Vehicle control method and device based on PPC and PID, equipment and medium

Abstract

A vehicle control method, apparatus and device based on PPC and PID, and a medium relate to the technical field of vehicle control, and the method comprises the steps of performing preset performance control PPC performance boundary calculation based on a real-time scene demand of a target vehicle to obtain a target performance boundary; mapping a real-time tracking error of the target vehicle to a PPC unconstrained space according to the target performance boundary to obtain a target unconstrained error; adjusting a PID parameter value through the target unconstrained error to generate a target PID parameter value; and performing PID control on the target vehicle according to the target PID parameter value and the real-time tracking error. According to the invention, the precision and response speed of vehicle control can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a vehicle control method, device, equipment and medium based on PPC and PID. Background Art

[0002] During the operation of unmanned commercial vehicles, achieving precise longitudinal following control and lateral lane keeping is extremely important for improving driving safety and road capacity. In related technologies, PID (Proportional, Integral, Differential) control algorithms are often used to achieve longitudinal following control and lateral lane keeping. However, parameter tuning in traditional PID control relies on experience and lacks a systematic performance guarantee mechanism. Especially when facing nonlinear, time-varying, or strong interference systems, fixed proportional, integral, and differential gains make it difficult to balance dynamic response and steady-state accuracy. This can easily lead to problems such as excessive overshoot, slow convergence, or steady-state errors, making it impossible to effectively achieve precise control and real-time dynamic response of the vehicle. Summary of the Invention

[0003] The present application provides a vehicle control method, device, equipment and medium based on PPC and PID, which can solve the technical problems existing in the prior art of low control accuracy and slow response caused by using traditional PID algorithm with fixed parameters to realize vehicle control.

[0004] In a first aspect, an embodiment of the present application provides a vehicle control method based on PPC and PID, the vehicle control method based on PPC and PID comprising: Perform preset performance control (PPC) performance boundary calculation based on the real-time scenario requirements of the target vehicle to obtain the target performance boundary; Mapping the real-time tracking error of the target vehicle to the PPC unconstrained space according to the target performance boundary to obtain the target unconstrained error; Adjusting PID parameter values according to the target unconstrained error to generate target PID parameter values; PID control is performed on the target vehicle according to the target PID parameter value and the real-time tracking error.

[0005] In combination with the first aspect, in one embodiment, adjusting the PID parameter value by the target unconstrained error to generate the target PID parameter value includes: Determining the PID parameters to be adjusted based on the magnitude relationship between the target unconstrained error and a preset error threshold; The parameter value of the PID parameter to be adjusted is adjusted according to the target unconstrained error and its corresponding derivative to obtain a target PID parameter value.

[0006] In combination with the first aspect, in one embodiment, when the target vehicle is in longitudinal control, determining the PID parameter to be adjusted based on the magnitude relationship between the target unconstrained error and a preset error threshold includes: If the target unconstrained error is less than or equal to a first error threshold, the integral gain parameter is used as the PID parameter to be adjusted; If the target unconstrained error is greater than or equal to a second error threshold, taking the proportional gain parameter as the PID parameter to be adjusted, and the second error threshold is greater than the first error threshold; If the target unconstrained error is greater than the first error threshold and less than the second error threshold, the proportional gain parameter and the differential gain parameter are used as PID parameters to be adjusted.

[0007] In combination with the first aspect, in one embodiment, when the target vehicle is in lateral control, determining the PID parameter to be adjusted based on the magnitude relationship between the target unconstrained error and a preset error threshold includes: If the absolute value of the target unconstrained error is less than a third error threshold, the integral gain parameter is used as the PID parameter to be adjusted; If the absolute value of the target unconstrained error is greater than or equal to a third error threshold and less than a fourth error threshold, the differential gain parameter is used as the PID parameter to be adjusted; If the absolute value of the target unconstrained error is greater than or equal to the fourth error threshold and less than or equal to the fifth error threshold, the proportional gain parameter and the differential gain parameter are used as the PID parameters to be adjusted; If the absolute value of the target unconstrained error is greater than the fifth error threshold, taking the proportional gain parameter as the PID parameter to be adjusted; The fifth error threshold is greater than the fourth error threshold and the fourth error threshold is greater than the third error threshold.

[0008] In combination with the first aspect, in one embodiment, adjusting the parameter value of the to-be-adjusted PID parameter according to the target unconstrained error and its corresponding derivative to obtain the target PID parameter value includes: A preset target relationship table is looked up according to the target unconstrained error and its corresponding derivative to determine the target parameter value corresponding to the PID parameter to be adjusted, wherein the target relationship table is used to store the mapping relationship between the PID parameter value and the target unconstrained error and the derivative corresponding to the target unconstrained error; The original parameter value of the PID parameter to be adjusted is updated to the target parameter value to generate a target PID parameter value.

[0009] In conjunction with the first aspect, in one embodiment, performing preset performance control (PPC) performance boundary calculation based on the real-time scenario requirements of the target vehicle to obtain the target performance boundary includes: Determine the target initial allowable error boundary, target steady-state error boundary and target control convergence speed in PPC based on the real-time scenario requirements; The target performance boundary is calculated based on the target initial allowable error boundary, the target steady-state error boundary, the target control convergence speed, and a preset PPC performance function.

[0010] In conjunction with the first aspect, in one embodiment, mapping the real-time tracking error of the target vehicle to a PPC unconstrained space according to the target performance boundary to obtain a target unconstrained error includes: Substituting the target performance boundary and the real-time tracking error into a PPC transfer function to obtain a target unconstrained error; The PPC conversion function is:

[0011] Where, represents the target unconstrained error, represents the real-time tracking error, Represents the target performance boundary.

[0012] In a second aspect, an embodiment of the present application provides a vehicle control device based on PPC and PID, the vehicle control device based on PPC and PID comprising: A boundary construction module is used to calculate the preset performance control (PPC) performance boundary based on the real-time scenario requirements of the target vehicle to obtain the target performance boundary; an error conversion module for mapping the real-time tracking error of the target vehicle to the PPC unconstrained space according to the target performance boundary to obtain a target unconstrained error; a parameter adjustment module, configured to adjust PID parameter values according to the target unconstrained error to generate target PID parameter values; A PID control module is used to perform PID control on the target vehicle according to the target PID parameter value and the real-time tracking error.

[0013] In conjunction with the second aspect, in one embodiment, the parameter adjustment module is specifically configured to: Determining the PID parameters to be adjusted based on the magnitude relationship between the target unconstrained error and a preset error threshold; The parameter value of the PID parameter to be adjusted is adjusted according to the target unconstrained error and its corresponding derivative to obtain a target PID parameter value.

[0014] In conjunction with the second aspect, in one embodiment, when the target vehicle is in longitudinal control, the parameter adjustment module is further configured to: If the target unconstrained error is less than or equal to a first error threshold, the integral gain parameter is used as the PID parameter to be adjusted; If the target unconstrained error is greater than or equal to a second error threshold, taking the proportional gain parameter as the PID parameter to be adjusted, and the second error threshold is greater than the first error threshold; If the target unconstrained error is greater than the first error threshold and less than the second error threshold, the proportional gain parameter and the differential gain parameter are used as PID parameters to be adjusted.

[0015] In conjunction with the second aspect, in one embodiment, when the target vehicle is in lateral control, the parameter adjustment module is further configured to: If the absolute value of the target unconstrained error is less than a third error threshold, the integral gain parameter is used as the PID parameter to be adjusted; If the absolute value of the target unconstrained error is greater than or equal to a third error threshold and less than a fourth error threshold, the differential gain parameter is used as the PID parameter to be adjusted; If the absolute value of the target unconstrained error is greater than or equal to the fourth error threshold and less than or equal to the fifth error threshold, the proportional gain parameter and the differential gain parameter are used as the PID parameters to be adjusted; If the absolute value of the target unconstrained error is greater than the fifth error threshold, taking the proportional gain parameter as the PID parameter to be adjusted; The fifth error threshold is greater than the fourth error threshold and the fourth error threshold is greater than the third error threshold.

[0016] In conjunction with the second aspect, in one embodiment, the parameter adjustment module is further configured to: A preset target relationship table is looked up according to the target unconstrained error and its corresponding derivative to determine the target parameter value corresponding to the PID parameter to be adjusted, wherein the target relationship table is used to store the mapping relationship between the PID parameter value and the target unconstrained error and the derivative corresponding to the target unconstrained error; The original parameter value of the PID parameter to be adjusted is updated to the target parameter value to generate a target PID parameter value.

[0017] In conjunction with the second aspect, in one embodiment, the boundary construction module is specifically configured to: Determine the target initial allowable error boundary, target steady-state error boundary and target control convergence speed in PPC based on the real-time scenario requirements; The target performance boundary is calculated based on the target initial allowable error boundary, the target steady-state error boundary, the target control convergence speed, and a preset PPC performance function.

[0018] In conjunction with the second aspect, in one implementation, the error conversion module is specifically configured to: Substituting the target performance boundary and the real-time tracking error into a PPC transfer function to obtain a target unconstrained error; The PPC conversion function is:

[0019] Where, represents the target unconstrained error, represents the real-time tracking error, Represents the target performance boundary.

[0020] In the third aspect, an embodiment of the present application provides a vehicle control device based on PPC and PID, wherein the vehicle control device based on PPC and PID includes a processor, a memory, and a vehicle control program based on PPC and PID stored in the memory and executable by the processor, wherein when the vehicle control program based on PPC and PID is executed by the processor, the steps of the vehicle control method based on PPC and PID as described above are implemented.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a vehicle control program based on PPC and PID is stored, wherein when the vehicle control program based on PPC and PID is executed by a processor, the steps of the aforementioned vehicle control method based on PPC and PID are implemented.

[0022] The beneficial effects of the technical solutions provided in the embodiments of the present application include: The target performance boundary is obtained by calculating the PPC performance boundary based on the real-time scenario requirements of the target vehicle, and the real-time tracking error of the target vehicle is mapped to the PPC unconstrained space according to the target performance boundary to obtain the target unconstrained error, so as to explicitly limit the convergence trajectory of vehicle control and avoid overshoot and oscillation; then, the PID parameter value is dynamically adjusted based on the target unconstrained error to generate the target PID parameter value, so as to break through the limitations of traditional PID fixed gain and avoid problems such as excessive overshoot, slow convergence or steady-state error; finally, the target vehicle is PID controlled according to the standard PID parameter value and the real-time tracking error, which can effectively achieve precise control and real-time dynamic response of the vehicle, thereby improving the accuracy and response speed of vehicle control. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1This is a flow chart of an embodiment of a vehicle control method based on PPC and PID in this application; Figure 2 For this application Figure 1 Detailed flow chart of step S10; Figure 3 For this application Figure 1 Detailed flow chart of step S30; Figure 4 This is a functional module diagram of an embodiment of a vehicle control device based on PPC and PID in this application; Figure 5 This is a schematic diagram of the hardware structure of the vehicle control device based on PPC and PID involved in the embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0026] In a first aspect, an embodiment of the present application provides a vehicle control method based on PPC and PID.

[0027] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of an embodiment of the vehicle control method based on PPC and PID in this application. Figure 1 As shown, the vehicle control method based on PPC and PID includes: Step S10: Calculate the preset performance control (PPC) performance boundary based on the real-time scenario requirements of the target vehicle to obtain the target performance boundary.

[0028] For example, it should be understood that traditional PID control is a classic feedback control algorithm that controls the controlled object through three adjustment actions: proportional, integral, and differential. Its basic form is: Where, It represents the deviation between the expected value and the actual value. 、 and They represent proportional gain, integral gain and differential gain respectively. The main disadvantage of traditional PID control is that its parameter tuning relies on experience and lacks a systematic performance guarantee mechanism. Especially when facing nonlinear, time-varying or strong interference systems, fixed proportional, integral and differential gains are difficult to take into account both dynamic response and steady-state accuracy, and are prone to problems such as excessive overshoot, slow convergence or steady-state error. At the same time, the accumulation of integral terms may lead to saturation effects, and the differential terms are sensitive to noise and require additional filtering. In addition, under the influence of factors such as noise and load disturbances, system parameters and even model structures will change with time and changes in the working environment, ultimately resulting in the system being unable to meet control requirements and having limited anti-interference capabilities.

[0029] Prescribed Performance Control (PPC) is a more advanced control method. Its core is to impose bounds on the tracking error through a preset performance function to achieve the desired dynamic and steady-state performance. Based on this, this embodiment combines PPC with PID control to effectively constrain the dynamic trajectory of the tracking error, ensuring that the system maintains good performance even under complex operating conditions.

[0030] It should be noted that the target vehicle in this embodiment refers to a vehicle that needs to be controlled such as longitudinal following control or lateral lane keeping control, which can be a commercial vehicle, a passenger car, or other types of vehicles, which are not limited here; the real-time scenario requirement refers to the control requirement required by the target vehicle in the current scenario, such as the longitudinal control scenario of emergency braking on a highway, where the preceding vehicle In case of emergency braking, the vehicle needs to reduce the following distance error from 10m to 5m within 1.5s. The real-time scenario requirements include that the vehicle needs to reduce the following distance error from 10m to 5m within 1.5s. For example, in the lateral deviation correction scenario on a highway, the vehicle's friction coefficient on the road is μ The road surface with a value of 0.25 is laterally offset due to crosswind =0.35m, and needs to return to the lane center within 2s. The real-time scenario requirements at this time include returning the vehicle to the lane center from a lateral offset of 0.35m within 2s. In addition, in addition to specific control requirements, real-time scenario requirements can also include dynamic performance indicators such as overshoot and convergence time used to evaluate the response characteristics of the PID system when subjected to disturbances or set value changes. For example, the preset overshoot should be ≤5% and the convergence time should be ≤3s.

[0031] Therefore, this embodiment uses real-time vehicle status (such as speed and posture), environmental perception data (such as the distance to the preceding vehicle and lane lines), and desired trajectory information as the input layer to model the dynamic performance boundary under PPC based on the real-time scenario requirements of the target vehicle. The target performance boundary obtained by modeling is then used to explicitly limit the convergence trajectory in control scenarios such as longitudinal following distance error and lateral trajectory offset error, thereby effectively avoiding overshoot and oscillation.

[0032] Further, see Figure 2 As shown, the preset performance control PPC performance boundary calculation is performed based on the real-time scene requirements of the target vehicle to obtain the target performance boundary, including: Step S101: determining the target initial allowable error boundary, target steady-state error boundary, and target control convergence speed in the PPC based on the real-time scenario requirements; Step S102: Calculating a target performance boundary according to the target initial allowable error boundary, the target steady-state error boundary, the target control convergence speed, and a preset PPC performance function.

[0033] For example, in this embodiment, the performance function under PPC is defined by real-time scenario requirements to describe the allowable range of error; wherein, the performance function can be an exponential decay performance function, a hyperbolic tangent performance function, or other functions that can be used to describe the allowable range of error, which are not limited here. Taking the exponential decay performance function as an example, first determine the target initial allowable error boundary, target steady-state error boundary and target control convergence speed at the current time t according to the real-time scenario requirements. For example, for longitudinal following control, the target initial allowable error boundary can be Set to 0.5m, target steady-state error boundary Set to 0.05m and target control convergence speed Set to 2; for lateral lane keeping, the target initial allowable error boundary can be Set to 0.4m, target steady-state error boundary Set to 0.02m and the target controls the convergence speed Set to 1.5. It should be noted that the target control convergence speed corresponds to the control scenario. For example, a larger value is used in longitudinal control, while a smaller value is used in lateral control to avoid steering buffeting. The specific value can be determined through experimental calibration or empirical value, and is not limited here.

[0034] Then the target initial allowable error boundary , target steady-state error bound And the target control convergence speed Substituting the following exponentially decaying performance function, the target performance boundary can be calculated:

[0035] Where, Represents the target performance boundary.

[0036] It can be seen that this embodiment provides clear boundary constraints for the dynamic trajectory of the system error by reasonably setting the initial allowable error boundary, the steady-state error boundary, and controlling the convergence rate, so as to ensure that the error converges within the allowable range.

[0037] Step S20: Mapping the real-time tracking error of the target vehicle to the PPC unconstrained space according to the target performance boundary to obtain the target unconstrained error.

[0038] In this embodiment, the dynamic error boundary constraint is implemented, that is, the convergence trajectory of the control error is explicitly limited by the performance function, effectively avoiding overshoot and oscillation. Specifically, the error conversion is performed based on the PPC control principle to convert the real-time tracking error of the target vehicle into Mapping to unconstrained space, i.e. real-time tracking error Convert to target unconstrained error through nonlinear mapping To ensure that the error is always within the target performance boundary. It should be noted that the real-time tracking error Refers to the deviation between the expected value and the actual value; for longitudinal control, the real-time tracking error is the following distance error, and for lateral control, the real-time tracking error is the trajectory offset error.

[0039] Furthermore, in one embodiment, mapping the real-time tracking error of the target vehicle to the PPC unconstrained space according to the target performance boundary to obtain the target unconstrained error includes: Substituting the target performance boundary and the real-time tracking error into a PPC transfer function to obtain a target unconstrained error; The PPC conversion function is:

[0040] Where, represents the target unconstrained error, represents the real-time tracking error, Represents the target performance boundary.

[0041] For example, in this embodiment, the PPC conversion function can be a logarithmic conversion function or a hyperbolic tangent function. Of course, it can also be other functions that can achieve error conversion, which is not limited here. This embodiment takes the PPC conversion function as a logarithmic conversion function as an example, and sets the target performance boundary and real-time tracking error Substituting into the following logarithmic conversion function and solving it, the target unconstrained error can be calculated :

[0042] It can be understood that the original real-time tracking error can be converted into ∈( - , ) is mapped to an unconstrained variable ∈(-∞,+∞).

[0043] Step S30: adjusting the PID parameter value according to the target unconstrained error to generate a target PID parameter value.

[0044] For example, in this embodiment, adaptive PID parameters are dynamically adjusted based on the conversion error, that is, according to the target unconstrained error after conversion. To dynamically adjust the specific values of PID parameters to generate target PID parameter values, and then break through the limitations of traditional PID fixed gains, it can effectively avoid problems such as excessive overshoot, slow convergence or steady-state error, thereby achieving fast response and high-precision control under different working conditions; for example, when When it is larger, the proportional term is enhanced , in order to achieve the purpose of rapid response; when When approaching steady state, increase the integral term , in order to eliminate the residual error.

[0045] Further, see Figure 3 As shown, the adjusting the PID parameter value by the target unconstrained error to generate the target PID parameter value includes: Step S301: determining a PID parameter to be adjusted based on a magnitude relationship between the target unconstrained error and a preset error threshold; Step S302: adjusting the parameter value of the PID parameter to be adjusted according to the target unconstrained error and its corresponding derivative to obtain a target PID parameter value.

[0046] For example, it should be noted that the specific value of the error threshold can be determined according to the control scenario and control requirements, and is not limited here. The relationship between the size of the error threshold is used to characterize the target unconstrained error The size of the target unconstrained error The size of determines the PID parameters to be adjusted; for example, in longitudinal control, when When the proportional term is large (such as in emergency braking conditions), As the PID parameter to be adjusted, when When approaching steady state, the integral term As the PID parameter to be adjusted; for example, in horizontal control, when When it is large, the proportional term As the PID parameter to be adjusted, when When it is small, the differential term As the PID parameter to be adjusted.

[0047] After determining the PID parameters to be adjusted, and its derivatives Dynamically adjust the specific value of the PID parameter to be adjusted, that is, = ( , ), = ( , )as well as = ( , );in, f () indicates a table lookup action, that is, to find the corresponding and its derivatives The corresponding PID parameter value is used as the target PID parameter value of the PID parameter to be adjusted.

[0048] Furthermore, in one embodiment, when the target vehicle is in longitudinal control, determining the PID parameter to be adjusted based on the magnitude relationship between the target unconstrained error and a preset error threshold includes: If the target unconstrained error is less than or equal to a first error threshold, the integral gain parameter is used as the PID parameter to be adjusted; If the target unconstrained error is greater than or equal to a second error threshold, taking the proportional gain parameter as the PID parameter to be adjusted, and the second error threshold is greater than the first error threshold; If the target unconstrained error is greater than the first error threshold and less than the second error threshold, the proportional gain parameter and the differential gain parameter are used as PID parameters to be adjusted.

[0049] For example, it should be noted that the specific values of the first error threshold and the second error threshold can be determined according to actual needs and are not limited here, as long as the second error threshold > the first error threshold. In this embodiment, when the target vehicle is in longitudinal control, the first error threshold can be preferably set to 0.3 and the second error threshold can be preferably set to 1.2; based on this, if the target unconstrained error ≤0.3, then the integral term As a PID parameter to be adjusted, such as increasing the integral term , to eliminate the residual error; if the target has no constraint error ≥1.2, then the proportional term As PID parameters to be adjusted, such as the enhanced proportional term , to quickly reduce the following distance; and if 0.3 < target unconstrained error <1.2, then the proportional term and the differential term As the PID parameter to be adjusted.

[0050] It should be understood that when the target unconstrained error When it is infinitely close to 0, the PID parameter adjustment of the current round will be stopped, that is, the PID parameters to be adjusted will no longer be determined in the current round; for example, the target unconstrained error When the error is less than 0.05, the PID parameter adjustment of the current round is stopped. In other words, when the target unconstrained error >0.05, it is necessary to continue to adjust the PID parameters, for example, when 0.05 < target unconstrained error When ≤0.3, the integral term needs to be As the PID parameter to be adjusted.

[0051] Furthermore, in one embodiment, when the target vehicle is in lateral control, determining the PID parameter to be adjusted based on the magnitude relationship between the target unconstrained error and a preset error threshold includes: If the absolute value of the target unconstrained error is less than a third error threshold, the integral gain parameter is used as the PID parameter to be adjusted; If the absolute value of the target unconstrained error is greater than or equal to a third error threshold and less than a fourth error threshold, the differential gain parameter is used as the PID parameter to be adjusted; If the absolute value of the target unconstrained error is greater than or equal to the fourth error threshold and less than or equal to the fifth error threshold, the proportional gain parameter and the differential gain parameter are used as the PID parameters to be adjusted; If the absolute value of the target unconstrained error is greater than the fifth error threshold, taking the proportional gain parameter as the PID parameter to be adjusted; The fifth error threshold is greater than the fourth error threshold and the fourth error threshold is greater than the third error threshold.

[0052] For example, it should be noted that the specific values of the third error threshold, the fourth error threshold, and the fifth error threshold can be determined according to actual needs and are not limited here, as long as the fifth error threshold > the fourth error threshold > the third error threshold is satisfied. In this embodiment, when the target vehicle is in lateral control, the third error threshold can be preferably set to 0.1, the fourth error threshold can be preferably set to 0.2, and the fifth error threshold can be preferably set to 1; based on this, if the absolute value of the target unconstrained error | |<0.1, then the integral term As the PID parameter to be adjusted; if 0.1≤ absolute value of target unconstrained error| |<0.2 (ie close to the center of the lane), then the differential term As PID parameters to be adjusted, such as enhanced differential terms , to suppress the steering angle oscillation; and if 0.2≤ the absolute value of the target unconstrained error | |≤1, then the proportional term and the differential term As the PID parameter to be adjusted; and if the absolute value of the target unconstrained error | |>1 (such as cutting into a curve), then the proportional term As PID parameters to be adjusted, such as the enhanced proportional term , to quickly correct direction.

[0053] It should be understood that when the absolute value of the target unconstrained error | |When it is infinitely close to 0, the PID parameter adjustment of the current round will be stopped, that is, the PID parameters to be adjusted will no longer be determined in the current round; for example, the absolute value of the target unconstrained error| When |≤0.05, stop the PID parameter adjustment of the current round. In other words, when the absolute value of the target unconstrained error | When |>0.05, it is necessary to continue adjusting the PID parameters. For example, when 0.05<the absolute value of the target unconstrained error| When |<0.1, the integral term needs to be As the PID parameter to be adjusted.

[0054] Furthermore, in one embodiment, adjusting the parameter value of the to-be-adjusted PID parameter according to the target unconstrained error and its corresponding derivative to obtain the target PID parameter value includes: A preset target relationship table is looked up according to the target unconstrained error and its corresponding derivative to determine the target parameter value corresponding to the PID parameter to be adjusted, wherein the target relationship table is used to store the mapping relationship between the PID parameter value and the target unconstrained error and the derivative corresponding to the target unconstrained error; The original parameter value of the PID parameter to be adjusted is updated to the target parameter value to generate a target PID parameter value.

[0055] For example, in this embodiment, a target relationship table is created in advance to store the mapping relationship between the PID parameter value and the target unconstrained error and the derivative corresponding to the target unconstrained error. That is, the target relationship table is a two-dimensional table that records 、 as well as and 、 The mapping relationship between is A and When B, Equal to C, Equal to D and Equal to E, then the corresponding relationship between A, B and C, the corresponding relationship between A, B and D, and the corresponding relationship between A, B and E are constructed. Based on this, when the calculation is =A、 =B and the PID parameters to be adjusted are When the target relationship table is looked up, C corresponding to A and B can be found, and C is used as The target parameter value is updated, that is, the original parameter value of the PID parameter to be adjusted is updated to C, thereby completing the determination of the target PID parameter value.

[0056] Step S40: performing PID control on the target vehicle according to the target PID parameter value and the real-time tracking error.

[0057] For example, in this embodiment, after determining the target PID parameter value, the target PID parameter value and the real-time tracking error can be substituted into the PID control equation: , to output the control results ; Among them, for longitudinal following control, The amount can be adjusted for the throttle opening, and for lateral lane keeping, Can be the steering wheel angle adjustment amount; then the control result is outputted By controlling the driving of the target vehicle, precise control and real-time dynamic response of the vehicle can be achieved, thereby improving the accuracy and response speed of vehicle control.

[0058] It should be noted that this embodiment also allows for rolling optimization of the PID output to ensure that the control variable is within the physical limits of the vehicle actuators (such as motor torque and steering wheel angle), thereby avoiding saturation failure and improving system reliability and safety. For example, when the steering wheel angle adjustment is greater than the steering wheel angle threshold, the steering wheel angle threshold is used as the control variable to control the target vehicle's movement. If the steering wheel angle adjustment is less than or equal to the steering wheel angle threshold, the steering wheel angle adjustment is used as the control variable to control the target vehicle's movement. Furthermore, the error can be monitored in real time to ensure that it meets the performance limit, allowing for dynamic adjustment of the control strategy to achieve a closed-loop feedback loop.

[0059] In summary, this embodiment combines the boundary constraint mechanism of PPC with the adjustment characteristics of PID, that is, it utilizes the flexibility of PID and the stability of PPC to achieve high-precision and strong robust control under complex working conditions by adjusting PID parameters and applying dynamic performance constraints, thus solving the problem of traditional PID parameter solidification (i.e. fixed 、 and ) caused by excessive overshoot, slow convergence, steady-state error and other problems. It is particularly suitable for longitudinal following control and lateral lane keeping of unmanned commercial vehicles in highway scenarios.

[0060] The workflow of this embodiment will be explained below by taking highway emergency braking longitudinal control as an example.

[0061] Scenario: The car in front In case of emergency braking, the vehicle must reduce the following distance error from 10m to 5m within 1.5s.

[0062] Implementation process: (1) Performance Boundary Generation: Dynamic Adjustment =10m, =5m, = 2.5 (to accelerate convergence), generating performance function .

[0063] (2) Parameter adjustment: Initial stage ( =1.2): From 1.0 to 2.5, Reduced from 0.5 to 0.2 to quickly respond to errors; Convergence phase ( =0.3): Increase from 0.3 to 0.8 to eliminate residual errors.

[0064] The experimental results are shown in Table 1: Table 1

[0065] As shown in Table 1, the overshoot of this embodiment is only 3.2%, and the steady-state error is 0.3m, which is significantly improved compared with the traditional PID (i.e., overshoot of 12%).

[0066] The following further illustrates the workflow of this embodiment by taking the lateral deviation correction of a highway as an example.

[0067] Scenario: Friction coefficient of vehicle on road μ= 0.25 The road surface deflects laterally due to crosswind =0.35m, and needs to return to the center of the lane within 2s. Implementation process: (1) Performance boundary relaxation: temporary relaxation =0.1m, =0.05m, =0.8 to reduce control aggressiveness.

[0068] (2) Differential term enhancement: lateral error =0.9, Increased from 1.0 to 2.2 to suppress yaw oscillations; (3) Constraint optimization: The steering angle rate is limited to 15° / s, and the path smoothness is improved by 40%.

[0069] The experimental results are shown in Table 2: Table 2

[0070] Table 2 shows that the lateral error convergence time is 1.8s, and no sideslip occurs, which is significantly improved compared to the traditional PID (i.e., convergence time of 3.2s).

[0071] In summary, this embodiment not only effectively improves the control accuracy, that is, the longitudinal steady-state error is ≤0.05m (traditional PID: 1.2m), the overshoot is reduced from 12% to 3.2%, a decrease of 73.3%, and the lateral correction accuracy reaches the centimeter level (0.02m), with an accuracy improvement of 87%; it also achieves the optimization of dynamic response, that is, the convergence time of emergency braking scenario is shortened from 2.8s to 1.5s, a speed increase of 46.4%, the lateral correction speed is increased by 44%, and the maximum lateral acceleration of the lateral correction scenario is reduced by 40% (0.42g→0.25g), and the control amount adjustment delay under complex disturbances is <50ms, which is better than the 120ms of PPC; in addition, this embodiment enhances the robustness, that is, in μ =0.25 During lateral control on low-adhesion roads, the sideslip rate was reduced by 90%, and the brake torque compensation error under acceleration disturbances from the preceding vehicle was less than 5%. Overall, this embodiment significantly improves control accuracy and robustness in complex scenarios by combining the boundary constraint mechanism of PPC with the flexible adjustment characteristics of PID.

[0072] In a second aspect, an embodiment of the present application also provides a vehicle control device based on PPC and PID.

[0073] In one embodiment, referring to Figure 4 , Figure 4 This is a functional module diagram of an embodiment of a vehicle control device based on PPC and PID in this application. Figure 4 As shown, the vehicle control device based on PPC and PID includes: A boundary construction module is used to calculate the preset performance control (PPC) performance boundary based on the real-time scenario requirements of the target vehicle to obtain the target performance boundary; an error conversion module for mapping the real-time tracking error of the target vehicle to the PPC unconstrained space according to the target performance boundary to obtain a target unconstrained error; a parameter adjustment module, configured to adjust PID parameter values according to the target unconstrained error to generate target PID parameter values; A PID control module is used to perform PID control on the target vehicle according to the target PID parameter value and the real-time tracking error.

[0074] Furthermore, in one embodiment, the parameter adjustment module is specifically configured to: Determining the PID parameters to be adjusted based on the magnitude relationship between the target unconstrained error and a preset error threshold; The parameter value of the PID parameter to be adjusted is adjusted according to the target unconstrained error and its corresponding derivative to obtain a target PID parameter value.

[0075] Furthermore, in one embodiment, when the target vehicle is in longitudinal control, the parameter adjustment module is further configured to: If the target unconstrained error is less than or equal to a first error threshold, the integral gain parameter is used as the PID parameter to be adjusted; If the target unconstrained error is greater than or equal to a second error threshold, taking the proportional gain parameter as the PID parameter to be adjusted, and the second error threshold is greater than the first error threshold; If the target unconstrained error is greater than the first error threshold and less than the second error threshold, the proportional gain parameter and the differential gain parameter are used as PID parameters to be adjusted.

[0076] Furthermore, in one embodiment, when the target vehicle is in lateral control, the parameter adjustment module is further configured to: If the absolute value of the target unconstrained error is less than a third error threshold, the integral gain parameter is used as the PID parameter to be adjusted; If the absolute value of the target unconstrained error is greater than or equal to a third error threshold and less than a fourth error threshold, the differential gain parameter is used as the PID parameter to be adjusted; If the absolute value of the target unconstrained error is greater than or equal to the fourth error threshold and less than or equal to the fifth error threshold, the proportional gain parameter and the differential gain parameter are used as the PID parameters to be adjusted; If the absolute value of the target unconstrained error is greater than the fifth error threshold, taking the proportional gain parameter as the PID parameter to be adjusted; The fifth error threshold is greater than the fourth error threshold and the fourth error threshold is greater than the third error threshold.

[0077] Furthermore, in one embodiment, the parameter adjustment module is further configured to: A preset target relationship table is looked up according to the target unconstrained error and its corresponding derivative to determine the target parameter value corresponding to the PID parameter to be adjusted, wherein the target relationship table is used to store the mapping relationship between the PID parameter value and the target unconstrained error and the derivative corresponding to the target unconstrained error; The original parameter value of the PID parameter to be adjusted is updated to the target parameter value to generate a target PID parameter value.

[0078] Furthermore, in one embodiment, the boundary construction module is specifically configured to: Determine the target initial allowable error boundary, target steady-state error boundary and target control convergence speed in PPC based on the real-time scenario requirements; The target performance boundary is calculated based on the target initial allowable error boundary, the target steady-state error boundary, the target control convergence speed, and a preset PPC performance function.

[0079] Furthermore, in one embodiment, the error conversion module is specifically configured to: Substituting the target performance boundary and the real-time tracking error into a PPC transfer function to obtain a target unconstrained error; The PPC conversion function is:

[0080] Where, represents the target unconstrained error, represents the real-time tracking error, Represents the target performance boundary.

[0081] Among them, the functional implementation of each module in the above-mentioned vehicle control device based on PPC and PID corresponds to the various steps in the above-mentioned vehicle control method embodiment based on PPC and PID, and their functions and implementation processes will not be repeated here one by one.

[0082] On the third aspect, an embodiment of the present application provides a vehicle control device based on PPC and PID. The vehicle control device based on PPC and PID can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0083] Reference Figure 5 , Figure 5 Schematic diagram of the hardware structure of the vehicle control device based on PPC and PID involved in the embodiment of the present application. In the embodiment of the present application, the vehicle control device based on PPC and PID may include a processor, a memory, a communication interface and a communication bus.

[0084] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0085] Communication interfaces include input / output (I / O), physical, and logical interfaces, used to interconnect components within PPC and PID-based vehicle control devices, as well as interfaces used to interconnect PPC and PID-based vehicle control devices with other devices (such as other computing devices or user devices). Physical interfaces can include Ethernet, fiber optic, and ATM interfaces; user devices can include displays and keyboards.

[0086] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0087] The processor may be a general-purpose processor that can invoke a PPC- and PID-based vehicle control program stored in a memory and execute the PPC- and PID-based vehicle control method provided in the embodiments of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the PPC- and PID-based vehicle control program is invoked can be referenced in the various embodiments of the PPC- and PID-based vehicle control method of the present application and will not be further described here.

[0088] Those skilled in the art will understand that Figure 5 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0089] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0090] The readable storage medium of the present application stores a vehicle control program based on PPC and PID, wherein when the vehicle control program based on PPC and PID is executed by a processor, the steps of the vehicle control method based on PPC and PID as described above are implemented.

[0091] Among them, the method implemented when the vehicle control program based on PPC and PID is executed can refer to the various embodiments of the vehicle control method based on PPC and PID in this application, and will not be repeated here.

[0092] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0093] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0094] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0095] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0096] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0097] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of this application.

[0098] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A vehicle control method based on PPC and PID, characterized in that: The vehicle control method based on PPC and PID includes: Perform preset performance control (PPC) performance boundary calculation based on the real-time scenario requirements of the target vehicle to obtain the target performance boundary; Mapping the real-time tracking error of the target vehicle to the PPC unconstrained space according to the target performance boundary to obtain the target unconstrained error; Adjusting PID parameter values according to the target unconstrained error to generate target PID parameter values; PID control is performed on the target vehicle according to the target PID parameter value and the real-time tracking error.

2. The vehicle control method based on PPC and PID as claimed in claim 1, characterized in that: The adjusting the PID parameter value by the target unconstrained error to generate a target PID parameter value includes: Determining the PID parameters to be adjusted based on the magnitude relationship between the target unconstrained error and a preset error threshold; The parameter value of the PID parameter to be adjusted is adjusted according to the target unconstrained error and its corresponding derivative to obtain a target PID parameter value.

3. The vehicle control method based on PPC and PID as claimed in claim 2, characterized in that: When the target vehicle is in longitudinal control, determining the PID parameters to be adjusted based on the magnitude relationship between the target unconstrained error and a preset error threshold includes: If the target unconstrained error is less than or equal to a first error threshold, the integral gain parameter is used as the PID parameter to be adjusted; If the target unconstrained error is greater than or equal to a second error threshold, taking the proportional gain parameter as the PID parameter to be adjusted, and the second error threshold is greater than the first error threshold; If the target unconstrained error is greater than the first error threshold and less than the second error threshold, the proportional gain parameter and the differential gain parameter are used as PID parameters to be adjusted.

4. The vehicle control method based on PPC and PID as claimed in claim 2, characterized in that: When the target vehicle is in lateral control, determining the PID parameter to be adjusted based on the magnitude relationship between the target unconstrained error and a preset error threshold includes: If the absolute value of the target unconstrained error is less than a third error threshold, the integral gain parameter is used as the PID parameter to be adjusted; If the absolute value of the target unconstrained error is greater than or equal to a third error threshold and less than a fourth error threshold, the differential gain parameter is used as the PID parameter to be adjusted; If the absolute value of the target unconstrained error is greater than or equal to the fourth error threshold and less than or equal to the fifth error threshold, the proportional gain parameter and the differential gain parameter are used as the PID parameters to be adjusted; If the absolute value of the target unconstrained error is greater than the fifth error threshold, taking the proportional gain parameter as the PID parameter to be adjusted; The fifth error threshold is greater than the fourth error threshold and the fourth error threshold is greater than the third error threshold.

5. The vehicle control method based on PPC and PID according to any one of claims 2 to 4, characterized in that: The step of adjusting the PID parameter to be adjusted according to the target unconstrained error and its corresponding derivative to obtain a target PID parameter value includes: A preset target relationship table is looked up according to the target unconstrained error and its corresponding derivative to determine the target parameter value corresponding to the PID parameter to be adjusted, wherein the target relationship table is used to store the mapping relationship between the PID parameter value and the target unconstrained error and the derivative corresponding to the target unconstrained error; The original parameter value of the PID parameter to be adjusted is updated to the target parameter value to generate a target PID parameter value.

6. The vehicle control method based on PPC and PID as claimed in claim 1, characterized in that: The preset performance control PPC performance boundary calculation is performed based on the real-time scenario requirements of the target vehicle to obtain the target performance boundary, including: Determine the target initial allowable error boundary, target steady-state error boundary and target control convergence speed in PPC based on the real-time scenario requirements; The target performance boundary is calculated based on the target initial allowable error boundary, the target steady-state error boundary, the target control convergence speed, and a preset PPC performance function.

7. The vehicle control method based on PPC and PID as claimed in claim 1, characterized in that: Mapping the real-time tracking error of the target vehicle to the PPC unconstrained space according to the target performance boundary to obtain the target unconstrained error includes: Substituting the target performance boundary and the real-time tracking error into a PPC transfer function to obtain a target unconstrained error; The PPC conversion function is: Where, represents the target unconstrained error, represents the real-time tracking error, Represents the target performance boundary.

8. A vehicle control device based on PPC and PID, characterized in that: The vehicle control device based on PPC and PID includes: A boundary construction module is used to calculate the preset performance control (PPC) performance boundary based on the real-time scenario requirements of the target vehicle to obtain the target performance boundary; an error conversion module for mapping the real-time tracking error of the target vehicle to the PPC unconstrained space according to the target performance boundary to obtain a target unconstrained error; a parameter adjustment module, configured to adjust PID parameter values according to the target unconstrained error to generate target PID parameter values; A PID control module is used to perform PID control on the target vehicle according to the target PID parameter value and the real-time tracking error.

9. A vehicle control device based on PPC and PID, characterized in that: The PPC and PID-based vehicle control device includes a processor, a memory, and a PPC and PID-based vehicle control program stored on the memory and executable by the processor, wherein when the PPC and PID-based vehicle control program is executed by the processor, the steps of the PPC and PID-based vehicle control method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a vehicle control program based on PPC and PID, wherein when the vehicle control program based on PPC and PID is executed by a processor, the steps of the vehicle control method based on PPC and PID as described in any one of claims 1 to 7 are implemented.

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