Compact Model-free Adaptive Disturbance Compensation Control Method for Measurable Disturbance

Abstract

The present invention discloses a compact model-free adaptive disturbance compensation control method for measurable disturbances. The method establishes a dynamic linearized data model of a controlled object under the action of a measurable disturbance; constructs and solves a cost function, optimizes and updates a pseudo-Jacobi input matrix and a pseudo-Jacobi disturbance matrix; designs a compact model-free adaptive disturbance compensation control scheme for measurable disturbances; constructs and solves an energy function, optimizes and updates a compact adaptive input matrix and a compact adaptive disturbance matrix; and uses the control scheme of the present invention to control the controlled object under the action of a measurable disturbance. The control method of the present invention can significantly weaken the influence of measurable disturbances on the actual value of the output of the controlled object system, realizes effective tracking of the expected value trajectory, and significantly improves the disturbance compensation control performance.

Description

Technical Field

[0001] The invention belongs to the field of automatic control, and in particular relates to a compact format model-free adaptive disturbance compensation control method for measurable disturbances. Background Art

[0002] Disturbance is widely present in actual control systems, such as most controlled objects in industries such as oil refining, petrochemicals, chemicals, pharmaceuticals, food, papermaking, water treatment, thermal power, metallurgy, cement, rubber, machinery, electrical, transportation, and robotics, including reactors, distillation towers, machines, equipment, devices, production lines, workshops, factories, unmanned vehicles, unmanned ships, drones, autonomous mobile robots, etc. In fact, the presence of disturbances usually reduces the control performance of the system, and in severe cases may cause instability of the entire system, thereby affecting system safety.

[0003] The existing compact model-free adaptive control method was first proposed by Hou Zhongsheng and Jin Shangtai in their co-authored book Model-free Adaptive Control - Theory and Application (Science Press, 2013, page 92). On this basis, inventions CN107991866A and CN107991865A proposed a decoupling method based on SISO, which solved the control problem of strongly coupled multi-input multi-output systems; inventions CN108132600A and CN108345213A proposed a parameter self-tuning method based on neural network, which solved the problem of time-consuming and labor-intensive parameter selection; invention CN109782588A proposed a heterogeneous factor control method, which solved the control problem of different control channel characteristics of strongly nonlinear multi-input multi-output systems; invention CN111522235A expanded invention CN109782588A and proposed a heterogeneous factor control method with parameter self-tuning, which further solved the problem of time-consuming and labor-intensive tuning of heterogeneous factor parameters. It should be pointed out that the above-mentioned invention methods have not yet considered the control difficulties of the controlled objects under disturbance.

[0004] For a multi-input multi-output controlled object under measurable disturbance, how to efficiently use the input and output data measured in real time by the controlled object, analyze and design a disturbance compensation control method without relying on any mathematical model information, and the designed control method can weaken the influence of the measurable disturbance on the actual value of the controlled object system output, and effectively track the expected value of the system output, which has important industrial application value. To achieve the above goals, the present invention proposes a compact format model-free adaptive disturbance compensation control method for measurable disturbances. Summary of the invention

[0005] In order to solve the problems existing in the background technology, the purpose of the present invention is to provide a compact model-free adaptive disturbance compensation control method for measurable disturbances, wherein the control method runs on a hardware platform to control a controlled object under the action of a measurable disturbance, wherein the controlled object is a multi-input multi-output system including multiple control inputs and multiple system outputs, and the control method is characterized in that it includes the following steps:

[0006] Step (1): obtaining a measurable disturbance at k sampling moments, and establishing a dynamic linearized data model of a controlled object under the action of the measurable disturbance, wherein the dynamic linearized data model of the controlled object comprises a pseudo-Jacobi input matrix θ(k) and a pseudo-Jacobi disturbance matrix χ(k);

[0007] Step (2): constructing a cost function and solving the cost function using a function extremum method, optimizing and updating the pseudo-Jacobian input matrix θ(k) and the pseudo-Jacobian perturbation matrix χ(k) in step (1);

[0008] Step (3): using the measurable disturbance at the k sampling time, based on the dynamic linearized data model of the controlled object after optimizing the pseudo-Jacobi input matrix θ(k) and the pseudo-Jacobi disturbance matrix χ(k) in step (2), a compact format model-free adaptive disturbance compensation control scheme for the measurable disturbance is designed, wherein the control scheme includes a compact format adaptive input matrix π c (k) and the compact adaptive perturbation matrix ω c (k);

[0009] Step (4): Construct an energy function and use the momentum gradient descent method to solve the energy function, optimize and update the compact adaptive input matrix π in step (3) c (k) and the compact adaptive perturbation matrix ω c (k);

[0010] Step (5): Optimize the compact adaptive input matrix π using step (4) c (k) and the compact adaptive perturbation matrix ω c The control scheme after (k) controls the controlled object under the action of measurable disturbance, weakens the influence of measurable disturbance on the actual value of the controlled object system output, and realizes effective tracking of the expected value of the system output.

[0011] Furthermore, the measurable disturbance at k sampling moments is obtained in step (1), and the dynamic linearized data model of the controlled object under the measurable disturbance is established as follows:

[0012] Δy(k+1)=θ(k)Δu(k)+χ(k)Δd(k)

[0013] Where k is the sampling time, k is a positive integer; y(k+1) is the actual value vector of the system output of the controlled object at the k+1 sampling time, y(k+1)=[y 1 (k+1),…,y n (k+1)] T , Δy(k+1)=y(k+1)-y(k); n is the total number of system outputs of the controlled object, and n is an integer greater than 1; u(k) is the control input vector of the controlled object at sampling time k, u(k)=[u 1 (k),…,u m (k)] T , Δu(k)=u(k)-u(k-1); m is the total number of control inputs of the controlled object, and m is an integer greater than 1; d(k) is the measurable disturbance vector of the controlled object at sampling time k, d(k)=[d 1 (k),…,d q (k)] T , Δd(k)=d(k)-d(k-1); q is the total number of measurable disturbances to the controlled object, q is a positive integer; θ(k) is the pseudo-Jacobian input matrix at sampling time k, and χ(k) is the pseudo-Jacobian disturbance matrix χ(k) at sampling time k.

[0014] The step (2) of constructing a cost function and solving the cost function using a function extremum method, and optimizing and updating the pseudo-Jacobi input matrix θ(k) and the pseudo-Jacobi perturbation matrix χ(k) in step (1), mainly comprises the following steps:

[0015] Step (2.1): Input matrix θ(k) to the pseudo-Jacobi and construct the cost function

[0016] J(θ(k))=||Δy(k)-θ(k)Δu(k-1)-χ(k-1)Δd(k-1)|| 2 +μ 1 ||Δθ(k)|| 2

[0017] Among them, μ 1 is the first weight factor;

[0018] Step (2.2): Construct a cost function for the pseudo-Jacobi perturbation matrix χ(k)

[0019] J(χ(k))=||Δy(k)-θ(k-1)Δu(k-1)-χ(k)Δd(k-1)|| 2 +μ 2 ||Δχ(k)|| 2

[0020] Among them, μ2 is the second weight factor;

[0021] Step (2.3): Use the function extremum method to solve the cost function described in step (2.1), and optimize and update the pseudo-Jacobi input matrix θ(k).

[0022]

[0023] Among them, α 1 is the first step size factor;

[0024] Step (2.4): Use the function extremum method to solve the cost function described in step (2.2), and optimize and update the pseudo-Jacobi perturbation matrix χ(k).

[0025]

[0026] Among them, α 2 is the second step size factor.

[0027] The measurable disturbance at the k sampling time described in step (3) is used, based on the dynamic linearized data model of the controlled object after optimizing the pseudo-Jacobi input matrix θ(k) and the pseudo-Jacobi disturbance matrix χ(k) in step (2), to design a compact model-free adaptive disturbance compensation control scheme for the measurable disturbance:

[0028] u(k)=u(k-1)-π c (k)e(k)+ω c (k)Δd(k)

[0029] Wherein, e(k) is the system error vector of the controlled object at sampling time k, e(k)=y * (k)-y(k),e(k)=[e 1 (k),…,e n (k)] T , Δe(k)=e(k)-e(k-1);π c (k) is the compact format adaptive input matrix at sampling time k, ω c (k) is the compact format adaptive perturbation matrix at sampling time k.

[0030] The energy function described in step (4) is constructed and the momentum gradient descent method is used to solve the energy function, and the compact format adaptive input matrix π described in step (3) is optimized and updated. c (k) and the compact adaptive perturbation matrix ω c (k), mainly including the following steps:

[0031] Step (4.1): Construct energy function

[0032]

[0033] Among them, y * (k+1) is the system output expected value vector of the controlled object at the k+1 sampling time, λ is the penalty factor;

[0034] Step (4.2): Use the momentum gradient descent method to solve the energy function described in step (4.1) and optimize and update the compact adaptive input matrix π c (k)

[0035]

[0036] Among them, σ 1 is the first learning rate, η 1 is the first momentum factor; Δπ c (k-1)=π c (k-1)-π c (k-2); is the energy function W versus π c (k-1) partial derivative;

[0037] Step (4.3): Use the momentum gradient descent method to solve the energy function described in step (4.1) and optimize and update the compact adaptive perturbation matrix ω c (k)

[0038]

[0039] Among them, σ 2 is the second learning rate, η 2 is the second momentum factor; Δω c (k-1)=ω c (k-1)-ω c (k-2); is the energy function W for ω c (k-1) partial derivative.

[0040] The energy function W described in step (4.2) is c The formula for calculating the partial derivative of (k-1) is:

[0041]

[0042] The energy function W described in step (4.3) is c The formula for calculating the partial derivative of (k-1) is:

[0043]

[0044] Said The mathematical formula for this is:

[0045] The step (5) described in step (4) is to optimize the compact format adaptive input matrix π c (k) and the compact adaptive perturbation matrix ω c The control scheme after (k) controls the controlled object under the action of the measurable disturbance, and at each sampling time k includes the following steps:

[0046] Step (5.1): Obtain the measurable disturbance vector d(k) at the current sampling time;

[0047] Step (5.2): Get the expected value vector y of the system output at the current sampling time * (k), the system output actual value vector y(k), and the system error vector e(k) at the current sampling moment is calculated;

[0048] Step (5.3): Based on steps (5.1) and (5.2), use step (4) to optimize the compact adaptive input matrix π c (k) and the compact adaptive perturbation matrix ω c (k) The control scheme after calculation obtains the control input vector u(k) at the current sampling time;

[0049] Step (5.4): After the control input vector acts on the controlled object, the actual value vector of the system output of the controlled object at the next sampling time is obtained.

[0050] Furthermore, the present invention adopts the following technical solutions:

[0051] A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the above-mentioned compact-format model-free adaptive disturbance compensation control method for measurable disturbances is implemented.

[0052] Furthermore, the present invention adopts the following technical solutions:

[0053] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned compact format model-free adaptive disturbance compensation control method for measurable disturbances when executing the program.

[0054] Based on the existing theoretical foundation of compact model-free adaptive control methods, some inventive methods have made progress in solving the problems of strong coupling of controlled objects, different channel characteristics, and time-consuming and laborious parameter setting. However, these inventive methods have not yet considered the control problem of controlled objects under disturbance, which restricts their promotion and application. For multi-input and multi-output controlled objects under measurable disturbance, the present invention can efficiently use the input and output data measured in real time by the controlled objects, and does not rely on any mathematical model information to analyze and design disturbance compensation control methods. Moreover, the designed control method can weaken the influence of measurable disturbance on the actual value of the controlled object system output, and realize the effective tracking of the expected value of the system output, which has important industrial application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is the algorithm principle block diagram of the present invention;

[0056] Figure 2 It is a block diagram of the engineering application system of the present invention;

[0057] Figure 3 A schematic diagram of a hardware platform for running the present invention;

[0058] Figure 4 A control effect diagram of the output of the first system when a two-input and two-output system adopts the compact format model-free adaptive disturbance compensation control method for measurable disturbances proposed by the present invention and a comparative control method;

[0059] Figure 5 A control effect diagram of the output of the second system when a two-input and two-output system adopts the compact format model-free adaptive disturbance compensation control method for measurable disturbances proposed by the present invention and a comparative control method;

[0060] Figure 6 The first control input curve when the compact format model-free adaptive disturbance compensation control method and the comparative control method for measurable disturbances proposed by the present invention are adopted for a two-input and two-output system;

[0061] Figure 7 The second control input curve when the compact format model-free adaptive disturbance compensation control method and the comparative control method for measurable disturbances proposed by the present invention are adopted for a two-input and two-output system;

[0062] Figure 8 It is a refrigeration cycle flow chart of a vapor compression refrigeration system;

[0063] Fig. 9 are the two measurable disturbance curves of the vapor compression refrigeration system;

[0064] Fig.10A control effect diagram of the first system output when the steam compression refrigeration system adopts the compact format model-free adaptive disturbance compensation control method for measurable disturbances and the comparative control method proposed by the present invention;

[0065] Fig.11 A control effect diagram of the second system output when the steam compression refrigeration system adopts the compact format model-free adaptive disturbance compensation control method for measurable disturbances and the comparative control method proposed by the present invention;

[0066] Fig.12 The first control input curve when the compact format model-free adaptive disturbance compensation control method and the comparative control method for measurable disturbances proposed by the present invention are used for a vapor compression refrigeration system;

[0067] Fig.13 The second control input curve when the compact model-free adaptive disturbance compensation control method and the comparative control method for measurable disturbances proposed by the present invention are adopted for a vapor compression refrigeration system. DETAILED DESCRIPTION

[0068] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0069] Figure 1 The algorithm principle block diagram of the present invention is given. The present invention discloses a compact model-free adaptive disturbance compensation control method for measurable disturbances. The method establishes a dynamic linearized data model of the controlled object under the action of measurable disturbances, and the model includes a pseudo-Jacobi input matrix and a pseudo-Jacobi disturbance matrix; constructs and solves a cost function, optimizes and updates the pseudo-Jacobi input matrix and the pseudo-Jacobi disturbance matrix; designs a compact model-free adaptive disturbance compensation control scheme for measurable disturbances, and the scheme includes a compact adaptive input matrix and a compact adaptive disturbance matrix; constructs and solves an energy function, optimizes and updates the compact adaptive input matrix and the compact adaptive disturbance matrix; and uses the control scheme of the present invention to control the controlled object under the action of measurable disturbances. Below, the implementation steps of the compact model-free adaptive disturbance compensation control method for measurable disturbances provided by the present invention are further described as follows:

[0070] The control method is run on a hardware platform to control a controlled object under a measurable disturbance, wherein the controlled object is a multi-input multi-output system including multiple control inputs and multiple system outputs, and the control method is characterized in that it comprises the following steps:

[0071] Step (1): obtaining a measurable disturbance at k sampling moments, and establishing a dynamic linearized data model of a controlled object under the action of the measurable disturbance, wherein the dynamic linearized data model of the controlled object comprises a pseudo-Jacobi input matrix θ(k) and a pseudo-Jacobi disturbance matrix χ(k);

[0072] Step (2): constructing a cost function and solving the cost function using a function extremum method, optimizing and updating the pseudo-Jacobian input matrix θ(k) and the pseudo-Jacobian perturbation matrix χ(k) in step (1);

[0073] Step (3): using the measurable disturbance at the k sampling time, based on the dynamic linearized data model of the controlled object after optimizing the pseudo-Jacobi input matrix θ(k) and the pseudo-Jacobi disturbance matrix χ(k) in step (2), a compact format model-free adaptive disturbance compensation control scheme for the measurable disturbance is designed, wherein the control scheme includes a compact format adaptive input matrix π c (k) and the compact adaptive perturbation matrix ω c (k);

[0074] Step (4): Construct an energy function and use the momentum gradient descent method to solve the energy function, optimize and update the compact adaptive input matrix π in step (3) c (k) and the compact adaptive perturbation matrix ω c (k);

[0075] Step (5): Optimize the compact adaptive input matrix π using step (4) c (k) and the compact adaptive perturbation matrix ω c The control scheme after (k) controls the controlled object under the action of measurable disturbance, weakens the influence of measurable disturbance on the actual value of the controlled object system output, and realizes effective tracking of the expected value of the system output.

[0076] Furthermore, the measurable disturbance at k sampling moments is obtained in step (1), and the dynamic linearized data model of the controlled object under the measurable disturbance is established as follows:

[0077] Δy(k+1)=θ(k)Δu(k)+χ(k)Δd(k)

[0078] Where k is the sampling time, k is a positive integer; y(k+1) is the actual value vector of the system output of the controlled object at the k+1 sampling time, y(k+1)=[y 1 (k+1),…,y n (k+1)] T , Δy(k+1)=y(k+1)-y(k); n is the total number of system outputs of the controlled object, and n is an integer greater than 1; u(k) is the control input vector of the controlled object at sampling time k, u(k)=[u 1 (k),…,u m (k)] T, Δu(k)=u(k)-u(k-1); m is the total number of control inputs of the controlled object, and m is an integer greater than 1; d(k) is the measurable disturbance vector of the controlled object at sampling time k, d(k)=[d 1 (k),…,d q (k)] T , Δd(k)=d(k)-d(k-1); q is the total number of measurable disturbances to the controlled object, q is a positive integer; θ(k) is the pseudo-Jacobian input matrix at sampling time k, and χ(k) is the pseudo-Jacobian disturbance matrix χ(k) at sampling time k.

[0079] The step (2) of constructing a cost function and solving the cost function using a function extremum method, and optimizing and updating the pseudo-Jacobi input matrix θ(k) and the pseudo-Jacobi perturbation matrix χ(k) in step (1), mainly comprises the following steps:

[0080] Step (2.1): Input matrix θ(k) to the pseudo-Jacobi and construct the cost function

[0081] J(θ(k))=||Δy(k)-θ(k)Δu(k-1)-χ(k-1)Δd(k-1)|| 2 +μ 1 ||Δθ(k)|| 2

[0082] Among them, μ 1 is the first weight factor;

[0083] Step (2.2): Construct a cost function for the pseudo-Jacobi perturbation matrix χ(k)

[0084] J(χ(k))=||Δy(k)-θ(k-1)Δu(k-1)-χ(k)Δd(k-1)|| 2 +μ 2 ||Δχ(k)|| 2

[0085] Among them, μ 2 is the second weight factor;

[0086] Step (2.3): Use the function extremum method to solve the cost function described in step (2.1), and optimize and update the pseudo-Jacobi input matrix θ(k).

[0087]

[0088] Among them, α 1 is the first step size factor;

[0089] Step (2.4): Use the function extremum method to solve the cost function described in step (2.2), and optimize and update the pseudo-Jacobi perturbation matrix χ(k).

[0090]

[0091] Among them, α 2 is the second step size factor.

[0092] The measurable disturbance at the k sampling time described in step (3) is used, based on the dynamic linearized data model of the controlled object after optimizing the pseudo-Jacobi input matrix θ(k) and the pseudo-Jacobi disturbance matrix χ(k) in step (2), to design a compact model-free adaptive disturbance compensation control scheme for the measurable disturbance:

[0093] u(k)=u(k-1)-π c (k)e(k)+ω c (k)Δd(k)

[0094] Wherein, e(k) is the system error vector of the controlled object at sampling time k, e(k)=y*(k)-y(k), e(k)=[e 1 (k),…,e n (k)] T , Δe(k)=e(k)-e(k-1);π c (k) is the compact format adaptive input matrix at sampling time k, ω c (k) is the compact format adaptive perturbation matrix at sampling time k.

[0095] The energy function described in step (4) is constructed and the momentum gradient descent method is used to solve the energy function, and the compact format adaptive input matrix π described in step (3) is optimized and updated. c (k) and the compact adaptive perturbation matrix ω c (k), mainly including the following steps:

[0096] Step (4.1): Construct energy function

[0097]

[0098] Among them, y * (k+1) is the system output expected value vector of the controlled object at the k+1 sampling time, λ is the penalty factor;

[0099] Step (4.2): Use the momentum gradient descent method to solve the energy function described in step (4.1) and optimize and update the compact adaptive input matrix π c (k)

[0100]

[0101] Among them, σ 1 is the first learning rate, η 1 is the first momentum factor; Δπ c (k-1)=π c (k-1)-π c (k-2); is the energy function W versus π c (k-1) partial derivative;

[0102] Step (4.3): Use the momentum gradient descent method to solve the energy function described in step (4.1) and optimize and update the compact adaptive perturbation matrix ω c (k)

[0103]

[0104] Among them, σ 2 is the second learning rate, η 2 is the second momentum factor; Δω c (k-1)=ω c (k-1)-ω c (k-2); is the energy function W for ω c (k-1) partial derivative.

[0105] The energy function W described in step (4.2) is c The formula for calculating the partial derivative of (k-1) is:

[0106]

[0107] The energy function W described in step (4.3) is c The formula for calculating the partial derivative of (k-1) is:

[0108]

[0109] Said The mathematical formula for this is:

[0110] The step (5) described in step (4) is to optimize the compact format adaptive input matrix π c (k) and the compact adaptive perturbation matrix ω c The control scheme after (k) controls the controlled object under the action of the measurable disturbance, and at each sampling time k includes the following steps:

[0111] Step (5.1): Obtain the measurable disturbance vector d(k) at the current sampling time;

[0112] Step (5.2): Get the expected value vector y of the system output at the current sampling time * (k), the system output actual value vector y(k), and the system error vector e(k) at the current sampling moment is calculated;

[0113] Step (5.3): Based on steps (5.1) and (5.2), use step (4) to optimize the compact adaptive input matrix π c (k) and the compact adaptive perturbation matrix ω c (k) The control scheme after calculation obtains the control input vector u(k) at the current sampling time;

[0114] Step (5.4): After the control input vector acts on the controlled object, the actual value vector of the system output of the controlled object at the next sampling time is obtained.

[0115] Figure 2 The engineering application system block diagram of the present invention is shown in FIG. Figure 2 The hardware platform in the engineering application system block diagram, Figure 3 A schematic diagram of the hardware platform for running the present invention is given in the figure. Specifically, the present invention adopts a non-transitory computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the above-mentioned compact format model-free adaptive disturbance compensation control method for measurable disturbances is implemented; the present invention adopts an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the program, the above-mentioned compact format model-free adaptive disturbance compensation control method for measurable disturbances is implemented.

[0116] The following are two specific embodiments of the present invention. Specific embodiment 1:

[0118] The controlled object adopts a two-input and two-output nonlinear system:

[0119]

[0120]

[0121]

[0122]

[0123] y 1 (k+1)=x 11 (k+1)

[0124] y 2 (k+1)=x21 (k+1)

[0125] Among them, a(k)=1+0.1sin(2πk / 1500), b(k)=1+0.1cos(2πk / 1500) are two time-varying parameters; d 1 (k) = 0.15 sin (k / 10), d 2 (k) = 0.15sin(k / 10) is a measurable disturbance. Therefore, the controlled object two-input two-output nonlinear system is a two-input two-output nonlinear system under the action of a measurable disturbance.

[0126] The expected value trajectory of the system output y * (k) are as follows:

[0127]

[0128]

[0129] In specific embodiment 1, m=n=q=2.

[0130] In order to more clearly compare the control performance of the control method of the present invention and the comparative control method, the time-weighted integral absolute error (ITAE) is used as the control performance evaluation index:

[0131]

[0132] in, is the expected value of the j-th system output at sampling time k, y j (k) is the actual value of the j-th system output at the k-th sampling time, j = 1,…,n. ITAE(e j ) is smaller, indicating that the jth system outputs the actual value y j (k) and the expected value of the j-th system output The error is generally smaller, the control accuracy and speed are higher, and the control performance is better.

[0133] The hardware platform for running the control method of the present invention adopts an industrial control computer.

[0134] The control method of the present invention is used to control a two-input two-output system. The control method parameters of the present invention are set as: θ(1) = [0.6, -0.05; 0.1, 0.6], χ(1) = [0.1, 0; 0, 0.1], π c (1)=[-0.35,0;0,-0.27],ω c (1) = [-0.1, 0; 0, -0.8], α 1 =0.5,α2 =0.5, μ 1 =1,μ 2 =0.9,σ 1 =0.7,σ 2 =0.9,η 1 =0.3,η 2 =0.5,λ=2.

[0135] When the control method of the present invention is used to control a two-input and two-output system under a measurable disturbance, the following steps are included at each sampling time k: a) obtaining the measurable disturbance vector d(k) at the current sampling time; b) obtaining the system output expected value vector y at the current sampling time * (k), the system output actual value vector y(k), and the system error vector e(k) at the current sampling moment is calculated; c) based on steps a) and b), the step (4) is used to optimize the compact format adaptive input matrix π c (k) and the compact adaptive perturbation matrix ω c (k) The control scheme after calculation obtains the control input vector u(k) at the current sampling moment; d) After the control input vector acts on the two-input two-output system, the system output actual value vector of the two-input two-output system at the next sampling moment is obtained; e) Repeat a) to d) until the sampling moment ends.

[0136] The control effects of the control method of the present invention and the existing PID control method (comparison control method) are compared as follows: Figure 4 The control effect diagram of the first system output when the control method of the present invention and the comparative control method are adopted is shown in FIG. Figure 5 The control effect diagram of the second system output when the control method of the present invention and the comparative control method are adopted is shown in FIG. Figure 6 is the first control input curve when the control method of the present invention and the comparative control method are used, Figure 7 is the second control input curve when the control method of the present invention and the comparative control method are used; from the control performance evaluation index, the ITAE (e 1 ) is 15774, and the ITAE (e 2 ) is 10716, and the ITAE (e 1 ) is 24067, and the ITAE (e 2) is 25998, and the control performance evaluation index results are listed in Table 1; from the system output curve, the control method of the present invention can effectively suppress the influence of the measurable disturbance on the actual output value of the two-input and two-output system, and the control performance of the control method of the present invention is better than the control performance of the comparative control method. Based on the above investigation, it is fully demonstrated that the compact format model-free adaptive disturbance compensation control method for measurable disturbances provided by the present invention can significantly weaken the influence of the measurable disturbance on the actual output value of the controlled object system, realize the effective tracking of the expected value trajectory, and significantly improve the disturbance compensation control performance.

[0137] Table 1 Comparison of control performance of two-input and two-output systems

[0138] Specific embodiment 2:

[0140] Vapor Compression Refrigeration Systems (VCRS) are the most common refrigeration cycle equipment, which are widely used in households (such as household refrigerators, air conditioners), businesses (such as building and car air conditioners, refrigerated warehouses) and industries (such as petrochemical plants, natural gas processing plants). Figure 8 As shown. The two disturbances in the refrigeration cycle are the inlet temperature of the cooling medium and the inlet temperature of the cooled medium. Today, when high-energy-consuming refrigeration equipment is widely used, realizing disturbance compensation control of the steam compression refrigeration system is of great significance to the promotion of energy conservation and consumption reduction in my country and even the world.

[0141] The controlled object vapor compression refrigeration system is a two-input and two-output nonlinear system. The two control inputs u 1 ,u 2 are compressor frequency (Hz) and valve opening (%), respectively. The two system outputs y of the controlled object vapor compression refrigeration system are 1 ,y 2 are the superheat (℃) and the outlet temperature of the cooled medium (℃), and the two disturbances d 1 ,d 2 are the inlet temperature of the cooling medium (°C) and the inlet temperature of the cooled medium (°C), d 1 With d 2 The online measurements are respectively carried out through the corresponding temperature sensors, which are measurable disturbances. Fig. 9 are two measurable disturbance curves of the vapor compression refrigeration system. Therefore, the controlled object vapor compression refrigeration system is a two-input two-output nonlinear system under the action of measurable disturbance. In specific embodiment 2, m=n=q=2. The hardware platform for running the control method of the present invention adopts an industrial control computer.

[0142] The initial operating condition of the controlled object vapor compression refrigeration system is: 1 (0)=36.45Hz,u 2 (0) = 48.79%, y 1 (0) = 14.65 °C, y 2 (0) = -22.15°C. To meet the cooling demand of the cooled medium, the system outputs the expected value trajectory At the 2nd minute, the temperature was adjusted from 14.65℃ to 7.2℃ in steps, at the 9th minute, it was adjusted from 7.2℃ to 22.2℃ in steps, and finally at the 16th minute, it was adjusted from 22.2℃ to 11.65℃ in steps. The system outputs the expected value trajectory. At the 2nd minute, the temperature was adjusted stepwise from -22.15°C to -22.65°C.

[0143] The control method of the present invention is used to control the vapor compression refrigeration system. The control method parameters of the present invention are set as follows: θ(1) = [2, 0; 0, 0.1], χ(1) = [0.2, 0; 0, 0.2], π c (1) = [-1, 0; 0, -1], ω c (1) = [-1.2, 0; 0, -0.05], α 1 =0.5,α 2 =0.5, μ 1 =1,μ 2 =1,σ 1 =0.5,σ 2 =0.9,η 1 =0.2,η 2 =0.2, λ=0.1.

[0144] When the control method of the present invention is used to control a vapor compression refrigeration system under a measurable disturbance, the following steps are included at each sampling time k: a) obtaining a measurable disturbance vector d(k) at the current sampling time; b) obtaining a system output expected value vector y at the current sampling time * (k), the system output actual value vector y(k), and the system error vector e(k) at the current sampling moment is calculated; c) based on steps a) and b), the step (4) is used to optimize the compact format adaptive input matrix π c (k) and the compact adaptive perturbation matrix ω c (k) The control scheme after calculating the control input vector u(k) at the current sampling moment; d) After the control input vector acts on the steam compression refrigeration system, the system output actual value vector of the steam compression refrigeration system at the next sampling moment is obtained; e) repeat a) to d) until the sampling moment ends.

[0145] The control effects of the control method of the present invention and the existing PID control method (comparison control method) are compared as follows: Fig.10 The control effect diagram of the first system output when the control method of the present invention and the comparative control method are adopted is shown in FIG. Fig.11 The control effect diagram of the second system output when the control method of the present invention and the comparative control method are adopted is shown in FIG. Fig.12 is the first control input curve when the control method of the present invention and the comparative control method are used, Fig.13 is the second control input curve when the control method of the present invention and the comparative control method are used; from the control performance evaluation index, the ITAE (e 1 ) is 142904, and the ITAE (e 2 ) is 5898, and the ITAE (e 1 ) is 471529, and the ITAE (e 2 ) is 52531, and the control performance evaluation index results are listed in Table 2; from the system output curve, the control method of the present invention can effectively suppress the influence of the measurable disturbance on the actual value of the steam compression refrigeration system output, and the control performance of the control method of the present invention is better than the control performance of the comparative control method. Based on the above investigation, it is fully demonstrated that the compact model-free adaptive disturbance compensation control method for measurable disturbances provided by the present invention can significantly weaken the influence of the measurable disturbance on the actual value of the controlled object system output, realize the effective tracking of the expected value trajectory, and significantly improve the disturbance compensation control performance.

[0146] Table 2 Comparison of control performance of vapor compression refrigeration system

[0147]

[0148] Furthermore, the following two points should be particularly noted:

[0149] (1) Disturbances are widely present in actual control systems, such as most controlled objects in industries such as oil refining, petrochemicals, chemicals, pharmaceuticals, food, papermaking, water treatment, thermal power, metallurgy, cement, rubber, machinery, electrical, transportation, and robotics, including reactors, distillation towers, machines, equipment, devices, production lines, workshops, factories, unmanned vehicles, unmanned ships, drones, autonomous mobile robots, etc. For example, a steam compression refrigeration system will be affected by the continuous and complex influence of two measurable disturbances, the inlet temperature of the cooling medium and the inlet temperature of the cooled medium. Specific Example 2 shows that the control method of the present invention can significantly weaken the influence of the measurable disturbance on the actual value of the output of the controlled object system, and achieve effective tracking of the expected value trajectory, thereby significantly improving the disturbance compensation control performance. For another example, an unmanned boat is extremely susceptible to the influence of the wind field on the water surface during operation. Changes in wind speed and direction will not only affect the speed and heading of the unmanned boat, but may also cause the unmanned boat to capsize in severe cases. Online monitoring of the two measurable disturbances of wind speed and wind direction can be implemented based on wind speed sensors and wind direction sensors. The control method of the present invention can be used to compensate for the measurable disturbance and achieve smooth operation of the unmanned boat, which is of great significance to improving the safety and reliability of the unmanned boat.

[0150] (2) In the above-mentioned specific embodiments 1 and 2, the hardware platform for running the control method of the present invention is an industrial control computer; in actual application, according to the specific situation, any one or any combination of a single-chip microcomputer controller, a microprocessor controller, a field programmable gate array controller, a digital signal processing controller, an embedded system controller, a programmable logic controller, a distributed control system, a field bus control system, an industrial Internet of Things control system, and an industrial Internet control system can be selected as the hardware platform for running the control method of the present invention.

[0151] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that the implementation of the present invention can be implemented by means of software plus the necessary hardware platform. The embodiments of the present invention can be implemented using an existing processor, or by a dedicated processor used for this purpose or other purposes for an appropriate system, or by a hard-wired system. The embodiments of the present invention also include a non-transitory computer-readable storage medium, which includes a machine-readable medium for carrying or having a machine-executable instruction or data structure stored thereon; such a machine-readable medium can be any available medium that can be accessed by a general-purpose or special-purpose computer or other machine with a processor. For example, such a machine-readable medium can include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the required program code in the form of machine-executable instructions or data structures, and can be accessed by a general-purpose or special-purpose computer or other machine with a processor. When information is transmitted or provided to a machine via a network or other communication connection (hard-wired, or wireless, or a combination of hard-wired and wireless), the connection is also considered a machine-readable medium.

[0152] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A compact model-free adaptive disturbance compensation control method for measurable disturbances, wherein the control method runs on a hardware platform to control a controlled object under the action of a measurable disturbance, wherein the controlled object is a multi-input multi-output system including multiple control inputs and multiple system outputs, and the control method is characterized in that: The following steps are involved: Step (1): Obtain a measurable disturbance at k sampling time, and establish a dynamic linearized data model of the controlled object under the action of the measurable disturbance, wherein the dynamic linearized data model of the controlled object includes a pseudo-Jacobi input matrix θ(k) and a pseudo-Jacobi disturbance matrix χ(k), and the dynamic linearized data model is established as follows: Δy(k+1)=θ(k)Δu(k)+χ(k)Δd(k) Where k is the sampling time, k is a positive integer; y(k+1) is the actual value vector of the system output of the controlled object at the k+1 sampling time, y(k+1)=[y 1 (k+1),…,y n (k+1)] T , Δy(k+1)=y(k+1)-y(k); n is the total number of system outputs of the controlled object, and n is an integer greater than 1; u(k) is the control input vector of the controlled object at sampling time k, u(k)=[u 1 (k),…,u m (k)] T , Δu(k)=u(k)-u(k-1); m is the total number of control inputs of the controlled object, and m is an integer greater than 1; d(k) is the measurable disturbance vector of the controlled object at sampling time k, d(k)=[d 1 (k),…,d q (k)] T , Δd(k)=d(k)-d(k-1); q is the total number of measurable disturbances to the controlled object, q is a positive integer; θ(k) is the pseudo-Jacobian input matrix at sampling time k, χ(k) is the pseudo-Jacobian disturbance matrix χ(k) at sampling time k; Step (2): construct a cost function and solve the cost function using the function extremum method, optimize and update the pseudo-Jacobi input matrix θ(k) and the pseudo-Jacobi perturbation matrix χ(k) in step (1), mainly including the following steps: Step (2.1): Input matrix θ(k) to the pseudo-Jacobi and construct the cost function J(θ(k))=||Δy(k)-θ(k)Δu(k-1)-χ(k-1)Δd(k-1)|| 2 +m 1 ||Δθ(k)|| 2 Among them, μ 1 is the first weight factor; Step (2.2): Construct a cost function for the pseudo-Jacobi perturbation matrix χ(k) J(χ(k))=||Δy(k)-θ(k-1)Δu(k-1)-χ(k)Δd(k-1)|| 2 +m 2 ||Δχ(k)|| 2 Among them, μ 2 is the second weight factor; Step (2.3): Use the function extremum method to solve the cost function described in step (2.1), and optimize and update the pseudo-Jacobi input matrix θ(k). Among them, α 1 is the first step size factor; Step (2.4): Use the function extremum method to solve the cost function described in step (2.2), and optimize and update the pseudo-Jacobi perturbation matrix χ(k). Among them, α 2 is the second step size factor; Step (3): using the measurable disturbance at the k sampling time, based on the dynamic linearized data model of the controlled object after optimizing the pseudo-Jacobi input matrix θ(k) and the pseudo-Jacobi disturbance matrix χ(k) in step (2), a compact format model-free adaptive disturbance compensation control scheme for the measurable disturbance is designed, wherein the control scheme includes a compact format adaptive input matrix π c (k) and the compact adaptive perturbation matrix ω c (k), the control scheme is: u(k)=u(k-1)-π c (k)e(k)+ω c (k)Δd(k) Wherein, e(k) is the system error vector of the controlled object at sampling time k, e(k)=y * (k)-y(k),e(k)=[e 1 (k),…,e n (k)] T , Δe(k)=e(k)-e(k-1);π c (k) is the compact format adaptive input matrix at sampling time k, ω c (k) is the compact format adaptive perturbation matrix at sampling time k; Step (4): Construct an energy function and use the momentum gradient descent method to solve the energy function, optimize and update the compact adaptive input matrix π in step (3) c (k) and the compact adaptive perturbation matrix ω c (k), mainly including the following steps: Step (4.1): Construct energy function Among them, y * (k+1) is the system output expected value vector of the controlled object at the k+1 sampling time, λ is the penalty factor; Step (4.2): Use the momentum gradient descent method to solve the energy function described in step (4.1) and optimize and update the compact adaptive input matrix π c (k) Among them, σ 1 is the first learning rate, η 1 is the first momentum factor; Δπ c (k-1)=π c (k-1)-π c (k-2); is the energy function W versus π c (k-1) partial derivative; Step (4.3): Use the momentum gradient descent method to solve the energy function described in step (4.1) and optimize and update the compact adaptive perturbation matrix ω c (k) Among them, σ 2 is the second learning rate, η 2 is the second momentum factor; Δω c (k-1)=ω c (k-1)-ω c (k-2); is the energy function W for ω c (k-1) partial derivative; The energy function W described in step (4.2) is c The formula for calculating the partial derivative of (k-1) is: The energy function W described in step (4.3) is c The formula for calculating the partial derivative of (k-1) is: Said The mathematical formula for this is: Step (5): Optimize the compact adaptive input matrix π using step (4) c (k) and the compact adaptive perturbation matrix ω c The control scheme after (k) controls the controlled object under the action of the measurable disturbance, weakens the influence of the measurable disturbance on the actual value of the controlled object system output, and realizes the effective tracking of the expected value of the system output; at each sampling time k, the following steps are included: Step (5.1): Obtain the measurable disturbance vector d(k) at the current sampling time; Step (5.2): Get the expected value vector y of the system output at the current sampling time * (k), the system output actual value vector y(k), and the system error vector e(k) at the current sampling moment is calculated; Step (5.3): Based on steps (5.1) and (5.2), use step (4) to optimize the compact adaptive input matrix π c (k) and the compact adaptive perturbation matrix ω c (k) The control scheme after calculation obtains the control input vector u(k) at the current sampling time; Step (5.4): After the control input vector acts on the controlled object, the actual value vector of the system output of the controlled object at the next sampling time is obtained.

2. A non-transitory computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the compact model-free adaptive disturbance compensation control method for measurable disturbances as claimed in claim 1 is implemented.

3. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the program, the compact model-free adaptive disturbance compensation control method for measurable disturbances as claimed in claim 1 is implemented.

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