Nonlinear adaptive disturbance compensation control method based on bias form and non-model

Abstract

The application discloses a partial format non-model adaptive disturbance compensation control method for unmeasurable disturbance. The method establishes a dynamic linearization data model of a controlled object under the action of unmeasurable disturbance; constructs and solves a cost function, optimizes and updates a pseudo-Jacobian input matrix and a pseudo-Jacobian disturbance matrix; designs a partial format non-model adaptive disturbance compensation control scheme for unmeasurable disturbance; constructs and solves an energy function, optimizes and updates a partial format adaptive input matrix and a partial format adaptive disturbance matrix; and controls the controlled object under the action of unmeasurable disturbance by using the control scheme. The control method can obviously weaken the influence of unmeasurable disturbance on the actual output value of the controlled object system, effectively track the expected value track, and significantly improve the disturbance compensation control performance.

Description

Technical Field

[0001] This invention belongs to the field of automation control, and in particular relates to a partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances. Background Technology

[0002] Disturbances are widespread in practical control systems, including most controlled objects in industries such as oil refining, petrochemicals, chemicals, pharmaceuticals, food processing, papermaking, water treatment, thermal power, metallurgy, cement, rubber, machinery, electrical engineering, transportation, and robotics. These include reactors, distillation columns, machines, equipment, devices, production lines, workshops, factories, unmanned vehicles, unmanned ships, drones, and autonomous mobile robots. 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 partial-format 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, p. 104). Building upon this, inventions CN108107721A and CN108107722A proposed a SISO-based decoupling method, solving the control problem of strongly coupled multi-input multi-output systems; inventions CN108287470A and CN108287471A proposed a neural network-based parameter self-tuning method, solving the problem of time-consuming and laborious parameter selection; invention CN111522231A proposed a heterogeneous factor control method, solving the control problem of highly nonlinear multi-input multi-output systems with varying control channel characteristics; and invention CN111522229A extended invention CN111522231A, proposing a heterogeneous factor control method with parameter self-tuning, further solving the problem of time-consuming and laborious tuning of heterogeneous factor parameters. It should be noted that none of the above-mentioned invention methods have considered the control problem of the controlled object under disturbance.

[0004] For multi-input multi-output controlled objects subjected to unmeasurable disturbances, this invention proposes a partial scheme model-free adaptive disturbance compensation control method. This method efficiently utilizes real-time measured input and output data of the controlled object without relying on any mathematical model information, aims to mitigate the impact of unmeasurable disturbances on the actual output value of the controlled object system and effectively track the expected output value. This has significant industrial application value. Summary of the Invention

[0005] To address the problems existing in the background art, the present invention aims to provide a partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances. This control method operates on a hardware platform to control a controlled object under the influence of unmeasurable disturbances. The controlled object is a multi-input multi-output system containing multiple control inputs and multiple system outputs. The control method is characterized by comprising the following steps:

[0006] Step (1): At sampling time k, establish a dynamic linearized data model of the controlled object under unmeasurable disturbance. The dynamic linearized data model of the controlled object includes a pseudo-Jacobi input matrix. and pseudo-Jacobi perturbation matrix

[0007] Step (2): Construct the cost function and solve the cost function using the function extremum method to optimize and update the pseudo-Jacobi input matrix described in step (1). and the pseudo-Jacobi perturbation matrix

[0008] Step (3): Optimize the pseudo-Jacobi input matrix based on step (2). and the pseudo-Jacobi perturbation matrix Based on the dynamic linearized data model of the controlled object, a partial scheme model-free adaptive disturbance compensation control scheme for unmeasurable disturbances is designed. The control scheme includes a partial scheme adaptive input matrix. and the partial scheme adaptive perturbation matrix

[0009] Step (4): Construct the energy function and solve for the energy function using the momentum gradient descent method, and optimize and update the partial scheme adaptive input matrix described in step (3). and the partial scheme adaptive perturbation matrix

[0010] Step (5): Optimize the partial scheme adaptive input matrix using the method described in step (4). and the partial scheme adaptive perturbation matrix The subsequent control scheme controls the controlled object under the influence of unpredictable disturbances, weakens the impact of unpredictable disturbances on the actual output value of the controlled object system, and achieves effective tracking of the expected output value of the system.

[0011] Furthermore, the dynamic linearized data model of the controlled object under unmeasurable disturbance at sampling time k, as described in step (1), is as follows:

[0012]

[0013] Where k is the sampling time, and k is a positive integer; y(k+1) is the actual output value vector of the controlled object at sampling time k+1, y(k+1) = [y1(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, where n is an integer greater than 1; u(k) is the control input vector of the controlled object at sampling time k, u(k) = [u1(k), ..., u m (k)] T Δu(k) = u(k) - u(k-1); m is the total number of control inputs to the controlled object, where m is an integer greater than 1; 1 q×1 = [1; 1; ...; 1] q×1 q is the total number of unmeasurable disturbances experienced by the controlled object, and q is a positive integer; Let be the pseudo-Jacobi input matrix at sampling time k. Let be the pseudo-Jacobi perturbation matrix at sampling time k.

[0014] The construction of the cost function and the solution of the cost function using the function extremum method described in step (2) optimize and update the pseudo-Jacobi input matrix described in step (1). and the pseudo-Jacobi perturbation matrix The main steps include:

[0015] Step (2.1): Input matrix for the pseudo-Jacobi Constructing the cost function

[0016]

[0017] Where μ1 is the first weighting factor;

[0018] Step (2.2): For the pseudo-Jacobi perturbation matrix... Constructing the cost function

[0019]

[0020] Where μ2 is the second weighting factor;

[0021] Step (2.3): Solve the cost function described in step (2.1) using the function extremum method, and optimize and update the pseudo-Jacobi input matrix.

[0022]

[0023] Where α1 is the first step size factor;

[0024] Step (2.4): Solve the cost function described in step (2.2) using the function extremum method, and optimize and update the pseudo-Jacobi perturbation matrix.

[0025]

[0026] Here, α2 is the second step size factor.

[0027] The optimization of the pseudo-Jacobi input matrix based on step (2) described in step (3) is as follows. and the pseudo-Jacobi perturbation matrix Based on the dynamic linearized data model of the controlled object, a partial-form model-free adaptive disturbance compensation control scheme for unmeasurable disturbances is designed as follows:

[0028]

[0029] Where, ΔE(k)=[-e(k) T ,Δe(k)…,Δe(k-L+2) T ] T e(k) is the system error vector of the controlled object at sampling time k, e(k) = y * (k)-y(k), e(k)=[e1(k),…,e n (k)] T Δe(k) = e(k) - e(k-1); L is the linearization length constant; L is a positive integer; For the partial format adaptive input matrix at sampling time k, Let be the partial format adaptive perturbation matrix at sampling time k.

[0030] The energy function constructed in step (4) and solved using the momentum gradient descent method are used to optimize and update the partial scheme adaptive input matrix described in step (3). and the partial scheme adaptive perturbation matrix The main steps include:

[0031] Step (4.1): Construct the energy function

[0032]

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

[0034] Step (4.2): Solve for the energy function described in step (4.1) using the momentum gradient descent method, and optimize and update the partial scheme adaptive input matrix.

[0035]

[0036] Where σ1 is the first learning rate and η1 is the first momentum factor; For the energy function W pair The partial derivatives;

[0037] Step (4.3): Solve for the energy function described in step (4.1) using the momentum gradient descent method, and optimize and update the partial scheme adaptive perturbation matrix.

[0038]

[0039] Where σ2 is the second learning rate and η2 is the second momentum factor; For the energy function W, pair The partial derivatives of .

[0040] The energy function W described in step (4.2) is paired with The formula for calculating the partial derivative is:

[0041]

[0042] The energy function W described in step (4.3) is... The formula for calculating the partial derivative is:

[0043]

[0044] The The mathematical formula for calculation is:

[0045] The optimization of the partial scheme adaptive input matrix in step (4) described in step (5) is described in step (5). and the partial scheme adaptive perturbation matrix The subsequent control scheme controls the controlled object under unmeasurable disturbances, and includes the following steps at each sampling time k:

[0046] Step (5.1): Obtain the system output expected value vector y at the current sampling time. * (k) The actual value vector y(k) of the system output is used to calculate the system error vector e(k) at the current sampling time;

[0047] Step (5.2): Based on step (5.1), optimize the partial format adaptive input matrix using step (4). and the partial scheme adaptive perturbation matrix The control input vector u(k) at the current sampling time is obtained by calculating the subsequent control scheme;

[0048] Step (5.3): After the control input vector is applied to the controlled object, the actual output value vector of the controlled object at the next sampling time is obtained.

[0049] Furthermore, the present invention adopts the following technical solution:

[0050] A non-transitory computer-readable storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the above-described partial-format model-free adaptive disturbance compensation control method for unmeasurable disturbances.

[0051] Furthermore, the present invention adopts the following technical solution:

[0052] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the program, implements the aforementioned partial-format model-free adaptive disturbance compensation control method for unmeasurable disturbances.

[0053] Based on the existing theoretical foundation of partial-form model-free adaptive control methods, some inventive methods have made progress in solving problems such as strong coupling of controlled objects, diverse channel characteristics, and time-consuming and laborious parameter tuning. However, these inventive methods have not yet considered the control problem of controlled objects under disturbances, which restricts their widespread application. For multi-input multi-output controlled objects under unmeasurable disturbances, this invention can efficiently utilize the real-time measured input and output data of the controlled object, without relying on any mathematical model information for the analysis and design of disturbance compensation control methods. Furthermore, the designed control method can reduce the impact of unmeasurable disturbances on the actual output value of the controlled object system, and achieve effective tracking of the expected output value of the system, which has significant industrial application value. Attached Figure Description

[0054] Figure 1 This is a block diagram illustrating the algorithm principle of the present invention;

[0055] Figure 2 This is a block diagram of the engineering application system of the present invention;

[0056] Figure 3 A schematic diagram of the hardware platform for running this invention;

[0057] Figure 4 The control effect diagram of the first system output when the partial scheme model-free adaptive disturbance compensation control method and the comparative control method for unmeasurable disturbances proposed in this invention are used for a two-input two-output system.

[0058] Figure 5The control effect diagram of the second system output when the partial scheme model-free adaptive disturbance compensation control method and the comparative control method for unmeasurable disturbances proposed in this invention are used for a two-input two-output system.

[0059] Figure 6 The first control input curve when using the partial scheme model-free adaptive disturbance compensation control method and the comparative control method for unmeasurable disturbances proposed in this invention for a two-input two-output system;

[0060] Figure 7 The second control input curve when using the partial scheme model-free adaptive disturbance compensation control method and the comparative control method for unmeasurable disturbances proposed in this invention for a two-input two-output system;

[0061] Figure 8 This is a flow chart of the refrigeration cycle in a vapor compression refrigeration system.

[0062] Figure 9 The graphs show two unmeasurable disturbances experienced by the vapor compression refrigeration system.

[0063] Figure 10 The control effect diagram of the first system output when the partial scheme model-free adaptive disturbance compensation control method and the comparative control method for unmeasurable disturbances proposed in this invention are used for the vapor compression refrigeration system;

[0064] Figure 11 The control effect diagram of the second system output when the partial scheme model-free adaptive disturbance compensation control method and the comparative control method for unmeasurable disturbances proposed in this invention are used for the vapor compression refrigeration system;

[0065] Figure 12 The first control input curve when the partial scheme model-free adaptive disturbance compensation control method and the comparative control method for unmeasurable disturbances proposed in this invention are used for the vapor compression refrigeration system;

[0066] Figure 13 The second control input curve is used when the partial scheme model-free adaptive disturbance compensation control method and the comparative control method for unmeasurable disturbances proposed in this invention are applied to the vapor compression refrigeration system. Detailed Implementation

[0067] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0068] Figure 1A block diagram illustrating the algorithm principle of this invention is provided. This invention discloses a partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances. The method establishes a dynamic linearized data model of the controlled object under unmeasurable disturbances, which includes a pseudo-Jacobi input matrix and a pseudo-Jacobi disturbance matrix; constructs and solves a cost function, optimizing and updating the pseudo-Jacobi input matrix and pseudo-Jacobi disturbance matrix; designs a partial scheme model-free adaptive disturbance compensation control scheme for unmeasurable disturbances, which includes a partial scheme adaptive input matrix and a partial scheme adaptive disturbance matrix; constructs and solves an energy function, optimizing and updating the partial scheme adaptive input matrix and partial scheme adaptive disturbance matrix; and uses the control scheme of this invention to control the controlled object under unmeasurable disturbances. The implementation steps of the partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances provided by this invention are further explained below:

[0069] The control method operates on a hardware platform to control a controlled object under unmeasurable disturbances. The controlled object is a multi-input multi-output system containing multiple control inputs and multiple system outputs. The control method is characterized by including the following steps:

[0070] Step (1): At sampling time k, establish a dynamic linearized data model of the controlled object under unmeasurable disturbance. The dynamic linearized data model of the controlled object includes a pseudo-Jacobi input matrix. and pseudo-Jacobi perturbation matrix

[0071] Step (2): Construct the cost function and solve the cost function using the function extremum method to optimize and update the pseudo-Jacobi input matrix described in step (1). and the pseudo-Jacobi perturbation matrix

[0072] Step (3): Optimize the pseudo-Jacobi input matrix based on step (2). and the pseudo-Jacobi perturbation matrix Based on the dynamic linearized data model of the controlled object, a partial scheme model-free adaptive disturbance compensation control scheme for unmeasurable disturbances is designed. The control scheme includes a partial scheme adaptive input matrix. and the partial scheme adaptive perturbation matrix

[0073] Step (4): Construct the energy function and solve for the energy function using the momentum gradient descent method, and optimize and update the partial scheme adaptive input matrix described in step (3). and the partial scheme adaptive perturbation matrix

[0074] Step (5): Optimize the partial scheme adaptive input matrix using the method described in step (4). and the partial scheme adaptive perturbation matrix The subsequent control scheme controls the controlled object under the influence of unpredictable disturbances, weakens the impact of unpredictable disturbances on the actual output value of the controlled object system, and achieves effective tracking of the expected output value of the system.

[0075] Furthermore, the dynamic linearized data model of the controlled object under unmeasurable disturbance at sampling time k, as described in step (1), is as follows:

[0076]

[0077] Where k is the sampling time, and k is a positive integer; y(k+1) is the actual output value vector of the controlled object at sampling time k+1, y(k+1) = [y1(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, where n is an integer greater than 1; u(k) is the control input vector of the controlled object at sampling time k, u(k) = [u1(k), ..., u m (k)] T Δu(k) = u(k) - u(k-1); m is the total number of control inputs to the controlled object, where m is an integer greater than 1; 1 q×1 = [1; 1; ...; 1] q×1 q is the total number of unmeasurable disturbances experienced by the controlled object, and q is a positive integer; Let be the pseudo-Jacobi input matrix at sampling time k. Let be the pseudo-Jacobi perturbation matrix at sampling time k.

[0078] The construction of the cost function and the solution of the cost function using the function extremum method described in step (2) optimize and update the pseudo-Jacobi input matrix described in step (1). and the pseudo-Jacobi perturbation matrix The main steps include:

[0079] Step (2.1): Input matrix for the pseudo-Jacobi Constructing the cost function

[0080]

[0081] Where μ1 is the first weighting factor;

[0082] Step (2.2): For the pseudo-Jacobi perturbation matrix... Constructing the cost function

[0083]

[0084] Where μ2 is the second weighting factor;

[0085] Step (2.3): Solve the cost function described in step (2.1) using the function extremum method, and optimize and update the pseudo-Jacobi input matrix.

[0086]

[0087] Where α1 is the first step size factor;

[0088] Step (2.4): Solve the cost function described in step (2.2) using the function extremum method, and optimize and update the pseudo-Jacobi perturbation matrix.

[0089]

[0090] Here, α2 is the second step size factor.

[0091] The optimization of the pseudo-Jacobi input matrix based on step (2) described in step (3) is as follows. and the pseudo-Jacobi perturbation matrix Based on the dynamic linearized data model of the controlled object, a partial-form model-free adaptive disturbance compensation control scheme for unmeasurable disturbances is designed as follows:

[0092]

[0093] Where, ΔE(k)=[-e(k) T ,Δe(k)…,Δe(k-L+2) T ] T e(k) is the system error vector of the controlled object at sampling time k, e(k) = y * (k)-y(k), e(k)=[e1(k),…,e n (k)] T Δe(k) = e(k) - e(k-1); L is the linearization length constant; L is a positive integer; For the partial format adaptive input matrix at sampling time k, Let be the partial format adaptive perturbation matrix at sampling time k.

[0094] The energy function constructed in step (4) and solved using the momentum gradient descent method are used to optimize and update the partial scheme adaptive input matrix described in step (3). and the partial scheme adaptive perturbation matrix The main steps include:

[0095] Step (4.1): Construct the energy function

[0096]

[0097] Among them, y * (k+1) is the system output expected value vector of the controlled object at sampling time k+1. λ is the penalty factor;

[0098] Step (4.2): Solve for the energy function described in step (4.1) using the momentum gradient descent method, and optimize and update the partial scheme adaptive input matrix.

[0099]

[0100] Where σ1 is the first learning rate and η1 is the first momentum factor; For the energy function W, pair The partial derivatives;

[0101] Step (4.3): Solve for the energy function described in step (4.1) using the momentum gradient descent method, and optimize and update the partial scheme adaptive perturbation matrix.

[0102]

[0103] Where σ2 is the second learning rate and η2 is the second momentum factor; For the energy function W, pair The partial derivatives of .

[0104] The energy function W described in step (4.2) is paired with The formula for calculating the partial derivative is:

[0105]

[0106] The energy function W described in step (4.3) is... The formula for calculating the partial derivative is:

[0107]

[0108] The The mathematical formula for calculation is:

[0109] The optimization of the partial scheme adaptive input matrix in step (4) described in step (5) is described in step (5). and the partial scheme adaptive perturbation matrix The subsequent control scheme controls the controlled object under unmeasurable disturbances, and includes the following steps at each sampling time k:

[0110] Step (5.1): Obtain the system output expected value vector y at the current sampling time. * (k) The actual value vector y(k) of the system output is used to calculate the system error vector e(k) at the current sampling time;

[0111] Step (5.2): Based on step (5.1), optimize the partial format adaptive input matrix using step (4). and the partial scheme adaptive perturbation matrix The control input vector u(k) at the current sampling time is obtained by calculating the subsequent control scheme;

[0112] Step (5.3): After the control input vector is applied to the controlled object, the actual output value vector of the controlled object at the next sampling time is obtained.

[0113] Figure 2 This is a block diagram of the engineering application system of the present invention. Further, regarding... Figure 2 Hardware platform in engineering application system block diagram, Figure 3 The invention provides a schematic diagram of the hardware platform on which it runs. Specifically, the invention employs a non-transitory computer-readable storage medium storing a computer program. The computer program, when executed by a processor, implements the aforementioned partial-format model-free adaptive disturbance compensation control method for unmeasurable disturbances. The invention also employs an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor, when executing the program, implements the aforementioned partial-format model-free adaptive disturbance compensation control method for unmeasurable disturbances.

[0114] The following are two specific embodiments of the present invention. Specific Implementation Example 1:

[0116] The controlled object is a two-input two-output nonlinear system:

[0117]

[0118]

[0119]

[0120]

[0121] y1(k+1)=x 11 (k+1)

[0122] y2(k+1)=x21 (k+1)

[0123] Where a(k)=1+0.1sin(2πk / 1500) and b(k)=1+0.1cos(2πk / 1500) are two time-varying parameters;

[0124]

[0125] d1(k) and d2(k) are unmeasurable disturbances. It should be noted that the unmeasurable disturbances are given to generate input and output data for the two-input two-output nonlinear system, and are not used in the controller design itself. Therefore, the controlled two-input two-output nonlinear system is a two-input two-output nonlinear system under the influence of unmeasurable disturbances.

[0126] The system outputs the expected value trajectory y * (k) is as follows:

[0127]

[0128]

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

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

[0131]

[0132] in, Let y be the expected value of the system output at the j-th sampling time k. j (k) represents the actual system output value at the j-th sampling time k, where j = 1, ..., n. ITAE(e j The smaller the value of ), the greater the actual output value y of the j-th system. j (k) and the expected output value of the j-th system Overall, the error is smaller, the control accuracy and speed are higher, and the control performance is better.

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

[0134] The control method of this invention is used to control a two-input, two-output system. The parameters of the control method of this invention are set as follows: L = 3. α1=0.2, α2=1.5, μ1=1, μ2=1, σ1=0.5, σ2=0.5, η1=0.1, η2=0.3, λ=0.83.

[0135] When using the control method of this invention to control a two-input two-output system under unmeasurable disturbance, the following steps are included at each sampling time k: a) Obtain the system output expected value vector y at the current sampling time. * (k) The actual value vector y(k) of the system output is used to calculate the system error vector e(k) at the current sampling time; b) Based on step a), the partial format adaptive input matrix is ​​optimized using step (4). and the partial scheme adaptive perturbation matrix The control scheme is then used to calculate the control input vector u(k) at the current sampling time; c) After the control input vector is applied to the two-input two-output system, the actual output value vector of the two-input two-output system at the next sampling time is obtained; d) Repeat a) to c) until the sampling time ends.

[0136] The control effects of the control method of this invention are compared with those of the existing PID control method (comparative control method) as follows: Figure 4 This is a control effect diagram of the first system output when using the control method of the present invention and the comparative control method. Figure 5 This is a control effect diagram of the second system output when using the control method of the present invention and the comparative control method. Figure 6 This is the first control input curve when using the control method of the present invention and the comparative control method. Figure 7 The second control input curves are shown for the control method of this invention and the comparative control method. Examining the control performance evaluation indicators, the ITAE(e1) of the first system output using the control method of this invention is 20380, and the ITAE(e2) of the second system output is 9142. For the comparative control method, the ITAE(e1) of the first system output is 32190, and the ITAE(e2) of the second system output is 17413. The control performance evaluation results are listed in Table 1. Examining the system output curves, the control method of this invention can effectively suppress the influence of unmeasurable disturbances on the actual output value of the two-input, two-output system. The control performance of the control method of this invention is superior to that of the comparative control method. Based on the above examination, it is fully demonstrated that the partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances provided by this invention can significantly weaken the influence of unmeasurable disturbances on the actual output value of the controlled object system, achieve 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 two-output systems

[0138] Specific Implementation Example 2:

[0140] Vapor compression refrigeration systems (VCRS) are the most common type of refrigeration cycle equipment, widely used in residential (e.g., household refrigerators, air conditioners), commercial (e.g., building and automotive air conditioning, cold storage warehouses), and industrial (e.g., petrochemical plants, natural gas processing plants). Their refrigeration cycle process is as follows: 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 medium being cooled.

[0141] The controlled vapor compression refrigeration system is a two-input, two-output nonlinear system. The two control inputs, u1 and u2, are the compressor frequency (Hz) and valve opening (%), respectively. The two system outputs, y1 and y2, are the superheat (°C) and the outlet temperature of the cooled medium (°C), respectively. The two disturbances, d1 and d2, experienced by the controlled vapor compression refrigeration system are the inlet temperature of the cooling medium (°C) and the inlet temperature of the cooled medium (°C), respectively. Since d1 and d2 are not measured online using corresponding temperature sensors, they are considered unmeasurable disturbances. Figure 9 The diagram shows two unmeasurable disturbances acting on the vapor compression refrigeration system. It should be noted that these unmeasurable disturbances are provided to generate input and output data for the vapor compression refrigeration system and are not intended for controller design. Therefore, the controlled vapor compression refrigeration system is a two-input, two-output nonlinear system under the influence of unmeasurable disturbances. In specific embodiment 2, m = n = q = 2. The hardware platform for running the control method of this invention uses an industrial control computer.

[0142] The initial operating conditions of the controlled vapor compression refrigeration system are: u1(0) ​​= 36.45 Hz, u2(0) = 48.79%, y1(0) = 14.65℃, y2(0) = -22.15℃. To meet the refrigeration requirements of the cooled medium, the system outputs the desired value trajectory. The temperature was stepped down from 14.65℃ to 7.2℃ at the 2nd minute, then stepped down from 7.2℃ to 22.2℃ at the 9th minute, and finally stepped down from 22.2℃ to 11.65℃ at the 16th minute. The system output the desired value trajectory. At the second minute, the temperature was adjusted from -22.15℃ to -22.65℃ in a step.

[0143] The control method of this invention is used to control a vapor compression refrigeration system. The parameters of the control method of this invention are set as follows: L = 2. α1=0.5, α2=0.5, μ1=1, μ2=1, σ1=0.5, σ2=0.8, η1=0.1, η2=0.1, λ=0.1.

[0144] When using the control method of this invention to control a vapor compression refrigeration system under unmeasurable disturbances, the following steps are included at each sampling time k: a) Obtain the system output expected value vector y at the current sampling time. * (k) The actual value vector y(k) of the system output is used to calculate the system error vector e(k) at the current sampling time; b) Based on step a), the partial format adaptive input matrix is ​​optimized using step (4). and the partial scheme adaptive perturbation matrix The control scheme is then used to calculate the control input vector u(k) at the current sampling time; c) after the control input vector is applied to the vapor compression refrigeration system, the actual output value vector of the vapor compression refrigeration system at the next sampling time is obtained; d) repeat a) to c) until the sampling time ends.

[0145] The control effects of the control method of this invention are compared with those of the existing PID control method (comparative control method) as follows: Figure 10 This is a control effect diagram of the first system output when using the control method of the present invention and the comparative control method. Figure 11 This is a control effect diagram of the second system output when using the control method of the present invention and the comparative control method. Figure 12 This is the first control input curve when using the control method of the present invention and the comparative control method. Figure 13 The second control input curves are shown for the control method of this invention and the comparative control method. Examining the control performance evaluation indicators, the ITAE(e1) of the first system output using the control method of this invention is 258284, and the ITAE(e2) of the second system output is 5164. For the comparative control method, the ITAE(e1) of the first system output is 506970, and the ITAE(e2) of the second system output is 52244. The results of the control performance evaluation indicators are listed in Table 2. Examining the system output curves, the control method of this invention can effectively suppress the influence of unmeasurable disturbances on the actual output value of the vapor compression refrigeration system. The control performance of the control method of this invention is superior to that of the comparative control method. Based on the above examination, it is fully demonstrated that the partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances provided by this invention can significantly weaken the influence of unmeasurable disturbances on the actual output value of the controlled system, achieve 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 Systems

[0147]

[0148] Furthermore, the following two points should be specifically pointed out:

[0149] (1) Disturbances are widespread in practical control systems, such as most controlled objects in industries like oil refining, petrochemicals, chemicals, pharmaceuticals, food, papermaking, water treatment, thermal power, metallurgy, cement, rubber, machinery, electrical engineering, transportation, and robotics. These include reactors, distillation columns, machines, equipment, devices, production lines, workshops, factories, unmanned vehicles, unmanned ships, drones, and autonomous mobile robots. For example, a vapor compression refrigeration system is subject to the continuous and complex influence of two unmeasurable disturbances: the inlet temperature of the cooling medium and the inlet temperature of the cooled medium. Specific embodiment 2 shows that the control method of the present invention can significantly reduce the impact of unmeasurable disturbances on the actual output value of the controlled object system, achieve effective tracking of the desired value trajectory, and thus significantly improve the disturbance compensation control performance. For example, unmanned surface vessels (USVs) are highly susceptible to the influence of surface wind fields during operation. Changes in wind speed and direction can affect not only the speed and course of the USV, but may also cause it to capsize in severe cases. When the surface wind field exhibits turbulent characteristics due to complex environmental factors, the wind speed and direction become a random and irregular motion, which is an unpredictable disturbance. The control method of this invention can compensate for this unpredictable disturbance, thereby achieving stable operation of the USV and playing an important role in improving its safety and reliability.

[0150] (2) In the above specific embodiments 1 and 2, the hardware platform for running the control method of the present invention is an industrial control computer; in practical applications, any one or any combination of single-chip microcontroller, microprocessor controller, field-programmable gate array controller, digital signal processing controller, embedded system controller, programmable logic controller, distributed control system, fieldbus control system, industrial Internet of Things control system, and industrial Internet control system can be selected as the hardware platform for running the control method of the present invention, depending on the specific circumstances.

[0151] From the above description of the embodiments, those skilled in the art can clearly understand that the implementation of the present invention can be achieved by means of software plus the necessary hardware platform. Embodiments of the present invention can be implemented using existing processors, or by dedicated processors used for this or other purposes for suitable systems, or by hardwired systems. Embodiments of the present invention also include non-transitory computer-readable storage media, which include machine-readable media for carrying or having machine-executable instructions or data structures stored thereon; such machine-readable media can be any available medium accessible by a general-purpose or special-purpose computer or other machine with a processor. For example, such machine-readable media can include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, 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 (hardwired, or wireless, or a combination of hardwired and wireless), that connection is also considered a machine-readable medium.

[0152] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles 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 such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A partial-form model-free adaptive disturbance compensation control method for unmeasurable disturbances, wherein the control method runs on a hardware platform to control a controlled object under the influence of unmeasurable disturbances, the controlled object being a multi-input multi-output system containing multiple control inputs and multiple system outputs, the control method being characterized by comprising the following steps: Step (1): At sampling time k, establish a dynamic linearized data model of the controlled object under unmeasurable disturbance. The dynamic linearized data model of the controlled object includes a pseudo-Jacobi input matrix. and pseudo-Jacobi perturbation matrix Step (2): Construct the cost function and solve the cost function using the function extremum method to optimize and update the pseudo-Jacobi input matrix described in step (1). and the pseudo-Jacobi perturbation matrix Step (3): Optimize the pseudo-Jacobi input matrix based on step (2). and the pseudo-Jacobi perturbation matrix Based on the dynamic linearized data model of the controlled object, a partial scheme model-free adaptive disturbance compensation control scheme for unmeasurable disturbances is designed. The control scheme includes a partial scheme adaptive input matrix. and the partial scheme adaptive perturbation matrix Step (4): Construct the energy function and solve for the energy function using the momentum gradient descent method, and optimize and update the partial scheme adaptive input matrix described in step (3). and the partial scheme adaptive perturbation matrix Step (5): Optimize the partial scheme adaptive input matrix using the method described in step (4). and the partial scheme adaptive perturbation matrix The subsequent control scheme controls the controlled object under the influence of unpredictable disturbances, weakens the impact of unpredictable disturbances on the actual output value of the controlled object system, and achieves effective tracking of the expected output value of the system.

2. The partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances according to claim 1, characterized in that, The dynamic linearized data model of the controlled object under unmeasurable disturbance at sampling time k, as described in step (1), is as follows: Where k is the sampling time, and k is a positive integer; y(k+1) is the actual output value vector of the controlled object at sampling time k+1, y(k+1) = [y1(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, where n is an integer greater than 1; u(k) is the control input vector of the controlled object at sampling time k, u(k) = [u1(k), ..., u m (k)] T Δu(k) = u(k) - u(k-1); m is the total number of control inputs to the controlled object, where m is an integer greater than 1; 1 q×1 = [1; 1; ...; 1] q×1 q is the total number of unmeasurable disturbances experienced by the controlled object, and q is a positive integer; Let be the pseudo-Jacobi input matrix at sampling time k. Let be the pseudo-Jacobi perturbation matrix at sampling time k.

3. The partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances according to claim 1, characterized in that, The construction of the cost function and the solution of the cost function using the function extremum method described in step (2) optimize and update the pseudo-Jacobi input matrix described in step (1). and the pseudo-Jacobi perturbation matrix The main steps include: Step (2.1): Input matrix for the pseudo-Jacobi Constructing the cost function Where μ1 is the first weighting factor; Step (2.2): For the pseudo-Jacobi perturbation matrix... Constructing the cost function Where μ2 is the second weighting factor; Step (2.3): Solve the cost function described in step (2.1) using the function extremum method, and optimize and update the pseudo-Jacobi input matrix. Where α1 is the first step size factor; Step (2.4): Solve the cost function described in step (2.2) using the function extremum method, and optimize and update the pseudo-Jacobi perturbation matrix. Here, α2 is the second step size factor.

4. The partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances according to claim 1, characterized in that, The optimization of the pseudo-Jacobi input matrix based on step (2) described in step (3) is as follows. and the pseudo-Jacobi perturbation matrix Based on the dynamic linearized data model of the controlled object, a partial-form model-free adaptive disturbance compensation control scheme for unmeasurable disturbances is designed as follows: Where, ΔE(k)=[-e(k) T ,Δe(k)…,Δe(k-L+2) T ] T e(k) is the system error vector of the controlled object at sampling time k, e(k) = y * (k)-y(k), e(k)=[e1(k),…,e n (k)] T Δe(k) = e(k) - e(k-1); L is the linearization length constant; L is a positive integer; For the partial format adaptive input matrix at sampling time k, Let be the partial format adaptive perturbation matrix at sampling time k.

5. The partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances according to claim 1, characterized in that, The energy function constructed in step (4) and solved using the momentum gradient descent method are used to optimize and update the partial scheme adaptive input matrix described in step (3). and the partial scheme adaptive perturbation matrix The main steps include: Step (4.1): Construct the energy function Among them, y * (k+1) is the system output expected value vector of the controlled object at sampling time k+1. λ is the penalty factor; Step (4.2): Solve for the energy function described in step (4.1) using the momentum gradient descent method, and optimize and update the partial scheme adaptive input matrix. Where σ1 is the first learning rate and η1 is the first momentum factor; For the energy function W pair The partial derivatives; Step (4.3): Solve for the energy function described in step (4.1) using the momentum gradient descent method, and optimize and update the partial scheme adaptive perturbation matrix. Where σ2 is the second learning rate and η2 is the second momentum factor; For the energy function W pair The partial derivatives of .

6. The partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances according to claim 5, characterized in that, The energy function W described in step (4.2) is... The formula for calculating the partial derivative is: The energy function W described in step (4.3) is... The formula for calculating the partial derivative is:

7. The partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances according to claim 6, characterized in that, The The mathematical formula for calculation is:

8. The partial scheme model-free adaptive disturbance compensation control method for unmeasurable disturbances according to claim 1, characterized in that, The optimization of the partial scheme adaptive input matrix in step (4) described in step (5) is described in step (5). and the partial scheme adaptive perturbation matrix The subsequent control scheme controls the controlled object under unmeasurable disturbances, and includes the following steps at each sampling time k: Step (5.1): Obtain the system output expected value vector y at the current sampling time. * (k) The actual value vector y(k) of the system output is used to calculate the system error vector e(k) at the current sampling time; Step (5.2): Based on step (5.1), optimize the partial format adaptive input matrix using step (4). and the partial scheme adaptive perturbation matrix The control input vector u(k) at the current sampling time is obtained by calculating the subsequent control scheme; Step (5.3): After the control input vector is applied to the controlled object, the actual output value vector of the controlled object at the next sampling time is obtained.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the partial-format model-free adaptive disturbance compensation control method for unmeasurable disturbances as described in any one of claims 1 to 8.

10. An electronic device comprising 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, it implements the partial-format model-free adaptive disturbance compensation control method for unmeasurable disturbances as described in any one of claims 1 to 8.

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