Unmanned mine car slip rate control method based on superhelix second-order sliding mode control and computer system

Abstract

The invention discloses an unmanned mine car slip rate control method based on superhelix second-order sliding mode control. The method comprises the following steps: deriving a second-order slip rate system kinetic equation based on a wheel kinetic model; introducing a steepest differential tracker to carry out slip rate differential estimation; converting the second-order slip rate kinetic equation into an error variable form for expression; designing a non-singular sliding mode surface according to a fixed time convergence theorem, and calculating the control rate of second-order sliding mode control based on a superhelix algorithm: issuing a braking torque control signal to a hydraulic braking control unit of the unmanned mine car, and further adjusting the braking pressure of a wheel cylinder. The method has the advantages that the actual slip rate of the wheels is controlled to be close to the target slip rate, the locking phenomenon is prevented when the mine car is braked, and the braking efficiency and safety of the mine car are improved.

Description

Technical Field

[0001] The invention relates to an anti-lock braking system of an unmanned mining vehicle, and in particular to a slip rate control method of an unmanned mining vehicle based on super-helical second-order sliding mode control and a computer system. Background Art

[0002] Unmanned mining vehicles operate in complex road environments in mines, facing steep slopes, gravel, slippery roads and other harsh road conditions, as well as braking challenges under high-load conditions. Traditional braking systems can easily cause wheel locks, leading to safety hazards such as vehicle loss of control or excessive braking distances. The anti-lock braking system (ABS) adjusts the wheel slip rate to prevent wheel locks and improves the matching ability of braking force and adhesion. It can not only shorten the braking distance, but also maintain vehicle stability and steering ability on low-adhesion roads, becoming a key technology for the safe operation of unmanned mining vehicles.

[0003] The starting point of the design of the anti-lock braking system is to control the wheel slip rate near the peak slip rate when the vehicle brakes, so that the tire can obtain a higher braking force coefficient and lateral force coefficient, thereby improving the vehicle's braking efficiency, especially the directional stability during braking.

[0004] Through the advanced control algorithm, the slip rate is controlled to improve the response speed and adaptability of the system to complex working conditions. At present, the academic and industrial circles have done a lot of research in this regard, including sliding mode control and model predictive control. The first-order sliding mode control algorithm has strong anti-interference ability and is often used in slip rate control. In the first-order sliding mode control, the discontinuous control quantity explicitly exists in the first-order derivative of the sliding mode variable, which will cause system chattering, which is not conducive to the practical application of the algorithm. At present, there are some improved methods that approximate the symbolic function by continuous functions. Although these methods suppress system chattering, they reduce the robustness of the control. High-order sliding mode algorithms, such as second-order sliding modes, suppress chattering while ensuring robustness, thereby improving control accuracy. Therefore, it is necessary to design a slip rate control method based on second-order sliding film control to achieve accurate and smooth slip rate control during the braking process of unmanned mine vehicles.

[0005] This is an area where this application needs to be improved. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a slip rate control method and computer system for an unmanned mine car based on super-helical second-order sliding mode control, so as to control the actual slip rate of the wheel near the target slip rate, prevent the locking phenomenon during braking of the mine car, and improve the braking efficiency and safety of the mine car.

[0007] In order to solve the above technical problems, the present invention provides a method for controlling the slip rate of an unmanned mining vehicle based on super-helical second-order sliding mode control, comprising the following steps:

[0008] Step S1: deriving a second-order slip rate system dynamics equation based on a wheel dynamics model;

[0009] Step S11: obtaining a vehicle speed signal and a wheel speed signal from the chassis system of the unmanned mining vehicle;

[0010] The vehicle speed is obtained through the IMU inertial navigation system, and the wheel speed is converted from the transmission output shaft speed measured by the speed sensor;

[0011] Step S12: Obtain the wheel dynamics equation based on the force characteristics of the wheel:

[0012]

[0013] Among them, v x is the longitudinal speed, ω is the wheel speed, m is 1 / 4 of the vehicle mass, J is the tire moment of inertia, R is the tire radius, T b is the braking torque, F x is the tire longitudinal force, F z is the tire vertical force, μ is the adhesion coefficient;

[0014] Step S13: Calculate the wheel slip ratio, where the slip ratio λ is defined as:

[0015]

[0016] Where: v x is the longitudinal vehicle speed, ω is the wheel speed, and R is the tire radius;

[0017] Step S14: deriving a second-order slip rate system dynamics equation;

[0018] Taking the second-order derivative of equation (2), we get:

[0019]

[0020] Combining equation (3) with equation (1) and its first-order derivative, we get the second-order slip system dynamic equation:

[0021]

[0022] Step S2: performing slip ratio differential estimation based on the fastest differential tracker;

[0023] When designing the second-order sliding mode controller based on the super-helical algorithm, the differential form of the slip rate is introduced to construct the system state variable. In view of the phenomenon that the signal noise is drastically amplified when the signal is differentiated by the difference method, the fastest discrete differential tracker of the discrete system is introduced to solve the problem of fast and accurate differential estimation of noisy signals.

[0024] Step S21: Introduce the fastest differential tracker of the discrete system to estimate the slip rate differential, which is in the following form:

[0025]

[0026] Where: x1(k+1), x2(k+1), x1(k), x2(k) are the values ​​of state variables x1(t) and x2(t) at time k and time k+1 respectively; v(k) is the system input at time k, y(k) is the system output at time k, h is the step size, r0 is the tracking factor, h0 is the filtering factor, and fhan(x1,x2,r,h) is the fastest control comprehensive function;

[0027] Step S22: fhan(x1,x2,r,h) is derived by Han Jingqing, and its algorithm formula is as follows:

[0028]

[0029] Step S23: the output variable y(k) of the fastest differential tracker at time k is the differential estimate of the input variable v(k), and is also the differential estimate of the slip rate;

[0030] Step S3: converting the second-order slip rate dynamics equation into a form of error variable representation;

[0031] Step S31: Set the target slip rate to λ d , define the error variable z1 = λ-λ d , The second-order slip rate dynamics equation is expressed as:

[0032]

[0033] in:

[0034]

[0035] It is assumed that the external disturbance d is bounded and differentiable, that is, it satisfies |d|≤D, and D is a positive constant;

[0036] Step S4: Design a non-singular sliding mode surface according to the fixed time convergence theorem, and calculate the control rate of the second-order sliding mode control based on the super-helical algorithm:

[0037] Step S41: Considering the fixed time convergence of the system, construct a non-singular sliding surface:

[0038]

[0039] Among them, c1>0, c2>0, 1<q2<2, q1>q2 are the design parameters of the sliding surface;

[0040] Taking the derivative of s, we get:

[0041]

[0042] Step S42: Design the following control rate:

[0043] u=u f +u st (10) Where: The nonlinear control term is:

[0044]

[0045] The second-order sliding mode control term designed by the super-helical algorithm is:

[0046]

[0047] Among them: b1>0, b2>0, p2>1 are controller parameters, u1 is the auxiliary variable; then the braking torque change rate is:

[0048]

[0049] Step S43: The actual control amount of the unmanned mining vehicle is the braking torque T b , so it is necessary to convert the solved control rate; in discrete systems, the derivative term is replaced by the backward difference quotient, that is:

[0050]

[0051] Where: T b (n) is the braking torque input at time n, T b (n-1) is the braking torque input at time n-1, for The value at time n, h is the controller step size;

[0052] Step S5: Send the braking torque control signal to the hydraulic brake control unit of the unmanned mining vehicle to adjust the wheel cylinder brake pressure.

[0053] The present invention also provides a computer system, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the unmanned mining vehicle slip rate control method based on superhelical second-order sliding mode control.

[0054] The beneficial effects of the present invention are:

[0055] 1) Control the actual wheel slip rate near the target slip rate to prevent the mine car from locking during braking, thereby improving the braking efficiency and safety of the mine car;

[0056] 2) Compared with the traditional first-order sliding mode control algorithm, the method proposed in the present invention improves the system order, and the control quantity is changed from the driving torque to the driving torque change rate. Therefore, the discontinuous term in the control quantity is eliminated by integration, thereby suppressing the system chattering and ensuring the robustness of the sliding mode control;

[0057] 3) The method proposed in the present invention constructs a non-singular sliding surface for sliding mode controller design. Compared with the traditional linear sliding surface, the non-singular sliding surface constructed in the present invention can ensure that the system state converges to the sliding surface within a fixed time, thereby accelerating the response speed of the system and improving real-time performance;

[0058] 4) The method proposed in the present invention introduces a discrete system fastest differential tracker to estimate the slip rate derivative. Compared with the traditional differential method, the fastest differential tracker introduced in the present invention can better suppress noise signals and achieve faster response speed while ensuring stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0060] Figure 1 This is a tire force analysis diagram under braking conditions in a specific embodiment of the present invention;

[0061] Figure 2 This is a control flow chart of a specific embodiment of the present invention;

[0062] Figure 3 This is a schematic diagram of a control algorithm according to a specific embodiment of the present invention;

[0063] Figure 4 It is a schematic diagram of the slip rate control effect of a specific embodiment of the present invention. DETAILED DESCRIPTION

[0064] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0065] like Figure 2 As shown, the present invention provides a method for controlling the slip rate of an unmanned mining vehicle based on super-helical second-order sliding mode control, and the specific steps are as follows:

[0066] Step S100: Figure 1 is the tire force analysis diagram under vehicle braking conditions, Figure 1 The force characteristics of the wheel are listed in the following wheel dynamics equation:

[0067]

[0068] Among them, vx is the longitudinal speed, ω is the wheel speed, m is 1 / 4 of the vehicle mass, J is the tire moment of inertia, R is the tire radius, T b is the braking torque, F x is the tire longitudinal force, F z is the tire vertical force, and μ is the adhesion coefficient.

[0069] Step S110: Derivation of the second-order slip rate dynamics equation, the slip rate λ is defined as:

[0070]

[0071] Among them, v x is the longitudinal speed, ω is the wheel speed; v x Obtained by the IMU inertial navigation system, ω is converted from the gearbox output shaft speed measured by the speed sensor;

[0072] Taking the second-order derivative of equation (2), we get:

[0073]

[0074] Combining equation (3) with equation (1) and its first-order derivative, we get the second-order slip system dynamic equation:

[0075]

[0076] Step S200: Figure 3 As shown, when designing a controller based on the above dynamic model, it is necessary to introduce the slip rate derivative term for calculation, so it is necessary to perform slip rate differential estimation. The calculation of the wheel slip rate requires the vehicle's longitudinal speed and wheel speed information. The longitudinal speed of the unmanned mining vehicle is estimated by the fusion of IMU and GPS, and the wheel speed is converted from the gearbox output shaft speed measured by the speed sensor. Because the noise signal caused by the sensor characteristics and external disturbances is unavoidable, a simple differential method cannot be used to differentiate the slip rate, which will severely amplify the noise signal and cause system oscillation. The fastest differential tracker has good signal tracking performance and filtering performance, and can well suppress the noise amplification phenomenon in the differential signal. The fastest differential tracker of the discrete system is introduced to estimate the slip rate differential, and its form is as follows:

[0077]

[0078] Among them, x1(k+1), x2(k+1), x1(k), x2(k) are the values ​​of state variables x1(t) and x2(t) at time k and time k+1 respectively, v(k) is the system input at time k, y(k) is the system output at time k, h is the step size, r0 is the tracking factor, h0 is the filtering factor, and fhan(x1,x2,r,h) is the fastest control comprehensive function.

[0079] Step 2100: fhan(x1,x2,r,h) is derived by Han Jingqing, and its algorithm formula is as follows:

[0080]

[0081] Step S220: The output variable y(k) of the fastest differential tracker at time k is the differential estimate of the input variable v(k), which is the differential estimate of the slip rate in the present invention. Obtaining the differential estimate of the actual wheel slip rate based on the fastest differential tracker is the premise for designing a second-order sliding mode controller.

[0082] Step S300: converting the second-order slip rate dynamics equation into a form of error variable representation;

[0083] Assume the target slip rate is λ d , define the error variable z1 = λ-λ d , The second-order slip rate dynamics equation is expressed as:

[0084]

[0085] in:

[0086]

[0087] It is assumed that the external disturbance d is bounded and differentiable, that is, it satisfies |d|≤D, and D is a positive constant.

[0088] Step S310: Considering the fixed time convergence of the system, construct a non-singular sliding mode surface:

[0089]

[0090] Among them, c1>0, c2>0, 1<q2<2, q1>q2 are the design parameters of the sliding surface. Derivative of s yields:

[0091]

[0092] Step S320: Design the following control rate:

[0093] u=u f +u st (10)

[0094] Among them, the nonlinear control term is:

[0095]

[0096] The second-order sliding mode control term designed by the super-helical algorithm is:

[0097]

[0098] Among them, b1>0, b2>0, p2>1 are controller parameters, and u1 is an auxiliary variable; then the braking torque change rate is:

[0099]

[0100] Step S330: The actual control amount of the unmanned mining vehicle is the braking torque T b , so the control rate to be solved needs to be converted. In discrete systems, the derivative term is replaced by the backward difference quotient, that is:

[0101]

[0102] Among them, T b (n) is the braking torque input at time n, T b (n-1) is the braking torque input at time n-1, for The value at time n, h is the controller step size.

[0103] Step S340: sending the braking torque control signal to the hydraulic brake control unit of the unmanned mining vehicle, thereby adjusting the wheel cylinder brake pressure to improve the braking efficiency of the unmanned mining vehicle.

[0104] Figure 4 This is a schematic diagram of the slip rate control effect of the present invention. According to the actual road conditions and tire characteristics, the target slip rate is set to 0.26. When the vehicle brakes at an initial speed of 40 km / h, the actual wheel slip rate rises briefly and then begins to decline. Finally, it stabilizes near the target slip rate within a certain period of time. The control process is relatively smooth without obvious vibration.

[0105] The unmanned mining vehicle slip rate control method based on super-helical second-order sliding mode control provided in the present application directly controls the actual slip rate near the target slip rate. First, the actual slip rate of the wheel is calculated, and then the differential estimate of the slip rate is obtained based on the introduced differential tracker. Then, the difference between the actual slip rate and the target slip rate is used as the error variable. The control rate calculated based on the super-helical second-order sliding mode control algorithm controls the error variable near zero, that is, the actual slip rate is controlled near the target slip rate.

[0106] The present invention also provides a computer system, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the unmanned mining vehicle slip rate control method based on superhelical second-order sliding mode control.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for controlling the slip rate of an unmanned mining vehicle based on super-helical second-order sliding mode control, comprising the following steps: Step S1: deriving a second-order slip rate system dynamics equation based on a wheel dynamics model; Step S11: obtaining a vehicle speed signal and a wheel speed signal from the chassis system of the unmanned mining vehicle; Step S12: Obtain the wheel dynamics equation based on the force characteristics of the wheel: in: v x is the longitudinal speed, ω is the wheel speed, m is 1 / 4 of the vehicle mass, J is the tire moment of inertia, R is the tire radius, T b is the braking torque, F x is the tire longitudinal force, F z is the tire vertical force, μ is the adhesion coefficient; Step S13: Calculate the wheel slip ratio, where the slip ratio λ is defined as: Where: v x is the longitudinal vehicle speed, ω is the wheel speed, and R is the tire radius; Step S14: deriving a second-order slip rate system dynamics equation; Taking the second-order derivative of equation (2), we get: Combining equation (3) with equation (1) and its first-order derivative, we get the second-order slip system dynamic equation: Step S2: performing slip ratio differential estimation based on the fastest differential tracker; Step S21: Slip ratio differential estimation is performed based on the fastest differential tracker, and its form is as follows: Where: x1(k+1), x2(k+1), x1(k), x2(k) are the values ​​of state variables x1(t) and x2(t) at time k and time k+1 respectively; v(k) is the system input at time k, y(k) is the system output at time k, h is the step size, r0 is the tracking factor, h0 is the filtering factor, and fhan(x1,x2,r,h) is the fastest control comprehensive function; Step S22: fhan(x1,x2,r,h) is derived by Han Jingqing, and its algorithm formula is as follows: Step S23: the output variable y(k) of the fastest differential tracker at time k is the differential estimate of the input variable v(k), and is also the differential estimate of the slip rate; Step S3: converting the second-order slip rate dynamics equation into a form of error variable representation; Assume the target slip rate is λ d , define the error variable z1 = λ-λ d , The second-order slip rate dynamics equation is expressed as: in: It is assumed that the external disturbance d is bounded and differentiable, that is, it satisfies |d|≤D, and D is a positive constant; Step S4: Design a non-singular sliding mode surface according to the fixed time convergence theorem, and calculate the control rate of the second-order sliding mode control based on the super-helical algorithm: Step S41: Considering the fixed time convergence of the system, construct a non-singular sliding surface: Among them, c1>0, c2>0, 1<q2<2, q1>q2 are the design parameters of the sliding surface; Taking the derivative of s, we get: Step S42: Design the following control rate: in=in f +in st (10) Among them: The nonlinear control term is: The second-order sliding mode control term designed by the super-helical algorithm is: Among them: b1>0, b2>0, p2>1 are controller parameters, u1 is the auxiliary variable; then the braking torque change rate is: Step S43: The actual control amount of the unmanned mining vehicle is the braking torque T b , convert the solved control rate, and in discrete systems, use the backward difference quotient to replace the derivative term, that is: Where: T b (n) is the braking torque input at time n, T b (n-1) is the braking torque input at time n-1, for The value at time n, h is the controller step size; Step S5: Send the braking torque control signal to the hydraulic brake control unit of the unmanned mining vehicle to adjust the wheel cylinder brake pressure.

2. A computer system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the unmanned mining vehicle slip rate control method based on super-helical second-order sliding mode control as described in claim 1.

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