A fractional-order PID control method and system for microbial fuel cells

Abstract

The invention belongs to the technical field of energy control, and provides a fractional-order PID control method and system for a microbial fuel cell; wherein the fractional-order PID control method for a microbial fuel cell comprises: obtaining a mathematical model of the microbial fuel cell and constructing a fractional-order PID controller; optimizing the control parameters of the constructed fractional-order PID controller by adopting an improved particle swarm algorithm, controlling the dilution rate of the microbial fuel cell, obtaining the maximum output voltage of the fuel cell, and realizing the fractional-order PID control of the microbial fuel cell; the improved particle swarm algorithm uses a chaotic map with a sine function introduced to initialize a particle population, evaluates particle fitness based on an adaptively decreasing inertia weight, adaptively adjusts a learning factor according to a change in particle fitness, enhances local search and global optimization of particles by a golden sine method, completes optimization when the maximum number of iterations is reached, and obtains optimized fractional-order PID controller control parameters.

Description

Technical Field

[0001] The invention belongs to the technical field of energy control, and in particular relates to a fractional-order PID control method and system for a microbial fuel cell. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Microbial fuel cells are a new type of green energy technology that uses microorganisms in wastewater as biocatalysts to directly convert the chemical energy of organic matter in wastewater into electrical energy. Currently, humans mainly rely on the combustion of fossil fuels to meet their energy needs, but fossil fuels are limited resources. Their excessive use not only leads to energy crises, but also causes serious environmental pollution. Therefore, finding renewable energy to replace fossil fuels has become an urgent task. The research on microbial fuel cells came into being in this context, aiming to provide a sustainable energy solution.

[0004] The operating mechanism of microbial fuel cells is complex, covering multiple disciplines such as microbiology, electrochemistry, materials science and control engineering. An efficient dual-chamber microbial fuel cell is mainly composed of an anode, a cathode and a proton exchange membrane located between the two. When glucose is used as a substrate, the microorganisms at the anode will oxidize and decompose glucose to generate protons and electrons. Protons migrate to the cathode through the proton exchange membrane, and electrons reach the cathode through an external circuit, releasing electrical energy in the process. At the cathode, electrons combine with oxygen to generate water.

[0005] At present, the practical application of microbial fuel cells is still mainly in the laboratory stage. The main factors affecting its power generation performance include the selection of anode and cathode materials, as well as the type and control of power-producing microorganisms. Studies have shown that microbial fuel cells are a complex nonlinear system, and their power generation performance is affected by the interaction of multiple external environments and internal microorganisms. At present, microbial fuel cells have problems such as slow power generation, unstable output voltage, and susceptibility to internal and external uncertain factors. In addition, how to control the output voltage of microbial fuel cells by adjusting the input and substrate concentration is still a technical problem that needs to be solved urgently. Summary of the invention

[0006] To solve the above problems, the present invention proposes a fractional-order PID (FOPID) control method and system for a microbial fuel cell, optimizes the parameters of FOPID based on an improved particle swarm algorithm, and obtains the maximum output power of the microbial fuel cell by controlling the input amount (dilution rate) of the microbial fuel cell.

[0007] According to some embodiments, a first solution of the present invention provides a fractional-order PID control method for a microbial fuel cell, using the following technical solution:

[0008] A fractional-order PID control method for a microbial fuel cell, comprising:

[0009] Obtain mathematical models of microbial fuel cells;

[0010] Based on the obtained mathematical model, a fractional-order PID controller for the microbial fuel cell is constructed;

[0011] The improved particle swarm algorithm is used to optimize the control parameters of the constructed fractional-order PID controller, control the dilution rate of the microbial fuel cell, obtain the maximum output voltage of the microbial fuel cell, and realize the fractional-order PID control of the microbial fuel cell.

[0012] Among them, the improved particle swarm algorithm uses a chaotic mapping that introduces a sine function to initialize the particle population, evaluates the particle fitness based on an adaptively decreasing inertia weight, adaptively adjusts the learning factor according to the change of the particle fitness, and enhances the local search and global optimization of the particles through the golden sine method during the particle iterative update process. The optimization is completed when the maximum number of iterations is reached, and the optimized fractional-order PID controller control parameters are obtained.

[0013] As a further technical limitation, the chaotic map introduced with the sine function is x n+1 =μ*sin(x n )*(1-sin(x n )); where μ represents the control parameter, sin(.) represents the sine function, and a uniformly distributed chaotic map is obtained through the oscillation fluctuation of the sine function.

[0014] As a further technical limitation, the adaptive decreasing inertia weight is Where Δf current refers to the current fitness change, Δf initial refers to the change in initial fitness, T max represents the maximum number of iterations, and T represents the current number of iterations.

[0015] As a further technical limitation, during the iterative update of particles, the golden ratio is used to control the scaling of the search space, and the sine function is used to guide the search direction to enhance the capabilities of global and local searches. In each iteration, the search range is scaled by the golden ratio, and the search step and direction are adjusted by the sine function to find the global optimal solution and complete the iterative update of the particles.

[0016] As a further technical definition, the fractional-order PID controller of the microbial fuel cell is composed of a proportional coefficient K p , integral coefficient K i , differential coefficient K d , integral order α and differential order β, its transfer function is

[0017] As a further technical limitation, the mathematical model of the microbial fuel cell is

[0018]

[0019]

[0020] Among them, x1, x2, x3, and x4 represent the substrate concentration, microbial content, acetate concentration, and hydrogen ion concentration of the microbial fuel cell, respectively. max represents the maximum substrate utilization, p max represents the maximum microbial growth rate, u represents the dilution rate of the microbial fuel cell, b represents the attenuation coefficient of the microbial fuel cell, n represents the initial substrate concentration of the microbial fuel cell, and Q represents the half-saturation constant.

[0021] Furthermore, in the process of obtaining the mathematical model of the microbial fuel cell, microbial fuel cell parameters including at least substrate concentration, microbial content, acetate concentration, hydrogen ion concentration, microbial growth rate and substrate utilization are collected, and the mathematical model of the microbial fuel cell is constructed based on the collected parameters.

[0022] According to some embodiments, the second solution of the present invention provides a fractional-order PID control system for a microbial fuel cell, using the following technical solution:

[0023] A fractional-order PID control system for a microbial fuel cell, comprising:

[0024] an acquisition module configured to acquire a mathematical model of a microbial fuel cell;

[0025] A construction module is configured to construct a fractional-order PID controller for a microbial fuel cell according to the obtained mathematical model;

[0026] A control module is configured to optimize the control parameters of the constructed fractional-order PID controller by using an improved particle swarm algorithm, control the dilution rate of the microbial fuel cell, obtain the maximum output voltage of the microbial fuel cell, and realize the fractional-order PID control of the microbial fuel cell;

[0027] Among them, the improved particle swarm algorithm uses a chaotic mapping that introduces a sine function to initialize the particle population, evaluates the particle fitness based on an adaptively decreasing inertia weight, adaptively adjusts the learning factor according to the change of the particle fitness, and enhances the local search and global optimization of the particles through the golden sine method during the particle iterative update process. The optimization is completed when the maximum number of iterations is reached, and the optimized fractional-order PID controller control parameters are obtained.

[0028] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution:

[0029] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps of the fractional-order PID control method for a microbial fuel cell as described in the first embodiment of the present invention.

[0030] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution:

[0031] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor. When the processor executes the program, the steps in the fractional-order PID control method for a microbial fuel cell according to the first embodiment of the present invention are implemented.

[0032] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution:

[0033] A computer program product includes software codes, wherein the program in the software codes executes the steps in the fractional-order PID control method for a microbial fuel cell as described in the first embodiment of the present invention.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention optimizes the parameters of FOPID based on an improved particle swarm algorithm, takes the output power of the microbial fuel cell as a cost function for particle swarm optimization, and obtains the maximum output power of the microbial fuel cell by controlling the input amount (dilution rate) of the microbial fuel cell. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0037] Figure 1 This is a flow chart of the fractional-order PID control method for a microbial fuel cell in Example 1 of the present invention;

[0038] Figure 2 The present invention is a flowchart of optimizing the control parameters of the fractional-order PID controller constructed by using the improved particle swarm algorithm in the first embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0040] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0041] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0042] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention and should not be understood as limitations on the present invention.

[0043] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0044] Embodiment 1

[0045] Embodiment 1 of the present invention introduces a fractional-order PID control method for a microbial fuel cell.

[0046] FOPID is a special PID controller that not only inherits the advantages of traditional PID technology, but also effectively overcomes its shortcomings, making it more robust, sensitive and stable. This embodiment controls the input (dilution rate) of the microbial fuel cell through FOPID, and optimizes the FOPID parameters based on the improved particle swarm algorithm, so that the microbial fuel cell can quickly and stably generate the maximum output voltage, which greatly avoids the problem that the control theory cannot be combined with practice.

[0047] like Figure 1 The fractional-order PID control method for the microbial fuel cell shown in the figure adopts the control input based on the FOPID controller. The FOPID controller parameter K is optimized by the improved particle swarm algorithm.p , K i , K d , α, β, to achieve the maximum output power of the microbial fuel cell, thereby completing the FOPID control of the microbial fuel cell.

[0048] In this embodiment, microbial fuel cell parameters including at least substrate concentration, microbial content, acetate concentration, hydrogen ion concentration, microbial growth rate and substrate utilization rate are collected, and a mathematical model of the microbial fuel cell is constructed based on the collected parameters; the mathematical model of the microbial fuel cell constructed is:

[0049]

[0050] Among them, x1, x2, x3, and x4 represent the substrate concentration, microbial content, acetate concentration, and hydrogen ion concentration of the microbial fuel cell, respectively. max represents the maximum substrate utilization, p max represents the maximum microbial growth rate, u represents the dilution rate of the microbial fuel cell, b represents the attenuation coefficient of the microbial fuel cell, n represents the initial substrate concentration of the microbial fuel cell, and Q represents the half-saturation constant; therefore, it is necessary to analyze the states x1 and x2 to complete the control of the output voltage; by controlling the dilution rate u, the state of the microbial fuel cell is controlled so that the state can quickly and stably reach the expected value, thereby quickly and stably generating the maximum output power. The reference value of the state is x d , then e=x d -x.

[0051] FOPID introduces fractional-order differential and integral operators to give the controller a higher degree of freedom, which can better adjust the dynamic performance of the system and thus better meet the control requirements of complex systems.

[0052] The fractional-order PID controller of the microbial fuel cell is composed of the proportional coefficient K p , integral coefficient K i , differential coefficient K d , integral order α and differential order β, its transfer function is Find the 5 optimal parameters of FOPID controller through optimization algorithm——K p , K i , K d , α, β, can speed up the response speed of the microbial fuel cell system, so that it can quickly and stably generate the maximum output power.

[0053] In the particle swarm algorithm, the five parameters of FOPID are assigned to particles. Each particle represents a five-dimensional vector, which indicates a specific set of parameters of the FOPID controller. p , K i , K d , α, β]; the position of each particle represents the five parameters of the corresponding FOPID controller.

[0054] The particle swarm consists of a large number of particles, and the position of each particle (i.e., parameter set) is randomly distributed in a defined search space during initialization; this space is designed based on the characteristics of microbial fuel cells, namely:

[0055] K p ∈[K pmax ,K pmin ]

[0056] K i ∈[K imax ,K imin ]

[0057] K d ∈[K dmax ,K dmin ]

[0058] α∈[α max ,α min ]

[0059] β∈[β max ,β min ]

[0060] like Figure 2 As shown, in this embodiment, the initial particle population is divided into four sub-populations of equal size, which are named as population 1, population 2, population 3 and population 4 respectively. The four sub-populations adopt different optimization methods respectively; specifically:

[0061] Population 1: Set the inertia weight ω, individual learning factor c1, and group learning factor c2 to constants, and use the speed update formula v in the normal particle swarm algorithm i (t+1)=w·v i (t)+c1·r1·(pBest i -x i (t))+c2·r2·(gBest-x i (t)) and the position update formula x i (t+1)=x i (t)+v i (t+1) to find the best solution; among them, v i (t) is the velocity of particle i at time t, x i(t) is the position of particle i at time t, r1 and r2 are random numbers between [0,1].

[0062] Population 2: Considering the inertia weight ω, individual learning factor c1, and group learning factor c2 as constants, a new golden sine algorithm is introduced into the speed update formula and position update formula. By combining the golden ratio and the sine function, the speed update formula of the particle group is v i (t+1)=w·v i (t)+c1·r1·sin(φ)·(pBest i -x i (t))+c2·r2·sin(φ)·(gBest-x i (t)), the position update formula is x i (t+1)=x i (t)+v i (t+1)+sin(φ), which greatly enhances the local search and global optimization capabilities of the particle swarm algorithm; among them, φ is the golden angle φ g It's the golden ratio

[0063] Population 3: This embodiment uses a new improved Logistic chaotic map to initialize the population, that is, the traditional logistic chaotic map is combined with the sine function (Sin).

[0064] The traditional Logistic function is x n+1 =x n μ(1-x n ),μ∈[0,4],x n ∈(0,1); where μ∈[0,4], x n ∈[0,4], n=0,1,2…。

[0065] After combining the traditional Logistic function with the sine function, the S-Logistic chaotic map x n+1 =μ*sin(x n )*(1-sin(x n )); where μ represents the control parameter, sin(.) represents the sine function, and a uniformly distributed chaotic map is obtained by the oscillation of the sine function; in this embodiment, μ is 10. Therefore, compared with the Logistic chaotic map, the S-Logistic chaotic map has a more uniform distribution because the Sin function introduced by it can oscillate and fluctuate continuously. Using it for population initialization can provide a high-quality search space for the algorithm, thereby helping to improve the convergence accuracy of the algorithm.

[0066] In order to improve the convergence speed faster, this embodiment adopts a new adaptive decreasing inertia weight, namely Where Δf current refers to the current fitness change, Δf initial refers to the change in initial fitness, T max represents the maximum number of iterations, and T represents the current number of iterations; the first half of the inertia weight formula is the adaptive part of the inertia weight, which means that the inertia weight is adjusted according to the current fitness change rate. Specifically, as the fitness improves (i.e., the fitness change Δf current The inertia weight also decreases accordingly. This allows the particle to maintain a larger inertia weight when the fitness improves faster, so as to explore more search space; when the fitness improves slower, the inertia weight is reduced, so as to search the local optimal solution more finely; the second half of the inertia weight formula is an e negative exponential function. According to the graph of the e exponential function, its function value is infinite at the beginning, and as x increases, its function value is a process of continuous decrease, which is just suitable for the algorithm to focus on global search capabilities in the early stage and local development capabilities in the later stage, which greatly improves the convergence accuracy and speed of the algorithm.

[0067] This embodiment also designs an improved learning factor that combines time decrement and adaptive adjustment. The individual learning factor c1 and the group learning factor c2 both decrease over time and are adaptively adjusted according to fitness changes. This method maintains a large exploration capability in the early stage of the algorithm, enhances the development capability in the later stage of the algorithm, and dynamically adjusts according to fitness changes to meet current search needs; specifically, in, represent the minimum and maximum values ​​of c1 and c2 respectively, Δf represents the fitness change between the current and previous generations, and f avg Represents the average fitness of the group.

[0068] A new golden sine algorithm is introduced in the speed update formula and position update formula. It uses the golden ratio to control the scaling of the search space and uses the sine function to guide the search direction, enhancing the ability of global and local search. Specifically, the algorithm scales the search range by the golden ratio in each iteration and adjusts the search step and direction by the sine function to find the global optimal solution. By combining the golden ratio and the sine function, the speed update formula of the particle swarm is:

[0069]

[0070] The position update formula is (x i (t+1)=x i (t)+v i(t+1)+sin(φ), which greatly enhances the local search and global optimization capabilities of the particle swarm algorithm.

[0071] Population 4, randomly generates probability X. When X is less than 0.5, it randomly adopts any one of populations 1, 2 or 3 to search for the best solution. When X is greater than or equal to 0.5, the particle swarm performs random movement to search for the best solution.

[0072] When the four sub-populations have reached the maximum number of iterations, the different particle swarms share their global optimal solutions, thus merging the four sub-populations. Each particle can refer to the previously found global optimal solution in the next optimization search, use the output power value of the microbial fuel cell as the cost function, and use the traditional particle swarm to re-optimize to maximize the output power of the system; when the maximum number of iterations is reached, the optimal solution Y is output; at this time, Y = [K p_optimal , K i_optimal , K d_optimal , α optimal , β optimal ], which is the FOPID controller parameter that can enable the microbial fuel cell to produce the maximum output power.

[0073] In this embodiment, in the initial stage, the initial population size is set to 60, 60 binary strings are generated randomly, the inertia weight is 0.7, the individual learning factor is 2, and the group learning factor is 2; the population is divided into 4 sub-populations of equal size, and the 4 populations are optimized in different ways; the output power of the microbial fuel cell is used as the cost function, the maximum number of iterations is 200, and when the maximum number of iterations is reached, the 4 sub-populations are merged. The normal optimization operation of the particle swarm algorithm is continued for this population, and the maximum number of iterations is set to 200. When the maximum number of iterations is reached, the optimal solution Y is obtained; at this time, the microbial fuel cell is realized to output at the maximum power.

[0074] This embodiment optimizes the parameters of FOPID based on the improved particle swarm algorithm, takes the output power of the microbial fuel cell as the cost function for particle swarm optimization, and obtains the maximum output power of the microbial fuel cell by controlling the input amount (dilution rate) of the microbial fuel cell.

[0075] Embodiment 2

[0076] The second embodiment of the present invention introduces a fractional-order PID control system for a microbial fuel cell.

[0077] A fractional-order PID control system for a microbial fuel cell, comprising:

[0078] an acquisition module configured to acquire a mathematical model of a microbial fuel cell;

[0079] A construction module is configured to construct a fractional-order PID controller for a microbial fuel cell according to the obtained mathematical model;

[0080] A control module is configured to optimize the control parameters of the constructed fractional-order PID controller by using an improved particle swarm algorithm, control the dilution rate of the microbial fuel cell, obtain the maximum output voltage of the microbial fuel cell, and realize the fractional-order PID control of the microbial fuel cell;

[0081] Among them, the improved particle swarm algorithm uses a chaotic mapping that introduces a sine function to initialize the particle population, evaluates the particle fitness based on an adaptively decreasing inertia weight, adaptively adjusts the learning factor according to the change of the particle fitness, and enhances the local search and global optimization of the particles through the golden sine method during the particle iterative update process. The optimization is completed when the maximum number of iterations is reached, and the optimized fractional-order PID controller control parameters are obtained.

[0082] The detailed steps are the same as those of the fractional-order PID control method for the microbial fuel cell provided in Example 1, and will not be repeated here.

[0083] Embodiment 3

[0084] Embodiment 3 of the present invention provides a computer-readable storage medium.

[0085] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps of the fractional-order PID control method for a microbial fuel cell as described in the first embodiment of the present invention.

[0086] The detailed steps are the same as those of the fractional-order PID control method for the microbial fuel cell provided in Example 1, and will not be repeated here.

[0087] Embodiment 4

[0088] A fourth embodiment of the present invention provides an electronic device.

[0089] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the fractional-order PID control method for a microbial fuel cell as described in the first embodiment of the present invention are implemented.

[0090] The detailed steps are the same as those of the fractional-order PID control method for the microbial fuel cell provided in Example 1, and will not be repeated here.

[0091] Embodiment 5

[0092] Embodiment 5 of the present invention provides a computer program product.

[0093] A computer program product includes software codes, wherein the program in the software codes executes the steps in the fractional-order PID control method for a microbial fuel cell as described in the first embodiment of the present invention.

[0094] The detailed steps are the same as those of the fractional-order PID control method for the microbial fuel cell provided in Example 1, and will not be repeated here.

[0095] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A fractional-order PID control method for a microbial fuel cell, characterized in that: include: Obtain mathematical models of microbial fuel cells; According to the obtained mathematical model, a fractional-order PID controller for microbial fuel cells is constructed; The improved particle swarm algorithm is used to optimize the control parameters of the constructed fractional-order PID controller, control the dilution rate of the microbial fuel cell, obtain the maximum output voltage of the microbial fuel cell, and realize the fractional-order PID control of the microbial fuel cell. The improved particle swarm algorithm uses a chaotic map that introduces a sine function to initialize the particle population, evaluates the particle fitness based on an adaptively decreasing inertia weight, and adaptively adjusts the learning factor according to the change in particle fitness. In the process of particle iterative update, the local search and global optimization of particles are enhanced by the golden sine method. When the maximum number of iterations is reached, the optimization is completed to obtain the optimized fractional-order PID controller control parameters. The adaptive decreasing inertia weight is Where Δf current refers to the current fitness change, Δf initial refers to the change in initial fitness, T max represents the maximum number of iterations, and T represents the current number of iterations.

2. A fractional-order PID control method for a microbial fuel cell as claimed in claim 1, characterized in that: The chaotic map introduced by the sine function is x n+1 =μ*sin(x n )*(1-sin(x n )); where μ represents the control parameter, sin(.) represents the sine function, and a uniformly distributed chaotic map is obtained through the oscillation fluctuation of the sine function.

3. A fractional-order PID control method for a microbial fuel cell as claimed in claim 1, characterized in that: In the process of particle iterative update, the golden ratio is used to control the scaling of the search space, and the sine function is used to guide the search direction to enhance the capabilities of global and local searches. In each iteration, the search range is scaled by the golden ratio, and the search step and direction are adjusted by the sine function to find the global optimal solution and complete the iterative update of the particles.

4. A fractional-order PID control method for a microbial fuel cell as claimed in claim 1, characterized in that: The fractional-order PID controller of the microbial fuel cell is composed of a proportional coefficient K p , integral coefficient K i , differential coefficient K d , integral order α and differential order β, its transfer function is 5. A fractional-order PID control method for a microbial fuel cell as claimed in claim 1, characterized in that: The mathematical model of the microbial fuel cell is Among them, x1, x2, x3, and x4 represent the substrate concentration, microbial content, acetate concentration, and hydrogen ion concentration of the microbial fuel cell, respectively. max represents the maximum substrate utilization, p max represents the maximum microbial growth rate, u represents the dilution rate of the microbial fuel cell, b represents the attenuation coefficient of the microbial fuel cell, n represents the initial substrate concentration of the microbial fuel cell, and Q represents the half-saturation constant.

6. A fractional-order PID control system for a microbial fuel cell, based on the fractional-order PID control method for a microbial fuel cell according to any one of claims 1 to 5, characterized in that: include: an acquisition module configured to acquire a mathematical model of a microbial fuel cell; A construction module is configured to construct a fractional-order PID controller for a microbial fuel cell according to the obtained mathematical model; A control module is configured to optimize the control parameters of the constructed fractional-order PID controller by using an improved particle swarm algorithm, control the dilution rate of the microbial fuel cell, obtain the maximum output voltage of the microbial fuel cell, and realize the fractional-order PID control of the microbial fuel cell; Among them, the improved particle swarm algorithm uses a chaotic mapping that introduces a sine function to initialize the particle population, evaluates the particle fitness based on an adaptively decreasing inertia weight, adaptively adjusts the learning factor according to the change of the particle fitness, and enhances the local search and global optimization of the particles through the golden sine method during the particle iterative update process. The optimization is completed when the maximum number of iterations is reached, and the optimized fractional-order PID controller control parameters are obtained.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the fractional-order PID control method for a microbial fuel cell according to any one of claims 1 to 5 are implemented.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the fractional-order PID control method for a microbial fuel cell according to any one of claims 1 to 5 are implemented.

9. A computer program product comprising software code, characterized in that The program in the software code executes the steps of the fractional-order PID control method for a microbial fuel cell according to any one of claims 1 to 5.

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