An air conditioner compressor adaptive optimization control method, device, equipment and medium

Abstract

The application belongs to the technical field of automobile intelligent control, and specifically provides a kind of air conditioner compressor adaptive optimization control method, device, equipment and medium, the method comprises the following steps: establishing PID controller;Design adaptive particle swarm optimization algorithm and establish parameter adaptive model, adjust the proportion, integral, differential parameter in PID controller, obtain effective control parameter;PID controller controls air compressor duty cycle according to the control parameter obtained.The difference between the actual temperature of the passenger compartment and the target set temperature is used as the control input, and the change amount of the air compressor duty cycle is used as the control output to establish the PID controller.The proportion, integral and differential parameters in the PID controller are adjusted using the adaptive particle swarm optimization algorithm to obtain effective control parameters, thereby improving the control accuracy.

Description

Technical Field

[0001] This invention relates to the field of automotive intelligent control technology, specifically to an adaptive optimization control method, device, equipment, and medium for an air conditioning compressor. Background Technology

[0002] Air conditioning compressor speed control is a crucial part of automotive thermal management, as adjusting the compressor speed regulates the temperature of the passenger compartment. Currently, air conditioning compressor speed control methods mainly include PID control and fuzzy PID control, but these require parameter tuning, consuming significant manpower and resources, thus increasing personnel costs.

[0003] One control method connects the air conditioning panel, compressor controller, and VCU to a single CAN bus. The air conditioning panel collects evaporator temperature signals, indoor temperature signals, target temperature signals, fan speed signals, pipeline pressure switch signals, and air conditioning mode signals, and uploads them to the CAN bus. The VCU receives these signals and, combined with vehicle fault information and SOC information, enables and adjusts the compressor's speed. However, calibrating this control strategy requires extensive trial and error based on human experience, and this method cannot achieve precise control.

[0004] Another method employs an improved particle swarm optimization algorithm to optimize the speed control of the fuzzy PID compressor. This method uses fuzzy PID control and optimizes the parameters using the improved particle swarm optimization algorithm. However, the parameters in the improved particle swarm optimization algorithm are adjusted only based on the number of iterations, failing to adapt to the actual temperature difference and exhibiting poor environmental adaptability and optimization capability. Furthermore, the coefficients c1 and c2 in the speed update formula do not change and require manual setting, increasing the manual parameter tuning process. Summary of the Invention

[0005] To address the issue that air conditioning compressor speed control methods, such as PID control and fuzzy PID control, require parameter tuning, consuming significant manpower and resources and increasing personnel costs, this invention provides an adaptive optimization control method, device, equipment, and medium for air conditioning compressors.

[0006] In a first aspect, the present invention provides an adaptive optimization control method for an air conditioning compressor, comprising the following steps:

[0007] Establish a PID controller;

[0008] An adaptive particle swarm optimization algorithm is designed and a parameter adaptive model is established to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters.

[0009] The PID controller controls the duty cycle of the air compressor based on the acquired control parameters.

[0010] As a further limitation of the technical solution of the present invention, the steps of establishing the PID controller include:

[0011] A PID controller is established using the difference between the actual operating state and the target operating state as the control input and the change in the duty cycle of the air compressor as the control output.

[0012]

[0013] Where Δu(t) is the change in the duty cycle of the air compressor at time t, H p H is the proportionality coefficient. l H is the integral coefficient. d Here are the differential coefficients, e(t) = y * (t)-y(t) represents the control error at time t, and y * y(t) represents the setpoint for the running state, and y(t) represents the actual value of the running state.

[0014] As a further limitation of the technical solution of this invention, the steps of designing an adaptive particle swarm optimization algorithm and establishing a parameter adaptive model to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters include:

[0015] Set the number of iterations K for particle swarm optimization, and set the particle swarm size N.

[0016] Minimizing the control error is used as the objective function of adaptive particle swarm optimization.

[0017] The proportional, integral, and derivative parameters of the PID controller are used as the position input objective function of the adaptive particle swarm optimization algorithm. By solving the objective function, the individual optimal solution searched by a single particle and the global optimal solution searched by the entire particle swarm are determined.

[0018] Based on the actual operating state and the set operating state, establish a parameter adaptive model;

[0019] The established parameter adaptive model is used to update the particle velocity and position using the individual optimal solution and the global optimal solution to date;

[0020] When the current iteration count is greater than or equal to the set iteration step count K, the global optimal position is output and used as the PID controller parameter optimization setpoint.

[0021] As a further limitation of the technical solution of the present invention, the steps of using the proportional, integral, and derivative parameters of the PID controller as the position input objective function of the adaptive particle swarm optimization algorithm, and determining the individual optimal solution searched so far for a single particle and the global optimal solution searched so far for the entire particle swarm by solving the objective function include:

[0022] Minimizing the control error is taken as the objective of adaptive particle swarm optimization: min f(t)=e(t);

[0023] The proportional, integral, and derivative parameters of the PID controller are used as the positions of the adaptive particle swarm optimization algorithm to solve for f(t);

[0024] In the k-th optimization step, the particle position x(k) = [H p (k),H l (k),H d Using [k] as input to f(t), calculate the value of f(t);

[0025] By comparing the calculated values, the individual optimal solution searched so far for a single particle is obtained. The individual optimal solutions obtained by each particle are compared to obtain the global optimal solution of the population searched so far.

[0026] As a further limitation of the technical solution of the present invention, in the step of establishing a parameter adaptive model based on the actual operating state and the set operating state, the established parameter adaptive model includes:

[0027]

[0028]

[0029]

[0030] Where y(k) represents the actual running state of the k-th iteration, y * (k) represents the set running state for the k-th iteration. This represents the maximum value of the running state. This represents the minimum value of the running state.

[0031] As a further limitation of the technical solution of the present invention, in the step of updating the particle velocity and position based on the established parameter adaptive model using the individual optimal solution and the global optimal solution to date, the formula for updating the particle velocity and position is as follows:

[0032] v n (k+1)=w(k)·v n (k)+c1(k)r1(p n (k)-x n (k))+c2(k)r2(g(k)-x n (k)) (5)

[0033] x n (k+1)=x n (k)+v n(k+1) (6)

[0034] Among them, v n (k) is the velocity vector of the nth particle, x n (k) is the position vector of the nth particle, w(k) is the inertia weight, c1(k) is the self-awareness learning factor, c2(k) is the social awareness learning factor, and r1 and r2 are random values ​​in the range [0, 1].

[0035] Secondly, the present invention provides an adaptive optimization control device for an air conditioning compressor, comprising a controller establishment module, a controller parameter optimization module, and a control output module;

[0036] The controller creation module is used to create a PID controller;

[0037] The controller parameter optimization module is used to design an adaptive particle swarm optimization algorithm and establish a parameter adaptive model to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters.

[0038] The control output module is used to set the PID controller to control the duty cycle of the air compressor based on the acquired control parameters.

[0039] As a further limitation of the technical solution of the present invention, the controller establishment module is specifically used to establish a PID controller by taking the difference between the actual operating state and the target operating state as the control input and the change in the duty cycle of the air compressor as the control output.

[0040]

[0041] Where Δu(t) is the change in the duty cycle of the air compressor at time t, H p H is the proportionality coefficient. l H is the integral coefficient. d Here are the differential coefficients, e(t) = y * (t)-y(t) represents the control error at time t, and y * y(t) represents the setpoint for the running state, and y(t) represents the actual value of the running state.

[0042] As a further limitation of the technical solution of the present invention, the controller parameter optimization module includes an initialization unit, an objective function confirmation unit, a calculation processing unit, an adaptive model establishment unit, an optimization processing unit, and an output unit;

[0043] The initialization unit is used to set the number of particle swarm optimization iterations K and the particle swarm size N.

[0044] The objective function confirmation unit is used to take minimizing the control error as the objective function of adaptive particle swarm optimization.

[0045] The computational processing unit is used to take the proportional, integral, and derivative parameters of the PID controller as the position input objective function of the adaptive particle swarm optimization algorithm, and determine the individual optimal solution searched by a single particle and the global optimal solution searched by the entire particle swarm by solving the objective function.

[0046] The adaptive model building unit is used to build a parameter adaptive model based on the actual operating state and the set operating state.

[0047] The optimization processing unit is used to update the particle velocity and position based on the established parameter adaptive model using the individual optimal solution and the global optimal solution to date;

[0048] The output unit is used to output the global optimal position when the current iteration number is greater than or equal to the set iteration step number K, and uses the global optimal position as the PID controller parameter optimization setpoint.

[0049] As a further limitation of the technical solution of the present invention, the computational processing unit is specifically used to minimize the control error as the objective of adaptive particle swarm optimization: min f(t)=e(t); to solve f(t) using the proportional, integral, and derivative parameters of the PID controller as the position of the adaptive particle swarm optimization algorithm; and in the k-th optimization step, to set the particle position x(k)=[H p (k),H l (k),H d [k] is used as input to f(t), and the value of f(t) is calculated. By comparing the calculated values, the individual optimal solution searched by a single particle so far is obtained. The individual optimal solutions obtained by each particle are compared to obtain the global optimal solution of the population searched so far.

[0050] As a further limitation of the technical solution of the present invention, the parameter adaptive model established by the adaptive model establishment unit includes:

[0051]

[0052]

[0053]

[0054] Where y(k) represents the actual running state of the k-th iteration, y * (k) represents the set running state for the k-th iteration. This represents the maximum value of the running state. This represents the minimum value of the running state.

[0055] As a further limitation of the technical solution of the present invention, the formula for updating the particle velocity and position of the optimized processing unit is as follows:

[0056] v n (k+1)=w(k)·v n (k)+c1(k)r1(p n (k)-x n (k))+c2(k)r2(g(k)-x n (k)) (5)

[0057] x n (k+1)=x n (k)+v n (k+1) (6)

[0058] Among them, v n (k) is the velocity vector of the nth particle, x n (k) is the position vector of the nth particle, w(k) is the inertia weight, c1(k) is the self-awareness learning factor, c2(k) is the social awareness learning factor, and r1 and r2 are random values ​​in the range [0, 1].

[0059] Thirdly, the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing computer program instructions executable by the at least one processor, the computer program instructions being executed by the at least one processor to enable the at least one processor to execute the adaptive optimization control method for an air conditioning compressor as described in the first aspect.

[0060] Fourthly, the present invention also provides a non-transitory computer-readable storage medium that stores computer instructions that cause the computer to execute the adaptive optimization control method for the air conditioning compressor as described in the first aspect.

[0061] As can be seen from the above technical solutions, the present invention has the following advantages: it adopts an adaptive particle swarm optimization algorithm to optimize the parameters that the PID controller needs to adjust, and in order to improve the search capability of the optimization algorithm, a parameter adaptive mechanism is added, thereby achieving efficient control of the air conditioner compressor speed.

[0062] Furthermore, the design principle of this invention is reliable, the structure is simple, and it has a very wide range of application prospects.

[0063] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a schematic flowchart of a method according to an embodiment of the present invention.

[0066] Figure 2 This is a schematic flowchart of a method according to another embodiment of the present invention.

[0067] Figure 3 This is a diagram of the adaptive optimization control structure for the air conditioner compressor speed of the present invention.

[0068] Figure 4 This is a schematic block diagram of an apparatus according to an embodiment of the present invention. Detailed Implementation

[0069] This invention proposes an adaptive optimization control method for air conditioner compressor speed. It employs an adaptive particle swarm optimization algorithm to optimize the parameters that the PID controller needs to adjust. Furthermore, to improve the search capability of the optimization algorithm, a parameter adaptive mechanism is added, thereby achieving efficient control of the air conditioner compressor speed. To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0070] like Figure 1 As shown in the figure, an embodiment of the present invention provides an adaptive optimization control method for an air conditioning compressor, comprising the following steps:

[0071] Step 1: Establish a PID controller;

[0072] In this step, the difference between the actual operating state and the target operating state is used as the control input, and the change in the duty cycle of the air compressor is used as the control output to establish a PID controller.

[0073]

[0074] Where Δu(t) is the change in the duty cycle of the air compressor at time t, H p H is the proportionality coefficient. l H is the integral coefficient. dHere are the differential coefficients, e(t) = y * (t)-y(t) represents the control error at time t, and y * y(t) represents the setpoint for the running state, and y(t) represents the actual value of the running state.

[0075] Step 2: Design an adaptive particle swarm optimization algorithm and establish a parameter adaptive model to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters;

[0076] Step 3: The PID controller controls the duty cycle of the air compressor based on the acquired control parameters.

[0077] In some embodiments, such as Figure 2 As shown, the steps for designing an adaptive particle swarm optimization algorithm and establishing a parameter adaptive model to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters include:

[0078] Step 21: Set the number of iterations K for particle swarm optimization and the particle swarm size N.

[0079] Step 22: Minimize the control error as the objective function of adaptive particle swarm optimization;

[0080] Step 23: Use the proportional, integral, and derivative parameters of the PID controller as the position input objective function of the adaptive particle swarm optimization algorithm. By solving the objective function, determine the individual optimal solution searched by a single particle so far and the global optimal solution searched by the entire particle swarm so far.

[0081] Step 24: Based on the actual operating state and the set operating state, establish a parameter adaptive model;

[0082] Step 25: Update the particle velocity and position based on the established parameter adaptive model using the individual optimal solution and the global optimal solution to date;

[0083] Step 26: Check if the number of iterations is greater than or equal to the set number of iteration steps K;

[0084] If yes, proceed to step 27; otherwise, proceed to step 23.

[0085] Step 27: Output the global optimal position and use it as the PID controller parameter optimization setpoint.

[0086] It should be further explained that the steps of using the proportional, integral, and derivative parameters of the PID controller as the position input objective function of the adaptive particle swarm optimization algorithm, and determining the individual optimal solution found so far by a single particle and the global optimal solution found so far by solving the objective function, include:

[0087] Step 311: Minimize the control error as the objective of adaptive particle swarm optimization: min f(t)=e(t);

[0088] Step 312: Use the proportional, integral, and derivative parameters of the PID controller as the position of the adaptive particle swarm optimization algorithm to solve for f(t);

[0089] Step 313: During the k-th optimization step, the particle position x(k) = [H p (k),H l (k),H d Using [k] as input to f(t), calculate the value of f(t);

[0090] Step 314: By comparing the calculated values, determine the individual optimal solution searched by a single particle so far. Compare the individual optimal solutions obtained by each particle to obtain the global optimal solution of the population searched so far.

[0091] In some embodiments, the step of establishing a parameter adaptive model based on the actual operating state and the set operating state includes establishing the following parameter adaptive model:

[0092]

[0093]

[0094]

[0095] Where y(k) represents the actual running state of the k-th iteration, y * (k) represents the set running state for the k-th iteration. This represents the maximum value of the running state. This represents the minimum value of the running state.

[0096] In the step of updating particle velocity and position based on the established parameter adaptive model using the individual optimal solution and the global optimal solution to date, the formulas for updating particle velocity and position are as follows:

[0097] v n (k+1)=w(k)·v n (k)+c1(k)r1(p n (k)-x n (k))+c2(k)r2(g(k)-x n (k)) (5)

[0098] x n (k+1)=x n (k)+v n(k+1) (6)

[0099] Among them, v n (k) is the velocity vector of the nth particle, x n (k) is the position vector of the nth particle, w(k) is the inertia weight, c1(k) is the self-awareness learning factor, c2(k) is the social awareness learning factor, and r1 and r2 are random values ​​in the range [0, 1].

[0100] In a specific embodiment, when the operating state is temperature, the method provided by this embodiment uses the difference between the actual temperature of the passenger cabin and the target set temperature as the control input, and the change in the duty cycle of the air compressor as the control output. A PID controller is established, and an adaptive particle swarm optimization algorithm is used to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters and improve control accuracy. The specific steps are as follows:

[0101] (1) Establish a PID controller

[0102] PID controller is represented as

[0103]

[0104] Where Δu(t) is the change in the duty cycle of the air compressor at time t, H p H is the proportionality coefficient. l H is the integral coefficient. d Here are the differential coefficients, e(t) = y * (t)-y(t) represents the control error at time t, and y * y(t) is the temperature setpoint, and y(t) is the actual temperature value.

[0105] (2) Design an adaptive particle swarm optimization method

[0106] ① Set the number of iterations K for particle swarm optimization and the particle swarm size N;

[0107] ② Minimize the control error as the objective of adaptive particle swarm optimization: min f(t)=e(t), and use the proportional, integral, and derivative parameters of the PID controller as the positions of the adaptive particle swarm optimization algorithm;

[0108] ③ Solve for f(t). In the k-th optimization step, the particle position x(k) = [H p (k),H l (k),H d[(k)] is used as the input of f(t), and the value of f(t) is calculated. The optimal solution p(k) searched by each particle is obtained by comparing the fitness values. The fitness value comparison is to compare the target values ​​of the particles. If the target value of a particle is small, it means that the particle has a better fitness value. The individual optimal solutions obtained by each particle are compared to obtain the global optimal solution g(k) of the population.

[0109] ④ To improve the adaptive capability of the particle swarm and enhance its search performance, a parameter adaptive mechanism is established based on the actual temperature and the set temperature:

[0110]

[0111]

[0112]

[0113] Where y(k) is the actual temperature inside the crew cabin in the kth iteration, y * (k) represents the set temperature for the k-th iteration. This is the highest temperature that can be set inside the crew cabin. The lowest temperature that can be set inside the crew cabin;

[0114] The particle velocity and position are updated using the individual optimal solution p(k) and the global optimal solution g(k):

[0115] v n (k+1)=w(k)·v n (k)+c1(k)r1(p n (k)-x n (k))+c2(k)r2(g(k)-x n (k)) (5)

[0116] x n (k+1)=x n (k)+v n (k+1) (6)

[0117] Among them, v n (k) is the velocity vector of the nth particle, x n (k) is the position vector of the nth particle, w(k) is the inertia weight, c1(k) is the self-awareness learning factor, c2(k) is the social awareness learning factor, and r1 and r2 are random values ​​in the range [0, 1].

[0118] ⑤ Determine whether the current iteration k is greater than or equal to K. If it is greater than or equal to K, proceed to step ⑥; if it is less than K, proceed to step ③.

[0119] ⑥ Using the globally optimal solution g(K) as the PID parameter optimization setpoint, it is expressed as:

[0120]

[0121] in, The optimal scaling factor is obtained in the Kth iteration of the particle swarm optimization. The optimal integral coefficients are obtained in the Kth iteration of the particle swarm optimization. The optimal differential coefficients are obtained in the Kth iteration of the particle swarm optimization.

[0122] (3) The input of the adaptive optimization control system for air conditioning compressor speed is the actual temperature of the passenger cabin and the set temperature, and the output is the actual temperature.

[0123] The structure diagram of the adaptive optimization control for air conditioning compressor speed is as follows: Figure 3 As shown in the figure, the actual temperature of the passenger compartment is compared with the set temperature to obtain the control error. The PID controller uses the control error as input and the duty cycle of the air compressor as output to control the air compressor. The proportional, integral, and derivative parameters in the PID controller do not need to be manually adjusted. They are adaptively obtained using an adaptive particle swarm optimization algorithm, which ultimately enables the actual temperature to accurately track the set temperature.

[0124] This invention is for ease of description only. It describes how to adaptively adjust the particle swarm parameters based on the actual temperature and the set temperature to optimize the proportional, integral, and derivative coefficients in the PID controller. Other methods for adaptively adjusting the parameters of the particle swarm optimization process based on the actual operating state should also fall within the scope of this invention.

[0125] like Figure 4 As shown, this embodiment of the invention provides an adaptive optimization control device for an air conditioning compressor, including a controller establishment module, a controller parameter optimization module, and a control output module;

[0126] The controller creation module is used to create a PID controller;

[0127] The controller parameter optimization module is used to design an adaptive particle swarm optimization algorithm and establish a parameter adaptive model to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters.

[0128] The control output module is used to set the PID controller to control the duty cycle of the air compressor based on the acquired control parameters.

[0129] It should be noted that the controller establishment module is specifically used to establish a PID controller by taking the difference between the actual operating state and the target operating state as the control input and the change in the duty cycle of the air compressor as the control output.

[0130]

[0131] Where Δu(t) is the change in the duty cycle of the air compressor at time t, H p H is the proportionality coefficient. l H is the integral coefficient. d Here are the differential coefficients, e(t) = y * (t)-y(t) represents the control error at time t, and y * y(t) represents the setpoint for the running state, and y(t) represents the actual value of the running state.

[0132] In some embodiments, the controller parameter optimization module includes an initialization unit, an objective function confirmation unit, a calculation processing unit, an adaptive model building unit, an optimization processing unit, and an output unit;

[0133] The initialization unit is used to set the number of particle swarm optimization iterations K and the particle swarm size N.

[0134] The objective function confirmation unit is used to take minimizing the control error as the objective function of adaptive particle swarm optimization.

[0135] The computational processing unit is used to take the proportional, integral, and derivative parameters of the PID controller as the position input objective function of the adaptive particle swarm optimization algorithm, and determine the individual optimal solution searched by a single particle and the global optimal solution searched by the entire particle swarm by solving the objective function.

[0136] The adaptive model building unit is used to build a parameter adaptive model based on the actual operating state and the set operating state.

[0137] The optimization processing unit is used to update the particle velocity and position based on the established parameter adaptive model using the individual optimal solution and the global optimal solution to date;

[0138] The output unit is used to output the global optimal position when the current iteration number is greater than or equal to the set iteration step number K, and uses the global optimal position as the PID controller parameter optimization setpoint.

[0139] In some embodiments, the computational processing unit is specifically used to minimize the control error as the objective of adaptive particle swarm optimization: min f(t) = e(t); to solve for f(t) using the proportional, integral, and derivative parameters of the PID controller as the position of the adaptive particle swarm optimization algorithm; and in the k-th optimization step, to set the particle position x(k) = [H p (k),H l (k),H d[k] is used as input to f(t), and the value of f(t) is calculated. By comparing the calculated values, the individual optimal solution searched by a single particle so far is obtained. The individual optimal solutions obtained by each particle are compared to obtain the global optimal solution of the population searched so far.

[0140] In some embodiments, the parameter adaptive model established by the adaptive model building unit includes:

[0141]

[0142]

[0143]

[0144] Where y(k) represents the actual running state of the k-th iteration, y * (k) represents the set running state for the k-th iteration. This represents the maximum value of the running state. This represents the minimum value of the running state.

[0145] Accordingly, the formula for updating particle velocity and position in the optimization processing unit is as follows:

[0146] v n (k+1)=w(k)·v n (k)+c1(k)r1(p n (k)-x n (k))+c2(k)r2(g(k)-x n (k)) (5)

[0147] x n (k+1)=x n (k)+v n (k+1) (6)

[0148] Among them, v n (k) is the velocity vector of the nth particle, x n (k) is the position vector of the nth particle, w(k) is the inertia weight, c1(k) is the self-awareness learning factor, c2(k) is the social awareness learning factor, and r1 and r2 are random values ​​in the range [0, 1].

[0149] This invention also provides an electronic device, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus. The communication bus can be used for information transmission between the electronic device and sensors. The processor can call logical instructions in the memory to execute the following methods: establishing a PID controller; designing an adaptive particle swarm optimization algorithm and establishing a parameter adaptive model to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters; and the PID controller controlling the duty cycle of the air compressor based on the obtained control parameters.

[0150] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0151] This invention provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to execute the methods provided in the above-described method embodiments. These instructions include, for example,: establishing a PID controller; designing an adaptive particle swarm optimization algorithm and establishing a parameter adaptive model; adjusting the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters; and controlling the duty cycle of the air compressor according to the obtained control parameters.

[0152] Although the present invention has been described in detail with reference to the accompanying drawings and preferred embodiments, the invention is not limited thereto. Various equivalent modifications or substitutions can be made to the embodiments of the invention by those skilled in the art without departing from the spirit and essence of the invention, and such modifications or substitutions should all be within the scope of the invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the invention should also be covered within the protection scope of the invention. Therefore, the protection scope of the invention should be determined by the scope of the claims.

Claims

1. An adaptive optimization control method for an air conditioning compressor, characterized in that, The steps include the following: Establish a PID controller; An adaptive particle swarm optimization algorithm is designed and a parameter adaptive model is established to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters. The PID controller controls the duty cycle of the air compressor based on the acquired control parameters; The steps to establish a PID controller include: A PID controller is established using the difference between the actual operating state and the target operating state as the control input and the change in the duty cycle of the air compressor as the control output. (1) Where Δu(t) is the change in the duty cycle of the air compressor at time t, Hp is the proportional coefficient, Hl is the integral coefficient, Hd is the differential coefficient, and e(t) = y (t)-y(t) represents the control error at time t, and y y(t) represents the setpoint for the running state, and y(t) represents the actual value of the running state. The steps involved in designing an adaptive particle swarm optimization algorithm and establishing a parameter adaptive model to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters include: Set the number of iterations K for particle swarm optimization, and set the particle swarm size; Minimizing the control error is used as the objective function of adaptive particle swarm optimization. The proportional, integral, and derivative parameters of the PID controller are used as the position input objective function of the adaptive particle swarm optimization algorithm. By solving the objective function, the individual optimal solution searched by a single particle and the global optimal solution searched by the entire particle swarm are determined. Based on the actual operating state and the set operating state, establish a parameter adaptive model; The established parameter adaptive model is used to update the particle velocity and position using the individual optimal solution and the global optimal solution to date; When the current iteration count is greater than or equal to the set iteration step count K, output the global optimal position and use the global optimal position as the PID controller parameter optimization setpoint; The step of establishing a parameter adaptive model based on the actual operating state and the set operating state includes the following: (2) (3) (4) Where y(k) represents the actual running state of the k-th iteration, y (k) represents the set running state for the k-th iteration. This represents the maximum value of the running state. This represents the minimum value of the running state; When the operating state is temperature, the difference between the actual temperature of the crew cabin and the target set temperature is used as the control input, and the change in the duty cycle of the air compressor is used as the control output. A PID controller is established, and an adaptive particle swarm optimization algorithm is used to adjust the proportional, integral, and derivative parameters in the PID controller.

2. The adaptive optimization control method for an air conditioning compressor according to claim 1, characterized in that, The steps of using the proportional, integral, and derivative parameters of the PID controller as the position input objective function of the adaptive particle swarm optimization algorithm, and determining the individual optimal solution found so far by a single particle and the global optimal solution found so far by solving the objective function, include: Minimizing the control error is taken as the objective of adaptive particle swarm optimization: min f(t)=e(t); The proportional, integral, and derivative parameters of the PID controller are used as the positions of the adaptive particle swarm optimization algorithm to solve for f(t); In the k-th optimization step, the particle position x(k)=[Hp(k),Hl(k),Hd(k)] is used as the input to f(t) to calculate the value of f(t); By comparing the calculated values, the individual optimal solution searched so far for a single particle is obtained. The individual optimal solutions obtained by each particle are compared to obtain the global optimal solution of the population searched so far.

3. The adaptive optimization control method for an air conditioning compressor according to claim 1, characterized in that, In the step of updating particle velocity and position based on the established parameter adaptive model using the individual optimal solution and the global optimal solution to date, the formulas for updating particle velocity and position are as follows: (5) (6) Where vn(k) is the velocity vector of the nth particle, xn(k) is the position vector of the nth particle, w(k) is the inertia weight, c1(k) is the self-awareness learning factor, c2(k) is the social-awareness learning factor, and r1 and r2 are random values ​​in the range [0, 1].

4. An adaptive optimization control device for an air conditioning compressor, applicable to the adaptive optimization control method for an air conditioning compressor as described in any one of claims 1-3, characterized in that, It includes a controller setup module, a controller parameter optimization module, and a control output module; The controller creation module is used to create a PID controller; The controller parameter optimization module is used to design an adaptive particle swarm optimization algorithm and establish a parameter adaptive model to adjust the proportional, integral, and derivative parameters in the PID controller to obtain effective control parameters. The control output module is used to set the PID controller to control the duty cycle of the air compressor based on the acquired control parameters.

5. The adaptive optimization control device for an air conditioning compressor according to claim 4, characterized in that, The controller parameter optimization module includes an initialization unit, an objective function verification unit, a calculation and processing unit, an adaptive model building unit, an optimization processing unit, and an output unit. The initialization unit is used to set the number of iterations K for particle swarm optimization and the particle swarm size N. The objective function confirmation unit is used to take minimizing the control error as the objective function of adaptive particle swarm optimization. The computational processing unit is used to take the proportional, integral, and derivative parameters of the PID controller as the position input objective function of the adaptive particle swarm optimization algorithm, and determine the individual optimal solution searched by a single particle and the global optimal solution searched by the entire particle swarm by solving the objective function. The adaptive model building unit is used to build a parameter adaptive model based on the actual operating state and the set operating state. The optimization processing unit is used to update the particle velocity and position based on the established parameter adaptive model using the individual optimal solution and the global optimal solution to date; The output unit is used to output the global optimal position when the current iteration number is greater than or equal to the set iteration step number K, and uses the global optimal position as the PID controller parameter optimization setpoint.

6. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores computer program instructions executable by the at least one processor, the computer program instructions being executed by the at least one processor to enable the at least one processor to perform the adaptive optimization control method for an air conditioning compressor as described in any one of claims 1 to 3.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute the adaptive optimization control method for the air conditioning compressor as described in any one of claims 1 to 3.

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