. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 is the impedance force-time curve diagram under the influence of different pre-impact speeds when the pre-impact firing point is fixed at 0 m in the present application.

[0013] Figure 2 is the impedance force-time curve diagram under the influence of different pre-impact positions when the pre-impact speed is fixed at-0.3 m / s in the present application.

[0014] Figure 3 is the multi-modal control strategy proposed in the present application, the reduction effect diagram of the impedance force peak value is shown by using a certain type of magneto-rheological damper under the working condition of continuous impact cycle of 35 ms and impact amplitude of 115000 N.

[0015] Figure 4 is the control method flow chart of the impact drill semi-active buffer device based on the magneto-rheological effect in the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0017] In the present application, the description such as "first", "second" and the like is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0018] In addition, the technical solutions of various embodiments of the present application can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it, and when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, and is not within the scope of protection required by the present application.

[0019] The specific embodiments, technical difficulties and points of the present application will be further introduced below in combination with the design examples.

[0020] In combination Figures 1-4 , the present application proposes a control method of a semi-active buffer device of an impact drill based on a magneto-rheological effect, comprising the following steps:

[0021] Step S1, a forward dynamics model of a magneto-rheological damper is established, and a mapping relationship of damping force-velocity-current is obtained, which is specifically as follows:

[0022] Based on the structural parameters of the magneto-rheological damper, a forward dynamics model of the magneto-rheological damper is established by combining a Bouc-Wen model and a bingham model, and a mapping relationship of damping force-velocity-current is fitted. The forward dynamics model of the magneto-rheological damper represents the output damping force characteristics of the damper under given input current and motion speed. The output damping force may be represented as:

[0023] (1),

[0024] In the formula, is the output damping force, is the viscous damping force, is the Coulomb damping force, is the passive viscous pressure drop, The active Coulomb pressure drop, Aeff is the effective area of the piston part, and n is the number of coil stages.

[0025] Wherein, the passive viscous pressure drop is the along-the-line loss pressure drop generated by the magnetorheological fluid flowing in the channel The pressure drop of the magnetorheological fluid flowing through the inlet and outlet , which are respectively represented by formula (2) and formula (3):

[0026] (2),

[0027] (3),

[0028] In the formula, The density of the magnetorheological fluid is The velocity of the liquid flowing through the damping channel is The Darcy friction coefficient is The contraction coefficient is The expansion coefficient is The damping channel length is The damping channel gap is.

[0029] The velocity of the liquid flowing through the damping channel is The following formula:

[0030] (4),

[0031] In the formula, The velocity of the piston rod is Aeff is the effective area of the piston part, The area of the piston rod is.

[0032] The calculation formula of the single-stage active Coulomb pressure drop is as follows:

[0033] (5),

[0034] In the formula, The active Coulomb pressure drop is Aeff is the effective length of the piston damping channel, The yield stress of the magnetorheological fluid affected by the magnetic field is.

[0035] By comprehensively considering the Coulomb pressure drop and the viscous pressure drop, the theoretical calculation model of the output damping force of the magnetorheological damper can be obtained:

[0036] (6),

[0037] Step S2, the impact drill is controlled by the above-mentioned magneto-rheological damper to form a buffer system, an overall dynamic model of the buffer system is established, and each physical parameter of the overall dynamic model is determined, in a Simulink simulation environment, the overall dynamic simulation model is built by combining each physical parameter of the overall dynamic model, and the specific process is as follows:

[0038] Step S21, considering that the equivalent mass of the system is , the spring stiffness is , the controllable damping force generated by the magneto-rheological damper is , the external excitation is , and the system displacement is , the dynamic equation of the overall dynamic model of the buffer system is:

[0039] (7),

[0040] wherein is the system displacement acceleration.

[0041] Step S22, the dynamic equation in step S21 is rewritten into a state space form:

[0042] (8),

[0043] In the formula, the state vector matrix is , the system matrix is , the control input matrix is , the disturbance input matrix is , u is the controller input, respectively represent the displacement and speed of the buffer system.

[0044] Step S23, the system model is constructed through a State-Space module in a Simulink simulation environment, and the overall dynamic simulation is completed by integrating the controller input u.

[0045] Step S3, according to the overall dynamic simulation model, the key influencing factors of the pre-impact energy consumption of the buffer system are the pre-impact speed and the pre-impact firing point, the specific influence of the pre-impact speed and the pre-impact firing point is theoretically analyzed, and the numerical simulation of MATLAB is used for verification, and the specific process is as follows:

[0046] Step S31, from the energy angle, there are many factors affecting the pre-impact energy consumption, including the spring stiffness, mass of the system and the damping of the buffer, and the frequency and amplitude of the impact load received by the system, but the final influence is reflected in the regulation and control of the kinetic energy and potential energy reserves of the system at the moment when the next impact comes. Therefore, essentially, the pre-impact speed and the pre-impact firing point affect the pre-impact energy consumption performance.

[0047] The static equilibrium position is taken as the coordinate origin, and the backward direction (same as the impact force) is taken as the positive direction. When the forward impact occurs, the system is located behind the static equilibrium position and has a forward velocity.

[0048] According to the law of conservation of energy:

[0049] (9),

[0050] In the formula, is the external excitation, is the damping force work, is the maximum backward displacement, is the forward impact point, is the spring stiffness, is the equivalent mass of the system, is the forward impact velocity.

[0051] As can be seen from equation (9), the forward impact kinetic energy directly offsets the impact input work. This shows that increasing the forward impact velocity will directly reduce the external excitation input work, thereby significantly reducing the maximum backward displacement and the peak impedance force, so the forward impact velocity is the most effective control variable for improving the forward impact energy dissipation performance.

[0052] The external excitation input work is proportional to , and increasing will reduce the integral interval of the impact input work, which is beneficial to reducing the system input energy. However, in actual systems, often changes with , forming a complex coupling relationship. When the spring stiffness is large, increasing will cause the value of to increase nonlinearly, resulting in an increase in system stiffness and an increase in the peak impedance force. Therefore, the reasonable selection of the forward impact trigger point is complex and requires numerical simulation for quantitative analysis and verification.

[0053] Figure 1 Step S32, in the overall dynamic simulation model established in step S2, the control variable method is used for systematic simulation analysis: first, while keeping the forward impact trigger point position, controller parameters and other conditions constant, the value of the forward impact velocity is adjusted for simulation, and the curve of the peak impedance force changing with the forward impact velocity is obtained, as shown in Figure 2 . Then, while keeping the forward impact velocity, controller parameters and other conditions unchanged, the forward impact trigger point is changed for simulation, and the curve of the peak impedance force changing with the forward impact trigger point is obtained, as shown in . By comparing the peak impedance forces under different forward impact velocities and forward impact trigger points, the specific influence law of the forward impact velocity and the impact trigger point position on the forward impact performance is revealed intuitively and quantitatively.

[0054] Step S4, according to the kinetic characteristics of the overall kinetic model, a multi-modal control strategy is designed, and the control flow chart is as shown in Figure 4 The multi-modal control strategy divides a single movement cycle into two stages of retreat and recovery, adopts H-∞ controller and LQR controller for the two stages respectively, and sets different controller gain matrices for different system modes caused by the change of dynamic equilibrium point due to the pre-impact effect, to obtain the target damping force, as follows:

[0055] Step S41, based on the overall kinetic model and the pre-impact principle, a single movement cycle is explicitly divided into a retreat stage with the core target of suppressing the peak value of the impedance force and a recovery stage with the core target of accurately controlling the reset state, and the most suitable control algorithm is designed for each movement stage. The H-∞ controller with strong robustness is adopted in the retreat stage to deal with impact interference, and the LQR controller with excellent tracking performance is adopted in the recovery stage to ensure the pre-impact speed and pre-impact point. The inputs of the H-∞ controller and the LQR controller are represented by 、

[0056] (10),

[0057] (11),

[0058] wherein, is the gain matrix of the H-∞ controller, is the gain matrix of the LQR controller. The controller switching logic based on the real-time monitoring of the system state vector (speed v) is designed, and the speed sign change is used to determine the start and end of the stage. Taking the static equilibrium position as the coordinate origin and the retreat direction (same as the impact force) as the positive direction, then:

[0059] (12),

[0060] wherein, is the controller input of the system, is the H-∞ controller input, is the LQR controller input, is the state vector matrix.

[0061] Step S42, the different system modes refer to the dynamic equilibrium point offset of the buffer system caused by the pre-impact effect, which is converted into several representative modes, denoted as mode A "mode-gain matrix mapping table" is pre-stored in the system, and the table configures a set of gain matrices of H-∞ controller and LQR controller for each mode .

[0062] ​

[0063] Step S43, in order to obtain the optimal control effect which makes the system pre-impact energy performance best, the particle swarm (PSO) optimization algorithm is used to optimize the controller parameters under each mode: the optimization variables include the key parameters of the H-infinity controller performance output matrix under each mode, and the parameters in the state weight matrix Q and the control weight matrix R of the LQR controller. For the first movement cycle, the optimization goal of the backward stage is to minimize the peak value of the impedance force, and at the same time, a constraint is added through the penalty function method, which is the peak value of the backward displacement; the optimization goal of the forward stage is to maximize the pre-impact velocity when the forward movement is completed, and the constraint is the time when the forward movement is completed, and the gain matrix 、 is obtained. For the subsequent movement cycles affected by pre-impact, the optimization goal is changed to track the pre-impact velocity and the pre-impact firing point of the previous movement cycle, and the gain matrix 、 is obtained. For the working condition of fixed interval and fixed amplitude of impact drilling, usually 2, that is, the first movement cycle is not affected by pre-impact, and the subsequent movement cycles are affected by pre-impact.

[0064] Step S44, the gain matrix is introduced into the overall dynamics model, and the ideal damping force is simulated. Considering the physical output characteristics of the magneto-rheological damper, that is, the output force has upper and lower limit constraints related to the real-time movement state. The ideal damping force-actual damping force conversion module containing saturation constraints is designed, and the feasible region model of the magneto-rheological damper output force is established. The feasible region is jointly defined by the maximum available damping force and the minimum available damping force under the current relative velocity, and the ideal control force calculated by the LQR controller is processed through the saturation constraint module to obtain the actual achievable target damping force, as shown in equation (13):

[0065] (13),

[0066] wherein 、 is calculated by the following formula:

[0067] (14),

[0068] (15),

[0069] wherein is the density of the magneto-rheological fluid, is the Darcy friction coefficient, is the contraction coefficient, is the expansion coefficient, Ld is the length of the damping channel, gd is the gap of the damping channel. Ae is the effective area of the piston part, and n is the number of coil turns. Lde is the effective length of the piston damping channel. vp is the velocity of the piston rod, Ap is the area of the piston rod, σy is the maximum yield stress of the MR fluid, σy is the minimum yield stress of the MR fluid.

[0070] Step S5, according to the target damping force, the required control current is solved by the inverse model of the magnetorheological damper. Specifically as follows:

[0071] Step S51, according to the forward dynamic model established in step S1, the inverse model of the magnetorheological damper is constructed by mathematical inversion or data fitting method. The above inverse model establishes the mapping relationship from the target damping force and the motion speed to the required control current I.

[0072] Step S52, according to the target damping force curve obtained by optimization in step S4 and the real-time collected displacement and speed signals, the required control current is solved by the inverse model, and the high-precision tracking control of the damping force is realized through the controller.

Claims

1. A control method of a semi-active buffer device of an impact drill based on a magneto-rheological effect, characterized by, The steps are as follows: S1, a forward dynamic model of the magnetorheological damper is established, and a mapping relationship of damping force-velocity-current is obtained; S2, a buffer system is formed by using the magnetorheological damper to control the impact drill, a whole dynamic model of the buffer system is established, and each physical parameter of the whole dynamic model is determined, in the Simulink simulation environment, the whole dynamic simulation model is built by combining each physical parameter of the whole dynamic model; S3, according to the whole dynamic simulation model, the key influencing factors of the pre-impact energy consumption of the buffer system are the pre-impact velocity and the pre-impact firing point, the specific influence of the pre-impact velocity and the pre-impact firing point is theoretically analyzed, and the numerical simulation of MATLAB is used for verification; S4, according to the dynamic characteristics of the whole dynamic model, a multi-modal control strategy is designed, the multi-modal control strategy divides a single motion cycle into two stages of retreat and advance, H-infinity controller and LQR controller are used for the two stages respectively, different controller parameters are set for different system modes due to the change of dynamic balance point caused by pre-impact in different cycles, and target damping force is obtained; S5, according to the target damping force, the required control current is solved through the inverse model of the magnetorheological damper.

2. The control method of the semi-active shock drill cushion device based on the magnetorheological effect according to claim 1, characterized in that, In step S1, the following is specifically done: based on the structural parameters of the magnetorheological damper, the forward dynamic model of the magnetorheological damper is established by combining the Bouc-Wen model and the bingham model, and the mapping relationship of damping force-velocity-current is fitted; The forward dynamic model of the magnetorheological damper represents the output damping force characteristics of the damper under given input current and motion velocity; Considering the coulomb pressure drop and the viscous pressure drop, the theoretical calculation model of the output damping force of the magnetorheological damper can be obtained: (6)， wherein, is the output damping force, is the viscous damping force, is the Coulomb damping force, is the passive viscous pressure drop, is the active Coulomb pressure drop, is the effective area of the piston portion, n is the number of coil turns, is the pressure drop due to the frictional loss of the MR fluid flowing in the channel, is the pressure drop of the MR fluid flowing through the inlet and outlet.

3. The control method of the semi-active impact drill shock absorber based on the magnetorheological effect according to claim 2, characterized in that, In step S2, the following is specifically done: Step S21, the dynamic equation of the whole dynamic model of the buffer system is established; Step S22, the dynamic equation of the whole dynamic model of the buffer system is rewritten into a state space form; Step S23, the system model is constructed by State-Space module in the Simulink simulation environment, and the whole dynamic simulation is completed by integrating the controller input u.

4. The control method of the semi-active impact drill shock absorber based on the magnetorheological effect according to claim 3, characterized in that, In step S21, the equivalent mass of the system is considered as , the spring stiffness is , the controllable damping force generated by the magneto-rheological damper is , the external excitation is , and the system displacement is , the dynamic equation of the overall dynamic model of the buffer system is established. (7)， wherein, is the system displacement acceleration.

5. The control method of the semi-active impact drill shock absorber based on the magnetorheological effect according to claim 4, characterized in that, In step S22, the dynamic equation of the whole dynamic model of the buffer system is rewritten into a state space form: (8)， where the state vector matrix , the system matrix , the control input matrix , the disturbance input matrix , u is the controller input, is the external excitation, respectively represent the displacement and velocity of the buffer system.

6. The control method of the semi-active shock drill cushion device based on the magnetorheological effect according to claim 5, characterized in that, In step S3, the following is specifically done: Step S31, the factors affecting the pre-impact energy consumption performance are the pre-impact velocity and the pre-impact firing point; Taking the static balance position as the coordinate origin and the backward direction of the impact force as the positive direction, when the pre-impact occurs, the system is located behind the static balance position and has a forward velocity; According to the energy conservation law: (9)， wherein is the external excitation, is the damping force work, is the maximum rearward displacement, is the front impact origin, is the spring stiffness, is the system equivalent mass, is the front impact velocity; Step S32, in the overall dynamics simulation model, the control variable method is used for systematic simulation analysis: first, the value of the pre-impact speed is adjusted for simulation under the premise of keeping the pre-impact launch point position, controller parameters and other conditions constant, and a curve of the peak impedance force changing with the pre-impact speed is obtained; then, the pre-impact launch point is changed for simulation under the condition of keeping the pre-impact speed, controller parameters and other conditions unchanged, and a curve of the peak impedance force changing with the pre-impact launch point is obtained; by comparing the peak impedance forces under different pre-impact speeds and pre-impact launch points, the specific influence law of the pre-impact speed and the launch point position on the pre-impact performance is intuitively and quantitatively revealed.

7. The control method of the semi-active shock drill cushion device based on the magnetorheological effect according to claim 6, characterized in that, Step S4 is specifically as follows: Step S41, based on the overall dynamics model and the pre-impact principle, a single motion cycle is explicitly divided into a retreat stage with the peak impedance force suppression as the core target and a return stage with the accurate control of the reset state as the core target; the H-infinity controller with strong robustness is used for the retreat stage to cope with the impact disturbance, and the LQR controller with excellent tracking performance is used for the return stage to ensure the pre-impact speed and the pre-impact launch point in the reset state; Step S42, the different system modal refers to the dynamic balance point offset caused by the front impact effect of the buffer system, which is converted into several representative modes, denoted as mode A "modal-gain matrix mapping table" is pre-stored in the system, and the table configures a set of gain matrices of H-infinity controllers and LQR controllers for each mode ​ Step S43, in order to obtain the optimal control effect of the system pre-impact energy consumption performance, the PSO optimization algorithm is used to optimize the controller parameters under each mode: the optimization variables include the key parameters of the H-infinity controller performance output matrix under each mode, and the parameters in the state weight matrix Q and the control weight matrix R of the LQR controller; For the first movement cycle, the optimization goal of the backward phase is to minimize the peak value of the impedance force, while adding a constraint by the penalty function method, which is the peak value of the backward displacement; the optimization goal of the forward phase is to maximize the forward speed when the forward displacement is completed, and the constraint is the time of the forward displacement, and the gain matrix is obtained , ; for the subsequent movement cycles affected by the forward, the optimization goal is changed to track the forward speed and the forward impact point of the previous movement cycle, and the gain matrix is obtained , ; for the working condition of the fixed interval and fixed amplitude of the impact drill, , that is, the first movement cycle is not affected by the forward, and the subsequent movement cycles are affected by the forward. Step S44, import the gain matrix into the overall dynamics model, simulate to obtain the ideal damping force, consider the physical output characteristics of the magnetorheological damper, that is, the output force has upper and lower limit constraints related to the real-time motion state; design an ideal damping force-actual damping force conversion module containing saturation constraints, and establish a feasible region model of the magnetorheological damper output force; the feasible region is jointly defined by the maximum available damping force and the minimum available damping force of the magnetorheological damper at the current relative speed The ideal control force calculated by the LQR controller is processed through the saturation constraint module to obtain the actual achievable target damping force, as shown in equation (13): (13)， wherein K∞ is a gain matrix of an H-∞ controller, Klqr is a gain matrix of an LQR controller; , By the following formula: (14)， (15)， wherein is the density of the MR fluid, is the Darcy friction factor, is the contraction coefficient, is the expansion coefficient, is the length of the damping channel, is the gap of the damping channel; is the effective area of the piston portion, n is the number of coil turns; is the effective length of the piston damping channel; is the velocity of the piston rod, is the area of the piston rod, is the maximum yield stress of the MR fluid, is the minimum yield stress of the MR fluid.

8. The control method of the semi-active shock drill cushion device based on the magnetorheological effect according to claim 7, characterized in that, In S41, the H-infinity controller input and the LQR controller input are represented by , respectively. (10)， (11)， wherein K∞ is a gain matrix of the H-∞ controller, Klqr is a gain matrix of the LQR controller; And a controller switching logic for real-time monitoring of the system speed v in the state vector is designed, and the start and end of the stage are determined by the sign change of the speed; taking the static equilibrium position as the coordinate origin and the retreat direction as the positive direction, then: (12)， wherein is the controller input to the system, is the state vector matrix.

9. The control method of the semi-active shock drill cushion device based on the magnetorheological effect according to claim 8, characterized in that, Step S5 is specifically as follows: Step S51, according to the forward dynamics model established in step S1, the inverse model of the magnetorheological damper is constructed by mathematical inversion or data fitting method, and the above inverse model establishes the mapping relationship from the target damping force and the motion speed to the required control current I; Step S52, according to the target damping force curve obtained by optimization in step S4 and the real-time collected displacement and speed signals, the required control current is solved through the inverse model, and the high-precision tracking control of the damping force is realized through the controller.

Images