A parameter setting method, a self-setting device, a storage medium and a controller
By using a self-tuning device and method, and employing a differential evolution algorithm to optimize PID parameters, the problem of long tuning time and poor consistency of PID parameters in existing technologies is solved, and the rapid and accurate adjustment of the VVT system is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- UNITED AUTOMOTIVE ELECTRONICS SYST
- Filing Date
- 2023-11-23
- Publication Date
- 2026-07-10
AI Technical Summary
In existing technologies, PID parameter tuning relies on manual experience, resulting in a time-consuming testing process and poor consistency of optimization results, making it difficult to quickly and accurately adjust variable valve timing (VVT) under dynamic operating conditions.
By employing a self-tuning device and method, the target operating conditions of an engine or vehicle are simulated by acquiring first-group data. The PID parameters are optimized using a differential evolution algorithm, and the parameters of the PID controller are automatically tuned by combining the weighted values of overshoot and absolute average error.
It achieves automated tuning of PID parameters, reduces manual intervention, improves tuning efficiency and consistency of results, and ensures rapid and accurate adjustment of VVT under dynamic operating conditions.
Smart Images
Figure CN117569937B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of engine control technology, and particularly relates to a parameter tuning method, a self-tuning device, a storage medium, and a controller. Background Technology
[0002] Variable Valve Timing (VVT) technology is often used to improve engine performance, economy, and environmental parameters. It optimizes the target intake and exhaust positions under different operating conditions to an optimal value and stores it in the corresponding target position map. When the engine is actually running, the control system queries the corresponding target intake and exhaust positions from the map according to the current operating conditions, and then the actuator adjusts the position according to the preset parameters.
[0003] To speed up the adjustment process, a PID controller can be added to correct the parameters. To ensure that the engine control system can quickly and accurately adjust the VVT to the target position under dynamic conditions, the parameters of the PID controller need to be tuned.
[0004] In related technologies, PID parameter tuning is done manually using empirical methods. Due to differences in vehicle and engine parameters, this process requires extensive manual testing and repeated optimization verification. The testing process is time-consuming, relies on personal experience, and the consistency of optimization results is poor. Therefore, there is an urgent need to improve the above process. Summary of the Invention
[0005] This invention discloses a parameter tuning method for engine valve timing control. Its core process includes a fifth parameter processing step; this fifth parameter processing step acquires a first group of data; the nth parameter vector in the first group of data replaces the calibration value corresponding to proportional-integral-derivative (PID) control; furthermore, based on the timestamp corresponding to the target position array SP[], the target position in the target position array SP[] is modified in real time; wherein, different VVT positions can be simulated by changing the target position array SP[].
[0006] The target position is the parameter of the target intake and exhaust position map Map of the engine valve timing system; at the same time, the actual position of the timing system VVT is recorded and stored in the actual position array RPn[]; then, the fitness set of the target position array SP[] and the actual position array RPn[] is calculated according to the weighted value of the overshoot and absolute average error of the PID control, that is, the parameter vector of the individual corresponding to the smallest element of the fitness set is used as the tuning value output to complete the tuning process of the PID control parameters.
[0007] Specifically, the parameter vector of the first group data can be composed of M PID parameter vectors, where M is a positive integer; the nth parameter vector can be a vector composed of at least one of the first proportional parameter kpn, the second integral parameter kin, and the third differential parameter kdn, i.e., kPIDn(kpn, kin, kdn).
[0008] Furthermore, this parameter tuning method can also tune and optimize global parameters by setting a third operating condition simulation step; this third operating condition simulation step adjusts the operating state of the engine or vehicle and makes the engine or vehicle operate under the target operating condition to simulate the intake and exhaust positions of the engine valve timing system.
[0009] The target operating conditions mentioned above can be adjusted by adjusting the speed and / or torque of the engine or vehicle test bench and dynamometer.
[0010] Furthermore, the parameter tuning method may also include a first data excitation step to provide simulation data to the above tuning and optimization process; that is, to simulate the target operating conditions of the engine or vehicle, and to use the coordinates corresponding to the target intake and exhaust position map of the engine valve timing system as input values to the above tuning and optimization process, so that the tuned parameters are more comprehensive and can adapt to the actual working scenarios of the engine.
[0011] The first data excitation step can also obtain the boundary conditions of the above parameter vector to avoid outliers or non-convergence. The boundary conditions include the upper and lower limit sets KP, KI, and KD of the first proportional parameter kpn, the second integral parameter kin, and the third differential parameter kdn, respectively.
[0012] Furthermore, its first data activation step can further optimize the first parameter set of the differential evolution process; wherein, the elements of the first parameter set include the optimized value OptM of the size M of the first group data Group, the maximum number of iterations imax, the mutation factor F, the crossover probability CR (Crossover Rate) and / or the overshoot weight f; its first parameter set is used to improve the iterative process of the tuned differential evolution.
[0013] If the iteration process has not yet reached the maximum number of iterations (imax), then selection, mutation, and / or crossover operations are performed on the differential evolution process to evolve the population to the next generation and repeat the fifth parameter processing step until the maximum number of iterations (imax) is reached.
[0014] Accordingly, this invention also discloses a self-tuning device for optimizing engine valve timing control parameters. Its core includes a fifth parameter processing unit, which is used to obtain a first type of group data Group and replace the calibration value corresponding to proportional-integral-derivative control (PID control) with the nth parameter vector in the first type of group data Group; and modify the target position in the target position array SP[] in real time according to the timestamp corresponding to the target position array SP[]; wherein, the target position is the parameter of the target intake and exhaust position map Map of the engine valve timing system.
[0015] Meanwhile, the actual position of the timing system VVT is recorded and stored in the actual position array RPn[]. Then, the fitness set of the target position array SP[] and the actual position array RPn[] is calculated based on the weighted value of the overshoot and absolute average error of the PID control, and the parameter vector of the individual corresponding to the smallest element of the fitness set is used as the tuning value output.
[0016] The parameter vector of the first group data includes M PID parameter vectors, where M is a positive integer; its nth parameter vector includes at least one of the first proportional parameter kpn, the second integral parameter kin, and the third differential parameter kdn.
[0017] Furthermore, the self-tuning device may also be equipped with a third operating condition simulation unit to adjust the operating state of the engine or vehicle and to make the engine or vehicle operate under a target operating condition; the target operating condition is used to simulate the intake and exhaust positions of the engine valve timing system; wherein, the target operating condition can be adjusted by adjusting the speed and / or torque of the engine or vehicle test bench and dynamometer.
[0018] Furthermore, in order to obtain the tuning of global parameters, the self-tuning device may also be equipped with a first data excitation unit to simulate the target operating conditions of the engine or vehicle; the target operating condition information includes the coordinates corresponding to the target intake and exhaust position map Map of the engine valve timing system; wherein, the first data excitation unit may also obtain the boundary conditions of the parameter vector; the boundary conditions include the upper and lower limit sets KP, KI, and KD of the first proportional parameter kpn, the second integral parameter kin, and the third differential parameter kdn, respectively.
[0019] The first data activation unit can also acquire the first parameter set of the differential evolution process. The elements of the first parameter set include the optimized value OptM of its first population data Group size M, the maximum number of iterations imax, the mutation factor F, the crossover probability CR (Crossover Rate) and / or the weight f of the overshoot OS (Over Shoot). The first parameter set is used to improve the iterative process of the tuned differential evolution. If the iterative process has not yet reached the maximum number of iterations imax, selection, mutation and / or crossover operations can be performed on the differential evolution process to evolve the population to the next generation and repeat the fifth parameter processing unit until the maximum number of iterations imax is reached.
[0020] Similarly, embodiments of the present invention also disclose a computer storage medium and a controller; the computer storage medium includes a storage medium body for storing a computer program; when the computer program is executed by a microprocessor, any of the parameter tuning methods described above can be implemented; the controller includes any of the self-tuning devices described above and / or any of the computer storage media, to solve the same technical problem.
[0021] In summary, this invention can be used for self-tuning of valve position PID parameters in a variable valve timing (VVT) system. Its fifth parameter processing step / unit replaces the calibration value of the engine valve timing control system with PID parameters, optimizes the parameters based on the fitness set obtained by weighting the overshoot and absolute average error of the PID control, and obtains the tuning value corresponding to the optimized parameter vector. Its third operating condition simulation step and first data excitation step improve the data simulation and traversal process, providing support for the selection, mutation, and / or cross-operation of the first parameter set and model in the differential evolution process. This can replace physical experiments and obtain optimized PID parameters for the engine or vehicle under target operating conditions.
[0022] It should be noted that the terms "first," "second," and similar terms used in this article are merely for describing the constituent elements of the technical solution and do not constitute a limitation on the technical solution, nor should they be interpreted as an indication or implication of the importance of the corresponding elements; elements with terms such as "first," "second," or similar terms indicate that at least one of the elements is included in the corresponding technical solution. Attached Figure Description
[0023] To more clearly illustrate the technical solution of the present invention and facilitate a further understanding of its technical effects, features, and objectives, the present invention will be described in detail below with reference to the accompanying drawings. The drawings constitute an essential part of the specification and are used together with Embodiment 1 of the present invention to illustrate the technical solution of the present invention, but do not constitute a limitation on the present invention.
[0024] The same reference numerals in the attached diagrams represent the same parts, specifically:
[0025] Figure 1 Here is an example of the intake VVT target curve.
[0026] Figure 2 This is a flowchart of parameter processing in an embodiment of the present invention.
[0027] Figure 3 This is an example of data sampling in an embodiment of the present invention.
[0028] Figure 4 This is a schematic diagram of the process of an embodiment of the method of the present invention.
[0029] Figure 5 This is a schematic diagram of the structural composition of an embodiment of the device of the present invention.
[0030] Figure 6 This is a schematic diagram of the composition structure of an embodiment of the product of the present invention. Figure 1 .
[0031] Figure 7 This is a schematic diagram of the composition structure of an embodiment of the product of the present invention. Figure 2 .
[0032] Figure 8 This is a schematic diagram of the composition structure of an embodiment of the product of the present invention. Figure 3 .
[0033] Figure 9 This is a schematic diagram of the composition structure of an embodiment of the product of the present invention. Figure 4 .
[0034] Figure 10 This is a schematic diagram of the composition structure of an embodiment of the product of the present invention. Figure 5 .
[0035] Figure 11 This is a schematic diagram of the composition structure of an embodiment of the product of the present invention. Figure 6 .
[0036] in:
[0037] 001-VVT target curve;
[0038] 010 - Data sampling;
[0039] 100 - First data stimulus step;
[0040] 101 - First parameter set;
[0041] 110 - The first type of group data, i.e., Group;
[0042] 121 - Preparation of intermediate data;
[0043] 123 - Intermediate data discrimination;
[0044] 125 - Iterative Data Processing;
[0045] 300 - Third Working Condition Simulation Steps;
[0046] 500 - Fifth parameter processing steps;
[0047] 510 - Parameter Initialization;
[0048] 520 - Parameter Filtering;
[0049] 555 - Fitness set;
[0050] 599 - Setting value;
[0051] 600 - Self-tuning equipment;
[0052] 610 - First Data Excitation Unit;
[0053] 630 - Third Working Condition Simulation Unit;
[0054] 650 - Fifth Parameter Processing Unit;
[0055] 900 - Engine or vehicle;
[0056] 901 - Controller;
[0057] 903 - Computer storage media. Implementation
[0058] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described below are merely illustrative of the technical solutions of the present invention, and not intended to limit the invention. Furthermore, the parts described in the embodiments or drawings are merely illustrative examples of relevant parts of the present invention, and not the entirety of the invention.
[0059] like Figure 2 , Figure 4 The parameter tuning method shown can be used for engine valve timing control. Its fifth parameter processing step 500 obtains the first type group data 110; and replaces the calibration value corresponding to proportional integral derivative control (PID control) with the nth parameter vector in the first type group data 110; and modifies the target position in the target position array SP[] in real time according to the timestamp corresponding to the target position array SP[]; wherein, the target position is the parameter of the target intake and exhaust position map Map of the engine valve timing system.
[0060] Simultaneously, the actual position of the timing system VVT is recorded and stored in the actual position array RPn[]. The fitness set 555 of the target position array SP[] and the actual position array RPn[] is calculated based on the weighted value of the overshoot and absolute average error of the PID control, and then the parameter vector of the individual corresponding to the smallest element of the fitness set 555 is used as the tuning value 599 for output.
[0061] Among them, the parameter vector of the first group data 110 includes M PID parameter vectors, where M is a positive integer; its nth parameter vector includes at least one of the first proportional parameter 111, the second integral parameter 112, and the third differential parameter 113.
[0062] Furthermore, the parameter tuning method also includes a third operating condition simulation step 300; the third operating condition simulation step 300 can adjust the operating state of the engine or vehicle 900 and make the engine or vehicle 900 operate under a target operating condition; the target operating condition is used to simulate the intake and exhaust positions of the engine valve timing system.
[0063] The target operating conditions can be adjusted by adjusting the speed and / or torque of the engine or vehicle 900 test bench and dynamometer.
[0064] Furthermore, the parameter tuning method also includes a first data excitation step 100; the first data excitation step 100 simulates the target operating condition of the engine or vehicle 900 to traverse the data; specifically, the target operating condition information includes the coordinates corresponding to the target intake and exhaust position map Map of the engine valve timing system.
[0065] Furthermore, its first data excitation step 100 can also obtain the boundary conditions of the parameter vector; wherein, the boundary conditions include the upper and lower limit sets KP, KI, and KD of the first proportional parameter 111, the second integral parameter 112, and the third differential parameter 113, respectively.
[0066] Specifically, the first data activation step 100 can also obtain a first parameter set 101 for the differential evolution process; the elements of the first parameter set 101 include the optimization value OptM of the size M of the first population data 110, the maximum number of iterations imax, the mutation factor F, the crossover probability CR (Crossover Rate) and / or the weight f of the overshoot OS (Over Shoot); through its first parameter set 101, the iterative process of differential evolution of the tuned value 599 can be improved.
[0067] If the iteration process has not yet reached the maximum number of iterations imax, selection, mutation and / or crossover operations can be performed on the differential evolution process to evolve the population to the next generation and repeat the fifth parameter processing step 500 until the maximum number of iterations imax is reached.
[0068] Similarly, such as Figure 5The self-tuning device 600 shown can be used to optimize engine valve timing control parameters. Its fifth parameter processing unit 650 acquires the first type group data 110 and replaces the calibration value corresponding to proportional integral derivative control (PID control) with the nth parameter vector in the first type group data 110. According to the timestamp corresponding to the target position array SP[], the target position in the target position array SP[] is modified in real time. The target position is the parameter of the target intake and exhaust position map Map of the engine valve timing system.
[0069] Meanwhile, the actual position of the timing system VVT is recorded and stored in the actual position array RPn[]. Then, the fitness set 555 of the target position array SP[] and the actual position array RPn[] is calculated based on the weighted value of the overshoot and absolute average error of the PID control, and the parameter vector of the individual corresponding to the smallest element of the fitness set 555 is output as the tuning value 599.
[0070] Specifically, the parameter vector of the first group data 110 includes M PID parameter vectors, where M is a positive integer; the nth parameter vector includes at least one of the first proportional parameter 111, the second integral parameter 112, and the third differential parameter 113.
[0071] Furthermore, the self-tuning device 600 is also equipped with a third operating condition simulation unit 630, which is used to adjust the operating state of the engine or vehicle 900 and make the engine or vehicle 900 operate under a target operating condition; the target operating condition is used to simulate the intake and exhaust positions of the engine valve timing system; wherein, the target operating condition can be adjusted by adjusting the speed and / or torque of the test bench and dynamometer of the engine or vehicle 900.
[0072] Furthermore, such as Figure 5 The self-tuning device 600 shown also includes a first data excitation unit 610 for simulating the target operating conditions of the engine or vehicle 900. The target operating condition information includes the coordinates corresponding to the target intake and exhaust position map Map of the engine valve timing system. The first data excitation unit 610 also acquires the boundary conditions of the parameter vector. The boundary conditions include the upper and lower limit sets KP, KI, and KD of the first proportional parameter 111, the second integral parameter 112, and the third differential parameter 113, respectively.
[0073] Specifically, its first data activation unit 610 also acquires a first parameter set 101 for the differential evolution process; the elements of the first parameter set 101 include the optimized value OptM of the size M of the first population data 110, the maximum number of iterations imax, the mutation factor F, the crossover probability CR (Crossover Rate) and / or the weight f of the overshoot OS (Over Shoot); the first parameter set 101 is used to improve the iterative process of differential evolution of the tuning value 599; if its iterative process has not yet reached the maximum number of iterations imax, selection, mutation and / or crossover operations can be performed on the differential evolution process to evolve the population to the next generation and repeat the fifth parameter processing unit 650 until the maximum number of iterations imax is reached.
[0074] Similarly, such as Figures 6 to 11 The computer storage medium 903 and controller 901 shown also employ the same inventive concept; the computer storage medium 903 includes a storage medium body for storing computer programs; when the computer program is executed by the microprocessor, it is used to implement any of the above parameter tuning methods; the controller 901 includes any of the above self-tuning devices 600 and / or computer storage medium 903 to solve the same technical problem.
[0075] In practical applications, the method or product of this invention can be used to automatically tune the parameters of PID control for the intake and exhaust target positions of a VVT system. Specifically, before automatic tuning, the target operating condition and boundary conditions, i.e., the algorithm parameters, need to be input. The target operating condition is used to determine the horizontal and vertical coordinates corresponding to the VVT target intake and exhaust position Map. Its boundary conditions include parameters such as the upper and lower limits of KP, KI, and KD, which are used to determine some limiting parameters of the optimization process. Its algorithm parameters include the population size M, the maximum number of iterations imax, the mutation factor F, the crossover probability CR (Crossover Rate), and the weight f of the overshoot OS (Over Shoot), which are used to construct a differential evolution process and implement corresponding adjustments.
[0076] Furthermore, it is necessary to simulate the change process of the target position by adjusting the engine's operating conditions; specifically, by using equipment such as engine test benches and chassis dynamometers, the engine can be controlled to the corresponding target operating conditions by changing parameters such as speed and torque.
[0077] Furthermore, in order to simulate the changes in the VVT target position and obtain a more refined data sequence of the target position, it is also necessary to simulate and generate a target position array; specifically, such as Figure 1As shown, taking intake VVT as an example, if the latest opening position of the engine's intake VVT is 25°CA and the earliest opening position is -35°CA, then a position slightly smaller than the latest opening position (such as 20°CA) can be used as the zero position. After stabilizing for Δt time, the target position is reduced by a fixed step size and stabilized again for Δt time. After that, this process must be repeated, and the target position is gradually reduced until it reaches the vicinity of the earliest opening position. Finally, the corresponding target position array SP[] and its timestamp array time[] can be generated with a time precision of 0.1s. Thus, the process of changing the VVT target position at different sizes can be simulated. °CA (Crank Angle) is the crank angle, that is, the angle of the crankshaft relative to the exhaust top dead center when the intake valve is open. It is negative before the exhaust top dead center and positive after the exhaust top dead center.
[0078] Among them, the iterative process of differential evolution adopts, as follows: Figure 2 The process is as follows: First, the system randomly generates an initial population with a population size of M individuals. Each individual in the population contains a set of PID parameters (kpn, kin, kdn), and the range of each parameter is limited to the upper and lower limits of the PID parameters mentioned above.
[0079] Subsequently, the system begins to test each individual in the population. During the test, the PID parameters of individual n are first input into the engine controller, that is, the corresponding KP, KI, and KD calibration values in the engine controller are modified. Then, the target position array SP[] is imported, and the target position Map is modified in real time according to its corresponding timestamp. At the same time, the actual position of VVT under the current parameters is recorded and saved to the array RPn[].
[0080] like Figure 3 As shown, a data sampling case is given. Specifically, after an individual's test is completed, the elements in the individual fitness set 555 are calculated using the fitness function F=(1+f*overshoot)*err and the arrays SP[] and RPn[]. During this process, one individual generates a pair of SP[] and RPn[] and calculates one fitness value (i.e., an element in the fitness set 555), which is then saved to obtain the fitness set 555. Overshoot is the absolute value of the maximum overshoot during the adjustment process, err is the absolute average error between the target position and the actual position during the entire test process, and f is the overshoot weight. The larger f is, the greater the influence of overshoot on the fitness value. After the calculation is completed, the individual fitness value is stored in the array fitness[], i.e., the fitness set 555, and the elements in the array fitness[] are reordered in ascending order.
[0081] After all individuals have been tested, determine whether the maximum number of iterations has been reached. If so, output the optimal solution, which is the PID parameter of the individual corresponding to the smallest element in the fitness[] array. Otherwise, the population needs to be evolved to the next generation through selection, mutation, and crossover operations. Then repeat the above process to test each individual in the new generation until the maximum number of iterations is reached and the optimization is completed.
[0082] It should be noted that the above embodiments are only for more clearly illustrating the technical solution of the present invention. Those skilled in the art will understand that the implementation of the present invention is not limited to the above content. Any obvious changes, substitutions or replacements made based on the above content do not exceed the scope of the technical solution of the present invention. Other implementations will also fall within the scope of the present invention without departing from the concept of the present invention.
Claims
1. A parameter tuning method for engine valve timing control, characterized in that... The process includes a fifth parameter processing step (500); the fifth parameter processing step (500) obtains the first population data (110) Group; and replaces the calibration value corresponding to the proportional integral derivative control (PID control) with the nth parameter vector in the first population data (110) Group; then, according to the timestamp corresponding to the target position array SP[], the target position in the target position array SP[] is modified in real time; the target position is the parameter of the target intake and exhaust position map Map of the engine valve timing system; at the same time, the actual position of the timing system VVT is recorded and stored in the actual position array RPn[]; the fitness set (555) of the target position array SP[] and the actual position array RPn[] is calculated according to the weighted value of the overshoot and the absolute average error of the PID control; and then the parameter vector corresponding to the individual with the smallest element of the fitness set (555) is output as the tuning value (599); The parameter vector of the first population data (110) includes M PID parameter vectors, where M is a positive integer; the nth parameter vector includes at least one of the first proportional parameter (111) kpn, the second integral parameter (112) kin, and the third differential parameter (113) kdn. The third operating condition simulation step (300) adjusts the operating state of the engine or vehicle (900) and makes the engine or vehicle (900) operate under the target operating condition; the target operating condition is used to simulate the intake and exhaust positions of the engine valve timing system.
2. The parameter tuning method as described in claim 1, wherein: The target operating condition is adjusted by adjusting the speed and / or torque of the test bench, dynamometer, and / or the engine or vehicle (900).
3. The parameter tuning method as described in claim 1 or 2 further includes a first data excitation step (100); the first data excitation step (100) simulates the target operating condition of the engine or vehicle (900), and the target operating condition information includes the coordinates corresponding to the target intake and exhaust position map Map of the engine valve timing system.
4. The parameter tuning method as described in claim 3, wherein: The first data excitation step (100) also obtains the boundary conditions of the parameter vector; the boundary conditions include the upper and lower limit sets KP, KI, and KD of the first proportional parameter (111) kpn, the second integral parameter (112) kin, and the third differential parameter (113) kdn.
5. The parameter tuning method as described in claim 4, wherein: The first data activation step (100) also obtains a first parameter set (101) for the differential evolution process; the elements of the first parameter set (101) include the optimized value OptM of the Group size M of the first population data (110), the maximum number of iterations imax, the mutation factor F, the crossover probability CR and / or the overshoot weight f; the first parameter set (101) is used to improve the iterative process of the differential evolution of the tuned value (599).
6. The parameter tuning method as described in claim 5, wherein: If the iteration process has not yet reached the maximum number of iterations imax, then selection, mutation and / or crossover operations are performed on the differential evolution process to evolve the population to the next generation and repeat the fifth parameter processing step (500) until the maximum number of iterations imax is reached.
7. A self-tuning device (600) for optimizing engine valve timing control parameters, comprising a fifth parameter processing unit (650) and a third operating condition simulation unit (630); the fifth parameter processing unit (650) acquires a first group data (110) Group; replaces the calibration value corresponding to proportional integral derivative control (PID control) with the nth parameter vector in the first group data (110) Group; modifies the target position in the target position array SP[] in real time according to the timestamp corresponding to the target position array SP[], wherein the target position is the parameter of the target intake and exhaust position map Map of the engine valve timing system; at the same time, records the actual position of the timing system VVT and stores it in the actual position array RPn[]; calculates the fitness set (555) of the target position array SP[] and the actual position array RPn[] according to the weighted value of the overshoot and absolute average error of the PID control, and outputs the parameter vector of the individual corresponding to the smallest element of the fitness set (555) as the tuning value (599); The parameter vector of the first population data (110) includes M PID parameter vectors, where M is a positive integer; the nth parameter vector includes at least one of the first proportional parameter (111) kpn, the second integral parameter (112) kin, and the third differential parameter (113) kdn. The third operating condition simulation unit (630) adjusts the operating state of the engine or vehicle (900) and makes the engine or vehicle (900) operate under the target operating condition; the target operating condition is used to simulate the intake and exhaust positions of the engine valve timing system.
8. The self-tuning device (600) of claim 7 adjusts the target operating condition by adjusting the speed and / or torque of the test bench and dynamometer of the engine or vehicle (900).
9. The self-tuning device (600) as claimed in claim 8 further includes a first data excitation unit (610); the first data excitation unit (610) simulates the target operating condition of the engine or vehicle (900), and the target operating condition information includes the coordinates corresponding to the target intake and exhaust position map Map of the engine valve timing system; wherein, The first data excitation unit (610) also acquires the boundary conditions of the parameter vector; the boundary conditions include the upper and lower limit sets KP, KI, and KD of the first proportional parameter (111) kpn, the second integral parameter (112) kin, and the third differential parameter (113) kdn, respectively.
10. The self-tuning device (600) as claimed in claim 9, wherein: The first data activation unit (610) also acquires a first parameter set (101) for the differential evolution process; the elements of the first parameter set (101) include the optimized value OptM of the Group size M of the first population data (110), the maximum number of iterations imax, the mutation factor F, the crossover probability CR and / or the overshoot weight f; the first parameter set (101) is used to improve the iterative process of the differential evolution of the tuned value (599); if the iterative process has not reached the maximum number of iterations imax, then selection, mutation and / or crossover operations are performed on the differential evolution process to make the population evolve to the next generation and repeat the fifth parameter processing unit (650) until the maximum number of iterations imax is reached.
11. A computer storage medium (903) comprising a storage medium body for storing a computer program; wherein the computer program, when executed by a microprocessor, implements the parameter tuning method as described in any one of claims 1 to 6.
12. A controller (901) comprising the self-tuning device (600) of any one of claims 7 to 10 and / or the computer storage medium (903) of claim 11.