Adaptive speed planning method based on fuzzy PID rule

Abstract

The invention relates to a self-adaptive speed planning method based on a fuzzy PID rule, and the method comprises the steps: inputting input data into a fuzzy PID controller, and then outputting PID parameters, the input values comprise an error e and an error change rate ec, the error e is obtained by subtracting a target vehicle speed from a current vehicle speed, and the error change rate ec is a current acceleration; the PID parameters comprise a scale factor kp of the error, an integral factor ki of the error and a differential factor kd of the error; a real-time planning value of an error e is calculated according to the value of the PID parameter, and a real-time planning value of the speed is obtained; the control method based on fuzzy logic realizes adaptive speed planning, has good robustness and adaptive capacity, can be widely applied to control of a complex system, and has important theoretical and practical significance.

Description

Technical Field

[0001] The invention relates to the field of speed planning, and in particular to an adaptive speed planning method based on fuzzy PID rules. Background Art

[0002] In the field of mechanical control, PID (Proportional-Integral-Derivative) controller is a common control algorithm, which is widely used in speed, position and force control. PID controller controls the system by weighted summation of the system error, integral term and differential term. However, traditional PID controllers often perform poorly when faced with nonlinear, uncertain and time-varying systems and cannot meet actual needs.

[0003] In complex environments, the parameters of traditional PID controllers often require a long time of debugging and optimization, and it is difficult to cope with changes in system parameters and loads, and it is difficult to adapt to the control requirements of nonlinear, uncertain and time-varying systems, resulting in a decrease in control performance. The parameters require a long time of debugging and optimization, and it is difficult to cope with changes in system parameters and loads. The stability performance is poor, and oscillation and overshoot are prone to occur. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides an adaptive speed planning method based on fuzzy PID rules. The adaptive speed planning is realized by a control method based on fuzzy logic. The method has good robustness and adaptability, can be widely used in the control of complex systems, and has important theoretical and practical significance.

[0005] According to a first aspect of the present invention, there is provided an adaptive speed planning method based on fuzzy PID rules, comprising: Step 1, inputting input data into the fuzzy PID controller and then outputting PID parameters, wherein the input value includes error e and error change rate ec, error e is the current vehicle speed minus the target vehicle speed, and error change rate ec is the current acceleration; the PID parameters include: error proportional factor kp, error integral factor ki and error differential factor kd; Step 2: Calculate the real-time planning value of the error e according to the value of the PID parameter to obtain the real-time planning value of the speed.

[0006] Based on the above technical solution, the present invention can also make the following improvements.

[0007] Optionally, the step 1 includes: Step 101, fuzzifying the input data to obtain fuzzy input variables; Step 102, constructing a fuzzy rule base, the fuzzy rule base including a corresponding relationship table of values ​​of fuzzy input variables and fuzzy output variables; obtaining fuzzy output variable values ​​of each PID parameter based on the fuzzy rule base; Step 103, defuzzifying the result of fuzzy reasoning to obtain control output.

[0008] Optionally, step 101 includes: Set the domain and determine the membership function to convert the input data into membership intervals.

[0009] Optionally, step 102 includes: Set the range values ​​of each interval of error e and error change rate ec, and construct a table of the size of the change value of each PID parameter and the interval range to which the error e and error change rate ec belong.

[0010] Optionally, the step 103 includes: deblurring using a centroid method according to the obtained fuzzy index and fuzzy degree.

[0011] Optionally, the adaptive speed planning method further includes: when the acceleration is greater than 0, using a segmented PID control algorithm for speed planning; when the acceleration is less than 0, using a fuzzy PID control algorithm for speed planning.

[0012] Optionally, the method further includes: Step 3: monitor the system status and environmental changes in real time, and adaptively adjust the parameters of the fuzzy PID controller according to the chassis control effect fed back in real time.

[0013] According to a second aspect of the present invention, there is provided an adaptive speed planning system based on fuzzy PID rules, comprising: a fuzzy PID controller and a speed planning module; After inputting the input data into the fuzzy PID controller, the PID parameters are outputted, wherein the input value includes the error e and the error change rate ec, the error e is the current vehicle speed minus the target vehicle speed, and the error change rate ec is the current acceleration; the PID parameters include: the proportional factor kp of the error, the integral factor ki of the error, and the differential factor kd of the error; The speed planning module is used to calculate the real-time planning value of the error e according to the value of the PID parameter, and obtain the real-time planning value of the speed.

[0014] According to a third aspect of the present invention, there is provided an electronic device comprising a memory and a processor, wherein the processor is used to implement the steps of an adaptive speed planning method based on fuzzy PID rules when executing a computer management program stored in the memory.

[0015] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium on which a computer management program is stored, and when the computer management program is executed by a processor, the steps of an adaptive speed planning method based on fuzzy PID rules are implemented.

[0016] The present invention provides an adaptive speed planning method, system, electronic device and storage medium based on fuzzy PID rules. The adaptive speed planning method based on fuzzy PID rules can better adapt to the control requirements of nonlinear, uncertain and time-varying systems, and has stronger adaptability than traditional PID controllers. The present invention introduces an adaptive adjustment module, which can realize automatic adjustment of fuzzy PID controller parameters, simplifies the parameter adjustment process, and improves the practicality and stability of the system. The method based on fuzzy PID rules can process the fuzzy relationship between input and output through fuzzy logic to realize accurate control of nonlinear, uncertain and time-varying systems, and has higher control accuracy than traditional PID controllers. Better system stability: through adaptive adjustment and a speed planning algorithm based on fuzzy logic, the present invention can improve the stability and robustness of the system, can better suppress oscillation and overshoot phenomena in complex environments, and improve the reliability of the system. The present invention can realize adaptive speed planning in complex environments, improve the practicality and applicability of the system, and can better meet actual application needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of an embodiment of an adaptive speed planning method based on fuzzy PID rules provided by the present invention; Figure 2 A processing flow chart of a fuzzy PID controller provided by an embodiment of the present invention; Figure 3 A schematic diagram of fuzzy processing of input data provided by an embodiment of the present invention; Figure 4 A flow chart of speed planning provided by an embodiment of the present invention; Figure 5 A structural block diagram of an adaptive speed planning system based on fuzzy PID rules provided by the present invention; Figure 6 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 7 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0018] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0019] Figure 1 The present invention provides an adaptive speed planning method based on fuzzy PID rules. Figure 1 As shown, the method includes: Step 1, input the input data into the fuzzy PID controller and then output the PID parameters, where the input values ​​include the error e and the error change rate ec, the error e is the current vehicle speed minus the target vehicle speed, and the error change rate ec is the current acceleration; the PID parameters include: the proportional factor kp of the error, the integral factor ki of the error and the differential factor kd of the error.

[0020] Step 2, calculate the real-time planning value of the error e according to the value of the PID parameter, and obtain the real-time planning value of the speed.

[0021] The present invention provides an adaptive speed planning method based on fuzzy PID rules, and realizes adaptive speed planning based on fuzzy logic control method, improves the adaptability of the controller to nonlinear, uncertain and time-varying systems, simplifies the parameter adjustment process, and improves the stability and robustness of the system. By introducing fuzzy logic and adaptive adjustment algorithms, more efficient and flexible speed planning control is achieved to meet the actual application requirements in complex environments; the shortcomings of traditional PID controllers in complex environments are solved, and it can be widely used in the control of complex systems, which has important theoretical and practical significance.

[0022] Example 1 Embodiment 1 provided by the present invention is an embodiment of an adaptive speed planning method based on fuzzy PID rules provided by the present invention, combined with Figure 1 It can be seen that the embodiment of the method includes: Step 1, input the input data into the fuzzy PID controller and then output the PID parameters, where the input values ​​include the error e and the error change rate ec, the error e is the current vehicle speed minus the target vehicle speed, and the error change rate ec is the current acceleration; the PID parameters include: the proportional factor kp of the error, the integral factor ki of the error and the differential factor kd of the error.

[0023] The embodiment of the present invention designs a PID controller based on fuzzy logic for realizing speed control of the system. The fuzzy PID controller uses fuzzy logic to process the fuzzy relationship between input and output to realize accurate control of nonlinear, uncertain and time-varying systems. Figure 2 Shown is a processing flow chart of a fuzzy PID controller provided in an embodiment of the present invention.

[0024] In a possible implementation manner, step 1 includes: Step 101, fuzzifying the input data to obtain fuzzy input variables.

[0025] In a possible implementation manner, step 101 includes: Set the domain and determine the membership function to convert the input data into membership intervals.

[0026] In a specific implementation, the membership function in the present invention uses a triangle membership function. Figure 3 It is a schematic diagram of fuzzy processing of input data provided by an embodiment of the present invention.

[0027] Step 102, constructing a fuzzy rule base, the fuzzy rule base includes a corresponding relationship table of the values ​​of fuzzy input variables and fuzzy output variables; based on the fuzzy rule base, obtaining the fuzzy output variable value of each PID parameter.

[0028] In a possible embodiment, step 102 includes: Set the range values ​​of each interval of error e and error change rate ec, and construct a table of the size of the change value of each PID parameter and the interval range to which the error e and error change rate ec belong.

[0029] In the specific implementation, the size of the error e and the error change rate ec is divided into seven levels from small to large: NB, NM, NS, ZO, PS, PM and PB, and the fuzzy rule base is a 7*7 table of the change values ​​of the error proportional factor kp, the error integral factor ki and the error differential factor kd when the error e and the error change rate ec belong to the 7 levels respectively.

[0030] Specifically, the value of the change Δkp of the error proportional factor kp is as follows:

[0031] The table of the value of the change Δki of the error integral factor ki is:

[0032] The table of the value of the change Δkd of the differential factor kd of the error is:

[0033] Step 103, defuzzifying the result of fuzzy reasoning to obtain control output.

[0034] In a possible implementation manner, step 103 includes: deblurring using a centroid method according to the obtained fuzzy index and fuzzy degree.

[0035] .

[0036] Step 2, calculate the real-time planning value of the error e according to the value of the PID parameter, and obtain the real-time planning value of the speed.

[0037] In a possible implementation manner, the adaptive speed planning method further includes: when the acceleration is greater than 0, using a segmented PID control algorithm for speed planning; when the acceleration is less than 0, using a fuzzy PID control algorithm for speed planning.

[0038] In the specific implementation, a speed planning algorithm based on fuzzy logic is designed to realize adaptive planning of speed by considering the dynamic characteristics of the system and changes in the environment. The throttle control adopts a segmented PID control algorithm, and the brake control adopts a fuzzy PID control algorithm. The algorithm can adjust the speed planning in real time according to the system status and environmental changes to ensure that the system can operate stably in a complex environment.

[0039] like Figure 4 FIG. 1 is a flow chart of speed planning provided by an embodiment of the present invention. Figure 3 In the given embodiment, each speed segment is set according to the speed, different PID parameter sets are set for different speed segments, and corresponding PID parameters are selected according to the speed segment that the current speed is in. When the current speed exceeds the speed segment, the speed segment that the current speed is in is re-determined and the corresponding PID parameters are re-selected.

[0040] In a specific implementation, the integral ratio of the integral factor ki of the error is determined according to each error level setting.

[0041] In a possible embodiment, the method further includes: Step 3: monitor the system status and environmental changes in real time, and adaptively adjust the parameters of the fuzzy PID controller according to the chassis control effect fed back in real time.

[0042] Monitor the system status and environmental changes in real time, and make adaptive adjustments to the parameters of the fuzzy PID controller based on real-time feedback. Through adaptive adjustment, the system can automatically optimize the parameters, improving the system's adaptability and robustness.

[0043] In the specific implementation, the fuzzy PID controller, speed planning algorithm and adaptive adjustment module are integrated, and the overall system is optimized to ensure that the system can operate stably in complex environments and achieve adaptive speed planning.

[0044] In specific implementation, the fuzzy rules and parameter adjustment methods of the fuzzy PID controller can be further optimized to improve the control accuracy and stability of the system for nonlinear, uncertain and time-varying systems.

[0045] The speed planning algorithm based on fuzzy logic can also be further improved to consider more system dynamic characteristics and environmental change factors to achieve more flexible and effective speed planning.

[0046] The algorithm and real-time performance of the adaptive adjustment module can be further optimized to achieve faster and more accurate parameter adjustment and improve the adaptive ability of the system.

[0047] Example 2 Embodiment 2 provided by the present invention is an embodiment of an adaptive speed planning system based on fuzzy PID rules provided by the present invention. Figure 5 A structural diagram of an adaptive speed planning system based on fuzzy PID rules provided by an embodiment of the present invention, combined with Figure 5 It can be seen that the embodiment of the system includes: a fuzzy PID controller and a speed planning module.

[0048] After the input data is input into the fuzzy PID controller, the PID parameters are output, where the input values ​​include the error e and the error change rate ec. The error e is the current vehicle speed minus the target vehicle speed, and the error change rate ec is the current acceleration. The PID parameters include: the error proportional factor kp, the error integral factor ki and the error differential factor kd.

[0049] Design fuzzy PID controllers to achieve accurate control of nonlinear, uncertain and time-varying systems. This involves fuzzy logic modeling and parameter optimization, as well as the design and optimization of fuzzy rules.

[0050] The speed planning module is used to calculate the real-time planning value of the error e according to the value of the PID parameter, and obtain the real-time planning value of the speed.

[0051] The speed planning algorithm based on fuzzy logic can adjust the speed planning in real time according to the system status and environmental changes to ensure that the system can operate stably in complex environments. The key point lies in the flexibility and real-time performance of the algorithm.

[0052] Furthermore, an adaptive adjustment module may be included, which can monitor the system status and environmental changes in real time, and adaptively adjust the parameters of the fuzzy PID controller according to real-time feedback to improve the adaptability and robustness of the system.

[0053] It can be understood that the adaptive speed planning system based on fuzzy PID rules provided by the present invention corresponds to the adaptive speed planning method based on fuzzy PID rules provided in the aforementioned embodiments. The relevant technical features of the adaptive speed planning system based on fuzzy PID rules can refer to the relevant technical features of the adaptive speed planning method based on fuzzy PID rules, which will not be repeated here.

[0054] See also Figure 6 , Figure 6 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 6As shown, an embodiment of the present invention provides an electronic device, including a memory 1310, a processor 1320, and a computer program 1311 stored in the memory 1310 and executable on the processor 1320. When the processor 1320 executes the computer program 1311, the following steps are implemented: inputting input data into the fuzzy PID controller and then outputting PID parameters, wherein the input values ​​include an error e and an error change rate ec, the error e is the current vehicle speed minus the target vehicle speed, and the error change rate ec is the current acceleration; the PID parameters include: an error proportional factor kp, an error integral factor ki, and an error differential factor kd; the real-time planning value of the error e is calculated according to the value of the PID parameters, and the real-time planning value of the speed is obtained.

[0055] See also Figure 7 , Figure 7 Schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. Figure 7 As shown, this embodiment provides a computer-readable storage medium 1400, on which a computer program 1411 is stored. When the computer program 1411 is executed by a processor, the following steps are implemented: input data into the fuzzy PID controller and then output PID parameters, wherein the input values ​​include an error e and an error change rate ec, the error e is the current vehicle speed minus the target vehicle speed, and the error change rate ec is the current acceleration; the PID parameters include: an error proportional factor kp, an error integral factor ki and an error differential factor kd; the real-time planning value of the error e is calculated according to the value of the PID parameters, and the real-time planning value of the speed is obtained.

[0056] An embodiment of the present invention provides an adaptive speed planning method, system, electronic device and storage medium based on fuzzy PID rules. The adaptive speed planning method based on fuzzy PID rules can better adapt to the control requirements of nonlinear, uncertain and time-varying systems, and has stronger adaptability than traditional PID controllers. The present invention introduces an adaptive adjustment module, which can realize automatic adjustment of fuzzy PID controller parameters, simplify the parameter adjustment process, and improve the practicality and stability of the system. The method based on fuzzy PID rules can process the fuzzy relationship between input and output through fuzzy logic to achieve precise control of nonlinear, uncertain and time-varying systems, and has higher control accuracy than traditional PID controllers. Better system stability: through adaptive adjustment and a speed planning algorithm based on fuzzy logic, the present invention can improve the stability and robustness of the system, can better suppress oscillation and overshoot phenomena in complex environments, and improve the reliability of the system. The present invention can realize adaptive speed planning in complex environments, improve the practicality and applicability of the system, and can better meet actual application needs.

[0057] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0058] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0062] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An adaptive speed planning method based on fuzzy PID rules, characterized in that: The adaptive speed planning method comprises: Step 1, inputting input data into the fuzzy PID controller and then outputting PID parameters, wherein the input value includes error e and error change rate ec, error e is the current vehicle speed minus the target vehicle speed, and error change rate ec is the current acceleration; the PID parameters include: error proportional factor kp, error integral factor ki and error differential factor kd; Step 2: Calculate the real-time planning value of the error e according to the value of the PID parameter to obtain the real-time planning value of the speed.

2. The method according to claim 1, characterized in that The step 1 comprises: Step 101, fuzzifying the input data to obtain fuzzy input variables; Step 102, constructing a fuzzy rule base, the fuzzy rule base including a corresponding relationship table of values ​​of fuzzy input variables and fuzzy output variables; obtaining fuzzy output variable values ​​of each PID parameter based on the fuzzy rule base; Step 103, defuzzifying the result of fuzzy reasoning to obtain control output.

3. The method according to claim 1, characterized in that The step 101 comprises: Set the domain and determine the membership function to convert the input data into membership intervals.

4. The method according to claim 1, characterized in that: The step 102 includes: Set the range values ​​of each interval of error e and error change rate ec, and construct a table of the size of the change value of each PID parameter and the interval range to which the error e and error change rate ec belong.

5. The method according to claim 1, characterized in that: The step 103 includes: deblurring using a centroid method according to the obtained fuzzy index and fuzzy degree.

6. The method according to claim 1, characterized in that The adaptive speed planning method also includes: when the acceleration is greater than 0, using a segmented PID control algorithm for speed planning; when the acceleration is less than 0, using a fuzzy PID control algorithm for speed planning.

7. The method according to claim 1, characterized in that The method further comprises: Step 3: monitor the system status and environmental changes in real time, and adaptively adjust the parameters of the fuzzy PID controller according to the chassis control effect fed back in real time.

8. An adaptive speed planning system based on fuzzy PID rules, characterized in that: The adaptive speed planning system comprises: a fuzzy PID controller and a speed planning module; After inputting the input data into the fuzzy PID controller, the PID parameters are outputted, wherein the input value includes the error e and the error change rate ec, the error e is the current vehicle speed minus the target vehicle speed, and the error change rate ec is the current acceleration; the PID parameters include: the proportional factor kp of the error, the integral factor ki of the error, and the differential factor kd of the error; The speed planning module is used to calculate the real-time planning value of the error e according to the value of the PID parameter, and obtain the real-time planning value of the speed.

9. An electronic device, characterized in that: It includes a memory and a processor, and the processor is used to implement the steps of the adaptive speed planning method based on fuzzy PID rules as described in any one of claims 1 to 7 when executing the computer management program stored in the memory.

10. A computer-readable storage medium, characterized in that: A computer management program is stored thereon, and when the computer management program is executed by a processor, the steps of the adaptive speed planning method based on fuzzy PID rules as described in any one of claims 1 to 7 are implemented.

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