Vehicle speed control methods, devices, computer equipment and storage media

Abstract

This application relates to the field of vehicle control technology, and in particular to a vehicle speed control method, device, computer equipment, and storage medium. The method includes: constructing a whole-vehicle dynamics model corresponding to the target vehicle, and designing a corresponding sliding-film robust control model for the target vehicle; optimizing the parameters of the whole-vehicle dynamics model and the sliding-film robust control model based on an iterative optimization algorithm; and controlling the vehicle speed according to the optimized whole-vehicle dynamics model and the optimized sliding-film robust control model. This application achieves vehicle speed control based on the optimized whole-vehicle dynamics model and the optimized sliding-film robust control model, preventing inaccurate vehicle speed control due to abnormal parameters in the whole-vehicle dynamics model and the sliding-film robust control model, which would affect normal driving and driving experience, and improve the control stability and driving safety of the target vehicle during driving.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle speed control method, device, computer equipment, and storage medium. Background Technology

[0002] In recent years, the rapid development of the global economy and technology has led to the rapid growth of the automotive industry. Considering that traditional gasoline-powered vehicles consume large amounts of gasoline and diesel fuel, and also generate significant amounts of greenhouse gases, ultimately contributing to resource scarcity and environmental problems, developing clean and pollution-free electric vehicles is an inevitable choice for my country in order to reduce greenhouse gas emissions and lower oil consumption. Even though gasoline-powered vehicles currently dominate the market, the electric vehicle industry is developing rapidly, with the market share of electric vehicles steadily increasing year by year.

[0003] Vehicle speed control is a crucial aspect of automotive control systems, as its accuracy and speed directly impact the safety and stability of the system. Currently, common speed control methods include PID control, sliding mode control (SMC), and model predictive control. While PID algorithms offer advantages such as simple structure and few parameters, they require high model accuracy, are susceptible to disturbances that severely affect control precision, and struggle to completely ignore sensor signal disturbances. Model predictive control offers high accuracy by incorporating future model states, but its complex structure and numerous parameters make it problematic. Sliding mode control boasts excellent anti-interference capabilities, exhibiting strong robustness against noise interference and parameter uncertainties in the dynamic equations, and is widely used in models where disturbance terms are unavoidable. The suitability of SMC parameters directly affects control performance; therefore, adjusting SMC model parameters to improve speed control performance is the focus of this invention. Summary of the Invention

[0004] Therefore, it is necessary to provide a vehicle speed control method, device, computer equipment, and storage medium that can accurately control the speed of a target vehicle in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a vehicle speed control method. The method includes:

[0006] Construct a vehicle dynamics model corresponding to the target vehicle, and design a sliding film robust control model corresponding to the target vehicle;

[0007] Based on the iterative optimization algorithm, the parameters of the vehicle dynamics model and the slippage robust control model are optimized.

[0008] Based on the optimized vehicle dynamics model and the optimized slicker robust control model, the vehicle speed is controlled.

[0009] In one embodiment, the parameter optimization of the vehicle dynamics model and the slippage robust control model based on an iterative optimization algorithm includes:

[0010] Obtain the parameters to be optimized in the vehicle dynamics model and the slippage robust control model;

[0011] Based on the iterative optimization algorithm, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0012] In one embodiment, the step of optimizing the parameters to be optimized according to the iterative optimization algorithm to obtain the optimized vehicle dynamics model and the optimized slippage robust control model includes:

[0013] Determine at least one initial parameter value corresponding to the parameter to be optimized;

[0014] Determine the fitness corresponding to each of the initial parameter values;

[0015] Based on the fitness corresponding to each of the initial parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0016] In one embodiment, the step of optimizing the parameters to be optimized based on the fitness corresponding to each of the initial parameter values ​​to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model includes:

[0017] Based on the fitness corresponding to each of the initial parameter values, the target initial parameter value is determined from each of the initial parameter values;

[0018] Based on the initial values ​​of the target parameters, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0019] In one embodiment, the step of optimizing the parameters to be optimized based on the target initial parameter values ​​to obtain the optimized vehicle dynamics model and the optimized slippage robust control model includes:

[0020] Based on the target initial parameter values, a first simulated migration and a second simulated migration are performed on each of the initial parameter values ​​to obtain the first population position corresponding to the first simulated migration and the second population position corresponding to the second simulated migration.

[0021] Based on the positions of the first and second populations, the initial parameter values ​​are adjusted to obtain candidate parameter values;

[0022] Based on the candidate parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0023] In one embodiment, the parameters to be optimized include: a first parameter to be optimized in the vehicle dynamics model and a second slippage robust control model in the slippage robust control model.

[0024] Secondly, this application also provides a vehicle speed control device. The device includes:

[0025] The module is used to construct the whole vehicle dynamics model corresponding to the target vehicle, and to design the sliding film robust control model corresponding to the target vehicle.

[0026] An optimization model is used to optimize the parameters of the vehicle dynamics model and the sliding film robust control model based on an iterative optimization algorithm.

[0027] The control model is used to control the speed of the target vehicle based on the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0028] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0029] Construct a vehicle dynamics model corresponding to the target vehicle, and design a sliding film robust control model corresponding to the target vehicle;

[0030] Based on the iterative optimization algorithm, the parameters of the vehicle dynamics model and the slippage robust control model are optimized.

[0031] Based on the optimized vehicle dynamics model and the optimized slicker robust control model, the vehicle speed is controlled.

[0032] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0033] Construct a vehicle dynamics model corresponding to the target vehicle, and design a sliding film robust control model corresponding to the target vehicle;

[0034] Based on the iterative optimization algorithm, the parameters of the vehicle dynamics model and the slippage robust control model are optimized.

[0035] Based on the optimized vehicle dynamics model and the optimized slicker robust control model, the vehicle speed is controlled.

[0036] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0037] Construct a vehicle dynamics model corresponding to the target vehicle, and design a sliding film robust control model corresponding to the target vehicle;

[0038] Based on the iterative optimization algorithm, the parameters of the vehicle dynamics model and the slippage robust control model are optimized.

[0039] Based on the optimized vehicle dynamics model and the optimized slicker robust control model, the vehicle speed is controlled.

[0040] The aforementioned vehicle speed control method, device, computer equipment, and storage medium construct a vehicle dynamics model corresponding to the target vehicle and design a corresponding slicker-film robust control model for the target vehicle. Based on an iterative optimization algorithm, the parameters of the vehicle dynamics model and the slicker-film robust control model are optimized. Based on the optimized vehicle dynamics model and the optimized slicker-film robust control model, the vehicle speed is controlled. As can be seen from the above, this application, by constructing a vehicle dynamics model and a slicker-film robust control model and optimizing the parameters of the vehicle dynamics model and the slicker-film robust control model through an iterative optimization algorithm, achieves vehicle speed control of the target vehicle based on the optimized vehicle dynamics model and the optimized slicker-film robust control model. This prevents inaccurate vehicle speed control due to abnormal parameters of the vehicle dynamics model and the slicker-film robust control model, which could affect normal driving and driving experience, and improves the control stability and driving safety of the target vehicle during driving. Attached Figure Description

[0041] Figure 1 An application environment diagram of a vehicle speed control method provided in this application embodiment;

[0042] Figure 2 A flowchart illustrating the first vehicle speed control method provided in this application embodiment;

[0043] Figure 3 This is a control flowchart of the sliding mode control module provided in an embodiment of this application;

[0044] Figure 4 The control flowchart of the gorilla force optimization algorithm provided in the embodiments of this application;

[0045] Figure 5 The control flowchart of the improved gorilla force optimization algorithm provided in the embodiments of this application;

[0046] Figure 6 A flowchart illustrating the second vehicle speed control method provided in this application embodiment;

[0047] Figure 7 The control flowchart based on the improved gorilla force optimized sliding mode control model is provided for the embodiments of this application;

[0048] Figure 8 A flowchart illustrating the third vehicle speed control method provided in this application embodiment;

[0049] Figure 9 A structural block diagram of the first type of vehicle speed control device provided in the embodiments of this application;

[0050] Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application. In the description of this application, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0053] The vehicle speed control method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on the cloud or other network servers. The process involves constructing a whole-vehicle dynamics model corresponding to the target vehicle and designing a corresponding sliding film robust control model for the target vehicle; optimizing the parameters of the whole-vehicle dynamics model and the sliding film robust control model based on an iterative optimization algorithm; and controlling the vehicle speed based on the optimized whole-vehicle dynamics model and the optimized sliding film robust control model. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0054] In one embodiment, Figure 2 As shown, a vehicle speed control method is provided, which is applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps:

[0055] S201, construct the vehicle dynamics model corresponding to the target vehicle, and design the sliding film robust control model corresponding to the target vehicle.

[0056] In one embodiment of this application, when it is necessary to construct a vehicle dynamics model corresponding to the target vehicle, the dynamic equations can be established based on the driving force and driving resistance equations in longitudinal dynamics. The driving force and driving resistance equations are as follows:

[0057] (1)

[0058] in, Indicates motor torque; Indicates the gear ratio of the transmission; Indicates the gear ratio of the main reducer; Indicates the mechanical efficiency of the transmission system; Indicates the wheel radius; Indicates the quality of the car; Represents gravitational acceleration; Indicates the rolling resistance coefficient; Indicates the slope the car is on; Indicates the air drag coefficient; Indicates the windward area; Indicates vehicle speed (in kilometers per hour); This indicates the conversion factor for the rotational mass of a vehicle. This indicates the speed of a car (in meters per second).

[0059] A vehicle dynamics model corresponding to the target vehicle is constructed by equating acceleration in the driving force and driving resistance equations to resistance; based on the above equations, displacement is selected. Speed As a state quantity x = [ v X ] T Request motor torque As a control quantity speed As output Establish the state-space equations. Considering the relationship between displacement, velocity, and acceleration, the state-space equations are as follows:

[0060] (2)

[0061] In this way, the information from the acceleration sensor is taken into account, so as to avoid updating the vehicle speed estimate without updating the slope estimate. This allows the acceleration sensor and the slope sensor to be used together to correct the estimation results, making the results closer to reality.

[0062] In another embodiment of this application, designing a sliding mode robust control model corresponding to the target vehicle may include the following: First, the sliding surface should be determined, and the relationship between the controlled variable and the target should be mapped onto the sliding surface; second, a specific approach rate should be designed, that is, the error between the output and the target value should be quantified, and then the speed at which the control system reaches the sliding surface should be controlled. Generally, there are constant velocity approach rate, exponential approach rate, power approach rate, etc. Among many commonly used approach rates, considering that the approach speed of the exponential approach rate gradually decreases, and that the constant velocity approach term is added to the exponential approach rate to ensure rapid approach while reducing chattering, this invention selects the exponential approach rate, as shown below:

[0063] (3)

[0064] in, It is the exponentially approaching term, when the parameter The larger the value, the faster the speed at which it approaches the sliding surface. It is a constant velocity approaching term. Adding a constant velocity approaching term can help eliminate flutter.

[0065] Based on this, a Lyapunov function is set to determine the stability of the selected system. The selection formula in this invention is as follows:

[0066] (4)

[0067] Its derivative is expressed as follows:

[0068] (5)

[0069] When satisfied and The system stability can be proven when the equation (4) and equation (5) are analyzed. When not equal to 0, the formula (4) always exists. That is, when formula (6) is used. When the condition is always true, it can be proven that the system is stable. Analyze formula (5). and The opposite sign means that it is always present. The system is stable. Analyzing formula (3), when the approach rate shown in formula (3) is established, the system can be stabilized.

[0070] To further explain, the Sliding Mode Control (SMC) control diagram is as follows: Figure 3 As shown, the specific design of the sliding mode control model includes: based on the vehicle driving force and driving resistance equation (2), considering that the ultimate goal of this study is to control the vehicle speed to a certain target speed by controlling the motor torque, that is, to be able to follow the target speed for the actual vehicle speed, the sliding surface is set as follows.

[0071] (6)

[0072] in, To track the error, it is numerically equal to the difference between the target value and the output value, and is set as follows:

[0073] (7)

[0074] By combining the sliding surface with the exponential convergence rate selected in this invention, the following equation is established:

[0075] (8)

[0076] Obtain the control quantity that satisfies system stability for:

[0077] (9)

[0078] Further explanation: Slippery diaphragm robust control may include: In actual engineering, sensors often suffer from error interference. If the collected real vehicle data contains noise interference, it will reduce the reliability of the driving force driving resistance equation. Considering that SMC has excellent anti-interference capabilities, this invention considers the influence of external noise interference and establishes a slippery diaphragm robust controller based on the driving force driving resistance equation. The driving force driving resistance equation model considering noise error is shown below:

[0079] (10)

[0080] in, To simulate external errors during real-world vehicle operation by introducing unknown disturbances that vary over time, the chosen rate of convergence for the above model that considers unknown disturbances is changed as follows:

[0081] (11)

[0082] Among them, when When, select ;when When, select That is, to ensure that it exists at all times. Since the sliding mode reachability condition is met, the sliding mode control system designed in this invention is asymptotically stable.

[0083] The simultaneous selection of exponential convergence rates becomes as follows:

[0084] (12)

[0085] Get input that meets the conditions for

[0086] (13)

[0087] S202 optimizes the parameters of the vehicle dynamics model and the sliding film robust control model based on an iterative optimization algorithm.

[0088] It should be noted that the iterative optimization algorithm mentioned here can be the Gorilla Troops Optimizer (GTO). The Gorilla Troops Optimizer (GTO) is an intelligent optimization algorithm. Compared to optimization algorithms such as Sparrow, Wolf, Snake, Whale, Goat, Ant Colony, and Butterfly, GTO has the fastest solution speed overall, the highest optimization performance with a finite number of iterations, and good performance in terms of both accuracy and convergence speed.

[0089] Specifically, GTO is mainly divided into two phases: the free exploration phase and the population development phase.

[0090] During the free exploration phase, the algorithm simulates the migratory behavior of a gorilla population. GTO designed three migration modes: individual gorillas migrating to a completely unknown area; migrating to the vicinity of a known area; and migrating to the vicinity of other gorillas' locations. These migration modes work together to constitute the migratory behavior of the gorilla population, and the three different migration modes enhance GTO's spatial search capabilities. The specific formula is as follows:

[0091] (14)

[0092] in, Indicates the upper boundary of the population's range. Indicates the lower boundary of the population's range. Indicates the first The position of the target individual in the next iteration. and This represents the location of a randomly selected individual. It is the individual's location that is updated through migration. , , , and It is represented as a random number between 0 and 1. It is a parameter that controls whether an individual undergoes a completely unknown migration. These are parameters used to simulate the leadership abilities of silverback gorillas (the best-fitting gorilla variant). It is a parameter related to the iteration period. yes arrive A random number between [a certain number of points].

[0093] The specific formulas for the above design parameters are as follows:

[0094] (15)

[0095] The expression is as follows:

[0096] (16)

[0097] in, It is a random number between -1 and 1.

[0098] (17)

[0099] in, It is a random number between 0 and 1.

[0100] Through the above three migration methods, we can obtain , to give The fitness values ​​are compared, and individuals with good fitness are retained while those with poor fitness values ​​are discarded.

[0101] In the population development phase, during each iteration, the solution corresponding to the optimal fitness is recorded as a silverback gorilla. The silverback, as the leader, plays a decisive role in the group's actions. However, the silverback may weaken, age, and eventually die. Other males in the group may then fight with the silverback, dominate the group, and become the new silverback. When the silverback is young and healthy, the males in the group will obediently follow the silverback's commands to find food sources in different locations. GTO simulates this behavior in the following way:

[0102] (18)

[0103] in, Representing the The optimal position information of the population in the next iteration. Represents the total number of the entire group.

[0104] As young gorillas grow, they compete with other males for adult females, a behavior that expands the group's range. This competition is often violent; at this stage, other males may fight with silverback gorillas to dominate the group. The following formula can be used to simulate this behavior.

[0105] (19)

[0106] in, and Parameters used to simulate the level of violence within a chimpanzee population. It is the identity matrix, used to quantify the impact of brute force on different dimensions, when random numbers... , This will equal the random value within the normal distribution and the dimension of the target problem; conversely, when the random number... , It will only equal a random value within a normal distribution. It is represented as a random number between 0 and 1.

[0107] The two behaviors described above are controlled by two different mechanisms. To control, that is, this parameter can control whether the above two behaviors occur during the development phase, when When simulating following the silverback gorillas, use formula (18); otherwise, refer to formula (19) to simulate competition between the population and the silverback gorillas. During the development phase, the obtained... and Fitness is compared, and individuals with good fitness are retained. The position of the optimal solution in the current iteration is considered the position of the silverback gorilla individual. The flowchart of the Gorilla Squadron Optimization Algorithm (GTO) is as follows: Figure 4 As shown.

[0108] To further explain, although GTO has advantages such as excellent optimization speed, fast convergence speed, and strong capabilities, it still suffers from problems such as population-based optimization algorithms getting trapped in local optima and potentially premature convergence. Therefore, the following improvements are proposed:

[0109] During the population development phase, the silverback orangutan, as the sole leader of the population, directly determines the optimization direction and efficiency. However, considering that the entire population's position is only affected by a single individual in each iteration, local search efficiency may be insufficient. Therefore, this invention, drawing on the ideas of the gray wolf optimization algorithm and the chimpanzee optimization algorithm, considers the top three fittest orangutan individuals as the leadership class in each iteration cycle, collectively making decisions and determining the population's behavior. That is, in addition to formulas (18-19), the following formula is established:

[0110] (20)

[0111] (twenty one)

[0112] (twenty two)

[0113] in, and These are the second and third best-fitting orangutan individuals, respectively. Formula (20) is used to calculate the results of a population led solely by a silverback orangutan. Instead, the algorithm incorporates multiple special individuals that collectively influence population behavior to enhance its optimization and local search capabilities.

[0114] The result and The fitness of individuals is compared, and those with good fitness are retained. The individuals in the leadership hierarchy are updated using the gorillas with the top three fitness values. The position of the population's optimal solution in the current iteration cycle is regarded as the position of the silverback gorilla individuals.

[0115] (2) During the free exploration phase, individual gorillas choose one of three different migration methods randomly. However, since different gorillas are located in different areas, random exploration is not necessarily the most efficient method. This invention combines gorilla population migration with gorilla location information. Gorillas with better fitness values ​​tend to randomly explore unknown areas, increasing their ability to escape local convergence; while individuals with poor fitness tend to migrate to areas near known regions or to the vicinity of other gorillas, learning from individuals with better fitness and increasing search accuracy. Specific manifestations are as follows:

[0116] (twenty three)

[0117] In each iteration, the fitness value of each individual is calculated, and the average value is taken. When the fitness value of the current individual is worse than the average fitness value of the population, the individual is allowed to explore the unknown region completely randomly. When the fitness value of the current individual is worse than the average fitness value of the population, the individual is allowed to learn from existing individuals and move to the vicinity of other gorillas.

[0118] Using formula (23) as a supplement to formula (14), in the development phase of each iteration process, the position obtained from formula (14) and the position obtained from formula (23) (denoted as the virtual exploration position) are compared with... The fitness of individuals is compared, and those with good fitness are retained while those with poor fitness are discarded. The flowchart of the improved gorilla army optimization algorithm IGTO is as follows: Figure 5 As shown.

[0119] S203 controls the vehicle speed of the target vehicle based on the optimized vehicle dynamics model and the optimized slicker membrane robust control model.

[0120] The aforementioned vehicle speed control method constructs a vehicle dynamics model corresponding to the target vehicle and designs a corresponding slick-film robust control model. Based on an iterative optimization algorithm, the parameters of the vehicle dynamics model and the slick-film robust control model are optimized. Based on the optimized vehicle dynamics model and the optimized slick-film robust control model, the vehicle speed is controlled. As can be seen from the above, this application, by constructing a vehicle dynamics model and a slick-film robust control model and optimizing their parameters through an iterative optimization algorithm, achieves vehicle speed control based on the optimized vehicle dynamics model and the optimized slick-film robust control model. This prevents inaccurate vehicle speed control due to abnormal parameters in the vehicle dynamics model and the slick-film robust control model, which could affect normal driving and driving experience, thus improving the control stability and driving safety of the target vehicle during driving.

[0121] In one embodiment, such as Figure 6 As shown, when it is necessary to optimize the parameters of the vehicle dynamics model and the sliding film robust control model based on iterative optimization algorithms, the following can be included:

[0122] S601, obtain the parameters to be optimized in the vehicle dynamics model and the sliding film robust control model.

[0123] The parameters to be optimized include: the first parameter to be optimized in the vehicle dynamics model and the second slippage robust control model in the slippage robust control model.

[0124] S602, based on the iterative optimization algorithm, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0125] Specifically, when it is necessary to optimize the parameters to be optimized, the following may be included: determining at least one initial parameter value corresponding to the parameter to be optimized; determining the fitness corresponding to each initial parameter value; and optimizing the parameters to be optimized based on the fitness corresponding to each initial parameter value to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0126] Furthermore, when optimizing the parameters to be optimized based on the fitness corresponding to each initial parameter value, the target initial parameter value can be determined from the initial parameter values ​​based on the fitness corresponding to each initial parameter value; based on the target initial parameter value, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0127] In one embodiment of this application, when it is necessary to optimize the parameters to be optimized based on the target initial parameter values ​​to obtain the optimized vehicle dynamics model and the optimized slicker-body robust control model, the initial parameter values ​​can be simulated using a first simulated migration and a second simulated migration to obtain the first population position corresponding to the first simulated migration and the second population position corresponding to the second simulated migration; the initial parameter values ​​can be adjusted based on the first population position and the second population position to obtain candidate parameter values; and the parameters to be optimized can be optimized based on the candidate parameter values ​​to obtain the optimized vehicle dynamics model and the optimized slicker-body robust control model.

[0128] As one embodiment, (1) the maximum number of iterations can be preset. The number of individual gorillas Fitness function Set the solution to the minimum value; that is, the smaller the fitness value, the better the representation effect; dimension. and the maximum value of the variable and minimum value The level of violence of individual gorillas Development phase behavioral mechanisms and conditions Current iteration number (2) Set the parameters of SMC , In the driving force and driving resistance model , As a population parameter X = [ ε , k , α , f ] Initialize gorilla position Calculate the fitness values ​​and sort them from smallest to largest. The individual with the smallest fitness value is designated as the silverback orangutan individual. (3) The orangutan population conducts exploratory migration behavior. Referring to formulas (16)-(19), the population location is obtained through the above three migration methods. (4) The orangutan population conducts regular exploration migration behavior. Referring to formula (25), individual fitness differences are incorporated into the migration behavior to obtain the virtual population location (distinguished from step three). (5) After the exploration phase is completed, the population location is updated, and the population location after the migration behavior in step three and step four is compared. and The fitness values ​​were compared, and individuals with good fitness were retained while those with poor fitness were discarded, thus updating the population position. (6) In the development stage, the orangutan population, under the leadership of the silverback orangutan, simulated two behaviors in the orangutan population: following the leader and males competing for adult females, as shown in formulas (20)-(21), to obtain the updated population position. (7) Select the top three individuals in fitness, establish a leadership hierarchy using formula (22), and determine their positions using formulas (23)-(24). Update. (8) Combine the results from step 6 and step 7. and Compare fitness values, retain individuals with good fitness, discard individuals with poor fitness values, update the population position, and record the position of the optimal solution as the position of the silverback orangutan. Update the leadership individuals using the top three best solutions. (9) Increment the iteration count by 1. If the iteration count reaches If the program fails, it will exit the program and record the optimal solution. The optimal solution is the first parameter to be optimized in this invention. , The second parameter to be optimized , The optimized solution. The flowchart of the improved sliding mode controller IGTO-SMC based on the "Gorilla Force" algorithm is as follows: Figure 7 As shown.

[0129] The above-mentioned vehicle speed control method prevents inaccurate vehicle speed control due to abnormal parameters of the vehicle dynamics model and the slicker robust control model, which would affect the normal driving and driving experience of the vehicle and improve the control stability and driving safety of the target vehicle during driving.

[0130] In one embodiment, such as Figure 8 As shown, when it is necessary to control the speed of a target vehicle, the following may be included:

[0131] S801, construct the whole vehicle dynamics model corresponding to the target vehicle, and design the sliding film robust control model corresponding to the target vehicle.

[0132] S802, obtain the parameters to be optimized in the vehicle dynamics model and the sliding film robust control model.

[0133] S803, determine at least one initial parameter value corresponding to the parameter to be optimized.

[0134] S804 determines the fitness corresponding to each initial parameter value.

[0135] S805, based on the fitness corresponding to each initial parameter value, determines the target initial parameter value from the initial parameter values.

[0136] S806, based on the initial parameter values ​​of the target, perform a first simulated migration and a second simulated migration for each initial parameter value to obtain the first population position corresponding to the first simulated migration and the second population position corresponding to the second simulated migration.

[0137] S807, based on the positions of the first and second populations, adjust the initial parameter values ​​to obtain candidate parameter values.

[0138] S808 optimizes the parameters to be optimized based on the candidate parameter values, resulting in the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0139] S809 controls the vehicle speed of the target vehicle based on the optimized vehicle dynamics model and the optimized slicker film robust control model.

[0140] The aforementioned vehicle speed control method constructs a vehicle dynamics model corresponding to the target vehicle and designs a corresponding slick-film robust control model. Based on an iterative optimization algorithm, the parameters of the vehicle dynamics model and the slick-film robust control model are optimized. Based on the optimized vehicle dynamics model and the optimized slick-film robust control model, the vehicle speed is controlled. As can be seen from the above, this application, by constructing a vehicle dynamics model and a slick-film robust control model and optimizing their parameters through an iterative optimization algorithm, achieves vehicle speed control based on the optimized vehicle dynamics model and the optimized slick-film robust control model. This prevents inaccurate vehicle speed control due to abnormal parameters in the vehicle dynamics model and the slick-film robust control model, which could affect normal driving and driving experience, thus improving the control stability and driving safety of the target vehicle during driving.

[0141] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0142] Based on the same inventive concept, this application also provides a vehicle speed control device for implementing the vehicle speed control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more vehicle speed control device embodiments provided below can be found in the limitations of the vehicle speed control method described above, and will not be repeated here.

[0143] In one embodiment, Figure 9 As shown, a vehicle speed control device is provided, including: a construction module 10, an optimization model 20, and a control model 30, wherein:

[0144] Module 10 is used to build the whole vehicle dynamics model corresponding to the target vehicle and to design the sliding film robust control model corresponding to the target vehicle.

[0145] Model 20 is used to optimize the parameters of the vehicle dynamics model and the sliding film robust control model based on an iterative optimization algorithm.

[0146] Control model 30 is used to control the speed of the target vehicle based on the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0147] In one embodiment, the parameters to be optimized in the vehicle dynamics model and the slippage robust control model are obtained;

[0148] Based on the iterative optimization algorithm, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0149] In one embodiment, at least one initial parameter value corresponding to the parameter to be optimized is determined;

[0150] Determine the fitness corresponding to each initial parameter value;

[0151] Based on the fitness corresponding to each initial parameter value, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0152] In one embodiment, the target initial parameter value is determined from the initial parameter values ​​based on the fitness corresponding to each initial parameter value;

[0153] Based on the initial target parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0154] In one embodiment, based on the target initial parameter values, a first simulated migration and a second simulated migration are performed on each initial parameter value to obtain the first population position corresponding to the first simulated migration and the second population position corresponding to the second simulated migration.

[0155] Based on the positions of the first and second populations, the initial parameter values ​​are adjusted to obtain candidate parameter values;

[0156] Based on the candidate parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0157] In one embodiment, the parameters to be optimized include: a first parameter to be optimized in the vehicle dynamics model and a second parameter to be optimized in the slicker film robust control model.

[0158] The aforementioned vehicle speed control device constructs a vehicle dynamics model corresponding to the target vehicle and designs a corresponding slick-film robust control model for the target vehicle. Based on an iterative optimization algorithm, it optimizes the parameters of the vehicle dynamics model and the slick-film robust control model. Based on the optimized vehicle dynamics model and the optimized slick-film robust control model, it controls the speed of the target vehicle. As can be seen from the above, this application, by constructing a vehicle dynamics model and a slick-film robust control model and optimizing the parameters of the vehicle dynamics model and the slick-film robust control model through an iterative optimization algorithm, achieves vehicle speed control based on the optimized vehicle dynamics model and the optimized slick-film robust control model. This prevents inaccurate speed control of the target vehicle due to abnormal parameters in the vehicle dynamics model and the slick-film robust control model, which would affect normal driving and driving experience, and improves the control stability and driving safety of the target vehicle during driving.

[0159] Each module in the aforementioned vehicle speed control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0160] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a vehicle speed control method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0161] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0162] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0163] Construct a vehicle dynamics model corresponding to the target vehicle, and design a sliding film robust control model corresponding to the target vehicle;

[0164] Based on iterative optimization algorithms, the parameters of the vehicle dynamics model and the sliding film robust control model are optimized.

[0165] Based on the optimized vehicle dynamics model and the optimized slicker robust control model, the vehicle speed is controlled.

[0166] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0167] Obtain the parameters to be optimized in the vehicle dynamics model and the sliding film robust control model;

[0168] Based on the iterative optimization algorithm, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0169] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0170] Determine at least one initial parameter value corresponding to the parameter to be optimized;

[0171] Determine the fitness corresponding to each initial parameter value;

[0172] Based on the fitness corresponding to each initial parameter value, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0173] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0174] Based on the fitness corresponding to each initial parameter value, the target initial parameter value is determined from each initial parameter value;

[0175] Based on the initial target parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0176] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0177] Based on the initial parameter values ​​of the target, a first simulated migration and a second simulated migration are performed for each initial parameter value to obtain the first population position corresponding to the first simulated migration and the second population position corresponding to the second simulated migration.

[0178] Based on the positions of the first and second populations, the initial parameter values ​​are adjusted to obtain candidate parameter values;

[0179] Based on the candidate parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0180] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0181] The parameters to be optimized include: the first parameter to be optimized in the vehicle dynamics model and the second parameter to be optimized in the sliding film robust control model.

[0182] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0183] Construct a vehicle dynamics model corresponding to the target vehicle, and design a sliding film robust control model corresponding to the target vehicle;

[0184] Based on iterative optimization algorithms, the parameters of the vehicle dynamics model and the sliding film robust control model are optimized.

[0185] Based on the optimized vehicle dynamics model and the optimized slicker robust control model, the vehicle speed is controlled.

[0186] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0187] Obtain the parameters to be optimized in the vehicle dynamics model and the sliding film robust control model;

[0188] Based on the iterative optimization algorithm, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0189] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0190] Determine at least one initial parameter value corresponding to the parameter to be optimized;

[0191] Determine the fitness corresponding to each initial parameter value;

[0192] Based on the fitness corresponding to each initial parameter value, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0193] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0194] Based on the fitness corresponding to each initial parameter value, the target initial parameter value is determined from each initial parameter value;

[0195] Based on the initial target parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0196] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0197] Based on the initial parameter values ​​of the target, a first simulated migration and a second simulated migration are performed for each initial parameter value to obtain the first population position corresponding to the first simulated migration and the second population position corresponding to the second simulated migration.

[0198] Based on the positions of the first and second populations, the initial parameter values ​​are adjusted to obtain candidate parameter values;

[0199] Based on the candidate parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0200] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0201] The parameters to be optimized include: the first parameter to be optimized in the vehicle dynamics model and the second parameter to be optimized in the sliding film robust control model.

[0202] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0203] Construct a vehicle dynamics model corresponding to the target vehicle, and design a sliding film robust control model corresponding to the target vehicle;

[0204] Based on iterative optimization algorithms, the parameters of the vehicle dynamics model and the sliding film robust control model are optimized.

[0205] Based on the optimized vehicle dynamics model and the optimized slicker robust control model, the vehicle speed is controlled.

[0206] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0207] Obtain the parameters to be optimized in the vehicle dynamics model and the sliding film robust control model;

[0208] Based on the iterative optimization algorithm, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0209] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0210] Determine at least one initial parameter value corresponding to the parameter to be optimized;

[0211] Determine the fitness corresponding to each initial parameter value;

[0212] Based on the fitness corresponding to each initial parameter value, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0213] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0214] Based on the fitness corresponding to each initial parameter value, the target initial parameter value is determined from each initial parameter value;

[0215] Based on the initial target parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0216] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0217] Based on the initial parameter values ​​of the target, a first simulated migration and a second simulated migration are performed for each initial parameter value to obtain the first population position corresponding to the first simulated migration and the second population position corresponding to the second simulated migration.

[0218] Based on the positions of the first and second populations, the initial parameter values ​​are adjusted to obtain candidate parameter values;

[0219] Based on the candidate parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

[0220] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0221] The parameters to be optimized include: the first parameter to be optimized in the vehicle dynamics model and the second parameter to be optimized in the sliding film robust control model.

[0222] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0223] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0224] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0225] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A vehicle speed control method, characterized in that, The method includes: Construct a vehicle dynamics model corresponding to the target vehicle, and design a sliding film robust control model corresponding to the target vehicle; Based on an iterative optimization algorithm, parameter optimization is performed on the vehicle dynamics model and the slippage robust control model. Specifically, this optimization includes: obtaining the parameters to be optimized in the vehicle dynamics model and the slippage robust control model; optimizing the parameters to be optimized according to the iterative optimization algorithm to obtain the optimized vehicle dynamics model and the optimized slippage robust control model; further, optimizing the parameters to be optimized according to the iterative optimization algorithm to obtain the optimized vehicle dynamics model and the optimized slippage robust control model includes: determining at least one initial parameter corresponding to the parameter to be optimized. The process involves: determining the fitness of each initial parameter value; optimizing the parameters to be optimized based on the fitness of each initial parameter value to obtain an optimized vehicle dynamics model and an optimized slicker-body robust control model; the optimization of the parameters to be optimized based on the fitness of each initial parameter value to obtain an optimized vehicle dynamics model and an optimized slicker-body robust control model includes: determining target initial parameter values ​​from the initial parameter values ​​based on the fitness of each initial parameter value; optimizing the parameters to be optimized based on the target initial parameter values ​​to obtain an optimized vehicle dynamics model and an optimized slicker-body robust control model. Based on the optimized vehicle dynamics model and the optimized slicker robust control model, the vehicle speed is controlled.

2. The method according to claim 1, characterized in that, The step of optimizing the parameters to be optimized based on the target initial parameter values ​​to obtain the optimized vehicle dynamics model and the optimized slippage robust control model includes: Based on the target initial parameter values, a first simulated migration and a second simulated migration are performed on each of the initial parameter values ​​to obtain the first population position corresponding to the first simulated migration and the second population position corresponding to the second simulated migration. Based on the positions of the first and second populations, the initial parameter values ​​are adjusted to obtain candidate parameter values; Based on the candidate parameter values, the parameters to be optimized are optimized to obtain the optimized vehicle dynamics model and the optimized sliding film robust control model.

3. The method according to claim 1, characterized in that, The parameters to be optimized include: the first parameter to be optimized in the vehicle dynamics model and the second parameter to be optimized in the slicker robust control model.

4. A vehicle speed control device, characterized in that, The device includes: The module is used to construct the whole vehicle dynamics model corresponding to the target vehicle, and to design the sliding film robust control model corresponding to the target vehicle. An optimization model is used to optimize the parameters of the vehicle dynamics model and the slip-film robust control model based on an iterative optimization algorithm. The optimization of the parameters based on the iterative optimization algorithm includes: obtaining the parameters to be optimized in the vehicle dynamics model and the slip-film robust control model; optimizing the parameters to be optimized according to the iterative optimization algorithm to obtain the optimized vehicle dynamics model and the optimized slip-film robust control model; the optimization of the parameters to be optimized according to the iterative optimization algorithm to obtain the optimized vehicle dynamics model and the optimized slip-film robust control model includes: determining at least one parameter corresponding to the parameter to be optimized. The process involves: determining initial parameter values; identifying the fitness corresponding to each initial parameter value; optimizing the parameters to be optimized based on the fitness corresponding to each initial parameter value to obtain an optimized vehicle dynamics model and an optimized slicker-body robust control model; the optimization of the parameters to be optimized based on the fitness corresponding to each initial parameter value to obtain an optimized vehicle dynamics model and an optimized slicker-body robust control model includes: determining target initial parameter values ​​from the initial parameter values ​​based on the fitness corresponding to each initial parameter value; optimizing the parameters to be optimized based on the target initial parameter values ​​to obtain an optimized vehicle dynamics model and an optimized slicker-body robust control model. The control model is used to control the speed of the target vehicle based on the optimized vehicle dynamics model and the optimized sliding film robust control model.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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