A Multivariable Coupled Throttle Control Method and System for Rotary Drilling Rigs

Abstract

This invention proposes a multivariable coupled throttle control method for rotary drilling rigs, comprising the following steps: acquiring the working parameter variables of the rotary drilling rig during the drilling process and setting expected values; inputting the actual values ​​of each variable into a neural network, predicting the influence factors of each variable on each control quantity in real time according to the working conditions, and inputting them into an NPI controller; inputting the error values ​​of each variable into the NPI controller, and obtaining the three-dimensional control quantity by combining the influence factors through nonlinear proportional and integral operations; inputting the three-dimensional control quantity into the engine throttle, pressurized cylinder valve port, and power motor valve port of the rotary drilling rig to control the power head torque, power head pressure, and advance per unit time to simultaneously reach the expected range. This invention uses nonlinear PI control, which can well adapt to the unstructured working environment of the rotary drilling rig and has strong robustness; by introducing the influence factors of the control quantity for multivariable coupling, it realizes automatic throttle control during the drilling process of the rotary drilling rig, greatly improving the efficiency of drilling operations and reducing the workload of operators.

Description

Technical Field

[0001] This invention relates to the field of automatic throttle control technology for rotary drilling rigs, and specifically to a multivariable coupled throttle control method for rotary drilling rigs. Background Technology

[0002] Rotary drilling rigs are a type of multi-purpose building foundation pile construction equipment, widely used in the construction of pile foundations for roads, bridges, docks, high-rise buildings, and other engineering projects.

[0003] Due to the complexity of construction and tight schedules, rotary drilling rigs often operate continuously for extended periods, especially during prolonged drilling operations. In typical rotary drilling operations, the operator relies entirely on sensing changes in the pilot handle to adjust the engine throttle, pressurized cylinder, and power motor to control the drilling. This results in low efficiency and high operator workload. Therefore, this invention proposes a multi-variable coupled throttle control method for rotary drilling rigs. This method automatically controls the opening of the engine throttle, pressurized cylinder valve, and power motor valve according to actual working conditions during drilling, achieving automated drilling. The operator only needs to perform simple operations or intervene in special circumstances. Summary of the Invention

[0004] This invention provides a multivariable coupled throttle control method and system for rotary drilling rigs. Addressing the problem of automatic control of throttle and valve openings during rotary drilling, this invention proposes a multivariable coupled throttle control method based on nonlinear PI (NPI) and neural networks, taking into full account the unstructured environment in which the rotary drilling rig operates, the uncertainty of operating conditions, and the complex coupling relationships between output variables. Using this method, the rotary drilling rig can automatically control the throttle and valve openings during drilling operations, ensuring that the power head torque, power head pressure, and drilling depth per unit time remain within the optimal desired range, thereby maintaining maximum automatic drilling efficiency. Furthermore, it also has advantages such as preventing damage to drill teeth due to improper operation and reducing the workload of operators.

[0005] To achieve the above objectives, the present invention provides a multivariable coupled rotary drilling rig throttle control method, the specific steps of which are as follows:

[0006] Step 1: Obtain the actual value T of the power head torque at the current time t. r Actual value of the applied pressure P of the power head r Actual value of advance per unit time H r The logic control unit compares and analyzes the above data to determine the drilling formation, and classifies the working conditions according to the drilling formation; based on the working conditions, it obtains the set expected values ​​T for each variable. d P d Hd ; Obtain the parameters of the previous time point t-Δt The parameters e represents the three-dimensional error value at the previous time point t-Δt. t-Δt The second-order difference is defined as follows: e represents the error value of the power head torque, power head pressure, and advance per unit time (T). e P e and H e The subscript t represents the current time point, t-Δt represents the previous time point, t-2Δt is the second time point before the current time point, and Δt is the sampling interval;

[0007] Step 2: Set the actual values ​​T of the three variables at the current time t: power head torque, power head pressure, and advance per unit time. r P r and H r The input layer of a neural network performs a non-linear transformation from input to output using activation functions in the hidden layers. The transformation relationship is as follows: and y (i) =f(W (i) y (i-1) In the formula, y represents the output of a neuron with 3 inputs, x represents the input, w represents the weights between the input and output, b is the bias term, and f represents the activation function; (i) W represents the output of the i-th neural network layer. (i) It is the weight between the (i-1)th layer and the ith layer; the control quantity influence factors ω1~ω7 are output by the output layer and input into the NPI controller;

[0008] Step 3: Set the error value T of the power head torque, power head pressure, and advance per unit time at the current time point t. e P e and H e Input to the NPI controller,

[0009] Combining the control quantity influence factor ω, the three-dimensional control quantity u is obtained by performing a composite calculation according to the following formula. v u l1 u l2

[0010]

[0011] K P K represents the proportional gain. I Indicates integral gain.

[0012]

[0013] Δt represents the sampling interval, G v G represents the integral factor of the engine throttle control quantity. l1 G represents the integral factor of the control quantity at the valve port of the pressurized hydraulic cylinder. l2 b represents the integral factor of the control quantity of the motor valve port; b represents the control factor, which determines the convergence speed. and These are the error values ​​T for the power head torque, power head pressure, and advance per unit time in step 1, respectively. e P e and H e Calculated parameters and The rate of change of the error;

[0014]

[0015] Three-dimensional control quantity u v u l1 u l2 These correspond to the increments of the engine throttle, the pressurized cylinder valve port, and the power motor valve port, respectively.

[0016] Step 4, set the three-dimensional control quantity u = [u v u l1 u l2 ] T Input the engine throttle, pressurized cylinder valve port, and power head motor valve port of the rotary drilling rig, and provide three error values ​​e. t =[T e P e H e ] T Set three corresponding error thresholds ε = [ε1 ε2 ε3] T , when e t When ε < ε, the torque of the power head, the pressure of the power head, and the advance per unit time all reach the desired range, thus completing automatic throttle control.

[0017] Preferably, the actual values ​​T of each variable r P r H r The expected value T is obtained from the torque sensor, pressure sensor, and displacement sensor, respectively. d P d H d The logic control unit then sets the parameters according to the operating conditions.

[0018] Preferably, the control quantity influence factor ω represents the influence coefficient value of each variable error value on each control quantity.

[0019] Another objective of this invention is to provide a multivariable coupled rotary drilling rig throttle control system, comprising the following components: a data acquisition module, a parameter prediction module, and a throttle increment control module.

[0020] The data acquisition module includes components for periodically collecting data on the power head torque, power head pressure, and drilling depth per unit time during rotary drilling operations at set time intervals, i.e., the actual power head torque value T. r Actual value of the applied pressure P of the power head r Actual value of advance per unit time H r ;

[0021] The parameter prediction module is used to perform the following steps:

[0022] Step 1, based on the actual value T of the power head torque at the current time point t. r Actual value of the applied pressure P of the power head r Actual value of advance per unit time H r Comparative analysis is used to determine the drilling formation, and working conditions are classified according to the drilling formation; the expected values ​​T of each variable are obtained based on the working conditions. d P d H d ;

[0023] Get the parameters of the previous time point t-Δt The parameters e represents the three-dimensional error value at the previous time point t-Δt. t-Δt The second-order difference is defined as follows: e represents the error value of the power head torque, power head pressure, and advance per unit time. e T e and H e The subscript t represents the current time point, t-Δt represents the previous time point, t-2Δt is the second time point before the current time point, and Δt is the sampling interval;

[0024] Step 2: Set the actual values ​​T of the three variables at the current time t: power head torque, power head pressure, and advance per unit time. r P r and H r The input layer of a neural network performs a non-linear transformation from input to output using activation functions in the hidden layers. The transformation relationship is as follows: and y (i) =f(W (i) y (i-1)In the formula, y represents the output of a neuron with I = 3 inputs, x represents the input, w represents the weights between the input and output, b is the bias term, and f represents the activation function; (i) W represents the output of the i-th neural network layer. (i) These are the weights between the (i-1)th and ith layers; the output layer outputs control quantity influence factors ω1~ω7.

[0025] Throttle increment control module: Based on a nonlinear PI controller, it uses the torque, force, and advance per unit time collected by the data acquisition module as feedback quantities, and calculates the throttle increment based on the error value T. e P e and H e The increments of each throttle and valve port are calculated using the control variable influence factors ω1~ω7.

[0026] The solution method is to obtain the three-dimensional control quantity u according to the following calculation formula. v u l1 u l2

[0027] K P K represents the proportional gain. I Indicates integral gain.

[0028]

[0029] Δt represents the sampling interval, G v G represents the integral factor of the engine throttle control quantity. l1 G represents the integral factor of the control quantity at the valve port of the pressurized hydraulic cylinder. l2 b represents the integral factor of the control quantity of the motor valve port; b represents the control factor, which determines the convergence speed. and These are the error values ​​T for the power head torque, power head pressure, and advance per unit time in step 1, respectively. e P e and H e Calculated parameters and The rate of change of the error;

[0030]

[0031] Three-dimensional control quantity u v u l1 u l2These correspond to the increments of the engine throttle, the pressurized cylinder valve port, and the power motor valve port, respectively.

[0032] The three-dimensional control quantity u = [u v u l1 u l2 ] T The throttle of the rotary drilling rig is automatically controlled by inputting the engine throttle, the pressurized cylinder valve port, and the power head motor valve port.

[0033] The present invention has the following beneficial effects: The present invention employs nonlinear PI control for throttle control, which is highly adaptable and robust, and can well adapt to the uncertainties of the rotary drilling rig's operating environment and the nonlinearity of the model; it can well adapt to nonlinear control, greatly improving control accuracy. Theoretical proof shows that the system stability of the nonlinear PI controller can be guaranteed, making it very suitable for harsh environments; the addition of a neural network introduces additional control variable influence factors, predicting the influence factors based on real-time operating conditions to achieve optimal system performance, resulting in excellent real-time performance; simultaneously, it solves the coupling problem between variables and greatly improves the efficiency of rotary drilling rig operations while reducing the workload of operators. Attached Figure Description

[0034] Figure 1 This is the control principle diagram of the present invention;

[0035] Figure 2 This is a flowchart of the control method of the present invention;

[0036] Figure 3 This is a graph showing the control results of the present invention;

[0037] Figure 4 This is a schematic diagram of the control system structure of the present invention. Detailed Implementation

[0038] To explain the technical content, objectives, and effects of this invention in detail, the following description is provided in conjunction with specific embodiments and accompanying drawings. This specific embodiment uses a rotary drilling rig operating in sandy soil as an example to illustrate throttle control.

[0039] like Figure 1As shown, this invention proposes a multivariable coupled throttle control method for rotary drilling rigs. Addressing the automatic control problem of various throttle and valve openings during rotary drilling, and considering the unstructured environment of the rotary drilling rig operation, the uncertainty of the operating conditions, the complex coupling relationships between output variables, and the need to improve operating efficiency, this invention proposes a multivariable coupled throttle control method based on nonlinear PI (hereinafter referred to as NPI) and neural networks. The neural network predicts the influence factors of each control quantity based on the actual values ​​of the input variables. The NPI controller combines the influence factors of the control quantities and the error values ​​of each variable to calculate the three-dimensional control quantities, including the engine throttle increment u. v Incremental u at the valve port of the pressurized hydraulic cylinder l1 Incremental u of the power motor valve port l2 This enables automatic throttle control of rotary drilling rigs through multi-variable coupling. Using this method, the rotary drilling rig can automatically control the throttle and valve opening during drilling operations, ensuring that the power head torque, power head pressure, and drilling depth per unit time remain within the optimal desired range, thus maintaining maximum automatic drilling efficiency. Furthermore, it offers advantages such as preventing damage to drill teeth and reducing the workload of operators.

[0040] The specific steps and procedures are as follows: Figure 2 As shown:

[0041] Step (1): Obtain the actual values ​​T of each variable at the current time point t using the sensor. r P r H r Based on the above data comparison and analysis, the drilling formation is determined, and the working conditions are divided according to the drilling formation; based on the working conditions, the expected values ​​T of each variable are obtained. d P d H d ; Obtain the parameters of the previous time point t-Δt

[0042] Where T, P, and H represent the torque of the power head, the pressure applied by the power head, and the advance per unit time, respectively. The actual values ​​of each variable are obtained by the torque sensor, pressure sensor, and displacement sensor, respectively, while the expected values ​​are set by the logic control unit according to the working conditions.

[0043] In the sandy soil hole-forming condition described in this embodiment, T d ∈[300, 320]kN·m, P d ∈[240, 260]kN, H d ∈[20, 30]m / h.

[0044] e represents the three-dimensional error value at the previous time point (t-Δt). t-Δt The second-order difference, the parameters obtained here. In subsequent steps, the NPI controller calculates the integral value I at the current time point t. t Required. Its definition is as follows:

[0045]

[0046] Step (2) sets the actual values ​​r of the power head torque, power head pressure, and advance per unit time at the current time point t. t =[T r P r H r ] T The input is fed into the neural network, where the influence factor ω of each variable on each control quantity is predicted in real time based on the operating conditions and then input into the NPI controller.

[0047] In this embodiment, the neural network used has an input layer containing 3 neurons, a hidden layer containing 7 neurons, and an output layer containing 7 neurons. Input layer neuron 1 represents the actual torque value T of the power head at the current time t. r Input layer neuron 2 represents the actual value P of the applied force P at the current time point t. r Input layer neuron 3 represents the actual advance per unit time H at the current time point t. r The values ​​are measured by multiple sensors. Output layer neurons 1-7 are the control input factors ω1-ω7. The activation function f in the hidden layer performs a non-linear transformation from the input layer to the output layer, and its calculation expression is as follows:

[0048]

[0049] y (i) =f(W (i) y (i-1) ),

[0050] In the formula, y represents the output of a neuron with I inputs, where I = 3 in this embodiment; x represents the input value, where x1 = T in this embodiment. r x2 = P r x3 = H r ; w represents the weights between the input and output; in this embodiment, w1, w2, and w3 are 7×3 matrices; b represents the bias term; f represents the activation function; y (i) W represents the output of the i-th neural network layer. (i) It is the weight between the (i-1)th layer and the i-th layer.

[0051] The three selected variables T, P, and H are interconnected, and these three variables are related to the three control quantities u to be determined. v u l1 u l2The relationship between them is not a simple one-to-one correspondence. Therefore, it is necessary to find a suitable multidimensional influence factor ω to couple the relationship between the three variables, and input ω into the NPI controller to solve for a reasonable control quantity u. v u l1 u l2 Table 1 is a table of control quantity influencing factors for neural network prediction, listing the variables, control quantities, and control quantum terms corresponding to each influencing factor.

[0052] Table 1

[0053]

[0054] In the sand drilling condition described in this embodiment, ω1, ω3, and ω5 are control variables u. v The influence factors have a total weight of 1; ω4 and ω6 are the control variables u. l1 The influencing factors have a total weight of 1; ω2 and ω7 are the control variables u. l2 The influencing factors have a total weight of 1. The actual values ​​r of each variable at the current time point t are... t =[T r P r H r ] T In this example, the input to the neural network yields ω = [0.36, 0.74, 0.42, 0.55, 0.22, 0.45, 0.26]. T The resulting control quantity influence factors indicate that: the engine throttle increment is most affected by the power head pressure error, followed by the power head torque error, and least affected by the advance error per unit time; the pressurized cylinder valve port increment is significantly affected by the power head pressure error, but less affected by the advance error per unit time; the power head motor valve port increment is significantly affected by the power head torque error, but less affected by the advance error per unit time, which is consistent with the actual situation of sand drilling in this embodiment.

[0055] Step (3) sets the error value e at the current time point t. t =[T e P e H e ] T The input to the NPI controller, combined with the influence factor ω obtained in step (2), yields the three-dimensional control quantity u through composite calculation. v u l1 u l2 The governing equations for the nonlinear PI converter are as follows:

[0056]

[0057] u=K P e+K I It ,

[0058] In the formula, t represents the sampling time, Δt represents the sampling interval, and I t I represents the integral factor at the current time point. t-Δt G represents the integral factor at the previous time point, b represents the integral factor of the control variable, e represents the control factor which determines the convergence rate, and e represents the error value. K represents the rate of change of error. P K represents the proportional gain. I This represents the integral gain.

[0059] In this embodiment, Δt = 0.01s.

[0060] The control equations described above, combined with the influence factor ω obtained in step (2), yield the control quantum terms:

[0061]

[0062]

[0063]

[0064]

[0065] Δt represents the sampling interval, G v G represents the integral factor of the engine throttle control quantity. l1 G represents the integral factor of the control quantity at the valve port of the pressurized hydraulic cylinder. l2 b represents the integral factor of the control quantity of the motor valve port; b represents the control factor, which determines the convergence speed. and These are the error values ​​P for the power head torque, power head pressure, and advance per unit time in step 1. e T e and H e Calculated parameters and The rate of change of the error;

[0066] The values ​​above correspond to the control quantum terms ω1 to ω7 in Table 1. Combining these values ​​yields the final three-dimensional control values ​​as follows:

[0067]

[0068] This completes the coupling of the variables, resulting in the three-dimensional control quantities required to control the engine throttle, the pressurized cylinder valve port, and the power head motor valve port.

[0069] Step (4), the three-dimensional control quantity u = [u] obtained in this embodiment v u l1 u l2 ] T Input the engine throttle, pressurized cylinder valve, and power head motor valve of the rotary drilling rig. Automatic throttle control during the drilling operation is achieved by controlling the increments of these throttles and valves. Three error values ​​e are provided. t =[T e P e H e ] T Set three corresponding error thresholds ε = [ε1 ε2 ε3] T , when e t When ε < ε, the torque of the power head, the pressure applied by the power head, and the advance per unit time all reach the desired range, thus completing automatic throttle control. The control results of this embodiment are as follows: Figure 3 As shown, the control effect is good and the convergence speed is fast.

[0070] like Figure 4 As shown, the multivariable coupled rotary drilling rig throttle control system of the present invention includes the following parts:

[0071] Data acquisition module: Considering the unstructured environment in which the rotary drilling rig operates, the uncertainty of the working conditions, and the complex relationships between various output variables, multiple sensors are used to acquire the power head torque, power head pressure, and advance per unit time during the rotary drilling rig's drilling operation.

[0072] Parameter prediction module: A neural network is added to the control system to predict the influencing factors of each control variable based on the operating conditions, so as to couple the variables and improve control accuracy and operation efficiency.

[0073] Throttle Increment Control Module: Based on a nonlinear PI controller, the module uses the power head torque, power head pressure, and advance per unit time as feedback quantities. It calculates the increment of each throttle and valve based on the error value and the influence factor of the control quantity, thereby realizing automatic throttle control for rotary drilling rig drilling operations.

[0074] This specific implementation adopts nonlinear PI control for throttle control, which is highly adaptable and robust, and can well adapt to the unstructured environment of rotary drilling rig operation and the nonlinearity of the model. It can well adapt to nonlinear control, and the control accuracy is greatly improved. Theoretically, it has been proven that the system stability of the nonlinear PI controller can be guaranteed, making it very suitable for harsh environments. The addition of a neural network introduces additional control variable influence factors, and the influence factors are predicted based on real-time operating conditions to achieve optimal system performance, resulting in excellent real-time performance. At the same time, it solves the coupling problem between variables, realizes automatic drilling of the rotary drilling rig, greatly improves the efficiency of rotary drilling operations, and reduces the workload of operators.

[0075] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multivariable coupled throttle control method for rotary drilling rigs, characterized in that, The specific steps of this method are as follows: Step 1: Obtain the actual value T of the power head torque at the current time t. r Actual value of the applied pressure P of the power head r Actual value of advance per unit time H r Based on the above data comparison and analysis, the drilling formation is determined, and the working conditions are divided according to the drilling formation; based on the working conditions, the expected values ​​T of each variable are obtained. d P d H d ; Obtain the parameters of the previous time point t-Δt The parameters e represents the three-dimensional error value at the previous time point t-Δt. t-Δt The second-order difference is defined as follows: The error values ​​e of the power head torque, power head pressure, and advance per unit time are respectively represented by T. e P e and H e In this diagram, the subscript t represents the current time point, t-Δt represents the previous time point, t-2Δt represents the second time point before the current time point, and Δt represents the sampling interval. Step 2: Set the actual values ​​T of the three variables at the current time t: power head torque, power head pressure, and advance per unit time. r P r and H r The input layer of a neural network performs a non-linear transformation from input to output using activation functions in the hidden layers. The transformation relationship is as follows: and y (i) =f(W (i) y (i-1) In the formula, y represents the output of a neuron with I = 3 inputs, x represents the input, w represents the weights between the input and output, b is the bias term, and f represents the activation function; (i) W represents the output of the i-th neural network layer. (i) These are the weights between the (i-1)th layer and the i-th layer; The control quantity influence factors ω1~ω7 are output from the output layer and input into the NPI controller. Step 3: Set the error value T of the power head torque, power head pressure, and advance per unit time at the current time point t. e P e and H e Input to the NPI controller, The three-dimensional control quantity u is obtained according to the following calculation formula. v u l1 u l2 K P K represents the proportional gain. I Indicates integral gain. Δt represents the sampling interval, G v G represents the integral factor of the engine throttle control quantity. l1 G represents the integral factor of the control quantity at the valve port of the pressurized hydraulic cylinder. l2 b represents the integral factor of the control quantity of the motor valve port; b represents the control factor, which determines the convergence speed. and These are the torque of the power head, the pressure applied by the power head, and the error value T of the advance per unit time in step 1. e P e and H e Calculated parameters and The rate of change of the error; Three-dimensional control quantity u v u l1 u l2 These correspond to the increments of the engine throttle, the pressurized cylinder valve port, and the power motor valve port, respectively. Step 4, set the three-dimensional control quantity u = [u v u l1 u l2 ] T Input the engine throttle, pressurized cylinder valve port, and power head motor valve port of the rotary drilling rig. Automatic throttle control during the drilling operation is achieved by controlling the increments of these throttles and valve ports. Three error values ​​e are provided. t =[T e P e H e ] T Set three corresponding error thresholds ε = [ε1 ε2 ε3] T , when e t When the torque, pressure, and advance per unit time of the power head all reach the desired range, throttle control is completed.

2. The multivariable coupled rotary drilling rig throttle control method according to claim 1, characterized in that, The expected value T of each variable d P d H d The logic control unit sets the parameters according to the operating conditions; the three-dimensional control quantity u = [u v u l1 u l2 ] T The NPI controller determines the error value T of each variable. e P e H e The results are obtained by combining the influence factors of each control quantity predicted by the neural network.

3. A multivariable coupled rotary drilling rig throttle control system, characterized in that, The system comprises the following components: a data acquisition module, a parameter prediction module, and a throttle increment control module. The data acquisition module includes components for periodically collecting data on the power head torque, power head pressure, and drilling depth per unit time during rotary drilling operations at set time intervals, i.e., the actual power head torque value T. r Actual value of the applied pressure P of the power head r Actual value of advance per unit time H r ; The parameter prediction module is used to perform the following steps: Step 1, based on the actual value T of the power head torque at the current time point t. r Actual value of the applied pressure P of the power head r Actual value of advance per unit time H r Comparative analysis is used to determine the drilling formation, and working conditions are classified according to the drilling formation; the expected values ​​T of each variable are obtained based on the working conditions. d P d H d ; Get the parameters of the previous time point t-Δt The parameters e represents the three-dimensional error value at the previous time point t-Δt. t-Δt The second-order difference is defined as follows: The error values ​​e of the power head torque, power head pressure, and advance per unit time are respectively represented by T. e P e and H e In this diagram, the subscript t represents the current time point, t-Δt represents the previous time point, t-2Δt represents the second time point before the current time point, and Δt represents the sampling interval. Step 2: Set the actual values ​​T of the three variables at the current time t: power head torque, power head pressure, and advance per unit time. r P r and H r The input layer of a neural network performs a non-linear transformation from input to output using activation functions in the hidden layers. The transformation relationship is as follows: and y (i) =f(W (i) y (i-1) In the formula, y represents the output of a neuron with I = 3 inputs, x represents the input, w represents the weights between the input and output, b is the bias term, and f represents the activation function; (i) W represents the output of the i-th neural network layer. (i) These are the weights between the (i-1)th and ith layers; the output layer outputs control quantity influence factors ω1~ω7. Throttle increment control module: Based on a nonlinear PI controller, it uses the torque, force, and advance per unit time collected by the data acquisition module as feedback quantities, and calculates the throttle increment based on the error value T. e P e and H e The increments of each throttle and valve port are calculated using the control variable influence factors ω1~ω7. The solution method is to obtain the three-dimensional control quantity u according to the following calculation formula. v u l1 u l2 K P K represents the proportional gain. I Indicates integral gain. Δt represents the sampling interval, G v G represents the integral factor of the engine throttle control quantity. l1 G represents the integral factor of the control quantity at the valve port of the pressurized hydraulic cylinder. l2 b represents the integral factor of the control quantity of the motor valve port; b represents the control factor, which determines the convergence speed. and These are the torque of the power head, the pressure applied by the power head, and the error value T of the advance per unit time in step 1. e P e and H e Calculated parameters and The rate of change of the error; Three-dimensional control quantity u v u l1 u l2 These correspond to the increments of the engine throttle, the pressurized cylinder valve port, and the power motor valve port, respectively. The three-dimensional control quantity u = [u v u l1 u l2 ] T The throttle of the rotary drilling rig is automatically controlled by inputting the engine throttle, the pressurized cylinder valve port, and the power head motor valve port.

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