CN104898420A - Dynamic batching active-disturbance-rejection control method - Google Patents

Abstract

The invention relates to a dynamic batching active-disturbance-rejection control method, and belongs to the field of a building material dynamic batching technology. The method comprises the following steps: step one, establishing a dynamic batching system motor frequency and material flow model; step two, establishing a belt scale material impact model in a dynamic batching process; step three, establishing a dynamic batching system weighing model; and step four, through combination with the dynamic batching system motor frequency and material flow model in step one and the belt scale material impact model in step two and the dynamic batching system weighing model in step three, establishing a dynamic batching system active-disturbance-rejection control model by use of a modern advanced active-disturbance-rejection control technology so as to realize dynamic batching active-disturbance-rejection control. According to the invention, through combination of a mechanism analysis and a modern advanced control technology, the ratio precision of the dynamic batching process is controlled, accurate batching of a building material dynamic batching system is realized, and the method has high robustness and high anti-interference performance in application of the dynamic batching system and thus has wide application prospect.

Description

A kind of dynamic batching Auto-disturbance-rejection Control

Technical field

The invention belongs to dynamic batching technical field, relate to a kind of dynamic batching Auto-disturbance-rejection Control, particularly a kind of dynamic batching Auto-disturbance-rejection Control utilizing Advanced Control Techniques to realize by simplified model.

Background technology

Along with the development and progress of science and technology, in current industrial products are produced, product is mixed according to certain ratio by several raw material mostly, such as cement, feed, chemical fertilizer, food, medicine etc.Thisly had a wide range of applications in industrial processes by the Dynamic Blending System that plurality of raw materials carries out mixing in the ratio of in advance setting, it is the proportion scale of different material according to the rules, carries out Dynamic Weighting batching in different feed proportioning systems.

In industries such as building, feed manufacturing, food processing, pharmacy, chemical industry, Dynamic Blending System all has a good application prospect, and for these production enterprises, product quality and productive capacity are the key factors determining enterprise development.The productive capacity of enterprise depends on the speed of product Dynamic Blending System, and product quality depends on the precision of feed proportioning system to a great extent.If the quality of fruit product is lower than the standard of industry, serious economic loss will be brought, cause enterprise to survive.Therefore improve the speed of Dynamic Blending System and control accuracy to the labour productivity and the product quality that improve these industries, reduce to consume etc. and play important effect.

Summary of the invention

In view of this, the object of the present invention is to provide a kind of dynamic batching Auto-disturbance-rejection Control, the method utilizes Auto Disturbances Rejection Control Technique in conjunction with Dynamic Blending System simplified model, controls, thus realize the accurate dosing of building materials Dynamic Blending System to dynamic batching process distributing precision.

For achieving the above object, the invention provides following technical scheme:

A kind of dynamic batching Auto-disturbance-rejection Control, comprises the following steps: step one: set up Dynamic Blending System electric machine frequency and mass flow model; Step 2: set up belt conveyer scale material impact model in dynamic batching process; Step 3: set up Dynamic Blending System weighting model; Step 4: the belt conveyer scale material impact model in the Dynamic Blending System electric machine frequency in integrating step one and mass flow model, step 2, the Dynamic Blending System weighting model in step 3, utilize modern advanced Auto Disturbances Rejection Control Technique to set up Dynamic Blending System Active Disturbance Rejection Control model, thus realize dynamic batching Active Disturbance Rejection Control.

Further, in step one, for Dynamic Blending System feed section technique, analyze feeding process mechanism, determine the principal element affecting feeding process, and pass through the foundation of mechanism based method analysis implementation model;

Set up Dynamic Blending System electric machine frequency and mass flow model is:

$Q_G=\frac{{2\mathrm{\πR}}_D{R}_G}{R_J}f\mathrm{}\mathrm{\ρwh}$

Wherein, Q _gfor feed proportioning system batcher feed flow, R _dfor the radius of gyration of motor, R _jfor the radius of gyration of reductor, R _gfor the feeding belt radius of gyration, ρ is material density, and w is belt conveyer scale width, and h is material height of drop, and f is the rotational frequency of motor.

Further, in step 2, the process that material in Dynamic Blending System falls into belt conveyer scale from batcher is studied, analyze material when falling into belt conveyer scale on the impact of belt balance weighing precision, and set up impulsive model;

Set up and based on belt conveyer scale material impact model in the dynamic batching process of theorem of impulse be:

$F\left(t\right)=G+m\left(t\right)\sqrt{2\mathrm{Hg}}$

Wherein, F (t) is the value of thrust of sensor suffered by moment t during consideration material impact power, and the quality of material of m (t) for falling, H is the height that material falls, and G is inventory on belt conveyer scale.

Further, in step 3, to weigh technique in conjunction with dynamic batching, set up the mechanism model of dynamic batching weighing process;

Described state feed proportioning system weighting model, adopts the mode of quantitative weighing, and set up in conjunction with the weighing technological process of dynamic batching belt conveyer scale and sensor weighing principle, weighting model comprises instantaneous flow model and integrated flux model:

Dynamic Blending System belt conveyer scale material instantaneous flow model is:

$Q=k*v*[\frac{(M_{\max }-M_{\min })}{10-0}*V-M_0]/L$

Dynamic Blending System belt conveyer scale accumulation of material discharge model is:

$M={\∫}_0^{T}Q\left(t\right)\mathrm{dt}={\∫}_0^T\{k*v*[\frac{(M_{\max }-M_{\min })}{10-0}*V\left(t\right)-M_0]/L\}\mathrm{dt}$

Wherein, k is for weighing coefficient, the rated speed that v (m/s) is belt, M _maxfor the maximum rated weighing value of LOAD CELLS, M _minfor the minimum specified weighing value of LOAD CELLS, M ₀(kg) be the belt weight of belted electronic balance self, L is the length of the effective weighing section of belt conveyer scale, and V is belt conveyer scale sensor output voltage value, and Q is instantaneous delivery, and M is integrated flux.

Further, in step 4, the realization of the transport function of Dynamic Blending System Active Disturbance Rejection Control model is by such as down conversion acquisition:

Relationship change between the input f of feed proportioning system and output is:

$Q_G=\frac{{2\mathrm{\πR}}_D{R}_G}{R_J}\mathrm{wh\ρf}=K^{*}\mathrm{\ρf}$

Wherein, for the constant relevant with Dynamic Blending System structure, do as down conversion to above formula:

$\left\{\begin{array}{c}{Q}_G\left(t\right)=K^{*}\mathrm{\ρf}\left(t\right)\\ \stackrel{\·}{f}\left(t\right)=u\left(t\right)\end{array}\right.$

Above formula being transformed to state equation is:

$\left\{\begin{array}{c}{\stackrel{\·}{Q}}_G\left(t\right)=K^{*}\mathrm{\ρu}\left(t\right)\\ y=Q_G\end{array}\right.{\mathrm{\ρ}}_{\min }\≤\mathrm{\ρ}\≤{\mathrm{\ρ}}_{\max }$

Then ssystem transfer function is:

$G\left(s\right)=\frac{Q_G\left(s\right)}{u\left(s\right)}=\frac{K^{*}\mathrm{\ρ}}{s}$

Designed the Nonlinear Tracking Differentiator of dynamic batching Active Disturbance Rejection Control model by the transport function obtained, extended state observer, and nonlinear state Error Feedback control law, realize the various piece of Active Disturbance Rejection Control model.

Further, described Nonlinear Tracking Differentiator adopts first-order tracking differentiator, and its aspect of model is:

$\left\{\begin{array}{c}{Z}_{11}=x_1+h*x_2\\ Z_{12}={\stackrel{\·}{Z}}_{11}=x_1+h*\mathrm{fhan}\end{array}\right.$

Wherein, Z ₁₁follow the tracks of input signal, Z ₁₂follow the tracks of the differential signal of input; H is the unknown parameter of tracker; x ₁, x ₂for system is in the output state value of adjacent moment, fhan () is time-optimal control comprehensive function;

Described extended state observer adopts Second Order Eso, and its aspect of model is:

$\left\{\begin{array}{c}{Z}_{21}=e=x_1-v\\ Z_{22}=x_2-{\mathrm{\β}}_1*e\\ Z_{23}=-{\mathrm{\β}}_2\mathrm{fal}(e,\mathrm{\α},h)+b*u\\ \mathrm{fal}(e,\mathrm{\α},h)=\left\{\begin{array}{cc}{\left|e\right|}^{\mathrm{\α}}*\mathrm{sign}\left(e\right),& \left|e\right|>h\\ \frac{e}{d^{\mathrm{\α}-1}},& \left|e\right|\≤h\end{array}\right.\end{array}\right.$

Wherein, Z ₂₁that Dynamic Blending System State Viewpoint is measured, Z ₂₂the differential that Dynamic Blending System State Viewpoint is measured, Z ₂₃be Dynamic Blending System disturbance observation amount, suppress outer and disturb the uncertainty with object.Parameter alpha, β ₁, β ₂, generally get 0.25,0.5,0.75, β according to adjustment experience α ₂the delayed of Dynamic Blending System disturbing signal estimation can be affected, β ₂more large time delay is less, but β ₂crossing conference makes system produce vibration, although increase β ₁vibration can be suppressed, but β ₁crossing conference makes system disperse, therefore β ₁, β ₂adjustment need mutually to coordinate, according to the application experience of state tracker, first can adjust β ₂, then increase β gradually ₁, constantly improve control effects, until optimum;

Described nonlinear state Error Feedback control law, its aspect of model is:

u ₀＝k ₁(Z ₁₁-Z ₂₁)+k ₂(Z ₁₂-Z ₂₂)

$u=u_0-\frac{Z_{22}}{b}$

Wherein, b is the gain of controlled input, due to ρ ₀the density value of material in most cases can be represented, then desirable b=K ^*ρ ₀, k ₁, k ₂for weight coefficient initial value, desirable k ₁=k ₂=1, follow-up system runs and can adjust according to effect.

Beneficial effect of the present invention is:

1, by the determination to proportioning process flow process major influence factors, simplify dynamic batching technological process, be divided into feed, weighing, batching three parts, and the major influence factors of various piece is deeply probed into, set up simplified model respectively, make the robustness of dynamic batching Active Disturbance Rejection Control model stronger, reliability is higher;

2, by setting up electric machine frequency and mass flow model, solve the problem affecting proportioning accuracy due to the uncertainty of proportion material size, density, shape, the realization for dynamic batching Auto-disturbance-rejection Control provides theoretical direction;

3, by setting up belt conveyer scale material impact model, processing affecting the weigh main interference of belt conveyer scale weighing precision of dynamic batching, improving weighing precision and then improving the distributing precision of Dynamic Blending System material;

4, by setting up Dynamic Blending System weighting model, comprising instantaneous flow model and integrated flux model, the principle of dynamic batching weighing part is analyzed, for the realization of dynamic batching Auto-disturbance-rejection Control provides theoretical direction.

5, by realizing Dynamic Blending System Auto-disturbance-rejection Control in conjunction with electric machine frequency and mass flow model, belt conveyer scale material impact model and Dynamic Blending System weighting model, accurate control for dynamic batching provides science, reliable, practical method, and the robustness of guarantee batching, anti-interference and high precision.

Accompanying drawing explanation

In order to make object of the present invention, technical scheme and beneficial effect clearly, the invention provides following accompanying drawing and being described:

Fig. 1 is feed section feed simplified pinciple schematic diagram;

Fig. 2 is that Dynamic Blending System leaks material schematic diagram;

Fig. 3 is belt impulsive force model schematic;

Fig. 4 is Dynamic Blending System Active Disturbance Rejection Control structural drawing;

Fig. 5 is the realization flow block diagram of the method for the invention;

Fig. 6 is the simulation result figure in embodiment.

Embodiment

Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.

Principle set up by Auto-disturbance-rejection Control model of the present invention:

On the basis of Dynamic Blending System simplified model, design system automatic disturbance rejection controller, first carries out certain conversion to the simplified model of the Dynamic Blending System set up, and from process modeling, the relationship change between the input f of feed proportioning system and output is:

$Q_G=\frac{{2\mathrm{\πR}}_D{R}_G}{R_J}\mathrm{wh\ρf}=K^{*}\mathrm{\ρf}$

Wherein, for the constant relevant with Dynamic Blending System structure, for design Active Disturbance Rejection Control system is done as down conversion:

$\left\{\begin{array}{c}{Q}_G\left(t\right)=K^{*}\mathrm{\ρf}\left(t\right)\\ \stackrel{\·}{f}\left(t\right)=u\left(t\right)\end{array}\right.$

Above formula being transformed to state equation is:

$\left\{\begin{array}{c}{\stackrel{\·}{Q}}_G\left(t\right)=K^{*}\mathrm{\ρu}\left(t\right)\\ y=Q_G\end{array}\right.{\mathrm{\ρ}}_{\min }\≤\mathrm{\ρ}\≤{\mathrm{\ρ}}_{\max }$

Then ssystem transfer function is:

$G\left(s\right)=\frac{Q_G\left(s\right)}{u\left(s\right)}=\frac{K^{*}\mathrm{\ρ}}{s}$

Due to ρ _min≤ ρ≤ρ _max, and ρ _minand ρ _maxbe minimum value and the maximal value of known material density, above formula be converted to:

$G\left(s\right)=\frac{K^{*}\mathrm{\ρ}}{s}=\frac{K^{*}{\mathrm{\ρ}}_0}{s}+\mathrm{\Δ\ρ}$

Wherein, get for the intermediate value of density, also get suitable value by field data measurement and data analysis, the physical significance of this value is the density value of material in most cases in blending process, and Δ ρ is the uncertain parameter of variable density and the disturbing signal of system.

To sum up, ρ is the uncertain parameter with a certain size, in the process of batching, many disturbing factors are also existed for Dynamic Blending System, the model set up is the simplified model of system, and be that belt conveyer scale weighing technology system also exists time delay due to what adopt, to sum up analysis and designation Active Disturbance Rejection Control system architecture diagram as shown in Figure 4, by w (t) for comprising the internal disturbance of system and the summation of external disturbance of uncertain parameter Δ ρ, v (t)=Q _int () is given reference stream value, x (t)=Q _gt () is system output stream amount.

Shown by above-mentioned analysis, dynamic batching Auto-disturbance-rejection Control model sets up on the basis of system Simplified flowsheet, do not need to set up complicated system mathematic model, and there is very strong interference free performance, this is the unique distinction that Auto-disturbance-rejection Control model is different from other control method models.

Fig. 5 is the realization flow block diagram of the method for the invention, and forecasting process of the present invention comprises: (1) sets up Dynamic Blending System electric machine frequency and mass flow model; (2) belt conveyer scale material impact model in dynamic batching process is set up; (3) Dynamic Blending System weighting model is set up; (4) steps such as Dynamic Blending System Active Disturbance Rejection Control model are set up:

(1) Dynamic Blending System electric machine frequency and mass flow model is set up

For the Auto-disturbance-rejection Control model needs that the present invention sets up, analysis principle for dynamic batching feed section technique simplified model is: in feed proportioning system, drive source is asynchronous motor, the general rotating speed by regulating the frequency of this motor to regulate batcher conveying belt, and then realize the change of mass flow on belt conveyer scale.Fig. 1 is feed section feed simplified pinciple schematic diagram, and Fig. 2 is that Dynamic Blending System leaks material schematic diagram.

The principle that drive motor subtracts through the deceleration of reductor is: the gear wheel on the gears meshing output shaft that fast drive motor is few by the number of teeth on the input shaft of reductor reaches and reduces the effect that rotating speed improves moment of torsion, the output speed of reductor can be drawn according to this principle, the relation of drive motor frequency and rotating speed, reductor and the connection of batcher adopt the angular velocity coaxially connecting then reductor equal with the angular velocity that batcher operates, therefore the pass can derived between drive motor and batcher feed flow is:

$Q_G=\frac{{2\mathrm{\πR}}_D{R}_G}{R_J}f\mathrm{}\mathrm{\ρwh}$

(2) belt conveyer scale material impact model in dynamic batching process is set up

Fig. 3 is belt impulsive force model schematic, and by the weighing principle of LOAD CELLS and the technological process of Dynamic Blending System feed section, analyze from amechanical angle by impacting representation model to the simplification shown in Fig. 3, obtaining belt conveyer scale material impact model is:

$F\left(t\right)=\frac{{\mathrm{\θk}}_o}{K_S}*U_O=G+\frac{m\left(t\right)g}{\mathrm{\Δt}}\sqrt{\frac{2H}{g}}=G+m\left(t\right)\sqrt{2\mathrm{Hg}}$

Wherein, F (t) is the value of thrust of sensor suffered by moment t during consideration material impact power, and the quality of material of m (t) for falling, H is the height that material falls, and G is inventory on belt conveyer scale.

(3) Dynamic Blending System weighting model is set up

Dynamic Blending System weighting model is for comprising instantaneous flow model and integrated flux model two parts:

The Dynamic Blending System belt conveyer scale material instantaneous flow model set up is:

$Q=k*v*[\frac{(M_{\max }-M_{\min })}{10-0}*V-M_0]/L$

The Dynamic Blending System belt conveyer scale accumulation of material discharge model set up is:

$M={\∫}_0^{T}Q\left(t\right)\mathrm{dt}={\∫}_0^T\{k*v*[\frac{(M_{\max }-M_{\min })}{10-0}*V\left(t\right)-M_0]/L\}\mathrm{dt}$

Wherein, k is for weighing coefficient, the rated speed that v (m/s) is belt, M _maxfor the maximum rated weighing value of LOAD CELLS, M _minfor the minimum specified weighing value of LOAD CELLS, M ₀(kg) be the belt weight of belted electronic balance self, L is the length of the effective weighing section of belt conveyer scale, and V is belt conveyer scale sensor output voltage value, and Q is instantaneous delivery, and M is integrated flux.

(4) Dynamic Blending System Active Disturbance Rejection Control model is set up

Auto Disturbances Rejection Control Technique is utilized to set up the Active Disturbance Rejection Control of dynamic batching shown in Fig. 4 model, comprise Nonlinear Tracking Differentiator (ESO), extended state observer (TD), and nonlinear state Error Feedback control law (NLSEF) three parts.

Nonlinear Tracking Differentiator adopts first-order tracking differentiator, and its aspect of model is;

$\left\{\begin{array}{c}{Z}_{11}=x_1+h*x_2\\ Z_{12}={\stackrel{\·}{Z}}_{11}=x_1+h*\mathrm{fhan}\end{array}\right.$

Wherein, Z ₁₁follow the tracks of input signal, Z ₁₂follow the tracks of the differential signal of input; H is the unknown parameter of tracker; x ₁, x ₂for system is in the output state value of adjacent moment, fhan () is time-optimal control comprehensive function.

Extended state observer adopts Second Order Eso, and its aspect of model is;

$\left\{\begin{array}{c}{Z}_{21}=e=x_1-v\\ Z_{22}=x_2-{\mathrm{\β}}_1*e\\ Z_{23}=-{\mathrm{\β}}_2\mathrm{fal}(e,\mathrm{\α},h)+b*u\\ \mathrm{fal}(e,\mathrm{\α},h)=\left\{\begin{array}{cc}{\left|e\right|}^{\mathrm{\α}}*\mathrm{sign}\left(e\right),& \left|e\right|>h\\ \frac{e}{d^{\mathrm{\α}-1}},& \left|e\right|\≤h\end{array}\right.\end{array}\right.$

Wherein, Z ₂₁that Dynamic Blending System State Viewpoint is measured, Z ₂₂the differential that Dynamic Blending System State Viewpoint is measured, Z ₂₃be Dynamic Blending System disturbance observation amount, suppress outer and disturb the uncertainty with object.Parameter alpha, β ₁, β ₂for relevant with feed proportioning system technique waiting adjusts parameter.

Nonlinear state Error Feedback control law is;

u ₀＝k ₁(Z ₁₁-Z ₂₁)+k ₂(Z ₁₂-Z ₂₂)

$u=u_0-\frac{Z_{22}}{b}$

Wherein, b is the gain of controlled input, from formula, due to ρ ₀the density value of material in most cases can be represented, then desirable b=K ^*ρ ₀, k ₁, k ₂for the desirable k of weight coefficient initial value ₁=k ₂=1, follow-up system runs and can adjust according to effect.

Simulation software is utilized to carry out simulation analysis to it, be illustrated in figure 6 simulation result, the control of dynamic batching Auto-disturbance-rejection Control (ADRC) can realize the elimination completely of system static difference, and curve can be found out, the change of active disturbance rejection to specified rate has good tracking performance, can reach stable following within the specified rate change short period.

What finally illustrate is, above preferred embodiment is only in order to illustrate technical scheme of the present invention and unrestricted, although by above preferred embodiment to invention has been detailed description, but those skilled in the art are to be understood that, various change can be made to it in the form and details, and not depart from claims of the present invention limited range.

Claims (6)

1. a dynamic batching Auto-disturbance-rejection Control, is characterized in that: comprise the following steps:

Step one: set up Dynamic Blending System electric machine frequency and mass flow model;

Step 2: set up belt conveyer scale material impact model in dynamic batching process;

Step 3: set up Dynamic Blending System weighting model;

Step 4: the belt conveyer scale material impact model in the Dynamic Blending System electric machine frequency in integrating step one and mass flow model, step 2, the Dynamic Blending System weighting model in step 3, utilize modern advanced Auto Disturbances Rejection Control Technique to set up Dynamic Blending System Active Disturbance Rejection Control model, thus realize dynamic batching Active Disturbance Rejection Control.

2. a kind of dynamic batching Auto-disturbance-rejection Control according to claim 1, it is characterized in that: in step one, for Dynamic Blending System feed section technique, analyze feeding process mechanism, determine the principal element affecting feeding process, and pass through the foundation of mechanism based method analysis implementation model;

Set up Dynamic Blending System electric machine frequency and mass flow model is:

$Q_G=\frac{2\mathrm{\π}{R}_D{R}_G}{R_J}\mathrm{f\ρwh}$

Wherein, Q _gfor feed proportioning system batcher feed flow, R _dfor the radius of gyration of motor, R _jfor the radius of gyration of reductor, R _gfor the feeding belt radius of gyration, ρ is material density, and w is belt conveyer scale width, and h is material height of drop, and f is the rotational frequency of motor.

3. a kind of dynamic batching Auto-disturbance-rejection Control according to claim 1, it is characterized in that: in step 2, the process that material in Dynamic Blending System falls into belt conveyer scale from batcher is studied, analyze material when falling into belt conveyer scale on the impact of belt balance weighing precision, and set up impulsive model;

Set up and based on belt conveyer scale material impact model in the dynamic batching process of theorem of impulse be:

$F\left(t\right)=G+m\left(t\right)\sqrt{2\mathrm{Hg}}$

Wherein, F (t) is the value of thrust of sensor suffered by moment t during consideration material impact power, and the quality of material of m (t) for falling, H is the height that material falls, and G is inventory on belt conveyer scale.

4. a kind of dynamic batching Auto-disturbance-rejection Control according to claim 1, is characterized in that: in step 3, to weigh technique, set up the mechanism model of dynamic batching weighing process in conjunction with dynamic batching;

Described state feed proportioning system weighting model, adopts the mode of quantitative weighing, and set up in conjunction with the weighing technological process of dynamic batching belt conveyer scale and sensor weighing principle, weighting model comprises instantaneous flow model and integrated flux model:

Dynamic Blending System belt conveyer scale material instantaneous flow model is:

$Q=k*v*[\frac{(M_{\max }-M_{\min })}{10-0}*V-M_0]/L$

Dynamic Blending System belt conveyer scale accumulation of material discharge model is:

$M={\∫}_0^{T}Q\left(t\right)\mathrm{dt}={\∫}_0^T\{k*v*[\frac{(M_{\max }-M_{\min })}{10-0}*V\left(t\right)-M_0]/L\}\mathrm{dt}$

Wherein, k is for weighing coefficient, the rated speed that v (m/s) is belt, M _maxfor the maximum rated weighing value of LOAD CELLS, M _minfor the minimum specified weighing value of LOAD CELLS, M ₀(kg) be the belt weight of belted electronic balance self, L is the length of the effective weighing section of belt conveyer scale, and V is belt conveyer scale sensor output voltage value, and Q is instantaneous delivery, and M is integrated flux.

5. a kind of dynamic batching Auto-disturbance-rejection Control according to claim 1, is characterized in that: in step 4, and the realization of the transport function of Dynamic Blending System Active Disturbance Rejection Control model is by such as down conversion acquisition:

Relationship change between the input f of feed proportioning system and output is:

$Q_G=\frac{2\mathrm{\π}{R}_D{R}_G}{R_J}\mathrm{wh\ρf}=K^{*}\mathrm{\ρf}$

Wherein, for the constant relevant with Dynamic Blending System structure, do as down conversion to above formula:

$\left\{\begin{array}{c}{Q}_G\left(t\right)=K^{*}\mathrm{\ρf}\left(t\right)\\ \stackrel{.}{f}\left(t\right)=u\left(t\right)\end{array}\right.$

Above formula being transformed to state equation is:

$\left\{\begin{array}{c}{\stackrel{.}{Q}}_G\left(t\right)=K^{*}\mathrm{\ρu}\left(t\right)\\ y=Q_G\end{array}\right.{\mathrm{\ρ}}_{\min }\≤\mathrm{\ρ}\≤{\mathrm{\ρ}}_{\max }$

Then ssystem transfer function is:

$G\left(s\right)=\frac{Q_G\left(s\right)}{u\left(s\right)}=\frac{K^{*}\mathrm{\ρ}}{s}$

Designed the Nonlinear Tracking Differentiator of dynamic batching Active Disturbance Rejection Control model by the transport function obtained, extended state observer, and nonlinear state Error Feedback control law, realize the various piece of Active Disturbance Rejection Control model.

6. a kind of dynamic batching Auto-disturbance-rejection Control according to claim 5, is characterized in that:

Described Nonlinear Tracking Differentiator adopts first-order tracking differentiator, and its aspect of model is:

$\left\{\begin{array}{c}{Z}_{11}=x_1+h*x_2\\ Z_{12}={\stackrel{.}{Z}}_{11}=x_1+h*\mathrm{fhan}\end{array}\right.$

Wherein, Z ₁₁follow the tracks of input signal, Z ₁₂follow the tracks of the differential signal of input; H is the unknown parameter of tracker; x ₁, x ₂for system is in the output state value of adjacent moment, fhan () is time-optimal control comprehensive function;

Described extended state observer adopts Second Order Eso, and its aspect of model is:

$\left\{\begin{array}{c}{Z}_{21}=e=x_1-v\\ Z_{22}=x_2-{\mathrm{\β}}_1*e\\ Z_{23}=-{\mathrm{\β}}_2\mathrm{fal}(e,\mathrm{\α},h)+b*u\\ \mathrm{fal}(e,\mathrm{\α},h)=\left\{\begin{array}{cc}{\left|e\right|}^{\mathrm{\α}}*\mathrm{sign}\left(e\right),& \left|e\right|>h\\ \frac{e}{d^{\mathrm{\α}-1}},& \left|e\right|\≤h\end{array}\right.\end{array}\right.$

Wherein, Z ₂₁that Dynamic Blending System State Viewpoint is measured, Z ₂₂the differential that Dynamic Blending System State Viewpoint is measured, Z ₂₃be Dynamic Blending System disturbance observation amount, suppress outer and disturb the uncertainty with object; α, β ₁, β ₂for parameter;

Described nonlinear state Error Feedback control law, its aspect of model is:

u ₀＝k ₁(Z ₁₁-Z ₂₁)+k ₂(Z ₁₂-Z ₂₂)

$u=u_0-\frac{Z_{22}}{b}$

Wherein, b is the gain of controlled input, k ₁, k ₂for weight coefficient initial value.