First-order improved active disturbance rejection control method based on model assistance and Smith predictor-like

Abstract

The present invention provides a first-order 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 first-order 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 applications 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 active disturbance rejection control algorithm, has been widely applied in motion systems, thermal systems, aerospace systems, etc.

[0003] However, the heat transfer and flow processes 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, and the change amount of the nitrogen oxide concentration in the denitration system corresponding to an ammonia injection amount of 1 ton is the input value. The time constant T refers to the time required for the system response to reach 63.2% of the steady-state value.

[0004] For the above high-order inertial system, there are a standard active disturbance rejection control algorithm and a Smith predictor-like active disturbance rejection control algorithm (Chinese Patent ZL202011125339.6 A Control Method for a Control System Based on Active Disturbance Rejection and Smith Predictor-like Estimation). The latter's first-order active disturbance rejection control algorithm based on Smith predictor-like estimation is as Figure 1 shown. This first-order active disturbance rejection control algorithm based on Smith predictor-like estimation has problems such as ineffective utilization of model information, excessive burden on the extended state observer, and low estimation accuracy when controlling the high-order inertial system and cannot very ideally balance the tracking and anti-interference performances. Summary of the Invention

[0005] The object of the present invention is to overcome the deficiencies of the prior art, and provide a first-order 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. A Smith predictor-like algorithm is designed based on the input and output of the high-order inertial system; the output of the obtained Smith predictor-like algorithm and the input of the high-order inertial system are used to design an extended state observer algorithm based on model assistance; the output of the obtained extended state observer algorithm and the system set value are used to design a control law; a new input value of the high-order inertial system is obtained, and the output of the high-order inertial system is adjusted and controlled 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.

[0006] The first aspect of the present invention provides a first-order improved active disturbance rejection control method based on model assistance and Smith predictor-like, including the following steps:

[0007] (1) Describe a class of controlled actual industrial systems by a high-order inertial system, and the mathematical expression is:

[0008]

[0009] 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 - 1) and u(Γ - 1) respectively represent the output and input of the actual industrial system at the previous calculation step; the value range of K is [-10 5 , 0) and (0, 10 5 , and the value range of T is (0, 10 5 ;

[0010] (2) For the class of controlled actual industrial systems in (1), design a Smith predictor-like algorithm based on the input and output of the actual industrial system:

[0011] y p (Γ) = y1(Γ - 1) - y2(Γ - 1) + y(Γ - 1)

[0012] 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. k1 is the gain of G1(s), and T1 is the time constant of G1(s) and G2(s); generally, k1 = K and T1 = T.

[0013] (3) Design a model-assisted extended state observer algorithm for the input of the actual industrial system in (1) and the output of the Smith predictor-like algorithm in (2):

[0014]

[0015] Among them, 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; z2(Γ + 1) and z2(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; u(Γ) is the actual industrial system output at the current calculation step Γ.

[0016] β1, β2, and b0 are calculation coefficients:

[0017]

[0018]

[0019]

[0020] Among them, ω o is the bandwidth of the model-assisted extended state observer algorithm and ω o ∈(0, 10 15 , ξ is an adjustable parameter and ξ ∈(0, 10 15 , and the value range of h is [0.001, 100].

[0021] (4) Design a control law algorithm based on the output of the extended state observer algorithm obtained in (3) and the set value of the actual industrial system as follows:

[0022]

[0023] Or

[0024]

[0025] 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, and k p is a calculation coefficient;

[0026] (5) Send the input 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, realize the adjustment of the control quantity of the actual industrial system, and thus realize the adjustment of the output quantity of the actual industrial system.

[0027] The second aspect of the present invention provides a first-order improved active disturbance rejection control system based on model assistance and Smith-like prediction, which is characterized in that it includes an actual industrial system to be controlled, a first controller for running the Smith-like prediction algorithm, a second controller for running the extended state observer algorithm based on model assistance, and a third controller for running the control law algorithm;

[0028] The actual industrial system, the first controller, the second controller, and the third controller are communicatively connected to each other to implement the first-order improved active disturbance rejection control method based on model assistance and Smith-like prediction.

[0029] The third aspect of the present invention provides a first-order improved active disturbance rejection control device, including:

[0030] A memory; and

[0031] A processor coupled to the memory, the processor being configured to execute the first-order improved active disturbance rejection control method based on model assistance and Smith-like prediction based on instructions stored in the memory.

[0032] 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 first-order improved active disturbance rejection control method based on model assistance and Smith-like prediction.

[0033] The features and beneficial effects of the present invention are as follows:

[0034] 1. The present invention proposes a first-order improved active disturbance rejection control method based on model assistance and Smith-like prediction, retaining the advantages of the existing improved active disturbance rejection control algorithm, such as simple structure and easy tuning;

[0035] 2. The designed 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;

[0036] 3. The designed control law algorithm can enhance the tracking ability and anti-interference ability, and achieve the disturbance-free tracking performance of the improved active disturbance rejection control based on model assistance and Smith predictor-like estimation;

[0037] 4. By combining partial model information of the known controlled object and the advantages of Smith predictor-like estimation, the present invention improves the extended state observer algorithm and the control law algorithm to achieve 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

[0038] Figure 1 It is the structural block diagram of the existing first-order improved active disturbance rejection control algorithm based on Smith predictor-like estimation.

[0039] Figure 2 It is the structural block diagram of the first-order improved active disturbance rejection control algorithm of the present invention.

[0040] Figure 3 It is the structural block diagram of another first-order improved active disturbance rejection control algorithm of the present invention.

[0041] 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 comparative method in Embodiment 2. Detailed Embodiments

[0042] The following further describes the technical solution of the present invention in detail through specific embodiments.

[0043] Embodiment 1

[0044] This embodiment proposes a first-order improved active disturbance rejection control method based on model assistance and Smith predictor-like estimation, including the following steps:

[0045] (1) Describe a class of controlled actual industrial systems using a high-order inertial system, and the mathematical expression is:

[0046]

[0047] 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, and n≥2; y(Γ-1) and u(Γ-1) respectively represent the output and input of the actual industrial system at the previous calculation step; the value range of K is [-10 5 , 0) and (0, 10 5 , and the value range of T is (0, 10 5 ;

[0048] (2) For a class of controlled actual industrial systems in (1), design a Smith predictor-like algorithm based on the input and output of the actual industrial system:

[0049] y p (Γ) = y1(Γ - 1) - y2(Γ - 1) + y(Γ - 1)

[0050] 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; G1(s) and G2(s) are the calculation expressions designed by the Smith predictor-like algorithm. k1 is the gain of G1(s), and T1 is the time constant of G1(s) and G2(s); generally, k1 = K and T1 = T.

[0051] (3) Design a model-based extended state observer algorithm based on the input of the actual industrial system in (1) and the output of the Smith predictor-like algorithm in (2):

[0052]

[0053] where 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; z2(Γ + 1) and z2(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; u(Γ) is the output of the actual industrial system at the current calculation step Γ.

[0054] β1, β2, and b0 are calculation coefficients:

[0055]

[0056]

[0057]

[0058] where ω o is the bandwidth of the model-based extended state observer algorithm and ω o ∈(0, 10 15 , and ξ is an adjustable parameter and ξ ∈(0, 10 15; The value range of h is [0.001, 100];

[0059] (4) Based on the output of the extended state observer algorithm obtained in (3) and the set value of the actual industrial system, the control law algorithm is designed as follows:

[0060] Figure 2 The control law algorithm shown in:

[0061]

[0062] Or Figure 3 The control law algorithm shown in:

[0063]

[0064] Wherein, u(Γ + 2) is the input of the actual industrial system for control rate calculation 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, and k p Is the calculation coefficient;

[0065] (5) Send the input 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, realize the adjustment of the control quantity of the actual industrial system, and thus realize the adjustment of the output quantity of the actual industrial system.

[0066] Embodiment 2

[0067] This embodiment takes the control of a certain actual industrial system as an example to illustrate the technical superiority of the method of this embodiment:

[0068] (1) Describe the controlled actual industrial system with a high-order inertial system, and the mathematical expression is:

[0069]

[0070] In this embodiment, K = 0.5, T = 10, and n = 2 for the actual industrial system;

[0071] (2) For the controlled actual industrial system in (1), design a Smith predictor-like algorithm based on the input and output of the actual industrial system:

[0072]

[0073]

[0074] In this embodiment, k1 = K and T1 = T;

[0075] (3) Design a model - assisted extended state observer algorithm for the input of the actual industrial system in (1) and the output of the Smith - like prediction algorithm in step (2):

[0076]

[0077] Among them, β1, β2, and b0 are calculation coefficients; generally, and In this embodiment, ω o = 0.5, ξ = 5, h = 0.1;

[0078] (4) Design a control law algorithm based on the output of the extended state observer algorithm obtained in (3) and the set value of the actual industrial system as Figure 3 shown, that is, as follows:

[0079]

[0080] In this embodiment, k p = 1.5;

[0081] (5) Send the input u(Γ + 2) of two calculation steps Γ + 2 of the actual industrial system obtained in (4) 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, and thus realize the adjustment of the output quantity of the actual industrial system.

[0082] Simulation comparison

[0083] Figure 4 is a comparison chart of the set value of the actual industrial object, the output value of the method in this embodiment, and the output value of the comparison method (Patent ZL202011125339.6) in this embodiment; among them, for the first - order active disturbance rejection control algorithm based on Smith - like prediction in the comparison method, its parameters are k1 = K, T1 = T, β1 = 1, β2 = 0.25, b0 = 0.25, and k p = 1.5; The thin solid line, the dotted line, and the thick solid line are respectively the set value of the actual industrial system, the output value of the comparison method, and the output value of the method in this embodiment.

[0084] 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 100 s, a disturbance of the control quantity is applied to the closed - loop circuit, changing from 0 to 1 until the simulation ends at 200 s. It can be seen from the simulation that the method in this embodiment makes full use of the known model information and has relatively fast tracking ability and strong anti - interference ability.

[0085] Embodiment 3

[0086] This embodiment provides a first-order improved active disturbance rejection control system based on model assistance and Smith predictor-like estimation, including an actual industrial system to be controlled, a first controller for running the Smith predictor-like estimation algorithm, a second controller for running the extended state observation algorithm based on model assistance, and a third controller for running the control law algorithm;

[0087] The actual industrial system, the first controller, the second controller, and the third controller are communicatively connected to each other to implement the first-order improved active disturbance rejection control method described in Embodiment 1.

[0088] Embodiment 4

[0089] This embodiment provides a first-order improved active disturbance rejection control device, including:

[0090] A memory; and

[0091] A processor coupled to the memory, the processor being configured to execute the first-order improved active disturbance rejection control method described in Embodiment 1 based on instructions stored in the memory.

[0092] The device of this embodiment may further include an input / output interface, a network interface, a storage interface, etc. These interfaces and the memory and the processor may be connected, for example, through a bus. Among them, the input / output interface provides a connection interface for input / output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface provides a connection interface for various networking devices. The storage interface provides a connection interface for external storage devices such as an SD card and a USB flash drive.

[0093] Embodiment 5

[0094] This embodiment 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 first-order improved active disturbance rejection control method described in Embodiment 1.

[0095] 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 memories, CD-ROMs, optical memories, etc.) containing computer program code.

[0096] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can 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, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more flows and / or one or more blocks in the flowcharts and / or block diagrams. Figure 1 in one or more flows and / or one or more blocks Figure 1 in the block or blocks.

[0097] 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, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more flows and / or one or more blocks in the flowcharts and / or block diagrams. Figure 1 in one or more flows and / or one or more blocks Figure 1 in the block or blocks.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or one or more blocks in the flowcharts and / or block diagrams. Figure 1 in one or more flows and / or one or more blocks Figure 1 in the block or blocks.

[0099] 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 it is still possible to modify the specific implementation manners of the present invention or perform equivalent replacements for some technical features; without departing from the spirit of the technical solutions of the present invention, they should all be covered by the scope of the technical solutions claimed in the present invention.

Claims

1. A first-order 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: 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, and n≥2; y(Γ-1) and u(Γ-1) respectively represent the output and input of the actual industrial system at the previous calculation step; the value range of K is [-10 5 , 0) and (0, 10 5 , and the value range of T is (0, 10 5 ; (2) For a class of controlled actual industrial systems in (1), design a Smith predictor-like algorithm based on the input and output 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. k1 is the gain of G1(s), and T1 is the time constant of G1(s) and G2(s). (3) Design a model-assisted extended state observer algorithm for the input of the actual industrial system in (1) and the output of the Smith predictor-like algorithm in (2): where 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; z2(Γ + 1) and z2(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; u(Γ) is the output of the actual industrial system at the current calculation step Γ; β1, β2, and b0 are calculation coefficients: Among them, ω o is the bandwidth of the model-assisted extended state observer algorithm and ω o ∈(0, 10 15 , ξ is an adjustable parameter and ξ ∈(0, 10 15 ; the value range of h is [0.001, 100]; (4) Design a control law algorithm based on the output of the extended state observer algorithm obtained in (3) and the set value of the actual industrial system as follows: Or 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, and k p is the calculation coefficient; (5) Send the input 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 of the actual industrial system.

2. A first-order improved active disturbance rejection control system based on model assistance and Smith predictor-like, characterized in that, It includes an actual industrial system to be controlled, a first controller for running the Smith predictor-like algorithm, a second controller for running the model-assisted extended state observer algorithm, and a third controller for running the 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 first-order improved active disturbance rejection control method based on model assistance and Smith predictor-like described in claim 1.

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

Images