Heavy-load drilling machine lifting control method for optimizing sliding mode by combining filtering self-adaptive chaos frost ice algorithm

Abstract

The invention provides a heavy-load drilling machine lifting control method for optimizing a sliding mode by combining a filtering self-adaptive chaos frost ice algorithm. The heavy-load drilling machine lifting control method comprises the steps that S1, an acceleration sensor is used for obtaining the drilling machine lifting motion acceleration adistob (t); s2, designing a Butterworth filter to filter the movement acceleration adistob (t) signal obtained in the step S1 to obtain a gas (t) signal, so as to filter out external unknown nonlinear disturbance and weaken the influence of buffeting of the sliding mode controller on the system; s3, a sliding mode controller about the acceleration error e and the sliding mode surface is designed to achieve accurate control over the lifting acceleration of the drilling machine; and S4, optimizing a sliding mode controller (SMC) by using a self-adaptive chaotic frost ice algorithm to improve the performance of the controller. According to the invention, self-adaptive stable control of lifting of the heavy-load drilling machine can be realized, so that the problem that the lifting acceleration of a drilling machine lifting system is unstable due to the influence of heavy load and external unknown nonlinear disturbance is solved.

Description

Technical Field

[0001] The present application relates to the field of oil drilling technology, and in particular to a heavy-duty drilling rig hoisting control method that combines a filtering adaptive chaotic frost and ice algorithm with an optimized sliding mode. Background Art

[0002] The hoisting system of the drilling rig consists of a winch system, wire rope, brake system, overhead crane system, traveling crane system, derrick, drill string and dead rope fixer. It is mainly used to raise and lower drilling tools, casing and control the feeding of the drill bit (such as Figure 1 As shown in Figure 2, it undertakes important drilling operations, and its control performance directly affects engineering efficiency, safety, and sustainability.

[0003] During the hoisting control process of the hoisting system, the conventional practice is for the driller to use the operating handle to set the hoisting speed based on experience, and then the control system to achieve stable control of the hoisting speed. However, as drilling depth increases, the drilling rig hoisting system is exposed to the influence of heavy loads and unknown external nonlinear disturbances, resulting in unstable hoisting acceleration, seriously affecting the safety of drilling operations. Therefore, it is necessary to achieve stable control of the hoisting of heavy-loaded drilling rigs.

[0004] Most of the existing control methods adopt fuzzy PID control. For example, Mao Yijian combined fuzzy control with PID control to solve the contradiction between stability and dynamics in the automatic drill feeding control system (Mao Yijian. Influencing factors and optimization countermeasures of automatic drill feeding system control of oil drilling rigs [J]. Chemical Engineering Management, 2020(11):177-178.); Wang Huanyu combined the variable domain idea with fuzzy PID control to improve the response speed and control accuracy of the automatic drill feeding control system (Wang Huanyu. Research on fuzzy PID control of automatic drilling of AC variable frequency oil drilling rigs [D]. Lanzhou: Lanzhou University of Technology, 2018.); Wu Zebing combined particle swarm algorithm with fuzzy PID control and applied it to the automatic drill feeding control system, reducing the overshoot of the system and improving the adaptive ability of the system (Wu Zebing. Simulation optimization of automatic drill feeding controller based on particle swarm algorithm [J]. Petroleum and Mining Machinery, 2019, 48(6):1-8.). Although the system using fuzzy PID control has a certain degree of adaptability, the establishment of fuzzy rules and the design of membership functions are based on human subjective experience, which cannot achieve high adaptability and it is difficult to meet the requirements of nonlinear and uncertain control systems for speed and stability. Summary of the Invention

[0005] In view of this, the present application provides a heavy-duty drilling rig lifting control method that combines a filtered adaptive chaotic frost algorithm to optimize the sliding mode to solve the problem of unstable lifting acceleration of the heavy-duty drilling rig lifting system under heavy load and disturbance conditions.

[0006] To achieve the above objectives, the technical solutions adopted in this application are as follows: A heavy-duty drilling rig hoisting control method combining a filtering adaptive chaotic frost-ice algorithm with an optimized sliding mode comprises: S1: Use the acceleration sensor to obtain the acceleration a of the drilling rig lifting motion disturb (t); S2: Design a Butterworth filter to calculate the motion acceleration a obtained in S1 disturb (t) signal is filtered to obtain a ture (t) signal to filter out external unknown nonlinear disturbances and weaken the impact of sliding mode controller chattering on the system; S3: Design a sliding mode controller based on the acceleration error e and the sliding mode surface to achieve precise control of the drilling rig lifting acceleration; S4: Use the Adaptive Chaos Frost Ice Algorithm (ACRIME) to optimize the sliding mode controller (SMC) and improve controller performance.

[0007] Furthermore, the S2 specifically includes: S2-1: Calculate the passband cutoff frequency of the Butterworth filter according to formulas (1) and (2) and the stopband start frequency ; (1) (2) where f v is the passband cutoff frequency, f q is the stopband start frequency; S2-2: Calculate the normalized frequency according to formulas (3) and (4): (3) (4) in, is the normalized passband cutoff frequency, is the normalized stopband start angular frequency; S2-3: Calculate the Butterworth filter order N and parameter C: (5) (6) (7) Among them, a v is the maximum attenuation in the passband, a q is the minimum attenuation in the stop band; S2-4: Using the calculated order N, check the Butterworth filter normalization system function table to obtain the normalized Butterworth low-pass filter H( p ); S2-5: Substitute H( p ) to eliminate the normalization and obtain the target filter, where s represents the s complex variable in the Laplace transform, p is the normalization coefficient; S2-6:a disturb (t) is filtered by an N-order Butterworth low-pass filter to obtain a ture (t).

[0008] Furthermore, the sliding mode controller designed in S3 regarding the acceleration error e and the sliding mode surface specifically includes: S3-1: Calculate reference acceleration a* and actual acceleration a ture The error: S3-2: Construct the sliding surface function z, where c is a custom hyperparameter, acceleration error e, 、 and All are functions of time t: S3-3: Design the Lyapunov function V(z) based on the function z of the sliding surface. The Lyapunov function is expressed as: ; S3-4: The derivative of the Lyapunov function V(z) with respect to time t is: S3-5: Construct the differential equation of the system without sliding mode controller based on e and is the second-order state equation of the state variable, A and B are the coefficient matrices: and T , T , T* is the control law of the sliding mode controller; S3-6: Designing the Control Law for a Sliding Mode Controller ,make: have to: Where M represents a function composed of x1 and x2; S3-7: Design the reaching law F(z) of the sliding mode controller, let: have to: Where a* is the expected acceleration, a ture is the actual acceleration signal of the drill string lifting motion after filtering by the Butterworth filter, and is the hyperparameter to be optimized.

[0009] Furthermore, the adaptive chaos frost ice algorithm (ACRIME) is used in S4 to optimize the sliding mode controller (SMC), specifically using the adaptive chaos frost ice algorithm to optimize the hyperparameters of the sliding mode controller. and ,include: S4-1: Use Tent Chaos Map to randomly generate a set of frost factors D i The frost population R consists of j frost particles x ij The initialization frost population sequence is formed, and each frost factor represents a set of candidate solutions for the optimized parameters, where the Tent chaos mapping formula is: The frost population is: S4-2: Based on the motion characteristics of frost particles, simulate the condensation process of each particle and calculate the position of frost; in represents the updated position of the jth particle in the i-th frost factor; represents the jth particle of the best frost factor in the frost population R; Controls the direction of particle movement, which is a random number between (-1, 1); and Together they control the direction of particle movement, which will change with the number of iterations; [·] indicates rounding; h is the adhesion, a random number in the range (-1, 1); r2 is a random number in the range (0, 1), which, together with the adhesion coefficient, controls whether frost condenses; T and t represent the maximum number of iterations and the current number of iterations, respectively. E To affect the condensation probability attachment factor; w is set to 5 to control the number of segments of the step function; Ub ij and Lb ijRepresents the upper and lower bounds of the search space, limiting the area where frost particles move; β is the environmental factor, which simulates the influence of the external environment with the number of iterations to ensure the convergence of the algorithm.

[0010] S4-3: By simulating the cross-condensation process of hard frost into ice, an information replacement strategy is proposed. During the search process, the position of the solution is actively changed to avoid falling into the local optimal solution, thereby enhancing the global search capability of the algorithm; Among them, r3 is a random number in the range of (-1, 1); Represents the normalized value of the current frost factor fitness value, which refers to the probability of the i-th frost being selected.

[0011] S4-4: Based on the control system structure and the ITAE criterion (Integral time multiplied by the Absolute value of the Error), the optimized fitness objective function of this application is calculated. The fitness values before and after the frost factor update are compared to determine whether to replace it. The optimized fitness objective function F(t) is: Where, e(t) is the difference between the reference acceleration a* and the actual acceleration a ture Error in (t); S4-5: The optimal hyperparameters found are fed into the sliding mode controller to improve the control effect.

[0012] Compared with the prior art, the present invention has the following advantages: 1. This application first uses a Butterworth filter (BF) to filter the actual acceleration signal to remove interference, so that the sliding mode controller can obtain a more accurate input signal, and also to some extent weaken the impact of the sliding mode controller's chattering on the system.

[0013] 2. This application uses sliding mode control, which has the advantages of fast response, insensitivity to parameter changes and disturbances, no need for system online identification, and simple physical implementation.

[0014] 3. This application modifies the reaching law of the sliding mode controller, i.e. adopting function, which solves the problem of chattering when the traditional sliding mode controller uses the sign function sgn(z) as the reaching law. In addition, It is continuous, the function has no time lag, and the hyperparameter є is set as a variable gain, so that the sliding mode controller has a certain degree of adaptability.

[0015] 4. This application uses Tent chaos mapping to replace the random initialization of the Frost and Ice algorithm to enhance and improve the distribution quality of the initial population in the search space, strengthen its global search capability, and thus improve the algorithm's solution accuracy.

[0016] 5. This application can make the control of the drilling rig hoisting system for hoisting acceleration tracking simpler and easier to debug. The proposed control method can accurately follow the reference lifting acceleration curve and significantly improve the control accuracy and stability under heavy-load conditions with disturbances. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is the structural diagram of the drilling rig hoisting system; Figure 2 This is a flow chart of a heavy-duty drilling rig hoisting control method combining a filtering adaptive chaotic frost and ice algorithm to optimize the sliding mode; Figure 3 This is the structural diagram of the acceleration control system of the drilling rig hoisting system of this application; Figure 4 is the normalized system function table of Butterworth filter; Figure 5 is a schematic diagram of sliding mode control; Figure 6 It is a flow chart of optimizing sliding mode controller (SMC) using adaptive chaotic frost ice algorithm (ACRIME). DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0020] like Figure 2 and Figure 3 As shown, a heavy-duty drilling rig hoisting control method combining a filtering adaptive chaotic frost-ice algorithm and an optimized sliding mode includes: S1: Use the acceleration sensor to obtain the acceleration a of the drilling rig lifting motion disturb (t); S2: Design a Butterworth filter to calculate the motion acceleration a obtained in S1 disturb (t) signal is filtered to obtain ature (t) signal to filter out external unknown nonlinear disturbances and weaken the impact of sliding mode controller chattering on the system; The Butterworth filter (BF) is a type of electronic filter, also known as a maximally flat filter. It has the advantages of balanced characteristics in terms of linear phase, attenuation slope, and loading characteristics.

[0021] The Butterworth low-pass filter is used for filtering because in the drill string lifting system, the acceleration change frequency is low, while the interference is generally high-frequency interference. Therefore, it is necessary to design a low-pass filter to filter out high-frequency interference signals and improve the control accuracy of the control algorithm. At the same time, it can weaken the impact of the sliding mode controller's chattering on the system and solve the shortcoming that sliding mode control will cause chattering.

[0022] As a further implementation, the S2 specifically includes: S2-1: Calculate the passband cutoff frequency of the Butterworth filter and the stopband start frequency ; In this example, the Butterworth filter design technical indicators are: passband cutoff frequency f v is 500Hz, the maximum passband attenuation is a v is 3dB, the stopband start frequency f q is 1000Hz, the minimum attenuation of the stop band is a q 18dB; From the design indicators we know: S2-2: Calculate the normalized frequency: =1 =2 in, is the normalized passband cutoff frequency, is the normalized stopband start angular frequency; S2-3: Calculate the Butterworth filter order N and parameter C: 7.900 =2.982 So the order N is 3, which is a 3rd order filter; S2-4: Use the calculated order N to check the normalized system function table of the Butterworth filter (such as Figure 4 ) to obtain a normalized third-order Butterworth low-pass filter: S2-5: Substitute H( p ) to eliminate the normalization and obtain the target filter, where s represents the s complex variable in the Laplace transform, p Represents the normalization coefficient: S2-6:a disturb (t) is filtered by a third-order Butterworth low-pass filter to obtain a ture (t).

[0023] S3: Design a sliding mode controller based on the acceleration error e and the sliding mode surface to achieve precise control of the drilling rig lifting acceleration; Sliding mode control (SMC), also known as variable structure control, is essentially a special type of nonlinear control, where nonlinearity manifests itself as control discontinuity. This control strategy differs from other control strategies in that the system's "structure" is not fixed but can be purposefully and continuously changed during the dynamic process based on the system's current state (such as the deviation and its derivatives), forcing the system to move along a predetermined "sliding mode" state trajectory. Because sliding modes can be designed and are independent of plant parameters and disturbances, sliding mode control offers advantages such as fast response, insensitivity to parameter changes and disturbances, the absence of online system identification, and simple physical implementation. However, it is still rarely used in the field of drilling rig hoisting control.

[0024] As a further implementation, the design of the sliding mode controller regarding the acceleration error e and the sliding mode surface in S3 specifically includes: S3-1: Calculate reference acceleration a* and actual acceleration a ture Error : S3-2: Construct the sliding surface function z, where c is a custom hyperparameter, acceleration error e, 、 and All are functions of time t: S3-3: Design the Lyapunov function V(z) based on the function z of the sliding surface. The Lyapunov function is expressed as: ; S3-4: The derivative of the Lyapunov function V(z) with respect to time t is: S3-5: Construct the differential equation of the system without sliding mode controller based on e and is the second-order state equation of the state variable, A and B are the coefficient matrices: and T , T , T* is the control law of the sliding mode controller; S3-6: Designing the Control Law for a Sliding Mode Controller ,make: have to: Where M represents a function composed of x1 and x2; The control law T* of the sliding mode controller, i.e., the motor torque, is calculated based on the acceleration error e(t) and the sliding surface. The electronic control system drives the motor according to the torque T* to lift the drill string.

[0025] S3-7: Design the reaching law F(z) of the sliding mode controller, let: have to: Where a* is the expected acceleration, a ture is the actual acceleration signal of the drill string lifting motion after filtering by the Butterworth filter, and is the hyperparameter to be optimized.

[0026] In order to solve the problem that chattering occurs when the traditional sliding mode controller uses the sign function sgn(z) as the reaching law, the present invention modifies the reaching law of the sliding mode controller, that is, adopts function. The upper and lower bounds of are the same, but it can further reduce the chattering of the sliding mode controller by replacing the discontinuous step function with a smooth curve control. In addition, The function is continuous and has no time lag, and є serves as a variable gain, making the sliding mode controller somewhat adaptive. The electronic control system then drives the motor to lift the drill string according to the torque T*.

[0027] like Figure 5 As shown, the design of the reaching law is to ensure that zz'<0, so that z can eventually tend to 0 stably, and because z=cx1+x2=ce+ , that is, to ensure x1=e=0, x2= =0; The reason why zz'<0 can ensure that z will eventually tend to 0 is that z=0 is an equilibrium state of z. According to Lyapunov stability, if V(z) satisfies the following two conditions, z is asymptotically stable, that is, z will eventually tend to 0 (equilibrium state). These two conditions are: (1) When V(z)≥0, only when z=0 can V(z)=0, and in the above V(z)=z 2 / 2, obviously meets this condition; (2) ≤0, only z=0 can =0, and =z Obviously, as long as z <0, z will eventually tend to 0.

[0028] S4: Use the Adaptive Chaos Frost Ice Algorithm (ACRIME) to optimize the sliding mode controller (SMC) and improve controller performance.

[0029] As a further embodiment, the adaptive chaos frost ice algorithm (ACRIME) is used in S4 to optimize the sliding mode controller (SMC), specifically using the adaptive chaos frost ice algorithm to optimize the hyperparameters of the sliding mode controller. and .

[0030] The current common practice is to continuously change c and є based on experience and perform traversal tests to find the appropriate c and є. However, this method takes a lot of time, so an adaptive algorithm is needed to adaptively search for the appropriate parameter combination.

[0031] The Rime Ice Algorithm (RIME) is an optimization algorithm inspired by the natural growth mechanisms of frost ice. It simulates the soft-time and hard-time growth of ice, constructing a soft-time search strategy and hard-time puncture to improve the quality of the global solution. The key idea of the Rime Ice Algorithm is to exploit the randomness of soft frost and the regularity of hard frost for search, optimizing the search by simulating the accumulation and growth of frost ice on a surface. In practical applications, the Rime Ice Algorithm has been used to solve optimization problems, such as optimizing searches to find the global optimal solution of a function by simulating the formation of frost ice.

[0032] Like other swarm intelligence algorithms, the original Rime Ice algorithm (RIME) initializes the positions of individuals in the swarm by randomly generating positions when solving complex problems. This results in low swarm diversity and slow convergence in optimizing the problem. To ensure that individuals have high global search capabilities at the beginning of the algorithm, the positions of the swarm need to be evenly distributed across the entire solution space. Therefore, this application uses a chaos operator to initialize the swarm.

[0033] Chaos, as a nonlinear natural phenomenon, is widely used in optimization search problems because of the advantages of ergodicity and randomness of its chaotic sequence. Searching with chaotic variables is obviously more advantageous than disordered random search. Currently, the commonly used chaotic perturbation equations include Logistic mapping and Tent mapping. It can be seen that the distribution characteristics of Logistic mapping are: the probability of taking values in the middle is relatively uniform, but the probability at both ends is particularly high. Therefore, when the global optimal point is not at the two ends of the design variable space, it is not conducive to finding the optimal point. The Tent chaotic mapping has better ergodic uniformity and faster search speed than the Logistic chaotic mapping. Therefore, this application uses the Tent chaotic mapping to replace the random initialization of the frost and ice algorithm, namely the adaptive chaotic frost and ice algorithm (ACRIME), to improve and improve the distribution quality of the initial population in the search space, strengthen its global search capability, and thus improve the accuracy of the algorithm solution.

[0034] like Figure 6 As shown, in this application, the adaptive chaotic frost algorithm is used to optimize the hyperparameters of the sliding mode controller and include: S4-1: Use Tent Chaos Map to randomly generate a set of frost factors D i The frost population R consists of j frost particles x ij The initialization frost population sequence is formed, and each frost factor represents a set of candidate solutions for the optimized parameters, where the Tent chaos mapping formula is: The frost population is: In this application, there are two optimized parameters, namely c and є, so the dimension is 2, that is, j=2.

[0035] S4-2: Based on the motion characteristics of frost particles, simulate the condensation process of each particle and calculate the position of frost; in represents the updated position of the jth particle in the i-th frost factor; represents the jth particle of the best frost factor in the frost population R; Controls the direction of particle movement, which is a random number between (-1, 1); and Together they control the direction of particle movement, which will change with the number of iterations; [·] indicates rounding; h is the adhesion, which is a random number in the range of (-1, 1); r2 is a random number in the range of (0, 1), which together with the adhesion coefficient controls whether frost condenses; T and t represent the maximum number of iterations and the current number of iterations, respectively; E is the adhesion factor that affects the probability of condensation; w is set to 5, which controls the number of segments of the step function; Ub ij and Lb ij Represents the upper and lower bounds of the search space, limiting the area where frost particles move; β is the environmental factor, which simulates the influence of the external environment with the number of iterations to ensure the convergence of the algorithm.

[0036] S4-3: By simulating the cross-condensation process of hard frost into ice, an information replacement strategy is proposed. During the search process, the position of the solution is actively changed to avoid falling into the local optimal solution, thereby enhancing the global search capability of the algorithm; Among them, r3 is a random number in the range of (-1, 1); Represents the normalized value of the current frost factor fitness value, which refers to the probability of the i-th frost being selected.

[0037] S4-4: Based on the control system structure and the ITAE criterion (Integral time multiplied by the Absolute value of the Error), the optimized fitness objective function of this application is calculated. The fitness values before and after the frost factor update are compared to determine whether to replace it. The optimized fitness objective function F(t) is: Where, e(t) is the difference between the reference acceleration a* and the actual acceleration a ture Error in (t); This step establishes an active greedy selection mechanism, which compares the fitness values before and after the frost factor update to decide whether to replace it. If the fitness after the update is better than the fitness before the update, the updated value is selected to replace the value before the update. By continuously selecting better solutions and retaining diversity, the search process can be continuously optimized to gradually approach the optimal solution.

[0038] S4-5: The optimal hyperparameters found are fed into the sliding mode controller to improve the control effect.

[0039] This application proposes a heavy-load drilling rig hoist control method that combines a filtered adaptive chaotic frost-ice algorithm with sliding mode optimization. Specifically, a Butterworth filter (BF) is first used to filter the actual acceleration signal to remove interference, allowing the sliding mode controller to obtain a more accurate input signal and, to a certain extent, reducing the impact of sliding mode controller chatter on the system. A sliding mode control (SMC) is then used to precisely control the hoisting acceleration. Furthermore, an adaptive chaotic frost-ice algorithm (ACRIME) sliding mode controller is used for adaptive optimization. This technical solution enables adaptive stable control of heavy-load drilling rig hoisting, addressing the issue of unstable hoisting acceleration caused by heavy loads and unknown external nonlinear disturbances in the drilling rig hoisting system. It also addresses the problem that existing control algorithms struggle to meet the requirements for rapidity and stability of nonlinear, uncertain control systems, requiring manual optimization.

[0040] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A heavy-duty drilling rig hoisting control method combining a filtering adaptive chaotic frost and ice algorithm to optimize the sliding mode, characterized in that: include: S1: Use the acceleration sensor to obtain the acceleration a of the drilling rig lifting motion disturb (t); S2: Design a Butterworth filter to calculate the motion acceleration a obtained in S1 disturb (t) signal is filtered to obtain a ture (t) signal to filter out external unknown nonlinear disturbances and weaken the impact of sliding mode controller chattering on the system; S3: Design a sliding mode controller based on the acceleration error e and the sliding mode surface to achieve precise control of the drilling rig lifting acceleration; S4: Use the Adaptive Chaos Frost Ice Algorithm (ACRIME) to optimize the sliding mode controller (SMC) and improve controller performance.

2. The heavy-duty drilling rig hoisting control method combining a filtering adaptive chaotic frost and ice algorithm with an optimized sliding mode as claimed in claim 1, characterized in that: The S2 specifically includes: S2-1: Calculate the passband cutoff frequency of the Butterworth filter according to formulas (1) and (2) and the stopband start frequency ; （1） （2） where f v is the passband cutoff frequency, f q is the stopband start frequency; S2-2: Calculate the normalized frequency according to formulas (3) and (4): （3） （4） in, is the normalized passband cutoff frequency, is the normalized stopband start angular frequency; S2-3: Calculate the Butterworth filter order N and parameter C: （5） （6） （7） Among them, a v is the maximum attenuation in the passband, a q is the minimum attenuation in the stop band; S2-4: Using the calculated order N, check the Butterworth filter normalization system function table to obtain the normalized Butterworth low-pass filter H( p ); S2-5: Substitute H( p ) to eliminate the normalization and obtain the target filter, where s represents the s complex variable in the Laplace transform, p is the normalization coefficient; S2-6:a disturb (t) is filtered by an N-order Butterworth low-pass filter to obtain a ture (t).

3. The heavy-duty drilling rig hoisting control method combining a filtering adaptive chaotic frost and ice algorithm with an optimized sliding mode as claimed in claim 2, characterized in that: The sliding mode controller designed in S3 regarding the acceleration error e and the sliding mode surface specifically includes: S3-1: Calculate reference acceleration a* and actual acceleration a ture The error: S3-2: Construct the sliding surface function z, where c is a custom hyperparameter, acceleration error e, 、 and All are functions of time t: S3-3: Design the Lyapunov function V(z) based on the function z of the sliding surface. The Lyapunov function is expressed as: ； S3-4: The derivative of the Lyapunov function V(z) with respect to time t is: S3-5: Construct the differential equation of the system without sliding mode controller based on e and is the second-order state equation of the state variable, A and B are the coefficient matrices: and T , T , T* is the control law of the sliding mode controller; S3-6: Designing the Control Law for a Sliding Mode Controller ,make: have to: Where M represents a function composed of x1 and x2; S3-7: Designing Reaching Laws for Sliding Mode Controllers ,make: have to: Where a* is the expected acceleration, a ture is the actual acceleration signal of the drill string lifting motion after filtering by the Butterworth filter, and is the hyperparameter to be optimized.

4. The heavy-duty drilling rig hoisting control method combining a filtering adaptive chaotic frost and ice algorithm with an optimized sliding mode as claimed in claim 3 is characterized in that: In S4, the adaptive chaos frost ice algorithm (ACRIME) is used to optimize the sliding mode controller (SMC). Specifically, the adaptive chaos frost ice algorithm is used to optimize the hyperparameters of the sliding mode controller. and ,include: S4-1: Use Tent Chaos Map to randomly generate a set of frost factors D i The frost population R consists of j frost particles x ij The initialization frost population sequence is formed, and each frost factor represents a set of candidate solutions for the optimized parameters, where the Tent chaos mapping formula is: The frost population is: S4-2: Based on the motion characteristics of frost particles, simulate the condensation process of each particle and calculate the position of frost; in represents the updated position of the jth particle in the i-th frost factor; represents the jth particle of the best frost factor in the frost population R; Controls the direction of particle movement, which is a random number between (-1, 1); and Together they control the direction of particle movement, which will change with the number of iterations; [·] indicates rounding; h is the adhesion, a random number in the range (-1, 1); r2 is a random number in the range (0, 1), which, together with the adhesion coefficient, controls whether frost condenses; T and t represent the maximum number of iterations and the current number of iterations, respectively. E To affect the condensation probability attachment factor; w is set to 5 to control the number of segments of the step function; Ub ij and Lb ij Represents the upper and lower bounds of the search space, limiting the area where frost particles move; β is the environmental factor, which simulates the influence of the external environment with the number of iterations to ensure the convergence of the algorithm; S4-3: By simulating the cross-condensation process of hard frost into ice, an information replacement strategy is proposed. During the search process, the position of the solution is actively changed to avoid falling into the local optimal solution, thereby enhancing the global search capability of the algorithm; Among them, r3 is a random number in the range of (-1, 1); Represents the normalized value of the current frost factor fitness value, which refers to the probability of the i-th frost being selected; S4-4: Based on the control system structure and the ITAE criterion (Integral of the Time multiplied by the Absolute value of the Error), the optimized fitness objective function of this application is calculated. The fitness values before and after the frost factor update are compared to determine whether to replace it. The optimized fitness objective function F(t) is: Where, e(t) is the difference between the reference acceleration a* and the actual acceleration a ture Error in (t); S4-5: The optimal hyperparameters found are fed into the sliding mode controller to improve the control effect.

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