Generalized Improved Active Disturbance Rejection Control Method Based on Model Aiding and Smith Predictor-like

Abstract

The present invention provides a generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like, belonging to the field of automatic control. The method includes the following steps: describing a class of actual industrial systems by using a high-order inertial system; designing a Smith predictor-like algorithm based on the input and output of the high-order inertial system; designing an extended state observer algorithm based on model assistance with the output of the obtained Smith predictor-like algorithm and the input of the high-order inertial system; designing a control law with the output of the obtained extended state observer algorithm and the system set value; obtaining a new input value of the high-order inertial system, and adjusting and controlling the output of the high-order inertial system according to this value. This method can make full use of the known model information, retain the characteristics of simple structure and easy parameter tuning of the active disturbance rejection control, enable the closed-loop system to better balance the tracking ability and anti-interference ability, and make the closed-loop system have strong robustness.

Description

Technical Field

[0001] The present invention relates to the field of industrial control, and particularly to a generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like estimation. Background Art

[0002] The active disturbance rejection control algorithm has received extensive attention and application due to its strong ability to handle system nonlinearity and system uncertainty, and its advantages such as simple structure and high reliability. The active disturbance rejection control algorithm, especially the first-order and second-order active disturbance rejection control algorithms, has been widely applied in motion systems, thermal systems, aerospace systems, etc.

[0003] However, the heat transfer and flow processes existing in the process control system are typical distributed parameters, which are generally described by using a high-order inertial system. Where s, K, T, and n respectively represent the differential operator, the gain of the high-order inertial system, the time constant of the high-order inertial system, and the order of the high-order inertial system, and n≥2. Y(s) and U(s) are respectively the output and input of the high-order inertial system. Taking the denitration system as an example, the meanings of the parameters in the above formula are as follows: the output Y(s) is the output value of the nitrogen oxide concentration in the denitration system, the input U(s) is the ammonia injection amount in the denitration system, the gain coefficient K refers to the amplification factor of the high-order system for the input value, the input value of 1 ton of ammonia injection amount corresponds to the change amount of the nitrogen oxide concentration in the denitration system, and the time constant T refers to the time required when the system response reaches 63.2% of the steady-state value.

[0004] For the above high-order inertial system, the existing standard active disturbance rejection control algorithm Figure 1 shown, which is characterized by including the following steps:

[0005] 1) Describing a class of actual industrial systems to be controlled by using a high-order inertial system, and the mathematical expression is:

[0006]

[0007] Where Y(s) and U(s) respectively represent the output and input of the actual industrial system, s, K, T, and n respectively represent the differential operator, the gain of the actual industrial system, the time constant of the actual industrial system, and the order of the actual industrial system, and n≥2; y(Γ) and u(Γ) respectively represent the output and input of the actual industrial system at the previous calculation step.

[0008] 2) Designing an (n + 1)-order extended state observer algorithm for the input and output of the actual industrial system in step 1):

[0009]

[0010] Among them, i represents the i-th variable, where 1 ≤ i < n and 1 ≤ j < m - 1;; z1(Γ + 1) and z1(Γ) are the tracking quantities of the actual industrial system outputs y(Γ + 1) and y(Γ) at the next calculation step Γ + 1 and the current calculation step Γ respectively; z i (Γ + 1) and z i (Γ) are the tracking values of the (i - 1)-th order derivative of the actual industrial system output at the next calculation step Γ + 1 and the current calculation step Γ respectively; z n (Γ + 1) and z n (Γ) are the tracking values of the (n - 1)-th order derivative of the actual industrial system output at the next calculation step Γ + 1 and the current calculation step Γ respectively; z n+1 (Γ + 1) and z n+1 (Γ) are the total disturbances of the actual industrial system at the next calculation step Γ + 1 and the current calculation step Γ respectively; h is the sampling step; β1, β i 、β n 、β n+1 and b0 are calculation coefficients;

[0011] 3) Design the control law algorithm based on the output of the extended state observer algorithm obtained in step 2) and the set value of the actual industrial system as follows:

[0012]

[0013] Among them, u(Γ + 2) is the input of the actual industrial system for calculating the control rate at the next two calculation steps Γ + 2, r(Γ + 1) is the set value of the actual industrial system at the next calculation step Γ + 1, k1, k2, k i 、k m are calculation coefficients;

[0014] 4) Send the input u(Γ + 2) of the actual industrial system at the next two calculation steps Γ + 2 obtained in step 3) to the actuator, adjust the opening of the actuator, and realize the adjustment of the control quantity of the closed-loop system, so as to realize the adjustment of the output of the actual industrial system. At this time, it is the implementation of the standard active disturbance rejection control algorithm.

[0015] The standard active disturbance rejection control algorithm has problems such as ineffective utilization of model information, excessive burden on the extended state observer, and low estimation accuracy when controlling high-order inertial systems and cannot very ideally balance the tracking and anti-interference performances. Summary of the Invention

[0016] The object of the present invention is to overcome the deficiencies of the prior art, and provide a generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like for a class of actual industrial systems described by high-order inertial systems. Design a Smith predictor-like algorithm based on the input and output of the high-order inertial system; design an extended state observer algorithm based on model assistance with the output of the obtained Smith predictor-like algorithm and the input of the high-order inertial system; design a control law with the output of the obtained extended state observer algorithm and the system set value; obtain a new input value of the high-order inertial system, and adjust and control the output of the high-order inertial system according to this value. This method can make full use of the known model information, retain the characteristics of simple structure and easy parameter tuning of the active disturbance rejection control, enable the closed-loop system to better balance the tracking ability and anti-interference ability, and make the closed-loop system have strong robustness, providing effective and reliable control strategy support for solving the control problems of a class of high-order inertial industrial systems.

[0017] The first aspect of the present invention provides a generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like, including the following steps:

[0018] (1) Describe a class of controlled actual industrial systems using a high-order inertial system, and the mathematical expression is:

[0019]

[0020] where Y(s) and U(s) respectively represent the output and input of the actual industrial system, s, K, T, and n respectively represent the differential operator, the gain of the actual industrial system, the time constant of the actual industrial system, and the order of the actual industrial system, n≥2, the value range of K is [-10 5 , 0) and (0, 10 5 , and the value range of T is (0, 10 5 ;

[0021] Let y(Γ - 1) and u(Γ - 1) respectively represent the output and input of the actual industrial system at the previous calculation step;

[0022] (2) For the controlled actual industrial system, design a Smith predictor-like algorithm based on the input and output of the actual industrial system:

[0023] y p (Γ) = y1(Γ - 1) - y2(Γ - 1) + y(Γ - 1)

[0024] y p(Γ) is the output of the Smith predictor-like algorithm at the current calculation step Γ; y1(Γ - 1) is the output of G1(s) at the previous calculation step Γ - 1, y2(Γ - 1) is the output of G2(s) at the previous calculation step Γ - 1, and the input of G1(s) is the actual industrial system input u(Γ - 1) at the previous calculation step, and the input of G2(s) is y1(Γ - 1) at the previous calculation step Γ - 1;

[0025] G1(s) and G2(s) are the calculation expressions designed by the Smith predictor-like algorithm:

[0026]

[0027]

[0028] where k1 and m are the gain and order of G1(s) respectively, 1 ≤ m < n, and T1 is the time constant of G1(s) and G2(s); k d1 = K and T1 = T;

[0029] (3) Design a model-assisted generalized extended state observer algorithm for the input of the actual industrial system and the output of the Smith predictor-like algorithm:

[0030]

[0031] where i and j represent the i-th variable and the j-th variable respectively, and 1 ≤ i < m - 1 and 1 ≤ j < m - 1;

[0032] represents the combination operation, represents the number of different cases of taking m objects from n objects;

[0033] z1(Γ + 1) and z1(Γ) are the tracking quantities of the actual industrial system outputs y(Γ + 1) and y(Γ) at the next calculation step Γ + 1 and the current calculation step Γ respectively; z i (Γ + 1), z i (Γ) are the tracking values of the (i - 1)-th order derivative of the actual industrial system output at the next calculation step Γ + 1 and the current calculation step Γ respectively; z m (Γ + 1), z m (Γ) are the tracking values of the (m - 1)-th order derivative of the actual industrial system output at the next calculation step Γ + 1 and the current calculation step Γ respectively; z m+1 (Γ + 1), z m+1 (Γ) are the observed quantities of the disturbances suffered by the actual industrial system at the next calculation step Γ + 1 and the current calculation step Γ respectively; h is the sampling step; the value range of h is [0.001, 100]; β1, β i, β m-1 , β m , β m+1 and b0 are calculation coefficients, and their numerical calculations are performed through the following formula:

[0034]

[0035] where ω o is the bandwidth of the model-assisted generalized extended state observation algorithm and ω o ∈(0, 10 15 , ξ is an adjustable parameter and ξ∈(0, 10 15 ;

[0036] (4) Design a generalized control law algorithm based on the output of the extended state observation algorithm obtained in (3) and the set value of the actual industrial system:

[0037]

[0038] Or

[0039]

[0040]

[0041] where u(Γ + 2) is the input of the actual industrial system calculated by the generalized control law in the next two calculation steps Γ + 2, r(Γ + 1) is the set value of the actual industrial system in the next calculation step Γ + 1, k1, k2, k i , k m are calculation coefficients, and their numerical calculations are performed through the following formula:

[0042]

[0043] where ω c is the bandwidth of the generalized control law algorithm and ω c ∈(0, 10 15 ;

[0044] (5) Send the input u(Γ + 2) of the actual industrial system in the next two calculation steps Γ + 2 obtained in (4) to the actuator of the actual industrial system, adjust the opening of the actuator, and realize the adjustment of the control quantity of the actual industrial system, so as to realize the adjustment of the output quantity of the actual industrial system.

[0045] The second aspect of the present invention provides a generalized improved active disturbance rejection control system based on model assistance and Smith-like prediction, including an actual industrial system to be controlled, a first controller for running the Smith-like prediction algorithm, a second controller for running the model-assisted generalized extended state observation algorithm, and a third controller for running the generalized control law algorithm;

[0046] An actual industrial system, a first controller, a second controller, and a third controller are communicatively connected to each other to implement the generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like estimation.

[0047] The third aspect of the present invention provides a generalized improved active disturbance rejection control device, including:

[0048] A memory; and

[0049] A processor coupled to the memory, the processor being configured to execute the generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like estimation based on instructions stored in the memory.

[0050] The fourth aspect of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like estimation.

[0051] The features and beneficial effects of the present invention are as follows:

[0052] 1. The present invention proposes a generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like estimation, retaining the advantages of the existing improved active disturbance rejection control algorithm, such as simple structure and easy tuning.

[0053] 2. The designed generalized extended state observer algorithm based on model information can make full use of the known partial model information, improve the upper bandwidth limit of the extended state observer algorithm, thereby improving the estimation accuracy of the extended state observer algorithm and reducing the online estimation burden of the extended state observer algorithm.

[0054] 3. The designed generalized control law algorithm can enhance the tracking ability and anti-interference ability, and achieve the disturbance-free tracking performance of the generalized improved active disturbance rejection control based on model assistance and Smith predictor-like estimation.

[0055] 4. By combining the partial model information of the known controlled object and the advantages of the Smith predictor-like estimation, the present invention improves the extended state observer algorithm and the control law algorithm, realizes the improvement of control performance, and provides an effective and reliable control strategy for solving the control problems of a class of high-order inertial industrial systems. Description of the Drawings

[0056] Figure 1 It is a block diagram of the existing standard active disturbance rejection control algorithm.

[0057] Figure 2 It is a block diagram of the generalized improved active disturbance rejection control algorithm of the present invention.

[0058] Figure 3This is the structural block diagram of another generalized improved active disturbance rejection control algorithm of the present invention.

[0059] Figure 4 It is the comparison diagram of the set value of the actual industrial object, the output value of the method of the present invention, and the output value of the comparison method in Embodiment 3. Specific embodiments

[0060] The technical solution of the present invention will be further described in detail below through specific embodiments.

[0061] Embodiment 1

[0062] This embodiment proposes a generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like, including the following steps:

[0063] (1) Describe a class of controlled actual industrial systems using a high-order inertial system, and the mathematical expression is:

[0064]

[0065] Among them, Y(s) and U(s) respectively represent the output and input of the actual industrial system, s, K, T, and n respectively represent the differential operator, the gain of the actual industrial system, the time constant of the actual industrial system, and the order of the actual industrial system, n≥2, and the value range of K is [-10 5 , 0) and (0, 10 5 , and the value range of T is (0, 10 5 ;

[0066] Let y(Γ - 1) and u(Γ - 1) respectively represent the output and input of the actual industrial system at the previous calculation step;

[0067] (2) For the controlled actual industrial system, design a Smith predictor-like algorithm based on the input and output of the actual industrial system:

[0068] y p (Γ) = y1(Γ - 1) - y2(Γ - 1) + y(Γ - 1)

[0069] y p (Γ) is the output of the Smith predictor-like algorithm at the current calculation step Γ; y1(Γ - 1) is the output of G1(s) at the previous calculation step Γ - 1, y2(Γ - 1) is the output of G2(s) at the previous calculation step Γ - 1, and the input of G1(s) is the input u(Γ - 1) of the actual industrial system at the previous calculation step, and the input of G2(s) is y1(Γ - 1) at the previous calculation step Γ - 1;

[0070] G1(s) and G2(s) are the calculation expressions designed for the Smith predictor-like algorithm:

[0071]

[0072]

[0073] where k1 and m are the gain and order of G1(s) respectively, 1 ≤ m < n, T1 is the time constant of G1(s) and G2(s); k d1 = K and T1 = T;

[0074] (3) Design a model-assisted generalized extended state observer algorithm for the input of the actual industrial system and the output of the Smith predictor-like algorithm:

[0075]

[0076] where i and j represent the i-th variable and the j-th variable respectively, and 1 ≤ i < m - 1 and 1 ≤ j < m - 1;

[0077] represents the combination operation, represents the number of different cases of taking m objects from n objects;

[0078] z1(Γ + 1) and z1(Γ) are the tracking quantities of the actual industrial system outputs y(Γ + 1) and y(Γ) at the next calculation step Γ + 1 and the current calculation step Γ respectively; z i (Γ + 1), z i (Γ) are the tracking values of the (i - 1)-th order derivative of the actual industrial system output at the next calculation step Γ + 1 and the current calculation step Γ respectively; z m (Γ + 1), z m (Γ) are the tracking values of the (m - 1)-th order derivative of the actual industrial system output at the next calculation step Γ + 1 and the current calculation step Γ respectively; z m+1 (Γ + 1), z m+1 (Γ) are the observed quantities of the disturbance received by the actual industrial system at the next calculation step Γ + 1 and the current calculation step Γ respectively; h is the sampling step; the value range of h is [0.001, 100]; β1, β i , β m-1 , β m , β m+1 and b0 are calculation coefficients, and their numerical calculations are carried out through the following formula:

[0079]

[0080] where ω o is the bandwidth of the model-assisted generalized extended state observer algorithm and ωo ∈(0, 10 15 , ξ is an adjustable parameter and ξ ∈(0, 10 15 ;

[0081] (4) Design a generalized control law algorithm based on the output of the extended state observation algorithm obtained in (3) and the set value of the actual industrial system:

[0082] Figure 2 The generalized control law algorithm shown in

[0083]

[0084] Or Figure 3 The generalized control law algorithm shown in

[0085]

[0086] Among them, u(Γ + 2) is the input of the actual industrial system calculated by the generalized control law in the next two calculation steps Γ + 2, r(Γ + 1) is the set value of the actual industrial system in the next calculation step Γ + 1, k1, k2, k i , k m are calculation coefficients, and their numerical calculations are carried out through the following formula:

[0087]

[0088] Among them, ω c is the bandwidth of the generalized control law algorithm and ω c ∈(0, 10 15 ;

[0089] (5) Send the input u(Γ + 2) of the actual industrial system in the next two calculation steps Γ + 2 obtained in (4) to the actuator of the actual industrial system, adjust the opening of the actuator, and realize the adjustment of the control quantity of the actual industrial system, so as to realize the adjustment of the output quantity of the actual industrial system.

[0090] Example 2

[0091] This example provides a generalized improved active disturbance rejection control system based on model assistance and Smith-like prediction, including the actual industrial system to be controlled, a first controller for running the Smith-like prediction algorithm, a second controller for running the generalized extended state observation algorithm based on model assistance, and a third controller for running the generalized control law algorithm;

[0092] The actual industrial system, the first controller, the second controller, and the third controller are interconnected for communication to implement the generalized improved active disturbance rejection control method described in Example 1.

[0093] Example 3

[0094] This embodiment is described by taking an actual industrial system in a thermal engineering system as an example:

[0095] 101: The actual industrial system in the thermal engineering system is described by a high-order inertial system, and the mathematical expression is:

[0096]

[0097] In this embodiment, for the actual industrial system, K = 1, T = 13, and n = 4;

[0098] 102: For the controlled actual industrial system in step 101, a Smith predictor-like algorithm is designed based on the input and output of the actual industrial system:

[0099] G1(s) and G2(s) are the calculation expressions designed by the Smith predictor-like algorithm:

[0100]

[0101]

[0102] k1 and m are the gain and order of G1(s) respectively, 1 ≤ m < n, and T1 is the time constant of G1(s) and G2(s);

[0103] In this embodiment, k d1 = K, T1 = T, and m = 3, that is

[0104]

[0105]

[0106] 103: A model-assisted generalized extended state observer algorithm is designed for the input of the actual industrial system in step 101 and the output of the Smith predictor-like algorithm in step 102:

[0107]

[0108] β1, β2, β3, β4, and b0 are calculated as follows:

[0109]

[0110] In this embodiment, ω o = 4, ξ = 25, h = 0.1;

[0111] 104: The output of the extended state observer algorithm obtained in step 103 and the set value of the actual industrial system are used to design the following generalized control law algorithm:

[0112]

[0113] k1, k2, and k3 are calculation coefficients, and their numerical calculations can be performed through the following formula:

[0114]

[0115] In this embodiment, ω c = 0.15;

[0116] Step 105: Send the input quantity u(Γ + 2) of the two calculation steps Γ + 2 in the actual industrial system obtained in step 104 to the actuator of the actual industrial system, adjust the opening of the actuator, realize the adjustment of the control quantity of the actual industrial system, so as to realize the adjustment of the output quantity of the actual industrial system, and complete the implementation of this embodiment.

[0117] Simulation comparison

[0118] Figure 4 It is a comparison diagram of the set value of the actual industrial object, the output value of the method of the present invention, and the output value of the comparison method in this embodiment; among them, for the standard active disturbance rejection control algorithm, its parameters are respectively k1 = 5.0625×10 -4 , k2 = 0.0135, k3 = 0.1350, k4 = 0.6, β1 = 2, β2 = 1.6, β2 = 1.6, β3 = 0.64, β4 = 0.1280, β5 = 0.0102, and b0 = 0.08; the thin solid line, the dashed line, and the thick solid line are respectively the set value of the actual industrial system, the output value of the comparison method (standard active disturbance rejection control algorithm), and the output value of the method of the present invention in this embodiment.

[0119] The specific simulation process is as follows: At the start time of the simulation, the system is in a steady state. At 10 s, the set value is changed from 0 to 1, and at 500 s, a disturbance of the control quantity is applied to the closed-loop circuit, changed from 0 to 1, until the simulation ends at 1000 s. It can be seen from the simulation that the active disturbance rejection control method of this embodiment has a relatively fast tracking ability and a strong anti-interference ability by making full use of the known model information.

[0120] Embodiment 4

[0121] This embodiment provides a generalized improved active disturbance rejection control device, including:

[0122] A memory; and

[0123] A processor coupled to the memory, the processor being configured to execute the generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like described in Embodiment 1 based on the instructions stored in the memory.

[0124] The device of this embodiment may further include an input / output interface, a network interface, a storage interface, etc. These interfaces, the memory, and the processor may be connected through a bus, for example. Among them, the input / output interface provides connection interfaces for input / output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface provides connection interfaces for various networking devices. The storage interface provides connection interfaces for external storage devices such as an SD card and a USB flash drive.

[0125] Embodiment 5

[0126] This embodiment provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the generalized improved active disturbance rejection control method based on model assistance and Smith predictor described in Embodiment 1.

[0127] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer non-transitory readable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer program code.

[0128] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0129] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0130] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 in one block or a plurality of blocks Figure 1 in the steps of the method.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the specific embodiments of the present invention may still be modified or some technical features may be equivalently replaced without departing from the spirit of the technical solutions of the present invention, and all of them should be covered by the scope of the technical solutions claimed by the present invention.

Claims

1. A generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like, characterized in that, It includes the following steps: (1) Describe a class of controlled actual industrial systems using a high-order inertial system, and the mathematical expression is: where Y(s) and U(s) respectively represent the output and input of the actual industrial system, s , K, T, and n respectively represent the differential operator, the gain of the actual industrial system, the time constant of the actual industrial system, and the order of the actual industrial system, n≥2, and the value range of K is [-10 5 , 0) and (0, 10 5 , and the value range of T is (0, 10 5 ; Let y(Γ - 1) and u(Γ - 1) represent the output quantity and the input quantity at the previous calculation step of the actual industrial system respectively; (2) For the controlled actual industrial system, design a Smith predictor-like algorithm based on the input and output quantities of the actual industrial system: y p (Γ) = y1(Γ - 1) - y2(Γ - 1) + y(Γ - 1) y p (Γ) is the output of the Smith predictor-like algorithm at the current calculation step Γ; y1(Γ - 1) is the output of G1(s) at the previous calculation step Γ - 1, y2(Γ - 1) is the output of G2(s) at the previous calculation step Γ - 1, and the input of G1(s) is the actual industrial system input u(Γ - 1) at the previous calculation step, and the input of G2(s) is y1(Γ - 1) at the previous calculation step Γ - 1; G1(s) and G2(s) are the calculation expressions designed by the Smith predictor-like algorithm: where k1 and m are the gain and order of G1(s) respectively, 1 ≤ m < n, and T1 is the time constant of G1(s) and G2(s); k d1 = K and T1 = T; (3) Design a model-assisted generalized extended state observer algorithm for the input quantity of the actual industrial system and the output quantity of the Smith predictor-like algorithm: wherein, i and j respectively represent the i-th variable and the j-th variable, and 1 ≤ i ＜m- ≤ 1 and 1 ≤ j < m - 1; Indicates a combinatorial operation, Indicates the number of different cases of taking m objects from n objects; z1(Γ + 1) and z1(Γ) are the tracking quantities of the actual industrial system output y(Γ + 1) and y(Γ) at the next calculation step Γ + 1 and the current calculation step Γ respectively; z i (Γ + 1) and z i (Γ) are the tracking values of the (i - 1)-th order derivative of the actual industrial system output at the next calculation step Γ + 1 and the current calculation step Γ respectively; z m (Γ + 1) and z m (Γ) are the tracking values of the (m - 1)-th order derivative of the actual industrial system output at the next calculation step Γ + 1 and the current calculation step Γ respectively; z m+1 (Γ + 1) and z m+1 (Γ) are the observed quantities of the interference received by the actual industrial system at the next calculation step Γ + 1 and the current calculation step Γ respectively; h is the sampling step size; the value range of h is [0.001, 100]; β1, β i , β m-1 , β m , β m+1 and b0 are calculation coefficients, and their numerical calculations are carried out through the following formula: Among them, ω o is the bandwidth of the model-assisted generalized extended state observer algorithm, and ω o ∈(0, 10 15 , ξ is an adjustable parameter and ξ ∈(0, 10 15 ; (4) Design a generalized control law algorithm based on the output quantity of the extended state observer algorithm obtained in (3) and the set value of the actual industrial system: Or Among them, u(Γ + 2) is the input of the actual industrial system calculated by the generalized control law at the next two calculation steps Γ + 2, r(Γ + 1) is the set value of the actual industrial system at the next calculation step Γ + 1, k1, k2, k i 、k m are calculation coefficients, and their numerical calculations are carried out through the following formula: where, ω c is the bandwidth of the generalized control law algorithm and ω c ∈(0, 10 15 ; (5) Send the input quantity u(Γ + 2) of the actual industrial system at the next two calculation steps Γ + 2 obtained in (4) to the actuator of the actual industrial system, adjust the opening of the actuator, and realize the adjustment of the control quantity of the actual industrial system, so as to realize the adjustment of the output quantity of the actual industrial system.

2. A generalized improved active disturbance rejection control system based on model assistance and Smith predictor-like, characterized in that, It includes a controlled actual industrial system, a first controller for running the Smith predictor-like algorithm, a second controller for running the model-assisted generalized extended state observer algorithm, and a third controller for running the generalized control law algorithm; The actual industrial system, the first controller, the second controller, and the third controller are communicatively connected to each other to implement the generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like described in claim 1.

3. A generalized improved active disturbance rejection control device, comprising: A memory; And A processor coupled to the memory, the processor being configured to execute the generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like described in claim 1 based on instructions stored in the memory.

4. A non-transitory computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the generalized improved active disturbance rejection control method based on model assistance and Smith predictor-like according to claim 1.

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