Speed control method and device for unmanned vehicle

Abstract

The invention discloses a speed control method and device for an unmanned vehicle, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining the current expected acceleration and the current actual acceleration of the unmanned vehicle at the current moment; acquiring target tracking acceleration data of the unmanned vehicle at the next moment based on the current expected acceleration; obtaining target observation acceleration data of the unmanned vehicle at the next moment and target disturbance data of the unmanned vehicle at the next moment based on the current actual acceleration; determining target tracking error data of the unmanned vehicle at the next moment by using the target tracking acceleration data and the target observation acceleration data; and based on the target tracking error data and the target disturbance data, determining the target speed control parameters of the unmanned vehicle, and according to the scheme, in the speed control process of the unmanned vehicle, the speed control deviation caused by errors and disturbance is effectively reduced.

Description

Technical Field

[0001] The present application relates to the field of unmanned driving technology, and in particular, to a speed control method and device for an unmanned vehicle. Background Art

[0002] The road surface on which unmanned mining vehicles operate is relatively poor, with many potholes and ramps. In addition, the wire control consistency of mining vehicles is relatively poor, and there are certain performance deviations in braking and driving between different vehicles. Conventional PID controllers cannot well identify external disturbances caused by the external environment and internal disturbances caused by the poor consistency of the vehicle's own wire control, which causes tracking speed deviations or frequent alternating control of the actuators during the actual vehicle speed following control process due to these disturbances. Summary of the invention

[0003] In order to overcome the deficiencies of the prior art, the present application provides a speed control method and device for an unmanned vehicle.

[0004] The technical solution adopted by this application to solve its technical problem is: In a first aspect, a speed control method for an unmanned vehicle is provided, comprising: Obtain the current expected acceleration and current actual acceleration of the unmanned vehicle at the current moment; Based on the current expected acceleration, obtaining target tracking acceleration data of the unmanned vehicle at the next moment; Based on the current actual acceleration, obtaining target observed acceleration data of the unmanned vehicle at the next moment and target disturbance data at the next moment; Determining target tracking error data of the unmanned vehicle at a next moment using the target tracking acceleration data and the target observation acceleration data; Based on the target tracking error data and the target disturbance data, a target speed control parameter of the unmanned vehicle is determined.

[0005] Furthermore, obtaining the target tracking acceleration data of the unmanned vehicle at the next moment includes: obtaining the target tracking acceleration data of the unmanned vehicle at the next moment based on an objective function, the objective function is used to adjust a differential operation parameter value, the differential operation parameter value includes a speed coefficient and a filter factor, the speed coefficient is related to the tracking speed, and the filter factor is related to the smoothness of the tracking.

[0006] Furthermore, obtaining the target observed acceleration data and the target disturbance data of the unmanned vehicle at the next moment includes: obtaining the target observed acceleration data and the target disturbance data of the unmanned vehicle at the next moment based on a correction function, the correction function being used to adjust the gain according to the tracking error, and the tracking error and the gain are inversely correlated.

[0007] Further, the target tracking acceleration data includes: target tracking acceleration and / or target tracking jerk; and / or, the target observation acceleration data includes: target observation acceleration and / or target observation jerk; and / or, the target tracking error data includes: target tracking acceleration error and / or target tracking jerk error.

[0008] Furthermore, the target tracking acceleration data includes target tracking acceleration and target tracking jerk, and obtaining the target tracking acceleration data of the unmanned vehicle at the next moment includes: using the current tracking acceleration and the current tracking jerk to obtain the target tracking acceleration of the unmanned vehicle at the next moment, the current tracking acceleration and the current tracking jerk are the target tracking acceleration data corresponding to the unmanned vehicle at the current moment; using the current expected acceleration, the current tracking acceleration, the current tracking jerk and the objective function to determine the target tracking acceleration of the unmanned vehicle at the next moment.

[0009] Further, the target observed acceleration data includes target observed acceleration and target observed jerk, and obtaining the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data of the unmanned vehicle at the next moment includes: using the current observed acceleration and the current actual acceleration to determine the current tracking error of the unmanned vehicle at the current moment, and the current observed acceleration is the observed acceleration of the unmanned vehicle at the current moment; using the current observed acceleration, the current observed jerk and the current tracking error to obtain the target observed acceleration of the unmanned vehicle at the next moment; according to the current observed jerk, the current tracking error, the current disturbance data and the correction function, the target observed jerk of the unmanned vehicle at the next moment is obtained, and the current disturbance data is the disturbance data of the unmanned vehicle at the current moment; using the current disturbance data, the current tracking error and the correction function to determine the target disturbance data of the unmanned vehicle at the next moment.

[0010] Furthermore, the target tracking error data includes a target tracking acceleration error and a target tracking jerk error. Based on the target tracking error data and the target disturbance data, determining the target speed control parameters of the unmanned vehicle includes: determining the target tracking acceleration deviation using the target tracking acceleration and the target observed acceleration; determining the target tracking jerk deviation using the target tracking jerk and the target observed jerk; determining target dynamics model parameters based on the target tracking acceleration deviation and the target tracking jerk deviation and using the correction function; and determining the target speed control parameters based on the target dynamics model parameters and the target disturbance data.

[0011] Furthermore, the target tracking acceleration data of the unmanned vehicle at the next moment is obtained by the following formula: ,in, is the current expected acceleration at the current moment, is the tracking acceleration at the current moment, is the tracking jerk at the current moment, is the velocity coefficient, is the filtering factor, () is the objective function, Tracking acceleration for the next moment, Track the jerk for the next target moment.

[0012] Furthermore, the objective function () is characterized by: , in, , , d = , =d , y= + , = , = , = , is the filtering factor, is the current expected acceleration at the current moment, is the tracking acceleration at the current moment, is the tracking jerk at the current moment, is the speed coefficient.

[0013] Furthermore, the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data at the next moment are obtained by the following formula: ; ; ; ; Among them, z1(k) is the current observed acceleration at the current moment, z2(k) is the current observed jerk at the current moment, z3(k) is the current disturbance data at the current moment, e(k) is the current tracking error at the current moment, and u(k) is the dynamic model parameter at the current moment. is an adjustable weight parameter, is the system parameter calibration quantity, y(k) is the actual acceleration at the current moment, is the target observed acceleration at the next moment, is the target observed acceleration at the next moment, is the target perturbation data for the next moment, () is the correction function, Is a constant.

[0014] Furthermore, the correction function () is characterized by: , = , = , is a constant and a is a calibration constant.

[0015] Further, based on the target tracking error data and the target disturbance data, determining the target speed control parameters of the unmanned vehicle includes: based on the target tracking error data and the target disturbance data, using a nonlinear feedback model to determine the target speed control parameters of the unmanned vehicle.

[0016] Furthermore, the nonlinear feedback model is characterized by: ; ; ; ; in, Tracking acceleration for the next moment, Track the jerk for the next target moment, is the target observed acceleration at the next moment, is the target observed acceleration at the next moment, is the target perturbation data for the next moment, is the target tracking acceleration deviation at the next moment, The target tracking jerk deviation for the next moment, , , is the system parameter calibration quantity, is a constant, is the target speed control parameter.

[0017] Furthermore, one or more of the following parameters are related to the load status of the unmanned vehicle: , , , , , , .

[0018] Furthermore, it also includes: judging whether the current expected acceleration is less than a preset speed value, and / or judging whether the target distance of the unmanned vehicle is a short walking distance; if the current expected acceleration is less than the preset speed value, and / or the target distance of the unmanned vehicle is a short walking distance, dynamically compensating the target speed control parameter.

[0019] Further, dynamically compensating the target speed control parameter includes: calculating a dynamic compensation value by the following formula: , k is the calibration quantity, t is the time constant, is the target tracking acceleration deviation of the unmanned vehicle at the next moment; the target speed control parameter is dynamically compensated using the dynamic compensation value through the following formula: , is the dynamic compensation value, is the target speed control parameter for the next moment, It is the target speed control parameter after compensation at the next moment.

[0020] In a second aspect, a speed control device for an unmanned vehicle is provided, comprising: The current vehicle data acquisition module is used to obtain the current expected acceleration and current actual acceleration of the unmanned vehicle at the current moment; An expected acceleration data acquisition module, used to acquire target tracking acceleration data of the unmanned vehicle at the next moment based on the current expected acceleration; An actual acceleration data acquisition module, used to acquire target observed acceleration data and target disturbance data of the unmanned vehicle at the next moment based on the current actual acceleration; An error calculation module, used to determine target tracking error data of the unmanned vehicle at a next moment by using the target tracking acceleration data and the target observation acceleration data; The output data determination module is used to determine the target speed control parameters of the unmanned vehicle based on the target tracking error data and the target disturbance data.

[0021] In a third aspect, an unmanned vehicle is provided, comprising: at least one processor and at least one memory; The memory stores executable instructions of the processor; The processor is configured to execute any of the above-mentioned speed control methods for an unmanned vehicle.

[0022] The technical solution of the present application provides a speed control method and device for an unmanned vehicle, which obtains the current expected acceleration and the current actual acceleration of the unmanned vehicle at the current moment, obtains the target tracking acceleration data of the unmanned vehicle at the next moment based on the current expected acceleration, obtains the target observation acceleration data of the unmanned vehicle at the next moment and the target disturbance data of the next moment based on the current actual acceleration, determines the target tracking error data of the unmanned vehicle at the next moment by using the target tracking acceleration data and the target observation acceleration data, and determines the target speed control parameters of the unmanned vehicle based on the target tracking error data and the target disturbance data, so that in the speed control process of the unmanned vehicle, the speed control deviation caused by errors and disturbances is effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 This is a flow chart of a speed control method for an unmanned vehicle provided in an embodiment of the present application; Figure 2 It is a schematic flow chart of another method for controlling the speed of an unmanned vehicle provided in an embodiment of the present application; Figure 3 It is a functional structure diagram of a speed control device for an unmanned vehicle provided in an embodiment of the present application; Figure 4 is a functional structure diagram of another speed control device for an unmanned vehicle provided in an embodiment of the present application; Figure 5 It is a functional structure diagram of an unmanned vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application is described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other implementation methods obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

[0026] To solve the above problems, refer to Figure 1 , the embodiment of the present application provides a speed control method for an unmanned vehicle, comprising: 101. Obtain the current expected acceleration and the current actual acceleration of the unmanned vehicle at the current moment.

[0027] 102. Based on the current expected acceleration, obtain the target tracking acceleration data of the unmanned vehicle at the next moment.

[0028] 103. Based on the current actual acceleration, obtain the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data at the next moment.

[0029] 104. Utilize the target tracking acceleration data and the target observation acceleration data to determine the target tracking error data of the unmanned vehicle at the next moment.

[0030] 105. Determine the target speed control parameters of the unmanned vehicle based on the target tracking error data and the target disturbance data.

[0031] The speed control method of an unmanned vehicle provided in this embodiment obtains a current expected acceleration and a current actual acceleration of the unmanned vehicle at a current moment; based on the current expected acceleration, obtains target tracking acceleration data of the unmanned vehicle at the next moment; based on the current actual acceleration, obtains target observation acceleration data of the unmanned vehicle at the next moment and target disturbance data of the next moment; uses the target tracking acceleration data and the target observation acceleration data to determine target tracking error data of the unmanned vehicle at the next moment; based on the target tracking error data and the target disturbance data, determines the target speed control parameters of the unmanned vehicle, identifies these disturbances in real time based on external disturbances of the operating environment and external disturbances of the vehicle's own line control consistency, and compensates for the disturbances, thereby ensuring smoothness during the automatic driving process.

[0032] As an improvement of the above embodiment, the embodiment of the present invention provides another speed control method for an unmanned vehicle. The embodiment of the present invention does not limit the implementation subject of the method, and can be selected by those skilled in the art according to actual engineering conditions, such as a vehicle-mounted device, which can be implemented by an unmanned driving management system. Figure 2 , the method comprising: Get the current expected acceleration and current actual acceleration of the unmanned vehicle at the current moment.

[0033] Optionally, the current expected acceleration can be obtained according to the trajectory of the decision plan, and the current actual acceleration can be obtained through inertial navigation.

[0034] 202. Based on the current expected acceleration, obtain target tracking acceleration data of the unmanned vehicle at the next moment.

[0035] In some optional embodiments, obtaining the target tracking acceleration data of the unmanned vehicle at the next moment includes: obtaining the target tracking acceleration data of the unmanned vehicle at the next moment based on the objective function, the objective function is used to adjust the differential operation parameter value, the differential operation parameter value includes a speed coefficient and a filter factor, the speed coefficient is related to the tracking speed, and the filter factor is related to the smoothness of the tracking.

[0036] The embodiment of the present invention adjusts the differential operation parameters through the objective function to adapt to different load states of the unmanned vehicle. A set of differential operation parameters is used in the light load state and a set of differential operation parameters is used in the heavy load state, making the control process more accurate and flexible, and greatly improving the speed control efficiency.

[0037] In some optional embodiments, the target tracking acceleration data includes: target tracking acceleration and / or target tracking jerk.

[0038] When the target tracking acceleration data includes the target tracking acceleration and the target tracking jerk, 202 may be implemented by, but is not limited to, the following process (not shown in the figure): 2021. Use the current tracking acceleration and the current tracking jerk to obtain the target tracking acceleration of the unmanned vehicle at the next moment.

[0039] Among them, the current tracking acceleration and the current tracking jerk are the target tracking acceleration data corresponding to the unmanned vehicle at the current moment.

[0040] 2022. Determine the target tracking jerk of the unmanned vehicle at the next moment using the current expected acceleration, the current tracking acceleration, the current tracking jerk and the objective function.

[0041] In some optional embodiments, the target tracking acceleration data of the unmanned vehicle at the next moment is obtained by the following formula: ,in, is the current expected acceleration at the current moment, is the tracking acceleration at the current moment, is the tracking jerk at the current moment, is the velocity coefficient, is the filtering factor, () is the objective function, Tracking acceleration for the next moment, Track the jerk for the next target moment.

[0042] In some optional embodiments, the objective function () is characterized by: , in, , , d = , =d , y= + , = , = , = , is the filtering factor, is the current expected acceleration at the current moment, is the tracking acceleration at the current moment, is the tracking jerk at the current moment, is the speed coefficient.

[0043] Based on the current actual acceleration, the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data at the next moment are obtained.

[0044] In some optional embodiments, obtaining the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data of the next moment includes: obtaining the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data of the next moment based on a correction function, the correction function is used to adjust the gain according to the tracking error, and the tracking error and the gain are inversely correlated.

[0045] The embodiment of the present invention adjusts the differential operation parameters through a correction function to adapt to different load states of the unmanned vehicle. The light load state uses a set of state observation parameters, and the heavy load state uses a set of state observation parameters, which makes the control process more accurate and flexible, and greatly improves the speed control efficiency.

[0046] In some optional embodiments, the target observed acceleration data includes: target observed acceleration and / or target observed jerk.

[0047] When the target observed acceleration data includes the target observed acceleration and the target observed jerk, 203 may be implemented, but not limited to, through the following process (not shown in the figure): 2031. Determine the current tracking error of the unmanned vehicle at the current moment using the current observed acceleration and the current actual acceleration.

[0048] Among them, the current observed acceleration is the observed acceleration of the unmanned vehicle at the current moment.

[0049] 2032. Obtain the target observed acceleration of the unmanned vehicle at the next moment using the current observed acceleration, the current observed jerk and the current tracking error.

[0050] 2033. Obtain the target observed acceleration of the unmanned vehicle at the next moment according to the current observed acceleration, the current tracking error, the current disturbance data and the correction function.

[0051] Among them, the current disturbance data is the disturbance data of the unmanned vehicle at the current moment.

[0052] 2034. Determine the target disturbance data of the unmanned vehicle at the next moment using the current disturbance data, the current tracking error and the correction function.

[0053] In some optional embodiments, the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data at the next moment are obtained by the following formula: ; ; ; ; Among them, z1(k) is the current observed acceleration at the current moment, z2(k) is the current observed jerk at the current moment, z3(k) is the current disturbance data at the current moment, e(k) is the current tracking error at the current moment, and u(k) is the dynamic model parameter at the current moment. is an adjustable weight parameter, is the system parameter calibration quantity, y(k) is the actual acceleration at the current moment, is the target observed acceleration at the next moment, is the target observed acceleration at the next moment, is the target perturbation data for the next moment, () is the correction function, Is a constant.

[0054] In some optional embodiments, the correction function () is characterized by: , = , = , is a constant, and a is a calibration constant. In engineering practice, it is set by technical personnel in this field according to engineering needs.

[0055] The target tracking acceleration data and the target observation acceleration data are used to determine the target tracking error data of the unmanned vehicle at the next moment.

[0056] In some optional embodiments, the target tracking error data includes: a target tracking acceleration error and / or a target tracking jerk error.

[0057] Based on the target tracking error data and the target disturbance data, the target speed control parameters of the unmanned vehicle are determined.

[0058] When the target tracking error data includes a target tracking acceleration error and a target tracking jerk error, 205 may be implemented, but not limited to, through the following process (not shown in the figure): 2051. Determine the target tracking acceleration deviation using the target tracking acceleration and the target observation acceleration.

[0059] 2052. Determine the target tracking jerk deviation using the target tracking jerk and the target observation jerk.

[0060] 2053. Based on the target tracking acceleration deviation and the target tracking jerk deviation, a correction function is used to determine the target dynamics model parameters.

[0061] 2054. Determine target speed control parameters based on target dynamics model parameters and target disturbance data.

[0062] Specifically, in some optional embodiments, based on the target tracking error data and the target disturbance data, a nonlinear feedback model is used to determine the target speed control parameters of the unmanned vehicle.

[0063] In some optional embodiments, the nonlinear feedback model is characterized by: ; ; ; ; in, Tracking acceleration for the next moment, Track the jerk for the next target moment, is the target observed acceleration at the next moment, is the target observed acceleration at the next moment, is the target perturbation data for the next moment, is the target tracking acceleration deviation at the next moment, The target tracking jerk deviation for the next moment, , , is the system parameter calibration quantity, is a constant, is the target speed control parameter.

[0064] In the related art, in order to achieve a better control effect in a relatively wide speed range of PID control, it is necessary to design different PID basic parameters corresponding to different speed segments. A large number of parameters will result in a more cumbersome calibration process, which takes a long time to calibrate. In order to solve this problem, in an embodiment of the present invention, one or more of the following parameters are related to the load state of the unmanned vehicle: , , , , , , In a specific embodiment, a person skilled in the art adjusts these parameters to adjust the objective function and the correction function, thereby adjusting the PID control parameters, that is, adjusting the vehicle speed control parameters.

[0065] In some optional embodiments, 1 times the standard value of adjustable weight, It is 3 times the standard value of adjustable weight. It is one ninth of the standard value of the adjustable weight.

[0066] In some optional embodiments, is 1 times the controller bandwidth, It is 3 times the controller bandwidth.

[0067] In some optional embodiments, the filter factor =0.02, speed coefficient =0.5.

[0068] Optionally, the standard value of the adjustable weight of the unmanned vehicle when overloaded is 0.52, and the controller bandwidth is 0.18. =-0.50.

[0069] In some optional embodiments, the standard value of the adjustable weight of the unmanned vehicle when lightly loaded is 0.45, and the controller bandwidth is 0.15. =-0.45.

[0070] In some optional embodiments, =0.25.

[0071] This embodiment uses the objective function and the correction function to match parameters for the state where the mass difference between the empty and loaded mine car is very large. A set of basic parameters is used for the empty and a set of basic parameters are used for the loaded, thereby avoiding the coupling of the empty and loaded during the parameter adjustment process, reducing the complexity of the parameter adjustment, and reducing the time spent on the parameter adjustment.

[0072] Determine whether the current expected acceleration is less than a preset speed value, and / or determine whether the target distance of the unmanned vehicle is a short walking distance.

[0073] 207. If the current expected acceleration is less than the preset speed value, and / or the target distance of the unmanned vehicle is a short walking distance, the target speed control parameter is dynamically compensated.

[0074] In some optional embodiments, the target speed control parameter may be dynamically compensated by, but not limited to, the following process (not shown in the figure): 2071. The dynamic compensation value is calculated by the following formula: , k is the calibration quantity, t is the time constant, Target tracking acceleration deviation for the unmanned vehicle at the next moment.

[0075] 2072. The target speed control parameter is dynamically compensated using the dynamic compensation value through the following formula: , is the dynamic compensation value, is the target speed control parameter for the next moment, It is the target speed control parameter after compensation at the next moment.

[0076] When the vehicle starts at a low speed, this embodiment adds recognition and dynamic gain compensation for the problem of slow accelerator acceleration in a wheel blocking scenario with a small target speed, thereby ensuring a quick start in this scenario.

[0077] The speed control method for the unmanned vehicle provided in this embodiment starts from the external disturbances of the operating environment and the external disturbances of the vehicle's own line control consistency, identifies these disturbances in real time, and compensates for the disturbances, thereby ensuring the smoothness of the automatic driving process. Starting from the actual operating status of the unmanned mine car, due to the large deviation of the unmanned mine car's empty and heavy load quality, the coupling degree of the adaptation is too high when using a set of basic parameters, and the problem of unloaded and heavy load needs to be re-adapted after the parameters are adjusted. This method decouples the basic parameters of empty and heavy loads, and reduces the difficulty of adaptation and the adaptation time by changing the control parameters when empty and heavy load. Real-time detection and identification of external interference on the road and external interference caused by the inconsistency of the vehicle's own line control.

[0078] The embodiment of the present invention provides another specific implementation of the speed control method of an unmanned vehicle, and the detailed process is as follows: Step 1: Get the expected acceleration based on the decision-making planned trajectory and get the actual acceleration based on the inertial navigation.

[0079] The expected acceleration is the current expected acceleration, and the actual acceleration is the current actual acceleration.

[0080] Step 2: Extract the differential tracking jerk corresponding to the expected acceleration, and obtain the over-processed tracking acceleration. The tracking acceleration will be smoother after processing, avoiding the large fluctuations of the actuator caused by the rapid acceleration and deceleration trajectory.

[0081] Optional, the process is as follows: , in, is the expected acceleration output by the decision planning, a1(k) and a2(k) are the tracking acceleration and tracking jerk, respectively. is the velocity coefficient, is the filtering factor.

[0082] fst() is a nonlinear function. By adjusting the speed coefficient and filter factor of the fst() function, a fast and smooth tracking acceleration and tracking jerk can be obtained, and the rapid acceleration and deceleration trajectory can be corrected. The fst() function structure is as follows: , where d = r , =d , y=x1+ , , .

[0083] The differential tracking acceleration is the target tracking acceleration data of the unmanned vehicle at the next moment.

[0084] It should be noted that the sign function is a function used to determine the positive or negative value. Its basic function is to return the corresponding sign value according to the input value. When the input value is positive, the sign function returns 1, when the input value is zero, it returns 0, and when the input value is negative, it returns -1.

[0085] Step 3: Estimate the state of the unmanned mining truck and the observed disturbance, observed acceleration and observed jerk obtained after the disturbance.

[0086] ; ; ; ; in, , , are observed acceleration, observed jerk, and observed disturbance respectively. The initial values ​​are generally 0. e(k) is the tracking error, and u(k) is the system control variable. are three adjustable weight parameters. These three parameters are related to the observer bandwidth. For the convenience of calibration, they are integrated into bandwidth linear relationship parameters. b0 is the system parameter calibration quantity, y(k) is the actual acceleration, fal() is a nonlinear function, and the structure of fal() is as follows: .

[0087] In the embodiment of the present invention, the observed acceleration is the actual acceleration obtained after the actual acceleration is processed, and is the actual acceleration obtained after the tracking error is taken into account.

[0088] Step 4: Obtain the acceleration error based on the tracked acceleration and observed acceleration, obtain the jerk error by tracking the jerk and observing the jerk, use the nonlinear feedback link to obtain the corresponding output, subtract the product of the observed disturbance and the inverse of the system gain from the output to obtain the final system control quantity.

[0089] The nonlinear feedback link is expressed as follows: ; ; ; ; in, , The tracking acceleration and jerk calculated in the second step are The observed acceleration, observed jerk, and observed disturbance calculated in the third step are For calibration, is a constant, are the outputs (variables of the dynamics model, e.g., rolling resistance coefficient).

[0090] For unmanned mining trucks, the difference between empty and loaded weights is large. If one set of parameters is used, the requirements for precise control cannot be achieved. Moreover, if the same set of parameters is used for empty and loaded, the difficulty and complexity of calibration will be increased. After the calibration is completed for the loaded and unloaded, the parameters have been modified during the control calibration. Because they also affect the loaded and unloaded scenes, the loaded and unloaded scenes need to be verified again.

[0091] In the scenario of following a small expected speed, in the related technology, the accelerator is slowly stepped on according to the small expected speed. At this time, if the wheels are blocked by potholes or stones, the small accelerator cannot start, and the slow accelerator will start slowly, causing business jams. For example, for the acceleration trajectory sent down (the expected speed is less than the speed threshold), but the actual speed is less than the speed threshold of 0.3. In order to identify this scenario, this embodiment performs gain compensation on the acceleration deviation parameter item. After triggering this scenario, the gain compensation will be dynamically increased according to time, and the dynamic gain will be recovered after the vehicle starts to ensure the subsequent control accuracy.

[0092] The dynamic compensation formula is as follows: , where k is a calibration quantity and t is a time constant. This is the deviation between the tracking acceleration calculated in the fourth step and the observed acceleration.

[0093] As described above, the speed control method of the unmanned vehicle provided by the embodiment of the present invention can more quickly identify the external disturbances of the unmanned mine car operating environment and the internal disturbances of the mine car itself, and compensate for these disturbances to achieve the purpose of precise control. According to the unique phenomenon that the weight difference between the unloaded and overloaded vehicles in the field is very large, a function with adjustable parameters is designed to facilitate the setting of two sets of basic parameters for unloaded and overloaded, decouple the calibration of unloaded and overloaded, and reduce the calibration workload. According to the individual business scenarios of unmanned mine cars, the problem of slow accelerator acceleration in the relevant technical algorithm when the wheels are blocked is identified, and dynamic compensation is performed for this scenario, and the gain compensation ratio is increased according to the time after the expected speed is received.

[0094] In order to implement the above-mentioned speed control method of the unmanned vehicle, the embodiment of the present invention provides a speed control device for an unmanned vehicle, see Figure 3 , the device comprises: The current vehicle data acquisition module 31 is used to obtain the current expected acceleration and the current actual acceleration of the unmanned vehicle at the current moment.

[0095] The expected acceleration data acquisition module 32 is used to acquire the target tracking acceleration data of the unmanned vehicle at the next moment based on the current expected acceleration.

[0096] The actual acceleration data acquisition module 33 is used to acquire the target observed acceleration data and the target disturbance data of the unmanned vehicle at the next moment based on the current actual acceleration.

[0097] The error calculation module 34 is used to determine the target tracking error data of the unmanned vehicle at the next moment by using the target tracking acceleration data and the target observation acceleration data.

[0098] The output data determination module 35 is used to determine the target speed control parameters of the unmanned vehicle based on the target tracking error data and the target disturbance data.

[0099] The speed control device for the unmanned vehicle provided in this embodiment, the current vehicle data acquisition module acquires the current expected acceleration and the current actual acceleration of the unmanned vehicle at the current moment. The expected acceleration data acquisition module acquires the target tracking acceleration data of the unmanned vehicle at the next moment based on the current expected acceleration. The actual acceleration data acquisition module acquires the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data at the next moment based on the current actual acceleration. The error calculation module determines the target tracking error data of the unmanned vehicle at the next moment using the target tracking acceleration data and the target observed acceleration data. The output data determination module determines the target speed control parameters of the unmanned vehicle based on the target tracking error data and the target disturbance data. The device identifies these disturbances in real time based on the external disturbances of the operating environment and the external disturbances of the vehicle's own line control consistency, and compensates for the disturbances, thereby ensuring the smoothness of the automatic driving process.

[0100] As an improvement of the above embodiment, the present invention provides another speed control device for an unmanned vehicle. Figure 4 , the device comprises: The current vehicle data acquisition module 41 is used to obtain the current expected acceleration and the current actual acceleration of the unmanned vehicle at the current moment.

[0101] The expected acceleration data acquisition module 42 is used to acquire the target tracking acceleration data of the unmanned vehicle at the next moment based on the current expected acceleration.

[0102] In some optional embodiments, the target tracking acceleration data of the unmanned vehicle at the next moment is obtained by: based on the objective function, the target tracking acceleration data of the unmanned vehicle at the next moment is obtained by differential operation, and the objective function is used to adjust the differential operation parameter value, and the differential operation parameter value includes a speed coefficient and a filter factor. The speed coefficient is related to the tracking speed, and the filter factor is related to the smoothness of the tracking.

[0103] In some optional embodiments, the target tracking acceleration data includes target tracking acceleration and target tracking jerk, and the expected acceleration data acquisition module 42 includes: The target tracking acceleration acquisition submodule 421 is used to obtain the target tracking acceleration of the unmanned vehicle at the next moment using the current tracking acceleration and the current tracking jerk. The current tracking acceleration and the current tracking jerk are the target tracking acceleration data corresponding to the unmanned vehicle at the current moment.

[0104] The target tracking acceleration determination submodule 422 is used to determine the target tracking acceleration of the unmanned vehicle at the next moment by using the current expected acceleration, the current tracking acceleration, the current tracking jerk and the objective function.

[0105] In some optional embodiments, the target tracking acceleration data of the unmanned vehicle at the next moment is obtained by the following formula: ,in, is the current expected acceleration at the current moment, is the tracking acceleration at the current moment, is the tracking jerk at the current moment, is the velocity coefficient, is the filtering factor, () is the objective function, Tracking acceleration for the next moment, Track the jerk for the next target moment.

[0106] Among them, the objective function () is characterized by: ,in, , , d = , =d , y= + , = , = , = , is the filtering factor, is the current expected acceleration at the current moment, is the tracking acceleration at the current moment, is the tracking jerk at the current moment, is the speed coefficient.

[0107] The actual acceleration data acquisition module 43 is used to acquire the target observed acceleration data and the target disturbance data of the unmanned vehicle at the next moment based on the current actual acceleration.

[0108] In some optional embodiments, the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data of the next moment are obtained as follows: the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data of the next moment are obtained based on a correction function, and the correction function is used to adjust the gain according to the tracking error, and the tracking error and the gain are inversely correlated.

[0109] In some optional embodiments, the target observed acceleration data includes the target observed acceleration and the target observed jerk, and the actual acceleration data acquisition module 43 includes: The tracking error determination submodule 431 is used to determine the current tracking error of the unmanned vehicle at the current moment by using the current observed acceleration and the current actual acceleration, where the current observed acceleration is the observed acceleration of the unmanned vehicle at the current moment.

[0110] The target observed acceleration acquisition submodule 432 is used to obtain the target observed acceleration of the unmanned vehicle at the next moment by using the current observed acceleration, the current observed jerk and the current tracking error.

[0111] The target observed acceleration acquisition submodule 433 is used to obtain the target observed acceleration of the unmanned vehicle at the next moment according to the current observed acceleration, the current tracking error, the current disturbance data and the correction function, and the current disturbance data is the disturbance data of the unmanned vehicle at the current moment.

[0112] The target disturbance data determination submodule 434 is used to determine the target disturbance data of the unmanned vehicle at the next moment by using the current disturbance data, the current tracking error and the correction function.

[0113] In some optional embodiments, the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data at the next moment are obtained by the following formula: ; ; ; ; Among them, z1(k) is the current observed acceleration at the current moment, z2(k) is the current observed jerk at the current moment, z3(k) is the current disturbance data at the current moment, e(k) is the current tracking error at the current moment, and u(k) is the dynamic model parameter at the current moment. is an adjustable weight parameter, is the system parameter calibration quantity, y(k) is the actual acceleration at the current moment, is the target observed acceleration at the next moment, is the target observed acceleration at the next moment, is the target perturbation data for the next moment, () is the correction function, Is a constant.

[0114] Among them, the correction function () is characterized by: , = , = , is a constant, and a is a calibration constant. In engineering practice, it is set by technical personnel in this field according to engineering needs.

[0115] The error calculation module 44 is used to determine the target tracking error data of the unmanned vehicle at the next moment by using the target tracking acceleration data and the target observation acceleration data.

[0116] In some optional embodiments, the target tracking acceleration data includes: target tracking acceleration and / or target tracking jerk. And / or, The target observed acceleration data includes: target observed acceleration and / or target observed jerk. And / or, The target tracking error data includes: a target tracking acceleration error and / or a target tracking jerk error.

[0117] The output data determination module 45 is used to determine the target speed control parameters of the unmanned vehicle based on the target tracking error data and the target disturbance data.

[0118] In some optional embodiments, the target tracking error data includes a target tracking acceleration error and a target tracking jerk error, and the output data determination module 45 includes: The target tracking acceleration deviation determination submodule 451 is used to determine the target tracking acceleration deviation using the target tracking acceleration and the target observation acceleration.

[0119] The target tracking jerk deviation determination submodule 452 is used to determine the target tracking jerk deviation using the target tracking jerk and the target observation jerk.

[0120] The target dynamics model parameter determination submodule 453 is used to determine the target dynamics model parameters based on the target tracking acceleration deviation and the target tracking jerk deviation and using a correction function.

[0121] The target speed control parameter determination submodule 454 is used to determine the target speed control parameters based on the target dynamics model parameters and the target disturbance data.

[0122] Specifically, in some optional embodiments, the output data determination module 45 determines the target speed control parameters of the unmanned vehicle using a nonlinear feedback model based on the target tracking error data and the target disturbance data.

[0123] In some optional embodiments, the nonlinear feedback model is characterized by: ; ; ; ; in, Tracking acceleration for the next moment, Track the jerk for the next target moment, is the target observed acceleration at the next moment, is the target observed acceleration at the next moment, is the target perturbation data for the next moment, is the target tracking acceleration deviation at the next moment, The target tracking jerk deviation at the next moment, , , is the system parameter calibration quantity, is a constant, is the target speed control parameter.

[0124] The compensation module 46 is used to determine whether the current expected acceleration is less than the preset speed value and / or whether the target distance of the unmanned vehicle is a short walking distance. If the current expected acceleration is less than the preset speed value and / or the target distance of the unmanned vehicle is a short walking distance, the target speed control parameter is dynamically compensated.

[0125] In some optional embodiments, the compensation module 46 includes: The compensation value calculation submodule 461 is used to calculate the dynamic compensation value by the following formula: , k is the calibration quantity, t is the time constant, Target tracking acceleration deviation for the unmanned vehicle at the next moment.

[0126] The compensation submodule 462 is used to dynamically compensate the target speed control parameter using the dynamic compensation value according to the following formula: , is the dynamic compensation value, is the target speed control parameter for the next moment, It is the target speed control parameter after compensation at the next moment.

[0127] It should be noted that one or more of the following parameters are related to the load status of the unmanned vehicle: , , , .

[0128] The speed control device for an unmanned vehicle provided in this embodiment, the current vehicle data acquisition module acquires the current expected acceleration and the current actual acceleration of the unmanned vehicle at the current moment. The expected acceleration data acquisition module acquires the target tracking acceleration data of the unmanned vehicle at the next moment based on the current expected acceleration. The actual acceleration data acquisition module acquires the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data at the next moment based on the current actual acceleration. The error calculation module determines the target tracking error data of the unmanned vehicle at the next moment using the target tracking acceleration data and the target observed acceleration data. The output data determination module determines the target speed control parameters of the unmanned vehicle based on the target tracking error data and the target disturbance data. The compensation module compensates the vehicle speed control parameters when the vehicle starts at a low speed. The device identifies these disturbances in real time based on the external disturbances of the operating environment and the external disturbances of the vehicle's own line control consistency, and compensates for the disturbances, thereby ensuring the smoothness of the automatic driving process.

[0129] Based on the same inventive concept, Figure 5 As shown, the present application also provides an unmanned vehicle, comprising: at least one processor 51 and at least one memory 52; The memory stores executable instructions of the processor; The processor is configured to execute the speed control method of the unmanned vehicle provided in the above embodiment.

[0130] The unmanned vehicle provided in the embodiment of the present application stores executable instructions of the processor in a memory. When the executable instructions are executed, the processor can identify these disturbances in real time based on external disturbances of the operating environment and external disturbances of the vehicle's own line control consistency, and compensate for the disturbances, thereby ensuring smoothness during autonomous driving.

[0131] It should be noted that, in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0132] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

Claims

1. A speed control method for an unmanned vehicle, characterized in that: include: Obtain the current expected acceleration and current actual acceleration of the unmanned vehicle at the current moment; Based on the current expected acceleration, obtaining target tracking acceleration data of the unmanned vehicle at the next moment; Based on the current actual acceleration, obtaining target observed acceleration data of the unmanned vehicle at the next moment and target disturbance data at the next moment; Determining target tracking error data of the unmanned vehicle at a next moment using the target tracking acceleration data and the target observation acceleration data; Based on the target tracking error data and the target disturbance data, a target speed control parameter of the unmanned vehicle is determined.

2. The method according to claim 1, characterized in that Obtaining target tracking acceleration data of the unmanned vehicle at the next moment, including: The target tracking acceleration data of the unmanned vehicle at the next moment is obtained based on the objective function, and the objective function is used to adjust the differential operation parameter value, and the differential operation parameter value includes a speed coefficient and a filter factor, the speed coefficient is related to the tracking speed, and the filter factor is related to the smoothness of the tracking.

3. The method according to claim 2, characterized in that Obtaining the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data at the next moment includes: The target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data at the next moment are obtained based on the correction function, and the correction function is used to adjust the gain according to the tracking error, and the tracking error and the gain are inversely correlated.

4. The method according to claim 1, wherein the target tracking acceleration data comprises: target tracking acceleration and / or target tracking jerk; and / or, The target observed acceleration data includes: target observed acceleration and / or target observed jerk; and / or, The target tracking error data includes: a target tracking acceleration error and / or a target tracking jerk error.

5. The method according to claim 1, 2 or 4, characterized in that: The target tracking acceleration data includes target tracking acceleration and target tracking jerk, Acquiring the target tracking acceleration data of the unmanned vehicle at the next moment includes: Obtaining the target tracking acceleration of the unmanned vehicle at the next moment by using the current tracking acceleration and the current tracking jerk, wherein the current tracking acceleration and the current tracking jerk are target tracking acceleration data corresponding to the unmanned vehicle at the current moment; The target tracking jerk of the unmanned vehicle at the next moment is determined using the current expected acceleration, the current tracking acceleration, the current tracking jerk and the objective function.

6. The method according to claim 1, 3 or 4, characterized in that: The target observed acceleration data includes target observed acceleration and target observed jerk, Obtaining the target observed acceleration data of the unmanned vehicle at the next moment and the target disturbance data at the next moment includes: Determining a current tracking error of the unmanned vehicle at a current moment using a current observed acceleration and the current actual acceleration, wherein the current observed acceleration is an observed acceleration of the unmanned vehicle at the current moment; Obtaining a target observed acceleration of the unmanned vehicle at a next moment using the current observed acceleration, the current observed jerk, and the current tracking error; Obtaining a target observed jerk of the unmanned vehicle at a next moment according to the current observed jerk, the current tracking error, the current disturbance data and the correction function, wherein the current disturbance data is the disturbance data of the unmanned vehicle at the current moment; The target disturbance data of the unmanned vehicle at the next moment is determined using the current disturbance data, the current tracking error and the correction function.

7. The method according to claim 6, characterized in that The target tracking error data includes a target tracking acceleration error and a target tracking jerk error. Based on the target tracking error data and the target disturbance data, determining a target speed control parameter of the unmanned vehicle includes: Determining the target tracking acceleration deviation using the target tracking acceleration and the target observation acceleration; Determining the target tracking jerk deviation using the target tracking jerk and the target observation jerk; Determining target dynamics model parameters based on the target tracking acceleration deviation and the target tracking jerk deviation and using the correction function; The target speed control parameter is determined based on the target dynamics model parameter and the target disturbance data.

8. The method according to claim 5, characterized in that The target tracking acceleration data of the unmanned vehicle at the next moment is obtained by the following formula: ,in, is the current expected acceleration at the current moment, is the tracking acceleration at the current moment, is the tracking jerk at the current moment, is the velocity coefficient, is the filtering factor, () is the objective function, Tracking acceleration for the next moment, Track the jerk for the next moment's goal.

9. The method according to claim 8, characterized in that The objective function () is characterized by: ， in, , , d = , =d , y= + , = , = , = , is the filtering factor, is the current expected acceleration at the current moment, is the tracking acceleration at the current moment, is the tracking jerk at the current moment, is the speed coefficient.

10. The method according to claim 6, characterized in that The target observed acceleration data and the target disturbance data of the unmanned vehicle at the next moment are obtained by the following formula: ； ； ； ； Among them, z1(k) is the current observed acceleration at the current moment, z2(k) is the current observed jerk at the current moment, z3(k) is the current disturbance data at the current moment, e(k) is the current tracking error at the current moment, and u(k) is the dynamic model parameter at the current moment. is an adjustable weight parameter, is the system parameter calibration quantity, y(k) is the actual acceleration at the current moment, is the target observed acceleration at the next moment, is the target observed acceleration at the next moment, is the target perturbation data for the next moment, () is the correction function, Is a constant.

11. The method according to claim 10, characterized in that The correction function () is characterized by: , = , = , is a constant and a is a calibration constant.

12. The method according to claim 10, characterized in that Determining a target speed control parameter of the unmanned vehicle based on the target tracking error data and the target disturbance data includes: Based on the target tracking error data and the target disturbance data, a target speed control parameter of the unmanned vehicle is determined using a nonlinear feedback model.

13. The method according to claim 12, characterized in that The nonlinear feedback model is characterized by: ； ； ； ； in, Tracking acceleration for the next moment, Track the jerk for the next target moment, is the target observed acceleration at the next moment, is the target observed acceleration at the next moment, is the target perturbation data for the next moment, is the target tracking acceleration deviation at the next moment, The target tracking jerk deviation for the next moment, , , is the system parameter calibration quantity, is a constant, is the target speed control parameter.

14. The method according to claim 13, characterized in that One or more of the following parameters are related to the load status of the unmanned vehicle: , , , , , , .

15. The method according to claim 13, characterized in that Also includes: Determining whether the current expected acceleration is less than a preset speed value, and / or determining whether the target distance of the unmanned vehicle is a short walking distance; If the current expected acceleration is less than a preset speed value, and / or the target distance of the driverless vehicle is a short walking distance, the target speed control parameter is dynamically compensated.

16. The method according to claim 15, characterized in that Dynamically compensating the target speed control parameter includes: The dynamic compensation value is calculated by the following formula: , k is the calibration quantity, t is the time constant, A target tracking acceleration deviation of the unmanned vehicle at the next moment; The target speed control parameter is dynamically compensated using the dynamic compensation value through the following formula: , is the dynamic compensation value, is the target speed control parameter for the next moment, It is the target speed control parameter after compensation at the next moment.

17. A speed control device for an unmanned vehicle, characterized in that: include: The current vehicle data acquisition module is used to obtain the current expected acceleration and current actual acceleration of the unmanned vehicle at the current moment; An expected acceleration data acquisition module, used to acquire target tracking acceleration data of the unmanned vehicle at the next moment based on the current expected acceleration; An actual acceleration data acquisition module, used to acquire target observed acceleration data and target disturbance data of the unmanned vehicle at the next moment based on the current actual acceleration; An error calculation module, used to determine target tracking error data of the unmanned vehicle at a next moment by using the target tracking acceleration data and the target observation acceleration data; The output data determination module is used to determine the target speed control parameters of the unmanned vehicle based on the target tracking error data and the target disturbance data.

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