WO2021109554A1 - Longitudinal control system and method for autonomous vehicle based on feed forward control - Google Patents

Longitudinal control system and method for autonomous vehicle based on feed forward control Download PDF

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WO2021109554A1
WO2021109554A1 PCT/CN2020/098223 CN2020098223W WO2021109554A1 WO 2021109554 A1 WO2021109554 A1 WO 2021109554A1 CN 2020098223 W CN2020098223 W CN 2020098223W WO 2021109554 A1 WO2021109554 A1 WO 2021109554A1
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Prior art keywords
output
reference model
vehicle
feed forward
control
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PCT/CN2020/098223
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French (fr)
Inventor
Pingliang HAN
Jiafeng CHAI
Li Rong
Lei Wang
Xun TONG
Xinshi ZHANG
Bohan SHANG
Zhishan LI
Wenbin Wang
Xiaofeng Zhang
Di Jiang
Fan Yang
Chen Zhao
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Suzhou Zhijia Science & Technologies Co., Ltd.
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Publication of WO2021109554A1 publication Critical patent/WO2021109554A1/en

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0008—Feedback, closed loop systems or details of feedback error signal
    • B60W2050/0011—Proportional Integral Differential [PID] controller
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0012—Feedforward or open loop systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0062—Adapting control system settings
    • B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
    • B60W2050/0083—Setting, resetting, calibration
    • B60W2050/0088—Adaptive recalibration

Definitions

  • the present disclosure belongs to the technical field of autonomous driving, and particularly relates to a longitudinal control system and method for an autonomous vehicle based on feed forward control.
  • the execution performance of a drive-by-wire platform of the heavy trucks is poorer than passenger vehicles due to the larger mass of the heavy trucks.
  • trailers may be connected in a non-rigid manner to the rear of heavy trucks, such that the problem of autonomous driving control of the heavy trucks is more challenging than for the passenger vehicles.
  • FF feed forward control algorithm
  • input data speed, acceleration and load
  • output data torque control percentages of an accelerator and a brake
  • a control module directly looks up the lookup table according to a required control value to obtain a corresponding output.
  • the method has the advantages of being simple in design, high in execution speed and capable of adequately covering the nonlinear drive-by-wire performance. However, this approach cannot adequately deal with the disturbance of parameters of the drive-by-wire platform and dynamic deviations in the control process.
  • the present disclosure provides a longitudinal control system and method for an autonomous vehicle based on feed forward control, which aim to solve at least one technical problem in the prior art by introducing a reference adaptive model.
  • the longitudinal control system comprises a reference model, a first fusion module and an adaptive law module.
  • the system further comprises a feed forward module and a second fusion module.
  • the reference model is configured to generate an output of the reference model according to a preset vehicle model and a reference input.
  • the first fusion is configured to obtain a first error module according to the output of the reference model and a current state of the system.
  • the adaptive law module is configured to adjust the output of the reference model according to the type of the vehicle and the first error to obtain the adjusted output of the reference model.
  • the feed forward module is configured to obtain a feed forward output according to an input and an output of the vehicle.
  • the second fusion module is configured to obtain a final value of the system according to the adjusted output of the reference model and the feed forward output.
  • a second aspect of the present disclosure provides a longitudinal control method for an autonomous vehicle based on feed forward control.
  • the longitudinal control method comprises the following steps:
  • the system is better able to cope with jerk caused by gear shifting and dynamic deviations in the control process. This may help to increase the longitudinal control accuracy of the autonomous vehicle, improve safety and deliver a more stable driving experience.
  • Fig. 1 is a schematic structural diagram of a control system for an autonomous vehicle using an FF algorithm in the prior art
  • Fig. 2 is a schematic structural diagram of a longitudinal control system for an autonomous vehicle based on feed forward control according to an embodiment of the present disclosure
  • Fig. 3 is a flow chart of a longitudinal control method for an autonomous vehicle based on feed forward control according to an embodiment of the present disclosure
  • Fig. 4 is a schematic structural diagram of a longitudinal control system for an autonomous vehicle based on feed forward control according to another embodiment of the present disclosure
  • Fig. 5 is a flow chart of a longitudinal control method for an autonomous vehicle based on feed forward control according to another embodiment of the present disclosure.
  • Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
  • fuse refers to a calculation which combines data derived from separate sources.
  • the information resulting from a “fuse” calculation may be used as an internal parameter within the system or as an output of the system.
  • Fig. 2 is a schematic structural diagram of a longitudinal control system for an autonomous vehicle based on feed forward control according to an embodiment of the present disclosure.
  • the longitudinal control system comprises a reference model, a first fusion module (also referred to as a feedback fusion module) and an adaptive law module.
  • the system further comprises a feed forward module and a second fusion module (also referred to as an algorithm fusion module) .
  • the reference model, the first fusion module and the adaptive law module may be collectively referred to as a model reference adaptive module.
  • the system may further comprises a drive-by-wire module between the model reference adaptive module and the vehicle for acquiring the status of the vehicle to provide to the control system and controlling the operation of the vehicle according to the control value of the control system.
  • the reference model generates an output of the reference model according to a preset vehicle model and a reference input.
  • the vehicle model in this embodiment of the present disclosure can be obtained generally through a system identification method or a big data modeling mode.
  • the first fusion module fuses the output of the reference model and a current state of the system to obtain a first error.
  • the state of the system may be a real-time state.
  • the first error is an error between the output of the reference model and the current state of the system.
  • the current state of the system in this embodiment may be from a signal fed back by the drive-by-wire module, such as data including the current percentage of an accelerator pedal.
  • the adaptive law module adjusts the output control of the reference model according to the type of the vehicle and the first error to obtain the adjusted output of the reference model.
  • the feed forward module obtains a feed forward output according to an input and an output of the vehicle.
  • the second fusion module fuses the adjusted output control of the reference model and the feed forward output control to obtain a final control value of the system.
  • the reference model is: where G m (S) represents a frequency-domain dynamics model of the reference model; Ta represents a time constant of a first-order inertia element, which depends on the dynamic performance of the vehicle, and generally has a reference range of [0.15-0.4] ; and S represents a frequency-domain independent variable.
  • the first error is the difference between the output of the reference model and the current state of the system. Specifically, the first error is calculated by the following formula:
  • CMD r represents the output of the reference model
  • CMD s represents the current state of the system
  • e represents the first error
  • the adaptive law module obtains the adjusted output of the reference model by using the following expression:
  • u 1 represents the adjusted output of the reference model
  • r represents an input of the reference model
  • gamma represents an adaptive law, which has a reference range of [1.0-2.3]
  • e represents the first error.
  • the second fusion module obtains the final control value (s) of the system by the following formula:
  • CMD final W MRAC *u 1 ++ W FF *u 2 ;
  • CMD final represents the final control value (s) of the system
  • W MRAC represents the weight of the adjusted output of the reference model, and preferably, a reference range of W MRAC is [0.6-1.0]
  • u 1 represents the adjusted output of the reference model
  • W FF represents the weight of the feed forward output, and preferably, a reference range of W FF is [0.5-1.0]
  • u 2 represents the feed forward output.
  • the feed forward output u 2 may be obtained by the following formula:
  • v represents the speed of the vehicle
  • w represents the load of the vehicle
  • a represents the acceleration of the vehicle
  • k 1 represents a coefficient of the speed of the vehicle
  • k 2 represents a coefficient of the load of the vehicle
  • k 3 represents a coefficient of the acceleration of the vehicle.
  • the second fusion module may allocate corresponding weights to the model reference adaptive module and the feed forward module according to different drive-by-wire performance indexes of the vehicle.
  • the adjusted output of the reference model and the feed forward output are fused by the second fusion module to obtain the final control value (s) of the system, so that the jerk problem caused by gear shifting can be adequately solved; and feedback regulation is rapidly conducted on the system, so that a smoother system experience is provided.
  • Fig. 3 is a flow chart of a longitudinal control method for an autonomous vehicle based on feed forward control according to an embodiment of the present disclosure.
  • the longitudinal control method comprises the following steps:
  • G m (S) represents a frequency-domain dynamics model of the reference model
  • Ta represents a time constant of a first-order inertia element
  • S represents a frequency-domain independent variable
  • u 1 represents the adjusted output of the reference model
  • r represents an input of the reference model
  • gamma represents an adaptive law
  • e represents the first error
  • CMD final W MRAC *u 1 + W FF *u 2 ;
  • CMD final represents the final control value of the system
  • W MRAC represents the weight of the adjusted output of the reference model
  • u 1 represents the adjusted output of the reference model
  • W FF represents the weight of the feed forward output
  • u 2 represents the feed forward output.
  • v represents the speed of the vehicle
  • w represents the load of the vehicle
  • a represents the acceleration of the vehicle
  • k 1 represents a coefficient of the speed of the vehicle
  • k 2 represents a coefficient of the load of the vehicle
  • k 3 represents a coefficient of the acceleration of the vehicle.
  • the longitudinal control method for the autonomous vehicle based on the feed forward control in this embodiment is basically consistent with the operation principle of the longitudinal control system of embodiment I, and will not be described in detail herein.
  • FIG. 4 a schematic structural diagram of a longitudinal control system for an autonomous vehicle based on feed forward control according to another embodiment of the present disclosure is shown in Fig. 4.
  • the system comprises a model reference adaptive module, a drive-by-wire module, a feed forward module, a PID module and a third fusion module, wherein the model reference adaptive module comprises a reference model, a first fusion module and an adaptive law module.
  • the reference model generates an output of the reference model according to a preset vehicle model and a reference input.
  • the first fusion module fuses the output of the reference model and a current state of the system to obtain a first error.
  • the first error is an error between the output of the reference model and the current state of the system.
  • the state of the system by be a real-time state.
  • the adaptive law module adjusts the output of the reference model according to the type of the vehicle and the first error to obtain the adjusted output of the reference model.
  • the feed forward module obtains a feed forward output according to an input and an output of the vehicle.
  • the PID module obtains a PID output according to the speed of the vehicle.
  • the third fusion module fuses the adjusted output of the reference model, the feed forward output and the PID output to obtain a final control value of the system.
  • the third fusion module obtains the final control value of the system by the following formula:
  • CMD final W MRAC *u 1 + W FF *u 2 + W PID *u3;
  • CMD final represents the final control value of the system
  • W MRAC represents the weight of the adjusted output of the reference model, and preferably has a reference range of [0.6-1.0]
  • u 1 represents the adjusted output of the reference model
  • W FF represents the weight of the feed forward output, and preferably, a reference range of W FF is [0.5-1.0]
  • u 2 represents the feed forward output
  • W PID represents the weight of the PID output, and preferably, a reference range of W PID is [0.3-1.0]
  • u 3 represents the PID output.
  • K p represents a P (proportional) parameter
  • K i represents an I (integral) parameter
  • K d represents a D (derivative) parameter
  • e (k) represents an error between the desired value and an observed value.
  • the third fusion module allocates corresponding weights to the model reference adaptive module, the feed forward module and the PID module according to different drive-by-wire performance indexes of the vehicle.
  • the adjusted output of the reference model, the feed forward output and the PID output are fused by the third fusion module to obtain the final control value of the system, so that the dynamic deviations of the system control can be reduced, and detection and the feedback can also be carried out in time, thereby greatly increasing the longitudinal control accuracy of the system.
  • the jerk problem caused by gear shifting can be adequately solved.
  • Feedback regulation is rapidly conducted on the system, so that a smoother system experience is provided, the longitudinal control accuracy and robustness of the autonomous vehicle can be greatly increased, thereby bringing about a safer and a smoother driving experience for the vehicle.
  • Fig. 5 is a flow chart of a longitudinal control method for an autonomous vehicle based on feed forward control according to another embodiment of the present disclosure.
  • the longitudinal control method comprises the following steps:
  • CMD final W MRAC *u 1 + W FF *u 2 + W PID *u3;
  • CMD final represents the final control value of the system
  • W MRAC represents the weight of the adjusted output of the reference model, and preferably has a reference range of [0.6-1.0]
  • u 1 represents the adjusted output of the reference model
  • W FF represents the weight of the feed forward output, and preferably, a reference range of W FF is [0.5-1.0]
  • u 2 represents the feed forward output
  • W PID represents the weight of the PID output, and preferably, a reference range of W PID is [0.3-1.0]
  • u 3 represents the PID output.
  • K p represents a P (proportional) parameter
  • K i represents an I (integral) parameter
  • K d represents a D (derivative) parameter
  • e (k) represents an error between the desired value and an observed value.
  • the weight of the adjusted output of the reference model, the weight of the feed forward output and the weight of the PID output are respectively allocated according to different drive-by-wire performance indexes of the vehicle.
  • the longitudinal control method for the autonomous vehicle based on the feed forward control in this embodiment is basically consistent with the operation principle of the longitudinal control system of embodiment III, and will not be described in detail herein.
  • Fig. 6 is a schematic structural diagram of an embodiment of an electronic device of the present disclosure.
  • an electronic device including, but not limited to, a smart phone, a fixed-line telephone, a tablet computer, a notebook computer, a wearable device and other electronic devices.
  • the electronic device comprises a processor, and a memory that stores computer-readable instructions, wherein the methods of the present disclosure described above are implemented when the computer-readable instructions are executed by the processor.
  • a further embodiment provides a non-transitory computer-readable storage medium, which may for example be a ROM (e.g., a read-only memory, a FLASH memory, a transfer apparatus, etc. ) , an optical storage medium (e.g., a CD-ROM, a DVD-ROM, a paper card, etc. ) , a magnetic storage medium (e.g., a magnetic tape, a magnetic disk drive, etc. ) , or other types of program memories.
  • the computer-readable storage medium has stored thereon computer programs, wherein the methods of the present disclosure described above are implemented when the computer programs run on a processor or a computer.
  • certain embodiments of the present disclosure may reduce or minimize jerk caused by gear shifting and dynamic deviations in the control process, which are particularly problematic in context of a control process for an autonomous driving heavy truck. In this way the longitudinal control accuracy of the autonomous vehicle may be greatly improved and a safer and more stable driving experience may be delivered.
  • the disclosed apparatuses and methods may be implemented in other ways.
  • the apparatus embodiments described above are merely illustrative.
  • the division of units is merely a logical functional division, and in actual implementations, there may be other division methods.
  • a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not implemented.
  • the mutual couplings or direct couplings or communicative connections shown or discussed may be indirect couplings or communicative connections via some interfaces, apparatuses or units, and may be electrical, mechanical or in other forms.
  • the units described as separate parts may or may not be physically separated, and parts displayed as units may or may not be physical units, that is, they may be located in one position, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual requirements to achieve the objectives of the solutions of the embodiments.
  • various functional units in the various embodiments of the present disclosure may be integrated into one processing unit, or various units may be physically present separately, or two or more units may be integrated into one unit.
  • the functions, if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a non-transitory computer-readable storage medium.
  • machine readable instructions may be stored on the non-transitory computer-readable storage medium, which when executed by a processor perform any of the above described methods.
  • the computer software product is stored in a storage medium, and includes several instructions used to cause a computer device (which may be a personal computer, a server, or a network device, etc. ) to perform all or some of the steps of the method in various embodiments of the present disclosure.
  • the aforementioned storage medium includes a USB flash disk, a mobile hard disk, an ROM, an RAM, a magnetic or optical disk, and various media in which program codes can be stored.

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Abstract

A longitudinal control system and method for an autonomous vehicle based on feed forward control are provided. The system comprises a reference model. An output of the reference model is generated according to a preset vehicle model and a reference input. The system obtains a first error according to the output of the reference model and a current state of the system. The output of the reference model is adjusted according to the type of the vehicle and the first error to obtain the adjusted output of the reference model. A feed forward output is obtained according to an input and an output of the vehicle. A control value of the system is obtained according to the adjusted output of the reference model and the feed forward output. The control value is output to a drive-by-wire module to change the operation of the vehicle.

Description

LONGITUDINAL CONTROL SYSTEM AND METHOD FOR AUTONOMOUS VEHICLE BASED ON FEED FORWARD CONTROL TECHNICAL FIELD
The present disclosure belongs to the technical field of autonomous driving, and particularly relates to a longitudinal control system and method for an autonomous vehicle based on feed forward control.
BACKGROUND
Autonomous driving techniques have been developed rapidly in the last decade, and the application of the autonomous driving techniques to heavy trucks also has attracted considerable attention in the recent years.
The execution performance of a drive-by-wire platform of the heavy trucks is poorer than passenger vehicles due to the larger mass of the heavy trucks. In addition, trailers may be connected in a non-rigid manner to the rear of heavy trucks, such that the problem of autonomous driving control of the heavy trucks is more challenging than for the passenger vehicles.
Existing control systems for autonomous vehicles do a poor job of addressing the technical challenges of autonomous trucks, especially the problems of non-linear and unstable drive-by-wire performance, jerk caused by gear shifting, etc.
At present, there is a longitudinal control method for a vehicle based on feed forward (FF, namely feed forward control algorithm) . Referring to Fig. 1, according to this method, input data (speed, acceleration and load) and output data (torque control percentages of an accelerator and a brake) are acquired firstly; then, a corresponding lookup table is established; and after receiving a trajectory instruction sent by an upstream planning module, a control module directly looks up the lookup table according to a required control value to obtain a corresponding output. The method has the advantages of being simple in design, high in execution speed and capable of adequately covering the nonlinear drive-by-wire performance. However, this approach cannot adequately deal with the disturbance of parameters of the drive-by-wire platform and dynamic deviations in the control process.
Therefore, in view of the above, there are several problems with prior art control systems including: poor robustness, jerk caused by gear shifting, and dynamic deviations in the control process.
SUMMARY OF THE DISCLOSURE
The present disclosure provides a longitudinal control system and method for an autonomous vehicle based on feed forward control, which aim to solve at least one technical problem in the prior art by introducing a reference adaptive model.
One aspect of the present disclosure provides a longitudinal control system for an autonomous vehicle based on feed forward control. The longitudinal control system comprises a reference model, a first fusion module and an adaptive law module. The system further comprises a feed forward module and a second fusion module.
The reference model is configured to generate an output of the reference model according to a preset vehicle  model and a reference input.
The first fusion is configured to obtain a first error module according to the output of the reference model and a current state of the system.
The adaptive law module is configured to adjust the output of the reference model according to the type of the vehicle and the first error to obtain the adjusted output of the reference model.
The feed forward module is configured to obtain a feed forward output according to an input and an output of the vehicle.
The second fusion module is configured to obtain a final value of the system according to the adjusted output of the reference model and the feed forward output.
A second aspect of the present disclosure provides a longitudinal control method for an autonomous vehicle based on feed forward control. The longitudinal control method comprises the following steps:
generating an output of a reference model according to a preset vehicle model and a reference input;
calculating a first error according to the output of the reference model and a current state of the system;
obtaining an adjusted output of the reference model by adjusting the output of the reference model according to the type of the vehicle and the first error;
calculating a feed forward output according to an input and an output of the vehicle; and
determining a final control value of the system according to the adjusted output of the reference model and the feed forward output.
By introducing the model reference adaptive module into the system, the system is better able to cope with jerk caused by gear shifting and dynamic deviations in the control process. This may help to increase the longitudinal control accuracy of the autonomous vehicle, improve safety and deliver a more stable driving experience.
Further features and aspects of the present disclosure are provided in the appended claims and the descriptions hereafter.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 is a schematic structural diagram of a control system for an autonomous vehicle using an FF algorithm in the prior art;
Fig. 2 is a schematic structural diagram of a longitudinal control system for an autonomous vehicle based on feed forward control according to an embodiment of the present disclosure;
Fig. 3 is a flow chart of a longitudinal control method for an autonomous vehicle based on feed forward control according to an embodiment of the present disclosure;
Fig. 4 is a schematic structural diagram of a longitudinal control system for an autonomous vehicle based on feed forward control according to another embodiment of the present disclosure;
Fig. 5 is a flow chart of a longitudinal control method for an autonomous vehicle based on feed forward control according to another embodiment of the present disclosure; and
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present  disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS
The present disclosure will be described in detail with reference to various embodiments shown in the drawings, but it should be noted that these embodiments do not limit the present disclosure, and equivalent alterations or alternatives in terms of the function, method or structure made by those skilled in the art according to these embodiments are all within the scope of protection of the present disclosure.
As described herein and in the claims, “fuse” refers to a calculation which combines data derived from separate sources. The information resulting from a “fuse” calculation may be used as an internal parameter within the system or as an output of the system.
Fig. 2 is a schematic structural diagram of a longitudinal control system for an autonomous vehicle based on feed forward control according to an embodiment of the present disclosure. Referring to Fig. 2, the longitudinal control system comprises a reference model, a first fusion module (also referred to as a feedback fusion module) and an adaptive law module. The system further comprises a feed forward module and a second fusion module (also referred to as an algorithm fusion module) . The reference model, the first fusion module and the adaptive law module may be collectively referred to as a model reference adaptive module. The system may further comprises a drive-by-wire module between the model reference adaptive module and the vehicle for acquiring the status of the vehicle to provide to the control system and controlling the operation of the vehicle according to the control value of the control system.
The reference model generates an output of the reference model according to a preset vehicle model and a reference input. The vehicle model in this embodiment of the present disclosure can be obtained generally through a system identification method or a big data modeling mode.
The first fusion module fuses the output of the reference model and a current state of the system to obtain a first error. For example, the state of the system may be a real-time state. The first error is an error between the output of the reference model and the current state of the system. The current state of the system in this embodiment may be from a signal fed back by the drive-by-wire module, such as data including the current percentage of an accelerator pedal.
The adaptive law module adjusts the output control of the reference model according to the type of the vehicle and the first error to obtain the adjusted output of the reference model.
The feed forward module obtains a feed forward output according to an input and an output of the vehicle.
The second fusion module fuses the adjusted output control of the reference model and the feed forward output control to obtain a final control value of the system.
Further, the reference model is: 
Figure PCTCN2020098223-appb-000001
where G m (S) represents a frequency-domain dynamics model of the reference model; Ta represents a time constant of a first-order inertia element, which depends on the dynamic performance of the vehicle, and generally has a reference range of [0.15-0.4] ; and S represents a frequency-domain independent variable.
In one exemplary embodiment, the first error is the difference between the output of the reference model and  the current state of the system. Specifically, the first error is calculated by the following formula:
e = CMD r-CMD s;
where CMD r represents the output of the reference model; CMD s represents the current state of the system; and e represents the first error.
In one exemplary embodiment, the adaptive law module obtains the adjusted output of the reference model by using the following expression:
u 1 = ∫ (gamma *e *r)
where u 1 represents the adjusted output of the reference model; r represents an input of the reference model; gamma represents an adaptive law, which has a reference range of [1.0-2.3] ; and e represents the first error.
In one exemplary embodiment, the second fusion module obtains the final control value (s) of the system by the following formula:
CMD final = W MRAC*u 1 ++ W FF*u 2;
where CMD final represents the final control value (s) of the system; W MRAC represents the weight of the adjusted output of the reference model, and preferably, a reference range of W MRAC is [0.6-1.0] ; u 1 represents the adjusted output of the reference model; W FF represents the weight of the feed forward output, and preferably, a reference range of W FF is [0.5-1.0] ; and u 2 represents the feed forward output.
The feed forward output u 2 may be obtained by the following formula:
u 2 = k 1 (v) + k 2 (w) + k 3 (a)
where v represents the speed of the vehicle, w represents the load of the vehicle, a represents the acceleration of the vehicle, k 1 represents a coefficient of the speed of the vehicle, k 2 represents a coefficient of the load of the vehicle, and k 3 represents a coefficient of the acceleration of the vehicle.
The second fusion module may allocate corresponding weights to the model reference adaptive module and the feed forward module according to different drive-by-wire performance indexes of the vehicle.
In this embodiment, the adjusted output of the reference model and the feed forward output are fused by the second fusion module to obtain the final control value (s) of the system, so that the jerk problem caused by gear shifting can be adequately solved; and feedback regulation is rapidly conducted on the system, so that a smoother system experience is provided.
Fig. 3 is a flow chart of a longitudinal control method for an autonomous vehicle based on feed forward control according to an embodiment of the present disclosure. Referring to Fig. 3, the longitudinal control method comprises the following steps:
generating an output of a reference model according to a preset vehicle model and a reference input;
fusing the output of the reference model and a current real-time state of the system to obtain a first error;
adjusting the output of the reference model according to the type of the vehicle and the first error to obtain the adjusted output of the reference model;
obtaining a feed forward output according to an input and an output of the vehicle; and
fusing the adjusted output of the reference model and the feed forward output to obtain a final control value of  the system.
Further, the reference model is:
Figure PCTCN2020098223-appb-000002
where G m (S) represents a frequency-domain dynamics model of the reference model; Ta represents a time constant of a first-order inertia element; and S represents a frequency-domain independent variable.
Further, the adjusted output of the reference model is obtained by the following formula:
u 1 = ∫ (gamma *e *r)
where u 1 represents the adjusted output of the reference model; r represents an input of the reference model; gamma represents an adaptive law; and e represents the first error.
Further, the final control value of the system is obtained by the following formula:
CMD final = W MRAC *u 1 + W FF *u 2;
where CMD final represents the final control value of the system; W MRAC represents the weight of the adjusted output of the reference model; u 1 represents the adjusted output of the reference model; W FF represents the weight of the feed forward output; and u 2 represents the feed forward output.
Further, the feed forward output u 2 is obtained by the following formula:
u 2 = k 1 (v) + k 2 (w) + k 3 (a)
where v represents the speed of the vehicle, w represents the load of the vehicle, a represents the acceleration of the vehicle, k 1 represents a coefficient of the speed of the vehicle, k 2 represents a coefficient of the load of the vehicle, and k 3 represents a coefficient of the acceleration of the vehicle.
The longitudinal control method for the autonomous vehicle based on the feed forward control in this embodiment is basically consistent with the operation principle of the longitudinal control system of embodiment I, and will not be described in detail herein.
Referring to Fig. 4, a schematic structural diagram of a longitudinal control system for an autonomous vehicle based on feed forward control according to another embodiment of the present disclosure is shown in Fig. 4. The system comprises a model reference adaptive module, a drive-by-wire module, a feed forward module, a PID module and a third fusion module, wherein the model reference adaptive module comprises a reference model, a first fusion module and an adaptive law module.
The reference model generates an output of the reference model according to a preset vehicle model and a reference input.
The first fusion module fuses the output of the reference model and a current state of the system to obtain a first error. The first error is an error between the output of the reference model and the current state of the system. For example, the state of the system by be a real-time state.
The adaptive law module adjusts the output of the reference model according to the type of the vehicle and the first error to obtain the adjusted output of the reference model.
The feed forward module obtains a feed forward output according to an input and an output of the vehicle.
The PID module obtains a PID output according to the speed of the vehicle.
The third fusion module fuses the adjusted output of the reference model, the feed forward output and the PID  output to obtain a final control value of the system.
It should be noted that in this embodiment, the methods for implementing the reference model and for obtaining the first error, the adjusted output of the reference model and the feed forward output are substantially identical to those in embodiment I, and will not be described in detail herein.
Further, the third fusion module obtains the final control value of the system by the following formula:
CMD final = W MRAC *u 1 + W FF *u 2 + W PID *u3;
where CMD final represents the final control value of the system; W MRAC represents the weight of the adjusted output of the reference model, and preferably has a reference range of [0.6-1.0] ; u 1 represents the adjusted output of the reference model; W FF represents the weight of the feed forward output, and preferably, a reference range of W FF is [0.5-1.0] ; u 2 represents the feed forward output; W PID represents the weight of the PID output, and preferably, a reference range of W PID is [0.3-1.0] ; and u 3 represents the PID output.
Further, the PID output u 3 is obtained by the following formula:
Figure PCTCN2020098223-appb-000003
K p represents a P (proportional) parameter, K i represents an I (integral) parameter, K d represents a D (derivative) parameter, and e (k) represents an error between the desired value and an observed value.
Preferably, the third fusion module allocates corresponding weights to the model reference adaptive module, the feed forward module and the PID module according to different drive-by-wire performance indexes of the vehicle.
In this the embodiment, the adjusted output of the reference model, the feed forward output and the PID output are fused by the third fusion module to obtain the final control value of the system, so that the dynamic deviations of the system control can be reduced, and detection and the feedback can also be carried out in time, thereby greatly increasing the longitudinal control accuracy of the system. Moreover, the jerk problem caused by gear shifting can be adequately solved. Feedback regulation is rapidly conducted on the system, so that a smoother system experience is provided, the longitudinal control accuracy and robustness of the autonomous vehicle can be greatly increased, thereby bringing about a safer and a smoother driving experience for the vehicle.
Fig. 5 is a flow chart of a longitudinal control method for an autonomous vehicle based on feed forward control according to another embodiment of the present disclosure. Referring to Fig. 5, the longitudinal control method comprises the following steps:
generating an output of a reference model according to a preset vehicle model and a reference input;
fusing the output of the reference model and a current real-time state of the system to obtain a first error, wherein the first error is an error between the output of the reference model and the current real-time state of the system;
adjusting the output of the reference model according to the type of the vehicle and the first error to obtain the adjusted output of the reference model;
obtaining a feed forward output according to an input and an output of the vehicle;
obtaining a PID output according to the speed of the vehicle; and
fusing the adjusted output of the reference model, the feed forward output and the PID output to obtain the final control value of the system.
Further, the final control value of the system is obtained by the following formula:
CMD final = W MRAC *u 1 + W FF *u 2 + W PID *u3;
where CMD final represents the final control value of the system; W MRAC represents the weight of the adjusted output of the reference model, and preferably has a reference range of [0.6-1.0] ; u 1 represents the adjusted output of the reference model; W FF represents the weight of the feed forward output, and preferably, a reference range of W FF is [0.5-1.0] ; u 2 represents the feed forward output; W PID represents the weight of the PID output, and preferably, a reference range of W PID is [0.3-1.0] ; and u 3 represents the PID output.
Further, the PID output u 3 is obtained by the following formula:
Figure PCTCN2020098223-appb-000004
K p represents a P (proportional) parameter, K i represents an I (integral) parameter, K d represents a D (derivative) parameter, and e (k) represents an error between the desired value and an observed value.
Preferably, the weight of the adjusted output of the reference model, the weight of the feed forward output and the weight of the PID output are respectively allocated according to different drive-by-wire performance indexes of the vehicle.
The longitudinal control method for the autonomous vehicle based on the feed forward control in this embodiment is basically consistent with the operation principle of the longitudinal control system of embodiment III, and will not be described in detail herein.
Fig. 6 is a schematic structural diagram of an embodiment of an electronic device of the present disclosure. Referring to Fig. 6, in this embodiment, provided is an electronic device including, but not limited to, a smart phone, a fixed-line telephone, a tablet computer, a notebook computer, a wearable device and other electronic devices. The electronic device comprises a processor, and a memory that stores computer-readable instructions, wherein the methods of the present disclosure described above are implemented when the computer-readable instructions are executed by the processor.
A further embodiment provides a non-transitory computer-readable storage medium, which may for example be a ROM (e.g., a read-only memory, a FLASH memory, a transfer apparatus, etc. ) , an optical storage medium (e.g., a CD-ROM, a DVD-ROM, a paper card, etc. ) , a magnetic storage medium (e.g., a magnetic tape, a magnetic disk drive, etc. ) , or other types of program memories. The computer-readable storage medium has stored thereon computer programs, wherein the methods of the present disclosure described above are implemented when the computer programs run on a processor or a computer.
By introducing the model reference adaptive module, certain embodiments of the present disclosure may reduce or minimize jerk caused by gear shifting and dynamic deviations in the control process, which are particularly problematic in context of a control process for an autonomous driving heavy truck. In this way the longitudinal control accuracy of the autonomous vehicle may be greatly improved and a safer and more stable driving experience may be delivered.
Those of ordinary skills in the art may be aware that the units and algorithm steps of various examples described in conjunction with the embodiments disclosed in the present disclosure can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to be beyond the scope of the present disclosure.
In the embodiments provided in the present application, it should be understood that the disclosed apparatuses and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division, and in actual implementations, there may be other division methods. For example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not implemented. In addition, the mutual couplings or direct couplings or communicative connections shown or discussed may be indirect couplings or communicative connections via some interfaces, apparatuses or units, and may be electrical, mechanical or in other forms.
The units described as separate parts may or may not be physically separated, and parts displayed as units may or may not be physical units, that is, they may be located in one position, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual requirements to achieve the objectives of the solutions of the embodiments.
In addition, various functional units in the various embodiments of the present disclosure may be integrated into one processing unit, or various units may be physically present separately, or two or more units may be integrated into one unit.
The functions, if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a non-transitory computer-readable storage medium. For example machine readable instructions may be stored on the non-transitory computer-readable storage medium, which when executed by a processor perform any of the above described methods. Based on such understanding, the technical solution of the present disclosure, in essence, or its contribution to the prior art, or part of the technical solution may be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions used to cause a computer device (which may be a personal computer, a server, or a network device, etc. ) to perform all or some of the steps of the method in various embodiments of the present disclosure. Moreover, the aforementioned storage medium includes a USB flash disk, a mobile hard disk, an ROM, an RAM, a magnetic or optical disk, and various media in which program codes can be stored.
The foregoing description merely relates to the specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or replacements that can be easily conceived by those skilled in the art within the technical scope disclosed by the present disclosure shall fall within the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure shall be subject to the scope of protection of the claims.

Claims (14)

  1. A longitudinal control system for an autonomous vehicle based on feed forward control, the longitudinal control system comprising a reference model, a first fusion module and an adaptive law module; the system further comprises a feed forward module and a second fusion module;
    wherein the reference model is configured to generate an output of the reference model according to a preset vehicle model and a reference input;
    the first fusion module is configured to obtain a first error according to the output of the reference model and a current state of the system;
    the adaptive law module is configured to adjust the output of the reference model according to the type of the vehicle and the first error to obtain the adjusted output of the reference model;
    the feed forward module is configured to obtain a feed forward output according to an input and an output of the vehicle; and
    the second fusion module is configured to determine a control value of the system according to the adjusted output of the reference model and the feed forward output.
  2. The longitudinal control system according to claim 1, wherein the control value of the system is output to a drive-by-wire module which is to control the operation of the vehicle based on the control value.
  3. The longitudinal control system according to claim 1 or 2, wherein the adaptive law module obtains the adjusted output of the reference model by the following formula:
    u 1 = ∫ (gamma *e *r) 
    wherein u 1 represents the adjusted output of the reference model; r represents an input of the reference model; gamma represents an adaptive law; and e represents the first error.
  4. The longitudinal control system according to claim 3, wherein the second fusion module obtains the control value of the system by the following formula:
    CMD final = W MRAC * u 1 + W FF * u 2;
    wherein CMD final represents the control value; W MRAC represents a weight of the adjusted output of the reference model; W FF represents a weight of the feed forward output; and u 2 represents the feed forward output.
  5. The longitudinal control system according to claim 4, wherein the feed forward output u 2 is obtained by the following formula:
    u 2 = k 1 (v) + k 2 (w) + k 3 (a) ,
    where v represents a speed of the vehicle, w represents a load of the vehicle, a represents an acceleration of the vehicle, k 1 represents a coefficient of the speed of the vehicle, k 2 represents a coefficient of the load of the vehicle, and k 3 represents a coefficient of the acceleration of the vehicle.
  6. The longitudinal control system according to any one of claims 1 to 5, further comprising a PID module and a third fusion module, wherein
    the PID module is configured to obtain a PID output according to a speed of the vehicle; and
    the third fusion module is configured to determine the control value of the system according to the adjusted output of the reference model, the feed forward output and the PID output.
  7. A longitudinal control method for an autonomous vehicle based on feed forward control, the longitudinal control method comprising:
    generating an output of a reference model according to a preset vehicle model and a reference input;
    calculating a first error according to the output control of the reference model and a current state of the system;
    obtaining an adjusted output of the reference model by adjusting the output of the reference model according to the type of the vehicle and the first error;
    calculating a feed forward output according to an input and an output of the vehicle; and
    determining a control value of the system according to the adjusted output of the reference model and the feed forward output.
  8. The longitudinal control method according to claim 7, wherein the adjusted output of the reference model is obtained by the following formula:
    u 1 = ∫ (gamma * e * r)
    wherein u 1 represents the adjusted output of the reference model; r represents an input of the reference model; gamma represents an adaptive law; and e represents the first error.
  9. The longitudinal control method according to claim 8, wherein the control value of the system is determined by the following formula:
    CMD final = W MRAC * u 1 + W FF * u 2;
    wherein CMD final represents the control value of the system; W MRAC represents a weight of the adjusted output of the reference model; W FF represents a weight of the feed forward output ; and u 2 represents the feed forward output .
  10. The longitudinal control method according to claim 9, wherein
    the feed forward output u 2 is obtained by the following formula:
    u 2 = k 1 (v) + k 2 (w) + k 3 (a)
    where v represents a speed of the vehicle, w represents a load of the vehicle, a represents an acceleration of the vehicle, k 1 represents a coefficient of the speed of the vehicle, k 2represents a coefficient of the load of the vehicle, and k 3 represents a coefficient of the acceleration of the vehicle.
  11. The longitudinal control method according to any one of claims 7 to 10, further comprising the following steps:
    calculating a PID output according to a speed of the vehicle; and
    determining the control value of the system according to the adjusted output of the reference model, the feed forward output and the PID output.
  12. The longitudinal control system according to claim 1, wherein the feed forward output is based on a speed, load and acceleration of the vehicle.
  13. A longitudinal control system for an autonomous vehicle based on feed forward control, the longitudinal control system comprising a model reference adaptive module and a drive-by-wire module, wherein the model reference adaptive module comprises a reference model, a first fusion submodule and an adaptive law submodule; the system further comprises a feed forward module and a second fusion module;
    the reference model generates an output control quantity of the reference model according to a preset vehicle model and a reference input quantity;
    the first fusion submodule fuses the output control quantity of the reference model and a current real-time state quantity of the system to obtain a first error;
    the adaptive law submodule adjusts the output control quantity of the reference model according to the type of the vehicle and the first error to obtain the adjusted output control quantity of the reference model;
    the feed forward module obtains a feed forward output control quantity according to an input value and an output value of the vehicle; and
    the second fusion module fuses the adjusted output control quantity of the reference model and the feed forward output control quantity to obtain a final control quantity of the system.
  14. A longitudinal control method for an autonomous vehicle based on feed forward control, the longitudinal control method comprising the following steps:
    generating an output control quantity of a reference model according to a preset vehicle model and a reference input quantity;
    fusing the output control quantity of the reference model and a current real-time state quantity of the system to obtain a first error;
    adjusting the output control quantity of the reference model according to the type of the vehicle and the first error to obtain the adjusted output control quantity of the reference model;
    obtaining a feed forward output control quantity according to an input value and an output value of the vehicle; and
    fusing the adjusted output control quantity of the reference model and the feed forward output control quantity to obtain a final control quantity of the system.
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