Control method of iodine working substance electric propulsion storage and supply system

Abstract

The present application relates to a kind of control method of iodine working substance electric propulsion storage and supply system, belong to space electric propulsion technical field, solve the problem that the thrust of existing iodine electric propulsion system is unstable and cannot meet the high-precision, high-stability requirement of space task.The control method of the present application includes: setting the first target temperature and the first temperature control range of the temperature control of the iodine tank of storage and supply system;First target temperature is in the first temperature control range;Detect the real-time temperature in the iodine tank, when the real-time temperature in the iodine tank is in the first temperature control range, the heating power of the first heating element of the iodine tank is adaptively controlled, to stabilize the temperature in the iodine tank at the first target temperature, wherein, in adaptive control, the parameter for adaptively controlling the first heating element is determined by sparrow search algorithm.In the present application, by accurate temperature control and adaptive adjustment, the stability of electric propulsion storage and supply system is improved, and the control difficulty is reduced.

Description

Technical Field

[0001] This invention relates to the field of space electric propulsion technology, and in particular to a control method for an iodine working propellant electric propulsion storage and supply system. Background Technology

[0002] Currently, with the continuous deepening of space exploration activities, space missions such as "drag compensation" for ultra-low orbit satellites, "imaging on the move" for high-resolution remote sensing satellites, and "precise orbit control" for low orbit constellations place increasingly higher demands on the control accuracy and stability of electric propulsion systems. However, in the deep space environment, disturbances such as temperature and pressure prevent the iodine working propellant from providing a stable airflow output to the electric propulsion system at a normal sublimation rate, thus causing thrust instability in the iodine-electric propulsion system and failing to meet the high precision and high stability requirements of space missions.

[0003] Furthermore, electric propulsion systems typically have low propellant flow rates but long continuous supply times, and iodine-based propellants are corrosive. Therefore, flow rates are usually not measured directly, but rather indirectly obtained by detecting temperature. However, due to the differences in temperature, pressure, and other environmental factors between space and Earth, the temperature of the storage and supply system cannot be used as a measure of airflow during space missions, making dynamic monitoring of flow rates difficult. Summary of the Invention

[0004] Based on the above analysis, the present invention aims to provide a control method for an iodine working propellant electric propulsion storage and supply system, in order to solve the problem that the thrust instability of existing iodine electric propulsion systems cannot meet the high precision and high stability requirements of space missions.

[0005] On one hand, embodiments of the present invention provide a control method for an iodine-based working fluid electric propulsion storage and supply system, the control method comprising:

[0006] A first target temperature and a first temperature control range are set for the iodine tank in the storage and supply system; the first target temperature is within the first temperature control range.

[0007] The real-time temperature inside the iodine container is detected. When the real-time temperature inside the iodine container is within the first temperature control range, the heating power of the first heating element of the iodine container is adaptively controlled to stabilize the temperature inside the iodine container at the first target temperature. In the adaptive control, a sparrow search algorithm is used to determine the parameters for adaptive control of the first heating element.

[0008] Based on a further improvement of the above method, the control method further includes:

[0009] When the real-time temperature inside the iodine container is lower than the lower limit of the first temperature control range, the first heating element is controlled to heat the iodine container at maximum heating power.

[0010] When the real-time temperature inside the iodine container is higher than the upper limit of the first temperature control range, the first heating element is controlled to stop heating the iodine container.

[0011] Based on a further improvement of the above method, the control method further includes:

[0012] A second target temperature and a second temperature control range are set for temperature control of the pipelines in the storage and supply system; the second target temperature is within the second temperature control range.

[0013] The real-time temperature inside the pipeline is collected. When the real-time temperature inside the pipeline is within the second temperature control range, the heating power of the second heating element is adaptively controlled to stabilize the temperature inside the pipeline at the second target temperature. In the adaptive control, a sparrow search algorithm is used to determine the parameters for adaptive control of the second heating element.

[0014] Based on a further improvement of the above method, the control method further includes:

[0015] When the real-time temperature inside the pipeline is lower than the lower limit of the second temperature control range, the second heating element is controlled to heat the pipeline with maximum heating power.

[0016] When the real-time temperature inside the pipeline is higher than the upper limit of the second temperature control range, the second heating element is controlled to stop heating the pipeline.

[0017] Based on a further improvement of the above method, the method further includes:

[0018] The pressure at the inlet and outlet of the pipeline is detected respectively;

[0019] The mass flow rate of the iodine working fluid in the pipeline is calculated based on the pressure at the pipeline inlet and outlet and the temperature inside the pipeline.

[0020] The control accuracy is evaluated based on the mass flow rate of the iodine working fluid in the pipeline.

[0021] Based on the above method, a further improvement is made to determine the parameters of adaptive control using a sparrow search algorithm, including the following steps:

[0022] The parameters of the adaptive control are regarded as the location information of sparrows, and the parameters of the adaptive control are expressed in the form of a sparrow population.

[0023] The fitness function is determined, and the butterfly optimization algorithm and adaptive inertia weight strategy are used to improve the position update formula of the discoverer. The Levy flight strategy is used to improve the position update formula of the follower. The hierarchy strategy in the gray wolf optimization algorithm is used to improve the position update formula of the watcher.

[0024] Initialize the sparrow population and divide it into discoverers, followers, and vigilants;

[0025] Randomly generate the location of sparrow populations within the search space;

[0026] Determine the fitness function, calculate the fitness value of all sparrows in the sparrow population according to the fitness function, sort them according to the fitness value, and select the position of the sparrow with the smallest fitness value as the optimal position;

[0027] Update the discoverer's position based on the improved discoverer position update formula;

[0028] Update the follower's position based on the improved follower position update formula;

[0029] Update the vigilant's position based on the improved vigilant position update formula;

[0030] Iteratively calculate the fitness value of all sparrows in the sparrow population, and replace the parent position with the position of the offspring with the better fitness value;

[0031] Determine whether the current iteration count has reached the set maximum iteration count. If the maximum iteration count has been reached, output the parameters of the adaptive control corresponding to the optimal position as the parameters of the optimal adaptive control. If the maximum iteration count has not been reached, continue iterating.

[0032] Based on a further improvement of the above method, the improved discoverer location update formula is as follows:

[0033]

[0034] In the formula, It represents the position of the j-th dimension of the i-th discoverer during the (t+1)-th iteration. It represents the position of the i-th discoverer in the j-th dimension at the t-th iteration, and ω(t) is the adaptive update weight. Let f be the globally optimal position in the t-th iteration, r be a random number in the interval (0,1), and f be the position of the global optimum. i is the butterfly scent of the i-th discoverer in the t-th iteration, Q is a random number following a normal distribution, L is a 1×d matrix of all 1s, R2 is the warning value, R2 is a random number uniformly distributed in the interval (0,1], and ST is the safety threshold, ST∈[0.5,1].

[0035] Based on a further improvement of the above method, the improved follower position update formula is as follows:

[0036]

[0037] In the formula, It represents the j-th dimension position of the i-th follower at the (t+1)-th iteration. Let be the position of the i-th follower in the j-th dimension during the t-th iteration, and Q be a random number following a normal distribution. Let be the global worst position in the t-th iteration. Let be the local optimal position in the (t+1)th iteration, Levy(d) be the Levy flight step size, and n be the total number of follower sparrows.

[0038] Based on the above method, when improving the guard position update formula using the hierarchy strategy in the gray wolf optimization algorithm, the optimal, second-best, and third-best positions from the previous iteration are selected as α wolf, β wolf, and γ wolf, respectively. The improved guard position update formula is as follows:

[0039]

[0040] In the formula, Let this be the position of the i-th guard in the j-th dimension at the (t+1)-th iteration. The position of the i-th vigilant in the j-th dimension at iteration t. Let x represent the positions of α wolf, β wolf, and γ wolf in the j-th dimension at the t-th iteration. 1,j x 2,j x 3,j Let f be the position of α wolf, β wolf, and γ wolf in the j-th dimension at the (t+1)-th iteration, μ be the ratio of discoverer to follower, and φ1, φ2, and φ3 be the weights of α wolf, β wolf, and γ wolf, respectively, where φ1 + φ2 + φ3 = 1, f i Let f be the fitness value of the i-th vigilant. α f β f γ represents the fitness values ​​of α wolf, β wolf, and γ wolf, respectively, and K is a constant used to control the step size for position updates. Let f be the global worst position in the t-th iteration. w ε is the fitness value of the worst position globally, and ε is a very small positive number.

[0041] Based on a further improvement of the above method, the adaptive control is PID control, and the control formula for the PID control is:

[0042]

[0043] In the formula, u(t) is the heating power of the heating element, e(t) is the temperature difference, and K is the kJ / m³. p K is a proportional parameter. i K is the integration parameter. d is the differential parameter.

[0044] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0045] 1. This invention improves the stability and efficiency of the electric propulsion storage and supply system while reducing control complexity through precise temperature control and adaptive adjustment. Specifically, when the real-time temperature of the iodine tank in the storage and supply system reaches a preset temperature range, the temperature inside the iodine tank is adjusted by adaptively controlling the heating power of the first heating element. This temperature control within a certain range results in smaller changes in the system's dynamic characteristics, simplifies the parameter tuning process, improves the system's response speed and stability, and makes the adaptive control algorithm more effective. This not only reduces control complexity but also improves control accuracy. Furthermore, this invention employs a sparrow search algorithm to determine the adaptive control parameters, improving the efficiency and accuracy of parameter determination, thereby enhancing the overall system control performance.

[0046] 2. In this invention, the sparrow search algorithm is improved based on multiple strategies. Specifically, the butterfly optimization algorithm and adaptive inertia weight strategy are used to improve the position update formula of the discoverer, the Levy flight strategy is used to improve the position update formula of the follower, and the hierarchy strategy in the gray wolf optimization algorithm is used to improve the position update formula of the watcher. This is to avoid getting stuck in local optima during the search, which is conducive to improving the global search capability, convergence speed, accuracy and robustness of the sparrow search algorithm, thereby ensuring the control accuracy of the system.

[0047] 3. In this invention, the iodine tank and pipeline are independently temperature controlled, and a stepped control strategy is adopted for both the iodine tank and pipeline. When the real-time temperature of the iodine tank or pipeline is lower than its corresponding temperature control range, the maximum heating power is used for heating, and heating is stopped when it is higher than its corresponding temperature control range. This enables the temperature of the iodine tank and pipeline to quickly reach the temperature control range for precise temperature adaptive control, which is beneficial to improving the accuracy and efficiency of system temperature control and saving on-board computing power.

[0048] 4. In this invention, the mass flow rate of the iodine working fluid in the pipeline is obtained based on the pressure and temperature parameters of the pipeline, realizing dynamic monitoring of the flow rate and thus evaluating the control accuracy.

[0049] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0050] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0051] Figure 1 This is a flowchart of a control method for an iodine working fluid electric propulsion storage and supply system according to an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the PID control principle in an embodiment of the present invention. Detailed Implementation

[0053] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0054] Embodiments of the present invention provide a control method for an iodine working fluid electric propulsion storage and supply system, such as... Figure 1 As shown, the control method includes:

[0055] A first target temperature and a first temperature control range are set for the iodine tank in the storage and supply system; the first target temperature is within the first temperature control range.

[0056] The real-time temperature inside the iodine container is detected. When the real-time temperature inside the iodine container is within the first temperature control range, the heating power of the first heating element of the iodine container is adaptively controlled to stabilize the temperature inside the iodine container at the first target temperature. In the adaptive control, a sparrow search algorithm is used to determine the parameters for adaptive control of the first heating element.

[0057] Compared with existing technologies, this invention improves the stability and efficiency of the electric propulsion storage and supply system through precise temperature control and adaptive adjustment, while reducing control complexity. Specifically, in this invention, when the real-time temperature of the iodine tank in the storage and supply system reaches a preset temperature range, the heating power of the first heating element is adaptively controlled to adjust the temperature inside the iodine tank. This temperature control within a certain range results in smaller changes in the system's dynamic characteristics, which simplifies the parameter tuning process, improves the system's response speed and stability, and makes the adaptive control algorithm more effective. Therefore, it not only reduces control complexity but also improves control accuracy.

[0058] Meanwhile, the sparrow search algorithm used in this invention to determine the parameters of adaptive control can improve the efficiency and accuracy of determining control parameters, thereby improving the control performance of the entire system.

[0059] When controlling an iodine-based electric propulsion storage and supply system, the first priority is to ensure a stable supply of gaseous iodine from the iodine tank. Within the iodine tank, the sublimation rate of iodine is determined by the heating temperature and the surface area of ​​the solid working medium. Since the surface area of ​​the solid working medium is generally fixed, controlling the temperature can stabilize the mass flow rate of gaseous iodine output from the tank. Specifically, the higher the heating temperature, the faster the sublimation rate of iodine. When the vapor pressure above the solid is the same as the saturated vapor pressure corresponding to the heating temperature, the sublimation rate and condensation rate reach a dynamic equilibrium.

[0060]

[0061] In the formula, α is the adhesion coefficient (usually denoted as 1); A is the surface area, m 2 M is the molar mass, g / mol; R is the universal gas constant, J / (mol·K); P is the ambient gas pressure, Pa; T is the ambient gas temperature, K; P vap , which is the vapor pressure at the corresponding temperature, in Pa.

[0062] Specifically, the control method further includes:

[0063] When the real-time temperature inside the iodine container is lower than the lower limit of the first temperature control range, the first heating element is controlled to heat the iodine container at maximum heating power.

[0064] When the real-time temperature inside the iodine container is higher than the upper limit of the first temperature control range, the first heating element is controlled to stop heating the iodine container.

[0065] Compared with the prior art, the present invention adopts a stepped control strategy for the iodine tank. When the real-time temperature of the iodine tank is lower than the first temperature control range, the maximum heating power is used for heating, and when it is higher than the first temperature control range, the heating is stopped. This enables the temperature of the iodine tank to quickly reach the temperature control range for precise temperature adaptive control, which is beneficial to improving the efficiency of the system and saving on-board computing power.

[0066] In practice, for example, the first target temperature for temperature control of the iodine tank in the storage and supply system is 85℃, and the first temperature control range is 83℃~87℃. When the temperature inside the iodine tank is less than 83℃, the first heating element heats at its maximum heating power (full power). When the temperature inside the iodine tank is greater than 87℃, the first heating element stops heating. When the temperature inside the iodine tank is between 83℃ and 87℃, adaptive control is activated for rapid and stable temperature regulation.

[0067] In one embodiment, the control method further includes:

[0068] A second target temperature and a second temperature control range are set for temperature control of the pipelines in the storage and supply system; the second target temperature is within the second temperature control range.

[0069] The real-time temperature inside the pipeline is collected. When the real-time temperature inside the pipeline is within the second temperature control range, the heating power of the second heating element is adaptively controlled to stabilize the temperature inside the pipeline at the second target temperature. The parameters for adaptive control of the second heating element are determined using a sparrow search algorithm.

[0070] In this invention, considering the different characteristics of the iodine tank and pipeline in the storage and supply system, the temperature of the iodine tank and pipeline is controlled independently.

[0071] The storage and supply system includes iodine tanks for storing iodine working fluid and pipelines for supplying iodine working fluid to the electric thrusters. The temperature and pressure inside the iodine tanks directly affect the sublimation rate of the iodine working fluid. Controlling the temperature inside the iodine tanks enables the sublimation rate of the iodine working fluid to stabilize rapidly in deep space. After sublimation, the iodine working fluid enters the pipelines. By controlling the temperature of the pipelines, rapid and stable control of the iodine working fluid mass flow rate in deep space can be achieved. Consequently, with high precision and stability in the iodine working fluid mass flow rate, the thrust of the iodine-based electric thrusters can also meet the requirements of high precision and stability.

[0072] Specifically, the formula for calculating the thrust of an iodine-based electric thruster is:

[0073]

[0074] In the formula, T is the thrust; For iodine mass flow rate; v i The velocity of iodide ions.

[0075] During implementation, the temperature control of the iodine tank is used to ensure the stable generation and supply of gaseous iodine, while the temperature control of the pipeline is used to ensure the stability and accuracy of gaseous iodine. The target temperature and temperature control range of the iodine tank and the pipeline are different, which are used to achieve their respective functions, thereby optimizing the performance of the entire system.

[0076] Specifically, the control method further includes:

[0077] When the real-time temperature inside the pipeline is lower than the lower limit of the second temperature control range, the second heating element is controlled to heat the pipeline with maximum heating power.

[0078] When the real-time temperature inside the pipeline is higher than the upper limit of the second temperature control range, the second heating element is controlled to stop heating the pipeline.

[0079] Similarly, in this embodiment of the invention, a stepped control strategy is adopted for the pipeline. When the real-time temperature of the pipeline is lower than the second temperature control range, the maximum heating power is used for heating, and when it is higher than the second temperature control range, the heating is stopped. This enables the pipeline temperature to quickly reach the temperature control range for precise temperature adaptive control, which is beneficial to improving system efficiency and saving on-board computing power.

[0080] In practice, for example, the second target temperature for temperature control of the pipeline in the storage and supply system is 105℃, and the second temperature control range is 103℃~107℃. When the temperature inside the pipeline is less than 103℃, the second heating element heats at its maximum heating power (full power). When the temperature inside the pipeline is greater than 107℃, the second heating element stops heating. When the temperature inside the pipeline is between 103℃ and 107℃, adaptive control is activated for rapid and stable temperature regulation.

[0081] In one embodiment, the control method of the present invention further includes:

[0082] The pressure at the inlet and outlet of the pipeline is detected respectively;

[0083] The mass flow rate of the iodine working fluid in the pipeline is calculated based on the pressure at the pipeline inlet and outlet and the temperature inside the pipeline.

[0084] The control accuracy is evaluated based on the mass flow rate of the iodine working fluid in the pipeline.

[0085] In this embodiment of the invention, the mass flow rate of the iodine working fluid in the pipeline is obtained based on the pressure and temperature parameters of the pipeline, realizing dynamic monitoring of the flow rate, and then evaluating the control accuracy, so as to achieve high-precision and high-stability control of the iodine electric propulsion storage and supply system.

[0086] Based on the principle of thermal throttling, controlling the gas temperature changes the gas viscosity, thus affecting the gas pressure inside the pipe and the mass flow rate of the iodine working fluid in the pipeline. The formula for calculation is:

[0087]

[0088] In the formula, P in The pressure at the pipeline inlet (Pa);

[0089] P out The pressure at the pipe outlet (Pa); D is the inner diameter of the pipe (mm);

[0090] L is the length of the thermal throttling tube (mm);

[0091] μ is the dynamic viscosity coefficient of the gas (Pa·s); considering the pressure range during the experiment, the fixed size of the pipeline, and the essentially unchanged properties of iodine vapor, the dynamic viscosity can be taken as a constant value of 179.5 × 10⁻⁶. -7 Pa·s;

[0092] T d The gas temperature inside the hot throttling tube (K); within the normal pressure range, at a temperature of 379K, take 179.5 × 10⁻⁶. -7 Pa·s;

[0093] R is the gas constant (J / kg·K); for iodine, R = 32.76 J / kg·K;

[0094] k is the shape factor, a dimensionless quantity that takes into account the design characteristics of the pipeline.

[0095] More specifically, the control accuracy is evaluated based on the convergence time or steady-state fluctuation of the mass flow rate of the iodine working fluid in the pipeline. A shorter convergence time and smaller steady-state fluctuation indicate higher control accuracy.

[0096] Specifically, the parameters for adaptive control are determined using the sparrow search algorithm, including the following steps:

[0097] The parameters of the adaptive control are regarded as the location information of sparrows, and the parameters of the adaptive control are expressed in the form of a sparrow population.

[0098] The fitness function is determined, and the butterfly optimization algorithm and adaptive inertia weight strategy are used to improve the position update formula of the discoverer. The Levy flight strategy is used to improve the position update formula of the follower. The hierarchy strategy in the gray wolf optimization algorithm is used to improve the position update formula of the watcher.

[0099] Initialize the sparrow population and divide it into discoverers, followers, and vigilants;

[0100] Randomly generate the location of sparrow populations within the search space;

[0101] Determine the fitness function, calculate the fitness value of all sparrows in the sparrow population according to the fitness function, sort them according to the fitness value, and select the position of the sparrow with the smallest fitness value as the optimal position;

[0102] Update the discoverer's position based on the improved discoverer position update formula;

[0103] Update the follower's position based on the improved follower position update formula;

[0104] Update the vigilant's position based on the improved vigilant position update formula;

[0105] Iteratively calculate the fitness value of all sparrows in the sparrow population, and replace the parent position with the position of the offspring with the better fitness value;

[0106] Determine whether the current iteration count has reached the set maximum iteration count. If the maximum iteration count has been reached, output the parameters of the adaptive control corresponding to the optimal position as the parameters of the optimal adaptive control. If the maximum iteration count has not been reached, continue iterating.

[0107] The sparrow search algorithm mainly achieves location optimization by imitating the foraging and anti-predation behaviors of sparrows. The sparrow search algorithm divides the sparrow population into three types: discoverers, followers, and vigilants. Discoverers have a high fitness value and are responsible for providing foraging areas and directions for followers. Followers will follow the discoverers and monitor them in order to get better food, and then compete for food. Vigilants will send a signal after discovering a predator, and the entire sparrow population will take anti-predation actions.

[0108] Compared with existing technologies, this invention improves the sparrow search algorithm based on multiple strategies. Specifically, it uses the butterfly optimization algorithm and adaptive inertial weight strategy to improve the finder's position update formula, the Levy flight strategy to improve the follower's position update formula, and the hierarchy strategy in the gray wolf optimization algorithm to improve the watcher's position update formula. This avoids getting stuck in local optima during the search, which helps to improve the global search capability, convergence speed, accuracy, and robustness of the sparrow search algorithm, thereby ensuring the control accuracy of the system.

[0109] To address the issue of the sparrow search algorithm easily getting trapped in the optimum, the butterfly optimization algorithm and adaptive inertia weight strategy are used to optimize the discoverer position update method.

[0110] The butterfly optimization algorithm involves both global search and local search processes. The process of a butterfly moving closer to another butterfly when it senses that another butterfly is emitting a stronger scent in a certain area is called global search. When a butterfly cannot sense a stronger scent than its own, it will move randomly, which is called local search.

[0111] The scent produced by butterflies involves three parameters: the sensory factor c, the stimulus intensity I, and the power exponent α. The stimulus intensity is related to the butterfly's fitness. The expression for the scent is as follows:

[0112] f = c·I α .

[0113] During the global search phase, the butterfly moves towards the optimal solution g. * Movement can be represented as follows:

[0114]

[0115] In the formula, f represents the solution for the i-th butterfly in the t-th iteration; i Let r be the scent of the i-th butterfly, and r be a random number between 0 and 1, i.e., r = rand(0,1).

[0116] The local search phase can be represented as:

[0117]

[0118] in, and It is randomly selected, r is a random number between 0 and 1, r = rand(0,1).

[0119] Adaptive inertia weighting is a method used in Particle Swarm Optimization (PSO) to balance global and local search capabilities. In PSO, to improve particle convergence and prevent particles from getting trapped in local optima, inertia weights are introduced into the velocity update formula. A typical linear decreasing strategy is as follows:

[0120]

[0121] In the formula, T represents the current iteration number, T max ω represents the maximum number of iterations, and ω represents the inertia weight. max ω represents the upper limit of the inertia weight; min This represents the lower bound of the inertia weight.

[0122] A method combining linear and nonlinear decay strategies is introduced. The nonlinear decay uses the Sigmoid function.

[0123]

[0124] Utilizing the non-linearly increasing and infinitely approaching constant characteristics of the Sigmoid function, an adaptive inertial weight is constructed for updating the discoverer's position in the sparrow search algorithm:

[0125]

[0126] Among them, S max The δ represents the x-axis limit value of the Sigmoid function. 2 γ represents the fitness variance; ω represents the variance threshold; max ω represents the upper limit of the inertia weight; min The lower bound of the inertia weight is represented; T represents the current iteration number; T max This indicates the maximum number of iterations.

[0127] The improved discoverer location update formula is as follows:

[0128]

[0129] In the formula, is the position of the j-th dimension of the i-th discoverer at the (t + 1)-th iteration, is the position of the j-th dimension of the i-th discoverer at the t-th iteration, ω(t) is the adaptive update weight, is the global optimal position at the t-th iteration, r is a random number in the interval (0, 1), f i is the butterfly fragrance of the i-th discoverer at the t-th iteration, Q is a random number obeying the normal distribution, L is a 1×d all-1 matrix, R2 is the warning value, R2 is a random number uniformly distributed in the interval (0, 1], ST is the safety threshold, and ST ∈ [0.5, 1].

[0130] When R2 < ST, it indicates that the environment where the current population is located is safe, and the discoverer can perform a global search and adopt the first position update method. When R2 ≥ ST, it indicates that some sparrows have discovered danger, and the second position update method is adopted.

[0131] When the followers perceive that the discoverer has found a better food source, a large number of them will flock in, making the population density around the discoverer too high, thus easily falling into local optimum. The Levy flight strategy can generate diverse step sizes and has certain ergodicity and randomness. The updated position of the followers optimized by the Levy flight strategy can expand the search space and improve the problem of being prone to falling into local optimum.

[0132] The Levy flight strategy is an optimization algorithm that simulates the random foraging behavior of animals in nature. It is realized through a non-Gaussian random process and is characterized by a probability density distribution with heavy tails, capable of simulating the behavior of animals occasionally making long-distance jumps.

[0133] In the embodiment of the present invention, the Levy flight strategy is as follows:

[0134]

[0135] In the formula, the scaling factor takes the value of 0.01, r1 and r2 are both uniformly distributed random numbers within the range of [0, 1], α is the exponent of the stable distribution, and α is a random number between (0, 1); θ is a parameter related to the exponent α of the stable distribution, Γ is the gamma function, where Γ(x) = (x - 1)!.

[0136] The improved updated formula for the position of the followers is:

[0137]

[0138] In the formula, is the position of the j-th dimension of the i-th follower at the (t + 1)-th iteration, Let be the position of the i-th follower in the j-th dimension during the t-th iteration, and Q be a random number following a normal distribution. Let be the global worst position in the t-th iteration. Let be the local optimal position in the (t+1)th iteration, Levy(d) be the Levy flight step size, and n be the total number of follower sparrows. At that time, the i-th follower with a poor fitness value may starve.

[0139] In the sparrow search algorithm, the escape methods of individual sparrows are monotonous and narrow, and the algorithm only considers the optimal solution of the current state during the update process. This leads to premature convergence of all individuals to the current best individual, making the entire algorithm prone to getting trapped in local optima. Applying a gray wolf hierarchy, the algorithm introduces the concept of a gray wolf pack, defining α wolves, β wolves, and γ wolves as the main leaders in the population, and updates the α wolf (optimal solution), β wolf (second-best solution), and γ wolf (third-best solution).

[0140] When improving the guard position update formula using the hierarchy strategy in the gray wolf optimization algorithm, the optimal, second-best, and third-best positions from the previous iteration are selected as α wolf, β wolf, and γ wolf, respectively. The improved guard position update formula is as follows:

[0141]

[0142] In the formula, Let this be the position of the i-th guard in the j-th dimension at the (t+1)-th iteration. The position of the i-th vigilant in the j-th dimension at iteration t. Let x represent the positions of α wolf, β wolf, and γ wolf in the j-th dimension at the t-th iteration. 1,j x 2,j x 3,j Let f be the position of α wolf, β wolf, and γ wolf in the j-th dimension at the (t+1)-th iteration, μ be the ratio of discoverer to follower, and φ1, φ2, and φ3 be the weights of α wolf, β wolf, and γ wolf, respectively, where φ1 + φ2 + φ3 = 1, f i Let f be the fitness value of the i-th vigilant. α f β f γ represents the fitness values ​​of α wolf, β wolf, and γ wolf, respectively, and K is a constant used to control the step size for position updates. Let f be the global worst position in the t-th iteration. w ε is the fitness value of the worst position globally, and ε is a very small positive number.

[0143] The weight values ​​(i.e., the importance of wolves) for α wolves, β wolves, and γ wolves are calculated using the following formulas:

[0144]

[0145] Where ω i It is an important factor.

[0146] Preferably, a Piecewise chaotic mapping strategy is adopted to generate chaotic sequences and convert them into the initial positions of the sparrow population through mapping. The expression for the Piecewise chaotic mapping is as follows:

[0147]

[0148] Among them, X k For the current sequence, X k+1 For the next sequence after the chaotic mapping, X k ∈[0,1], the initial value X1 is a random number that follows a uniform distribution; k∈(1,dim-1); p is a parameter that controls the mapping effect.

[0149] After chaotic mapping, the initial position of the sparrow population is:

[0150] X new =lb+X k ·(ub-lb)

[0151] Among them, X new The initial position of the sparrow population is indicated by ub and lb, which represent the upper and lower bounds of the population, respectively.

[0152] In this embodiment of the invention, considering that the existing SSA uses a strategy of randomly generating the initial position of the population, which leads to a lack of ergodicity in the algorithm, and thus differences in the distribution of individuals in the population, and even the fitness value of a certain individual in the population being much better than the average fitness value of the population, thereby reducing the search breadth of the algorithm, this embodiment of the invention adopts a Piecewise chaotic mapping strategy in order to ensure a uniform initial distribution of the population and improve the search ergodicity of the algorithm. The chaotic sequence is generated by mapping and converted into the initial position of the sparrow population.

[0153] Specifically, the formula for the fitness function of the sparrow search algorithm is:

[0154]

[0155] In the formula, f is the fitness value, e(t) is the difference between the temperature inside the tank and the first target temperature, u(t) is the heating power of the first heating element, w1, w2 and w3 are the parameter weight values, and w3 >> w1.

[0156] In the fitness function of this embodiment, considering the dynamic characteristics of the iterative process, the integral of the temperature difference with respect to the value is used, and a squared term of the control value (i.e., heating power) is added to avoid excessive control amplitude. The temperature difference term and the control value term are multiplied by corresponding weighting coefficients w1 and w2 to balance the importance of error and control amplitude in the fitness function. Simultaneously, to avoid overshoot, a parameter w3 is introduced into the fitness function for adjustment.

[0157] In practice, w1 = 0.999, w2 = 0.001, and w3 = 100.

[0158] In one embodiment, such as Figure 2 As shown, the adaptive control is PID control, and the control formula for PID control is:

[0159]

[0160] In the formula, u(t) is the heating power of the heating element, e(t) is the temperature difference, and K is the kJ / m³. p K is a proportional parameter. i K is the integration parameter. d is the differential parameter.

[0161] PID control is a commonly used adaptive control method that achieves precise control of the system through proportional, integral, and micro-adjustment actions. The proportional adjustment calculates the error between the current actual temperature and the set temperature by multiplying it by a proportional coefficient K. p The result is then used as input to the controlled object. The temperature error e(t+1) during the next control cycle will gradually decrease until the actual temperature approaches the set target temperature. Integral control further strengthens control based on proportional control. When a steady-state error occurs, proportional control alone is insufficient to bring the temperature back to the set value. In this case, the integral of the introduced error is multiplied by an integral coefficient K. i This allows the system to continue adjusting the temperature and eliminate static errors. Differential control is used to improve the response speed of the temperature control system. When the temperature changes drastically, an effective early correction signal is introduced into the system, namely the temperature change rate and a differential coefficient K. d This improves the system's response speed.

[0162] When using the sparrow search algorithm to optimize the PID control algorithm for the iodine tank, a certain number of sparrows are initialized in the three-dimensional search space, with each sparrow representing a set of PID control parameter values ​​(K). p K i K d ).

[0163] Specifically, when using the sparrow search algorithm to determine the parameters of the PID control for the iodine tank, the PID control parameters are expressed in the form of a sparrow population, and the expression is as follows:

[0164]

[0165] In the formula, X is the position matrix of the sparrow population, t is the number of iterations, and n is the number of sparrows in the sparrow population.

[0166] Correspondingly, the fitness matrix of the sparrow population is:

[0167]

[0168] When using the sparrow search algorithm to optimize the PID control algorithm, for the control of the iodine tank and pipeline, not only can different fitness functions be designed, but also different initial values ​​and search ranges of PID parameters can be set to optimize the control accuracy of the system.

[0169] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0170] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A control method for an iodine-based working fluid electric propulsion storage and supply system, characterized in that, The control method includes: A first target temperature and a first temperature control range are set for the iodine tank in the storage and supply system; the first target temperature is within the first temperature control range. The real-time temperature inside the iodine container is detected. When the real-time temperature inside the iodine container is within the first temperature control range, the heating power of the first heating element of the iodine container is adaptively controlled to stabilize the temperature inside the iodine container at the first target temperature. In the adaptive control, a sparrow search algorithm is used to determine the parameters for adaptive control of the first heating element.

2. The control method according to claim 1, characterized in that, The control method further includes: When the real-time temperature inside the iodine container is lower than the lower limit of the first temperature control range, the first heating element is controlled to heat the iodine container at maximum heating power. When the real-time temperature inside the iodine container is higher than the upper limit of the first temperature control range, the first heating element is controlled to stop heating the iodine container.

3. The control method according to claim 2, characterized in that, The control method further includes: A second target temperature and a second temperature control range are set for temperature control of the pipelines in the storage and supply system; the second target temperature is within the second temperature control range. The real-time temperature inside the pipeline is collected. When the real-time temperature inside the pipeline is within the second temperature control range, the heating power of the second heating element is adaptively controlled to stabilize the temperature inside the pipeline at the second target temperature. In the adaptive control, a sparrow search algorithm is used to determine the parameters for adaptive control of the second heating element.

4. The control method according to claim 3, characterized in that, The control method further includes: When the real-time temperature inside the pipeline is lower than the lower limit of the second temperature control range, the second heating element is controlled to heat the pipeline with maximum heating power. When the real-time temperature inside the pipeline is higher than the upper limit of the second temperature control range, the second heating element is controlled to stop heating the pipeline.

5. The control method according to claim 4, characterized in that, The method further includes: The pressure at the inlet and outlet of the pipeline is detected respectively; The mass flow rate of the iodine working fluid in the pipeline is calculated based on the pressure at the pipeline inlet and outlet and the temperature inside the pipeline. The control accuracy is evaluated based on the mass flow rate of the iodine working fluid in the pipeline.

6. The control method according to any one of claims 1-5, characterized in that, The parameters for adaptive control are determined using the sparrow search algorithm, which includes the following steps: The parameters of the adaptive control are regarded as the location information of sparrows, and the parameters of the adaptive control are expressed in the form of a sparrow population. The fitness function is determined, and the butterfly optimization algorithm and adaptive inertia weight strategy are used to improve the position update formula of the discoverer. The Levy flight strategy is used to improve the position update formula of the follower. The hierarchy strategy in the gray wolf optimization algorithm is used to improve the position update formula of the watcher. Initialize the sparrow population and divide it into discoverers, followers, and vigilants; Randomly generate the location of sparrow populations within the search space; Determine the fitness function, calculate the fitness value of all sparrows in the sparrow population according to the fitness function, sort them according to the fitness value, and select the position of the sparrow with the smallest fitness value as the optimal position; Update the discoverer's position based on the improved discoverer position update formula; Update the follower's position based on the improved follower position update formula; Update the vigilant's position based on the improved vigilant position update formula; Iteratively calculate the fitness value of all sparrows in the sparrow population, and replace the parent position with the position of the offspring with the better fitness value; Determine whether the current iteration count has reached the set maximum iteration count. If the maximum iteration count has been reached, output the parameters of the adaptive control corresponding to the optimal position as the parameters of the optimal adaptive control. If the maximum iteration count has not been reached, continue iterating.

7. The control method according to claim 6, characterized in that, The improved discoverer location update formula is as follows: In the formula, It represents the position of the j-th dimension of the i-th discoverer during the (t+1)-th iteration. It represents the position of the i-th discoverer in the j-th dimension at the t-th iteration, and ω(t) is the adaptive update weight. Let f be the globally optimal position in the t-th iteration, r be a random number in the interval (0,1), and f be the position of the global optimum. i is the butterfly scent of the i-th discoverer in the t-th iteration, Q is a random number following a normal distribution, L is a 1×d matrix of all 1s, R2 is the warning value, R2 is a random number uniformly distributed in the interval (0,1], and ST is the safety threshold, ST∈[0.5,1].

8. The control method according to claim 6, characterized in that, The improved follower position update formula is as follows: In the formula, It represents the j-th dimension position of the i-th follower at the (t+1)-th iteration. Let be the position of the i-th follower in the j-th dimension during the t-th iteration, and Q be a random number following a normal distribution. Let be the global worst position in the t-th iteration. Let be the local optimal position in the (t+1)th iteration, Levy(d) be the Levy flight step size, and n be the total number of follower sparrows.

9. The control method according to claim 6, characterized in that, When improving the guard position update formula using the hierarchy strategy in the gray wolf optimization algorithm, the optimal, second-best, and third-best positions from the previous iteration are selected as α wolf, β wolf, and γ wolf, respectively. The improved guard position update formula is as follows: In the formula, Let this be the position of the i-th guard in the j-th dimension at the (t+1)-th iteration. The position of the i-th vigilant in the j-th dimension at iteration t. Let x represent the positions of α wolf, β wolf, and γ wolf in the j-th dimension at the t-th iteration. 1,j x 2,j x 3,j Let f be the position of α wolf, β wolf, and γ wolf in the j-th dimension at the (t+1)-th iteration, μ be the ratio of discoverer to follower, and φ1, φ2, and φ3 be the weights of α wolf, β wolf, and γ wolf, respectively, where φ1 + φ2 + φ3 = 1, f i Let f be the fitness value of the i-th vigilant. α f β f γ represents the fitness values ​​of α wolf, β wolf, and γ wolf, respectively, and K is a constant used to control the step size for position updates. Let f be the global worst position in the t-th iteration. w ε is the fitness value of the worst position globally, and ε is a very small positive number.

10. The control method according to any one of claims 1-5, characterized in that, The adaptive control is PID control, and the control formula for PID control is: In the formula, u(t) is the heating power of the heating element, e(t) is the temperature difference, and K is the kJ / m³. p K is a proportional parameter. i K is the integration parameter. d is the differential parameter.

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