Solid waste sand making crusher level control method based on error symbol integral robustness

Abstract

The application discloses a kind of error symbol integral robust-based solid waste sand making crusher level control methods, adopt a kind of solid waste sand making crusher level control system;Establish the mathematical model of solid waste sand making crusher crushing process;Establish the dynamics model of solid waste sand making crusher crushing process;Establish error symbol integral robust-based solid waste sand making crusher level control model;Based on the stability analysis of error symbol integral robust-based solid waste sand making crusher level control method, the result of system global asymptotic stability is obtained;Using control input u as the control input of controller, adjust control gain parameters k1, k r So that the output h of the system = x asymptotically to the parameter level signal h d (t) = x d (t), realize the stable control of crusher level.The control method has strong robustness, high tracking performance, can effectively suppress system uncertainty and external disturbance, etc., can automatically realize the precise control of crusher level, can significantly improve the control performance of system and realize asymptotic tracking.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of solid waste sand making crusher control, and particularly relates to a solid waste sand making crusher material level control method based on error sign integral robustness. BACKGROUND

[0002] The crushing operation is an important link of solid waste sand making, and the primary task of realizing the automatic control of the crushing process is to effectively control the machine cavity material level of the crusher. When the material level is too high, the crusher is prone to be blocked, so that the particle size requirement of the crushed sandstone is not easy to meet; when the material level is too low, the crushing efficiency is greatly affected, thereby affecting the output of the sandstone and reducing the economic benefits. Therefore, realizing high-precision control of the machine cavity material level of the crusher can significantly improve the crushing capacity and overall operation efficiency of the crusher, so that the crusher can always work in the best state, which is beneficial to reducing the production cost.

[0003] However, the crushing process has process characteristics such as nonlinearity, uncertainty and time lag. The traditional control method for the machine cavity material level generally adopts the PID control mode, which has a simple control structure, but its robustness to system uncertainty and disturbance is poor, and it is difficult to meet the control requirements of the crushing process. In order to improve the robustness of the system to modeling uncertainty and external disturbance, an adaptive robust control method is proposed, which can obtain good tracking performance under the condition of modeling uncertainty and disturbance, but it can only obtain bounded stable tracking results, which is often not enough for systems that pursue high-precision control. The traditional sliding mode robust control method can effectively handle the modeling uncertainty and disturbance of the system by using the sign function, and can obtain asymptotically stable tracking performance, but due to the discontinuity of the sign function, the control signal is prone to chattering, which is not allowed for actual actuators.

[0004] In order to overcome the shortcomings of the traditional nonlinear robust control method, it is urgent to provide an advanced nonlinear robust control method to effectively suppress system uncertainty and external disturbance and the like. SUMMARY

[0005] In view of the problems existing in the prior art, the application provides a solid waste sand making crusher material level control method based on error sign integral robustness. The control method has strong robustness, high tracking performance, can effectively suppress system uncertainty and external disturbance and the like, can automatically realize precise control of the material level of the crusher, can significantly improve the control performance of the system and realize asymptotic tracking, and has very important economic and practical value for realizing automatic control of the solid waste sand making crushing process.

[0006] In order to achieve the above object, the present application provides a kind of based on error symbol integral robust solid waste sand making crusher material level control method, adopt a kind of solid waste sand making crusher material level control system, the solid waste sand making crusher material level control system includes storage bin, scraper conveyor, crusher and controller, the discharge end of the storage bin is arranged in the upper of scraper conveyor feed end, the feed end of the crusher is arranged in the lower of scraper conveyor discharge end, the controller is connected with storage bin, scraper conveyor and crusher respectively;Specifically include the following steps;

[0007] Step one: establish the mathematical model of solid waste sand making crusher crushing process;

[0008] S11: based on mass flow conservation theorem, the relationship between the material flow rate of storage bin output and the material flow rate of scraper conveyor is represented by formula (1), the material flow rate of scraper conveyor is represented by formula (2), and the relationship between the material flow rate of scraper conveyor output and the material flow rate of crusher input is represented by formula (3);

[0009] Q c (t)=Q b (t-τ) (1);

[0010] Q b (t-τ)=A1ρ1v(t-τ) (2);

[0011] Q i (t)=Q b (t-τ) (3);

[0012] In the formula, Q c (t) is the flow rate of the storage bin, Q b (t) is the flow rate of the scraper conveyor, τ is the lag time, A1 is the cross-sectional area of the material block in the direction of material flow of the scraper conveyor, ρ1 is the material block distribution density of the scraper conveyor, u is the running speed of the scraper conveyor, Q i (t) is the flow rate of the crusher;

[0013] S12: for the crushing process of the crusher, the dynamic mathematical model of the crusher is established according to formula (4) based on the material balance relationship in the cavity;

[0014]

[0015] In the formula, Q o (t) is the flow rate of the crusher, Q o (t)=αh(t), wherein α is the crushing coefficient, A2 is the cross-sectional area of the crusher in the vertical direction of material flow, ρ2 is the material density of the material block distribution in the crusher, and h(t) is the material height of the crushing cavity;

[0016] Step two: Establish the dynamics model of the solid waste sand making crusher crushing process;

[0017] S21: Combine formula (3) and formula (4) to obtain formula (5);

[0018]

[0019] In the formula, A2 is the cross-sectional area of the crusher in the vertical direction of the material flow, ρ2 is the material density of the material block distribution in the crusher, h(t) is the material height of the crushing cavity, Q o (t) is the flow rate out of the crusher;

[0020] S22: Combine formula (2) and formula (5) to obtain formula (6);

[0021]

[0022] S23: Laplace transform formula (6) to obtain formula (7);

[0023] (A2ρ2s+α)H(s)=A1ρ1V(s)e -τs (7);

[0024] S24: According to formula (7), the transfer function of the crushing process is obtained, as shown in formula (8);

[0025]

[0026] In the formula, K is a constant determined by the mechanical structure of the crushing process, K=A1ρ1 / α, T is a constant determined by the process structure of the crushing process, T=A2ρ2 / α;

[0027] S25: Define the state variable x=h(t), and use the state space equation in formula (9) to represent the dynamics model of the solid waste sand making crusher crushing process;

[0028]

[0029] In the formula: g=K / T, u(t)=v(t), f(x)=x / T, d(t)=g(u(t-τ)-u(t));

[0030] Step three: Establish a solid waste sand making crusher material level control model based on error sign integral robustness;

[0031] S31: Use formula (10) to define the crusher material level tracking error z;

[0032] z=x-x d (10);

[0033] where z represents the crusher level tracking error;

[0034] S32: define an auxiliary error signal r(t) using equation (11);

[0035]

[0036] where k1 is a positive control parameter;

[0037] S33: derive equation (12) by expanding equation (11) with respect to equation (10) and equation (9);

[0038]

[0039] S34: represent the control input u of the system using equation (13), represent the robust control term u s using equation (14), represent the model-based compensation term u a using equation (15), represent the linear robust control term u s1 using equation (16), and represent the nonlinear integral robust control term u s2 ;

[0040] u = (u a + u s ) / g (13);

[0041] u s = u s1 + u s2 (14);

[0042]

[0043] u s1 = -k1z-k r z (16);

[0044]

[0045] where the control gain k r is a positive number; β is a nonlinear robust control gain, β ≥ δ1+ δ2 / k2; sgn(z) represents a sign function,

[0046] S35: substitute equation (13), equation (14), equation (15), equation (16), and equation (17) into equation (12) to obtain equation (18);

[0047] r = -k r z + d + u s2 (18);

[0048] S36: Derivation of formula (18) obtains formula (19);

[0049]

[0050] Step four: based on the error symbol integral robustness of waste sand making crusher material level control method stability analysis, the system global asymptotic stability result is obtained;

[0051] S41: auxiliary function L(t) is defined by formula (20); auxiliary function P(t) is defined by formula (21);

[0052]

[0053]

[0054] In the formula, z(0) and respectively represent the initial value of z and ;

[0055] S42: Lyapunov function is defined by formula (22);

[0056]

[0057] S43: derivation of formula (22) and combination of formula (11) and formula (19) obtain formula (23);

[0058]

[0059] S44: function V is determined by formula (22) and formula (23), therefore, z, r are bounded, the derivative of W is determined by formula (11) and formula (19), therefore, function W is a uniform continuous function; formula (13), formula (14), formula (15), formula (16) and formula (17) are used as the control input of the controller, and the control gain parameters k1, k r Make the output h of the system = x asymptotically parameter material level signal h d (t) = x d (t), realize the stable control to the material level of the crusher.

[0060] As a preferred, the controller is a PLC controller.

[0061] The application provides an advanced nonlinear robust control method, compared with prior art, the method has the following advantages: (1) the uncertainty and disturbance in the crushing process of the crusher are processed by using the error symbol integral robust control, the control performance of global asymptotic tracking can be obtained, and good robust performance is obtained; (2) the control input of the designed controller is continuous, which is beneficial to application in engineering practice.

[0062] The control method has strong robustness, high tracking performance, and continuous control input of the controller, is convenient for application in engineering practice, can effectively inhibit system uncertainty and external interference, etc., can obtain higher control performance, can automatically realize accurate control of the crusher material level, can significantly improve the control performance of the system and realize asymptotic tracking, and has very important economic and practical value for realizing automatic control of the solid waste sand making crushing process. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 is a principle block diagram of a solid waste sand making crusher material level control method based on error sign integral robustness in the application;

[0064] Figure 2 is a principle diagram of a solid waste sand making crusher crushing process mathematical model in the application;

[0065] Figure 3 is a tracking effect diagram of system output to expected command under the action of a solid waste sand making crusher material level controller based on error sign integral robustness in the application;

[0066] Figure 4 is a tracking effect diagram of system output to expected command under the action of a traditional PID controller;

[0067] Figure 5 is a tracking error comparison curve diagram of a system under the action of a controller and a traditional PID controller in the application;

[0068] Figure 6 is a control input diagram of a system under the action of a controller in the application. DETAILED DESCRIPTION

[0069] The application will be further described below with reference to the drawings.

[0070] As shown in Figures 1 to 6 , the application provides a solid waste sand making crusher material level control method based on error sign integral robustness, adopts a solid waste sand making crusher material level control system, the solid waste sand making crusher material level control system comprises a storage bin, a scraper conveyor, a crusher and a controller, the discharge end of the storage bin is arranged above the feeding end of the scraper conveyor, the feeding end of the crusher is arranged below the discharge end of the scraper conveyor, and the controller is connected with the storage bin, the scraper conveyor and the crusher respectively; specifically comprising the following steps:

[0071] Step 1: establishing a mathematical model of a solid waste sand making crusher crushing process;

[0072] S11: According to the actual situation, in the solid waste sand making crusher stock level control system, only the speed of the scraper conveyor is adjustable, the crusher runs at a constant speed, the stock level of the crushing cavity can be controlled by controlling the speed of the scraper conveyor; ignoring the difference between the relative speed of the scraper conveyor and the material flow, the material speed and the scraper conveyor speed are consistent, and because of the scraper effect of the scraper conveyor, the distribution of the material on the scraper conveyor is uniform and continuous, there is no inconsistency in the front and rear flow; the inlet and outlet flow of the crusher is uniformly changed, the inside of the crushing cavity is a simple linear system, and the multi-stage chopping effect is not considered. Therefore, based on the mass flow conservation theorem, the relationship between the output material flow of the storage bin and the output material flow of the scraper conveyor is represented by formula (1), the material flow transported by the scraper conveyor is represented by formula (2), and the relationship between the output material flow of the scraper conveyor and the input material flow of the crusher is represented by formula (3);

[0073] Q c (t)=Q b (t-τ) (1);

[0074] Q b (t-τ)=A1ρ1u(t-τ) (2);

[0075] Q i (t)=Q b (t-τ) (3);

[0076] In the formula, Q c (t) is the flow out of the storage bin, Q b (t) is the flow out of the scraper conveyor, τ is the lag time, A1 is the cross-sectional area of the material block in the direction of the material flow of the scraper conveyor, ρ1 is the material block distribution density on the scraper conveyor, v is the running speed of the scraper conveyor, Q i (t) is the flow into the crusher;

[0077] S12: For the crushing process of the crusher, a dynamic mathematical model of the crusher is established according to formula (4) based on the material balance relationship in the cavity;

[0078]

[0079] In the formula, Q o (t) is the flow out of the crusher, which is approximately linear in an ideal case, Q o (t)=ah(t), where α is the crushing coefficient, A2 is the cross-sectional area of the crusher in the vertical direction of the material flow, ρ2 is the material density of the material block distribution in the crusher, and h(t) is the material height of the crushing cavity;

[0080] Step 2: Establish the dynamics model of the solid waste sand making crusher crushing process;

[0081] S21: combining formula (3) and formula (4) to obtain formula (5);

[0082]

[0083] wherein A2 is the cross-sectional area of the crusher in the vertical direction of the material flow, ρ2 is the material density of the lump distribution in the crusher, h(t) is the material height in the crushing chamber, Q o (t) is the flow rate out of the crusher;

[0084] S22: combining formula (2) and formula (5) to obtain formula (6);

[0085]

[0086] S23: Laplace transforming formula (6) to obtain formula (7);

[0087] (A2ρ2s+α)H(s)=A1ρ1V(s)e -τs (7);

[0088] S24: obtaining the transfer function of the crushing process according to formula (7), as shown in formula (8);

[0089]

[0090] wherein K is a constant determined by the mechanical structure of the crushing process, K=A1ρ1 / α, and T is a constant determined by the process structure of the crushing process, T=A2ρ2 / α;

[0091] Formula (8) is the transfer function between the material level in the crusher chamber and the speed of the scraper conveyor, and it can be seen that the crusher material level control is a typical first-order inertia lag system; the static gain K reflects the size that the crusher chamber material level can exceed the steady state when returning to equilibrium; the time constant T reflects the speed of the crusher chamber material level reaching the steady state value with the change of the scraper conveyor speed, and embodies the dynamic characteristics of the crushing process object; the entire crushing process control system has a lag, and the reason for the lag is the lag effect of the scraper conveyor on the transmission of the mineral material flow, and the lag time is τ.

[0092] S25: defining the state variable x=h(t), and using the state space equation in formula (9) to represent the solid waste sand crusher crushing process dynamics model;

[0093]

[0094] wherein g=K / T, u(t)=v(t), f(x)=x / T, and d(t)=g(u(t-τ)-u(t));

[0095] Step three: Establishing the robust control model of the solid waste sand making crusher level based on the integral of error symbols

[0096] The design goal of the solid waste sand making crusher crushing process controller is to give the system reference signal h d (t) = x d (t), design a bounded control input u to make the system output h = x as possible to track the system reference signal. In order to facilitate the design of the controller, the following assumptions and lemmas are given:

[0097] Assumption 1: The expected system reference signal h d (t) and its first order derivative are smooth and bounded.

[0098] Assumption 2: d(t) is smooth enough, its first and second order derivatives exist and are bounded, that is Where δ1, δ2 are known normal numbers;

[0099] S31: Use formula (10) to define the crusher level tracking error z;

[0100] z = x - x d (10);

[0101] In the formula: z represents the crusher level tracking error;

[0102] S32: In order to obtain an additional controller design degree of freedom, use formula (11) to define the auxiliary error signal r(t);

[0103]

[0104] In the formula: k1 is a positive control parameter;

[0105] S33: Derive formula (10) and expand formula (11) by combining formula (9) to obtain formula (12);

[0106]

[0107] S34: Use formula (13) to represent the system control input u, use formula (14) to represent the robust control term u s , use formula (15) to represent the model-based compensation term u a , use formula (16) to represent the linear robust control term u s1 , use formula (17) to represent the nonlinear integral robust control term u s2 ;

[0108] u = (u a + u s ) / g (13);

[0109] us = u s1 + u s2 (14) ;

[0110]

[0111] u s1 = -k1z - k r z (16) ;

[0112]

[0113] where the control gain k r is a positive number; β is a nonlinear robust control gain, β ≥ δ1+ δ2 / k2; sgn(z) represents a sign function,

[0114] S35: Substituting formula (13), formula (14), formula (15), formula (16) and formula (17) into formula (12) to obtain formula (18);

[0115] r = -k r z + d + u s2 (18) ;

[0116] S36: Deriving formula (18) to obtain formula (19);

[0117]

[0118] Step four: Based on the stability analysis of the error sign integral robust solid waste sand making crusher level control method, the global asymptotic stability of the system is obtained;

[0119] S41: Defining an auxiliary function L(t) using formula (20);

[0120]

[0121] Lemma: If β ≥ δ1+ δ2 / k2, then formula (a) and formula (b) are established;

[0122] Proof of the lemma:

[0123] Integrating both sides of formula (20) and combining formula (11) to obtain formula c ) ;

[0124]

[0125] Partially integrating formula (c) to obtain formula (d);

[0126]

[0127] Therefore, there are:

[0128]

[0129] From equation (e), if the selection of the control gain β satisfies β≥δ1+δ2 / k2, is established, that is, the lemma is proved.

[0130] An auxiliary function P(t) is defined by equation (21);

[0131]

[0132] In the formula, z(0) and respectively represent the initial values of z and ;

[0133] S42: According to the lemma, it is known that when the selection of the control gain β satisfies the condition shown in equation (17), P(t)≥0, therefore, a Lyapunov function is defined by equation (22);

[0134]

[0135] S43: Derivation of equation (22) and combination of equation (11) and equation (19) obtain equation (23);

[0136]

[0137] S44: The function V is bounded by equation (22) and equation (23), therefore, z and r are bounded, and according to the expression of the control input, it is easy to judge that it is bounded, therefore, all signals of the closed-loop system are bounded. The derivative of W is bounded by equation (11) and equation (19), therefore, the function W is a uniformly continuous function; according to the Barbalat lemma, when the time tends to infinity, W tends to 0, that is, z tends to 0, that is, the system is globally asymptotically stable.

[0138] Equations (13), (14), (15), (16) and (17) are taken as the control input of the controller, and the control gain parameters k1, k r are adjusted so that the output h=x of the system asymptotically approaches the parameter material level signal h d (t)=x d (t), realizing the stable control of the crusher material level.

[0139] As a preferred, the controller is a PLC controller.

[0140] The stability proof is made by using Lyapunov stability theory, and the global asymptotic stability of the system can be obtained, so the conclusion is that the error sign integral robust controller designed for the crushing process of the solid waste sand making crusher can make the system obtain the global asymptotic stability, and adjusting the control gain k r The tracking error of the system can tend to zero under the condition that the tracking error of the system tends to infinity with time. The block diagram of the error sign integral robust solid waste sand making crusher level control method is as shown in Figure 1

[0141] Embodiment

[0142] In order to examine the performance of the designed controller, the system control parameters are selected as follows: static gain K=6, time constant T=25, and lag time τ=6.

[0143] The desired level command of the given system is x d =330 (mm).

[0144] The following controller is taken in the simulation for comparison:

[0145] Error sign integral robust controller (Controller 1): the controller parameters k1=0.1, k r =0.05, and β=0.001 are taken.

[0146] PID controller (Controller 2): the controller parameters K p =0.15, K i =0.02, and K d =0.2 are taken.

[0147] The tracking effect of the system output on the desired command under the action of Controller 1 is as shown in Figure 3 The tracking effect of the system output on the desired command under the action of Controller 2 is as shown in Figure 4 The tracking error comparison curve of the system under the action of Controller 1 and Controller 2 is as shown in Figure 5 It can be found that the controller proposed in the application has greater improvement in control performance compared with the traditional PID controller, has excellent tracking performance, and has faster response speed.

[0148] Figure 6 is a curve diagram of the system control input changing with time under the action of Controller 1, and from the diagram, it can be seen that the obtained control input is a low-frequency continuous signal, which is more beneficial to the execution in actual application.

[0149] ​The application provides an advanced nonlinear robust control method, compared with the prior art, the method has the following advantages: (1) the uncertainty and disturbance in the crushing process of the crusher are processed by using the error symbol integral based robust control, the control performance of global asymptotic tracking can be obtained, and the robust performance is good; (2) the control input of the designed controller is continuous, and is beneficial to application in engineering practice.

[0150] The control method has strong robustness, high tracking performance, and the control input of the controller is continuous, and is beneficial to application in engineering practice, the method can effectively inhibit system uncertainty and external disturbance and the like, higher control performance can be obtained, the precise control of the material level of the crusher can be automatically realized, the control performance of the system can be significantly improved and asymptotic tracking can be realized, and the method has very important economic and practical value for realizing automatic control of the solid waste sand crushing process.

Claims

1. A method for controlling the material level of a solid waste sand making crusher based on error sign integral robustness, comprising a material level control system for the solid waste sand making crusher, the material level control system including a storage silo, a scraper conveyor, a crusher, and a controller, wherein the discharge end of the storage silo is located above the feed end of the scraper conveyor, the feed end of the crusher is located below the discharge end of the scraper conveyor, and the controller is connected to the storage silo, the scraper conveyor, and the crusher respectively; characterized in that, Specifically, it includes the following steps; Step 1: Establish a mathematical model of the crushing process of the solid waste sand making crusher; S11: Based on the mass flow conservation law, formula (1) is used to characterize the relationship between the output material flow of the storage bin and the output material flow of the scraper conveyor, formula (2) is used to characterize the flow of the material transported by the scraper conveyor, and formula (3) is used to characterize the relationship between the output material flow of the scraper conveyor and the input material flow of the crusher. Q c (t)=Q b (t-τ) (1); Q b (t-τ)=A1ρ1v(t-τ) (2); Q i (t)=Q b (t-τ) (3); In the formula, Q c (t) represents the flow rate out of the storage silo, Q b (t) represents the outflow rate from the scraper conveyor, τ represents the lag time, A1 represents the cross-sectional area of ​​the material block on the scraper conveyor in the material flow direction, ρ1 represents the material block distribution density on the scraper conveyor, v represents the operating speed of the scraper conveyor, and Q represents the flow rate of the scraper conveyor. i (t) represents the flow rate into the crusher; S12: For the crushing process of the crusher, a dynamic mathematical model of the crusher is established based on the material balance relationship in the machine cavity and formula (4); In the formula, Q o (t) represents the flow rate exiting the crusher, Q o (t)=αh(t), where α is the crushing coefficient, A2 is the cross-sectional area of ​​the crusher in the direction perpendicular to the material flow, ρ2 is the material density of the block distribution in the crusher, and h(t) is the material height in the crushing chamber; Step 2: Establish a dynamic model of the crushing process of the solid waste sand making crusher; S21: Combining formula (3) and formula (4) yields formula (5); In the formula, A2 is the cross-sectional area of ​​the crusher in the direction perpendicular to the material flow, ρ2 is the material density of the block distribution inside the crusher, h(t) is the material height in the crushing chamber, and Q... o (t) represents the flow rate exiting the crusher; S22: Combining formula (2) and formula (5) yields formula (6); S23: Perform Laplace transform on formula (6) to obtain formula (7); (A2ρ2s+α)H(s)=A1ρ1V(s)e -τs (7); S24: The transfer function of the crushing process is obtained according to formula (7), as shown in formula (8); In the formula, K is a constant determined by the mechanical structure of the crushing process, K = A1ρ1 / α, and T is a constant determined by the process structure of the crushing process, T = A2ρ2 / α; S25: Define the state variable x = h(t) and use the state space equation in formula (9) to characterize the dynamic model of the crushing process of the solid waste sand making crusher; In the formula: g=K / T, u(t)=v(t), f(x)=x / T, d(t)=g(u(t-τ)-u(t)); Step 3: Establish a robust material level control model for solid waste sand making crusher based on error sign integral; S31: Define the crusher material level tracking error z using formula (10); z=x-x d (10); In the formula: z represents the crusher material level tracking error; S32: Define the auxiliary error signal r(t) using formula (11); In the formula: k1 is a positive control parameter; S33: Differentiate formula (10) and expand formula (11) in combination with formula (9) to obtain formula (12); S34: Use formula (13) to characterize the system's control input u, and use formula (14) to characterize the robust control term u. s Formula (15) is used to characterize the model-based compensation term u. a The linear robust control term u is characterized using formula (16). s1 Formula (17) is used to characterize the nonlinear integral robust control term u. s2 ; u=(u a +in s ) / g (13); in s =in s1 +in s2 (14); u s1 =-k1z-k r z (16); In the formula, the control gain k r β is a positive number; β is the nonlinear robust control gain, β≥δ1+δ2 / k2; sgn(z) represents the sign function. S35: Substitute formulas (13), (14), (15), (16), and (17) into formula (12) to obtain formula (18); r=-k r z+d+u s2 (18); S36: Differentiating formula (18) yields formula (19); Step 4: Stability analysis of the solid waste sand making crusher material level control method based on error sign integral robustness, and obtain the result of global asymptotic stability of the system; S41: Define the auxiliary function L(t) using formula (20); define the auxiliary function P(t) using formula (21); In the formula, z(0) and Represent z and The initial value; S42: Define the Lyapunov function using formula (22); S43: Differentiate formula (22) and combine it with formula (11) and formula (19) to obtain formula (23); S44: The function V is bounded by formulas (22) and (23), therefore z and r are both bounded. The derivative of W is bounded by formulas (11) and (19), therefore the function W is a uniformly continuous function. Formulas (13), (14), (15), (16), and (17) are used as the control inputs of the controller to adjust the control gain parameters k1 and k2. r This makes the system output h = x asymptotically approximate the parameter level signal h. d (t)=x d (t) enables stable control of the material level in the crusher.

2. The method for material level control of a solid waste sand making crusher based on error sign integral robustness as described in claim 1, characterized in that, The controller is a PLC controller.

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