Longitudinal control method and device for unmanned vehicle

Abstract

The present invention discloses a longitudinal control method and device for an unmanned vehicle. The method obtains the desired driving torque of the unmanned vehicle to be controlled; determines the desired motion velocity trajectory sequence of the unmanned vehicle to be controlled based on the desired driving torque and a preset discrete nonlinear system used to characterize the longitudinal control of the unmanned vehicle; and upon receiving an open-loop and closed-loop iterative learning control instruction, repeatedly performs iterative calculation operations according to the open-loop and closed-loop iterative learning control instruction. During the iterative process, the actual driving torque sequence is updated by a generated closed-loop iterative learning controller. After each iteration, it is determined whether the updated actual driving torque sequence is less than a preset range. If so, the unmanned vehicle to be controlled is controlled based on the current updated actual driving torque sequence. By implementing the present invention, precise control of the motion velocity trajectory of an unmanned vehicle can be achieved.

Description

Technical Field

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

[0002] In the existing technology, unmanned driving technology generally consists of three parts, namely environmental perception, decision-making and planning, and motion control. Among them, motion control usually includes longitudinal control. The longitudinal controller adjusts the longitudinal speed to the desired speed through the power actuator. At present, the research on the longitudinal control method of unmanned vehicles mainly focuses on the following three directions: first, realizing automatic cruising; second, realizing the following of the vehicle in front; third, automatic emergency braking; but the above three directions are control methods for assisting the driver's operation according to actual road conditions. In fact, many applications may require the vehicle to move at a preset speed trajectory and repeat tasks. For example, unmanned driving can be used to make reciprocating movements along the same route every day. The bus is required to move along a predetermined speed trajectory, including decelerating to stop at a station, stopping to wait, accelerating to leave the station and other speed-changing trajectories, and using unmanned vehicles to collect road test information on sections. The above systems can all be represented by dynamic systems with repetitive motion properties. Therefore, in order to achieve the tracking of the longitudinal speed of unmanned vehicles, scholars have proposed PID control, model predictive control, empirical rule control, and neural network control. However, the tracking error convergence speed of these traditional control methods is slow, and the accuracy of the motion speed trajectory needs to be improved. Therefore, how to achieve precise control of the motion speed trajectory of unmanned vehicles is an urgent problem to be solved. Summary of the Invention

[0003] The present invention provides a longitudinal control method and device for an unmanned vehicle. The method iteratively updates the driving torque through an open-closed loop iterative learning controller until the actual motion speed trajectory approaches the desired motion speed trajectory, thereby achieving precise control of the motion speed trajectory of the unmanned vehicle.

[0004] An embodiment of the present invention provides a longitudinal control method for an unmanned vehicle, comprising:

[0005] Obtaining a desired driving torque of the unmanned vehicle to be controlled;

[0006] Determining a desired motion velocity trajectory sequence of the unmanned vehicle to be controlled based on the desired driving torque and a preset discrete nonlinear system for characterizing longitudinal control of the unmanned vehicle;

[0007] When receiving an open-loop and closed-loop iterative learning control instruction, repeatedly performing an iterative calculation operation according to the open-loop and closed-loop iterative learning control instruction until a target driving torque is obtained;

[0008] controlling the unmanned vehicle to be controlled according to the target driving torque;

[0009] The iterative calculation operation includes:

[0010] Determining a current actual motion velocity trajectory sequence of the unmanned vehicle to be controlled based on a current actual driving torque sequence and the discrete nonlinear system; wherein the actual driving torque sequence corresponding to the initial iterative calculation is generated based on a random driving torque;

[0011] Obtaining an error sequence and an iterative error index according to the expected motion speed trajectory sequence and the actual motion speed trajectory sequence;

[0012] forming an open-loop and closed-loop iterative learning controller according to a preset open-loop iterative learning control gain, a preset closed-loop iterative learning control gain, and the error sequence;

[0013] updating the actual driving torque sequence according to the error sequence, the open-loop iterative learning control gain, the closed-loop iterative learning control gain, and the open-loop and closed-loop iterative learning controller;

[0014] It is determined whether the iterative error index is less than a preset range. If so, the updated actual driving torque sequence is used as the target driving torque; if not, the next iterative calculation operation is performed according to the updated actual driving torque sequence.

[0015] Furthermore, determining the desired motion velocity trajectory sequence of the unmanned vehicle to be controlled based on the desired driving torque and a preset discrete nonlinear system for characterizing the longitudinal control of the unmanned vehicle includes:

[0016] The expected driving torque is sampled according to a preset sampling period and a preset iterative expected time length to obtain a discretized expected driving torque sequence;

[0017] The discretized desired driving torque sequence is used as the input of the discrete nonlinear system, so that the discrete nonlinear system outputs the desired motion velocity trajectory sequence of the unmanned vehicle to be controlled.

[0018] Furthermore, the step of using the discretized desired driving torque sequence as the input of the discrete nonlinear system so that the discrete nonlinear system outputs a desired motion velocity trajectory sequence of the unmanned vehicle to be controlled includes:

[0019] The expected motion velocity trajectory sequence of the unmanned vehicle to be controlled is calculated according to the following formula:

[0020]

[0021] in, C = 1; is the expected state of the system; is the expected driving torque sequence; is the desired motion speed trajectory sequence; T r is the expected time length of the preset iteration, T s is the preset sampling period; m is the mass of the unmanned vehicle to be controlled; I w is the equivalent moment of inertia of the wheel; R w is the effective rolling radius of the wheel; ρ is the air density; C d is the air resistance coefficient; S is the vehicle's frontal area; V w is the wind speed.

[0022] Furthermore, the discrete nonlinear system satisfies the following conditions:

[0023]

[0024]

[0025] Among them, f is a Lipschitz constant greater than 0; is the initial state of the system at the jth iteration; x r (0)=0.

[0026] Furthermore, according to the current actual driving torque sequence and the discrete nonlinear system, determining the current actual motion velocity trajectory sequence of the unmanned vehicle to be controlled includes:

[0027] The actual motion velocity trajectory sequence of the unmanned vehicle to be controlled is calculated according to the following formula:

[0028]

[0029] in, C = 1; is the actual state of the system at the jth iteration; is the actual driving torque sequence of the jth iteration; is the actual motion velocity trajectory sequence of the jth iteration; T r is the expected time length of the preset iteration, T s is the preset sampling period; m is the mass of the unmanned vehicle to be controlled; I w is the equivalent moment of inertia of the wheel; R w is the effective rolling radius of the wheel; ρ is the air density; C dis the air resistance coefficient; S is the vehicle's frontal area; V w is the wind speed.

[0030] Furthermore, according to the expected motion speed trajectory sequence and the actual motion speed trajectory sequence, an error sequence and an iterative error index are obtained, including:

[0031] The error sequence is calculated according to the following formula:

[0032]

[0033] in, is the error sequence of the jth iteration; is the actual motion velocity trajectory sequence of the jth iteration; is the desired motion speed trajectory sequence;

[0034] The iteration error index is calculated according to the following formula:

[0035]

[0036] Among them, SE j is the iterative error index of the jth iteration; T r The expected duration of the preset iteration; is the actual motion velocity trajectory sequence of the jth iteration; is the desired motion velocity trajectory sequence.

[0037] Furthermore, the open-closed loop iterative learning controller is:

[0038]

[0039]

[0040]

[0041] Among them, G, H are open-loop control gains; M is closed-loop control gain; is the actual driving torque sequence of the jth iteration; is the actual driving torque sequence of the jth iteration of the open loop, is the actual driving torque sequence of the jth iteration of the closed loop; Updated actual driving torque sequence.

[0042] Furthermore, the preset open-loop iterative learning control gain satisfies the following convergence conditions:

[0043]

[0044] Wherein, G is the preset open-loop iterative learning control gain; H is the preset open-loop iterative learning control gain.

[0045] An embodiment of the present invention provides a longitudinal control device for an unmanned vehicle, comprising: a desired driving torque acquisition module, a desired motion speed trajectory sequence determination module, an iterative calculation module, and an unmanned vehicle control module;

[0046] The desired driving torque acquisition module is used to acquire the desired driving torque of the unmanned vehicle to be controlled;

[0047] The expected motion speed trajectory sequence determination module is used to determine the expected motion speed trajectory sequence of the unmanned vehicle to be controlled based on the expected driving torque and a preset discrete nonlinear system for characterizing the longitudinal control of the unmanned vehicle;

[0048] The iterative calculation module is configured to, upon receiving an open-loop and closed-loop iterative learning control instruction, repeatedly perform an iterative calculation operation according to the open-loop and closed-loop iterative learning control instruction until a target driving torque is obtained;

[0049] The unmanned vehicle control module is configured to control the unmanned vehicle to be controlled according to the target driving torque;

[0050] The iterative calculation operation includes:

[0051] Determining a current actual motion velocity trajectory sequence of the unmanned vehicle to be controlled based on a current actual driving torque sequence and the discrete nonlinear system; wherein the actual driving torque sequence corresponding to the initial iterative calculation is generated based on a random driving torque;

[0052] Obtaining an error sequence and an iterative error index according to the expected motion speed trajectory sequence and the actual motion speed trajectory sequence;

[0053] forming an open-loop and closed-loop iterative learning controller according to a preset open-loop iterative learning control gain, a preset closed-loop iterative learning control gain, and the error sequence;

[0054] updating the actual driving torque sequence according to the error sequence, the open-loop iterative learning control gain, the closed-loop iterative learning control gain, and the open-loop and closed-loop iterative learning controller;

[0055] It is determined whether the iterative error index is less than a preset range. If so, the updated actual driving torque sequence is used as the target driving torque; if not, the next iterative calculation operation is performed according to the updated actual driving torque sequence.

[0056] The following beneficial effects are achieved by implementing the present invention:

[0057] The present invention provides a longitudinal control method and device for an unmanned vehicle. The method obtains a desired motion velocity trajectory sequence of the unmanned vehicle to be controlled based on basic parameters of the unmanned vehicle to be controlled and a desired driving torque. Upon receiving an open-loop and closed-loop iterative learning control instruction, the method uses the actual driving torque sequence obtained from the previous iteration update as the system input for this iteration to obtain the actual motion velocity trajectory sequence of the unmanned vehicle to be controlled, and obtains an error sequence and an iteration error index based on the desired motion velocity trajectory sequence and the actual motion velocity trajectory sequence. An open-loop and closed-loop iterative learning controller is formed based on a preset open-loop and closed-loop iterative learning control gain, a closed-loop and closed-loop iterative learning control gain, and the error sequence. The actual driving torque sequence for the next iteration is updated based on the error sequence, the open-loop and closed-loop iterative learning control gain, the closed-loop and closed-loop iterative learning controller. When it is determined that the iteration error index is less than a preset range, the actual driving torque sequence corresponding to the time when the iteration error index is less than the preset range is used as the target driving torque, and the unmanned vehicle to be controlled based on the target driving torque.

[0058] Therefore, the present application obtains the difference between the expected motion speed trajectory sequence and the actual motion speed trajectory sequence, that is, the error sequence, and then forms an open-loop and closed-loop iterative learning controller according to the preset open-loop iterative learning control gain, closed-loop iterative learning control gain and the error sequence, and updates the driving torque based on the iterative learning controller, and repeatedly performs iterative calculation operations according to the open-loop and closed-loop iterative learning control instructions. As the number of iterations increases, the convergence effect of the output actual motion speed trajectory on the expected motion speed trajectory is improved, and significant error reduction can be achieved with a smaller number of iterations, so that the system can follow the expected trajectory more accurately, thereby improving the safety of unmanned vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0060] Figure 1 This is a flow chart of a longitudinal control method for an unmanned vehicle provided by the present application;

[0061] Figure 2 is the actual iteration time length T of the present invention j Change curve diagram under different iteration numbers;

[0062] Figure 3is the initial state of the discrete nonlinear system of the present invention at each iteration Change curve diagram under different iteration numbers;

[0063] Figure 4 1. It is a diagram showing the tracking of the actual motion velocity trajectory sequence to the desired motion velocity trajectory sequence by the open-closed loop iterative learning controller of the present invention at different iteration numbers;

[0064] Figure 5 This is a comparison diagram of the error convergence effect between the open-loop and closed-loop iterative learning controller of the present invention and the traditional iterative learning controller;

[0065] Figure 6 This is a schematic structural diagram of a longitudinal control device for an unmanned vehicle provided in this application. DETAILED DESCRIPTION

[0066] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0068] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0069] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0070] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0071] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0072] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0073] See also Figure 1 , is a flow chart of a longitudinal control method for an unmanned vehicle provided in this application, comprising:

[0074] S1. Obtaining the expected driving torque of the unmanned vehicle to be controlled;

[0075] Schematically, before implementing the longitudinal control method of the unmanned vehicle, the longitudinal dynamics model of the unmanned vehicle needs to be represented as a discrete system;

[0076] Specifically, the longitudinal dynamics model of the unmanned vehicle is as follows:

[0077]

[0078] Among them, t∈[0,T j ] is the actual duration of each iteration, j∈(0,1,2,...) is the number of iterations; m=1500kg is the mass of the unmanned vehicle; I w =65.35kg·m 2 is the equivalent moment of inertia of the wheel; R w =0.35m is the effective rolling radius of the wheel; V j (t) is the actual motion velocity trajectory of the unmanned vehicle at the jth iteration t; is the actual driving torque of the unmanned vehicle at the jth iteration t; ρ = 1.20 kg / m 3 is the air density; Cd =0.32 is the air resistance coefficient; S = 1.87m 2 is the frontal area of ​​the vehicle, V w =20m / s is the wind speed; according to the definition of derivative,

[0079] Next, let the sampling period T s =1s, yes Perform periodic sampling to obtain the sampling sequence of the driving torque in make is the control input of the discrete system, that is, the actual driving torque sequence V j (t) Perform periodic sampling to obtain the sampling sequence V j (i·T s ), let V j (i·T s ) is the actual state of the discrete system

[0080] Therefore, the longitudinal dynamics model of the unmanned vehicle can be expressed as the following discrete system:

[0081]

[0082] in, is the actual state of the system at the jth iteration; is the actual driving torque sequence of the jth iteration; is the actual motion velocity trajectory sequence of the jth iteration; T j is the actual iteration time length of the jth time, T s is the preset sampling period; m is the mass of the unmanned vehicle to be controlled; I w is the equivalent moment of inertia of the wheel; R w is the effective rolling radius of the wheel; ρ is the air density; C d is the air resistance coefficient; S is the vehicle's frontal area; V w is the wind speed;

[0083] Specifically, it is easy to see that the discrete system (2) contains a nonlinear part:

[0084] Therefore, a nonlinear function f(·) is defined to represent the system state: Therefore, the discrete system (2) can be transformed into the following discrete nonlinear system:

[0085]

[0086] in, C = 1;

[0087] Therefore, the calculation formula for the expected motion velocity trajectory sequence of the unmanned vehicle to be controlled is easily obtained as follows:

[0088]

[0089] S2. Determining a desired motion velocity trajectory sequence of the unmanned vehicle to be controlled based on the desired driving torque and a preset discrete nonlinear system for characterizing longitudinal control of the unmanned vehicle;

[0090] In a preferred embodiment, determining the desired motion velocity trajectory sequence of the unmanned vehicle to be controlled based on the desired driving torque and a preset discrete nonlinear system for characterizing the longitudinal control of the unmanned vehicle includes:

[0091] The expected driving torque is sampled according to a preset sampling period and a preset iterative expected time length to obtain a discretized expected driving torque sequence;

[0092] Using the discretized desired driving torque sequence as input to the discrete nonlinear system, so that the discrete nonlinear system outputs a desired motion velocity trajectory sequence for the unmanned vehicle to be controlled;

[0093] In a preferred embodiment, the method of using the discretized desired driving torque sequence as the input of the discrete nonlinear system so that the discrete nonlinear system outputs the desired motion velocity trajectory sequence of the unmanned vehicle to be controlled includes:

[0094] The expected motion velocity trajectory sequence of the unmanned vehicle to be controlled is calculated according to the following formula:

[0095]

[0096] in, C = 1; is the expected state of the system; is the expected driving torque sequence; is the desired motion speed trajectory sequence; T r is the expected time length of the preset iteration, T s is the preset sampling period; m is the mass of the unmanned vehicle to be controlled; I w is the equivalent moment of inertia of the wheel; R w is the effective rolling radius of the wheel; ρ is the air density; C d is the air resistance coefficient; S is the vehicle's frontal area; V w is the wind speed;

[0097] Specifically, the sampling period of the discrete nonlinear system is set to T S , the sampled discrete sequence is Among them, T r is the expected time length of each iteration of the discrete nonlinear system, then the corresponding discrete time series is The desired driving torque sequence Input into the discrete nonlinear system (4), thereby obtaining the desired motion velocity trajectory sequence ,

[0098] S3. When receiving an open-loop and closed-loop iterative learning control instruction, repeatedly performing an iterative calculation operation according to the open-loop and closed-loop iterative learning control instruction until a target driving torque is obtained;

[0099] The iterative calculation operation includes:

[0100] Determining a current actual motion velocity trajectory sequence of the unmanned vehicle to be controlled based on a current actual driving torque sequence and the discrete nonlinear system; wherein the actual driving torque sequence corresponding to the initial iterative calculation is generated based on a random driving torque;

[0101] Obtaining an error sequence and an iterative error index according to the expected motion speed trajectory sequence and the actual motion speed trajectory sequence;

[0102] forming an open-loop and closed-loop iterative learning controller according to a preset open-loop iterative learning control gain, a preset closed-loop iterative learning control gain, and the error sequence;

[0103] updating the actual driving torque sequence according to the error sequence, the open-loop iterative learning control gain, the closed-loop iterative learning control gain, and the open-loop and closed-loop iterative learning controller;

[0104] determining whether the iterative error index is less than a preset range; if so, using the updated actual driving torque sequence as the target driving torque; if not, performing the next iterative calculation operation based on the updated actual driving torque sequence;

[0105] Schematically, the current actual motion velocity trajectory sequence of the unmanned vehicle to be controlled is determined based on the current actual driving torque sequence and the discrete nonlinear system; wherein the actual driving torque sequence corresponding to the initial iterative calculation is generated based on a random driving torque;

[0106] In a preferred embodiment, the discrete nonlinear system satisfies the following conditions:

[0107]

[0108]

[0109] Among them, f is a Lipschitz constant greater than 0; is the initial state of the system at the jth iteration;

[0110]

[0111] Specifically, for the discrete nonlinear system, the following conditions are set:

[0112] Condition 1: For each iteration, the initial state of the discrete nonlinear system satisfies in,

[0113] Condition 2: It is easy to see that the nonlinear function f(·) in the discrete nonlinear system is differentiable with respect to i, and is a global Lipschitz variable, so for all i, and There will always be a Lipschitz constant ζ greater than 0 f satisfy

[0114] Specifically, let the randomly generated driving torque in the first iteration of the discrete nonlinear system be u0(t), and the corresponding initial input signal be:

[0115]

[0116] After discretizing the driving torque u0(t) input during the first iteration, the actual driving torque sequence input during the first iteration is obtained: Input into system (3) to obtain the actual motion velocity trajectory sequence during the first iteration calculation

[0117] In a preferred embodiment, obtaining an error sequence and an iterative error index according to the expected motion speed trajectory sequence and the actual motion speed trajectory sequence includes:

[0118] The error sequence is calculated according to the following formula:

[0119]

[0120] in, is the error sequence of the jth iteration; is the actual motion velocity trajectory sequence of the jth iteration; is the desired motion speed trajectory sequence;

[0121] The iterative error index is calculated according to the following formula:

[0122]

[0123] Among them, SE j is the iterative error index of the jth iteration; T r The expected duration of the preset iteration; is the actual motion velocity trajectory sequence of the jth iteration; is the desired motion speed trajectory sequence;

[0124] Schematically, an error sequence and an iterative error index are obtained according to the expected motion speed trajectory sequence and the actual motion speed trajectory sequence;

[0125] In a preferred embodiment, the preset open-loop iterative learning control gain satisfies the following convergence conditions:

[0126]

[0127] Wherein, G is the preset open-loop iterative learning control gain; H is the preset open-loop iterative learning control gain;

[0128] In a preferred embodiment, the open-closed loop iterative learning controller is:

[0129]

[0130]

[0131]

[0132] Among them, G, H are open-loop control gains; M is closed-loop control gain; is the actual driving torque sequence of the jth iteration; is the actual driving torque sequence of the jth iteration of the open loop, is the actual driving torque sequence of the jth iteration of the closed loop; Updated actual driving torque sequence;

[0133] Specifically, the error sequence of the jth iteration is calculated Then, an open-loop iterative learning controller is formed according to the preset open-loop iterative learning control gain, the preset closed-loop iterative learning control gain and the error sequence; and according to the error sequence, the open-loop iterative learning control gain, the closed-loop iterative learning control gain and the open-loop iterative learning controller, an updated actual driving torque sequence is obtained, i.e., the input of the next iteration

[0134] In addition, set the iteration error index It is used to determine whether the error of each iteration is less than the allowable range; if so, the actual driving torque sequence updated in the current iteration is used as the target driving torque; if not, the next iterative calculation operation is performed based on the updated actual driving torque sequence;

[0135] S4. controlling the unmanned vehicle to be controlled according to the target driving torque;

[0136] Specifically, when it is determined that the error of the actual driving torque sequence after the iterative update is less than the allowable range, the actual driving torque sequence after the iterative update is used as the target driving torque, and the unmanned vehicle to be controlled is controlled according to the target driving torque.

[0137] To better illustrate the present invention, let the number of iterations be j=150, the expected duration of each iteration be 100s, and the sampling period T be s = 1s, the expected duration is T r =100; set the open-loop control gain G=15, H=10, and the closed-loop control gain M=10 in the open-loop and closed-loop iterative learning controller, and conduct experiments. For specific experimental results, see Figure 2-Figure 5 ;

[0138] See also Figure 2 , is the actual iteration time length T of the present invention j The change curve diagram under different iteration times shows that the actual iteration time length of each iteration of the discrete nonlinear system described in this application is not fixed to the expected time length, but will change dynamically according to the actual situation. The actual iteration time length T j ={90,91,...,115}, Figure 2 Reflects the actual iteration time length T j Specific values ​​at different iteration times;

[0139] Specifically, by Figure 2 It can be obtained that the actual iteration time length T of each iteration of the system is j ={90,91,...,115} satisfies the uniform distribution, so there is obviously a lower limit With upper limit

[0140] See also Figure 3 , is the initial state of the discrete nonlinear system in each iteration of the present invention The curve diagram of the change under different iteration times shows the initial state of the system for the traditional iterative learning controller. The discrete nonlinear system of the present invention requires that the initial input of each iteration satisfies Figure 3 Reflects the initial state of the system Specific values ​​at different iteration times;

[0141] Specifically, compared with the traditional iterative learning controller, the present invention proposes a more relaxed initial condition: setting the system initial state for each iteration satisfy That is, the initial actual motion velocity trajectory of each iteration is allowed to fluctuate around 0;

[0142] See also Figure 4 , is a diagram showing the tracking of the actual motion velocity trajectory sequence to the expected motion velocity trajectory sequence by the open-closed loop iterative learning controller of the present invention at different iteration numbers, showing that when the iteration numbers are 10, 20, and 50, the system output To the expected output It can be seen that with the increase of the number of iterations, the convergence effect of the system output trajectory on the expected trajectory will also improve. When j = 50, the system output trajectory is already very close to the expected trajectory;

[0143] See also Figure 5 , is a comparison diagram of the error convergence effect between the open-closed-loop iterative learning controller of the present invention and the traditional iterative learning controller. It can be seen that the open-closed-loop iterative learning controller of the present invention has reduced the iteration error index to a considerable range near the 40th iteration, while the traditional iterative learning controller needs nearly 80 iterations to achieve this effect. This not only greatly reduces the control time, but also saves system storage space, thereby achieving the effect of energy saving.

[0144] See also Figure 6 , is a longitudinal control device for an unmanned vehicle provided in one embodiment of the present application, comprising: a desired driving torque acquisition module, a desired motion speed trajectory sequence determination module, an iterative calculation module, and an unmanned vehicle control module;

[0145] The desired driving torque acquisition module is used to acquire the desired driving torque of the unmanned vehicle to be controlled;

[0146] The expected motion speed trajectory sequence determination module is used to determine the expected motion speed trajectory sequence of the unmanned vehicle to be controlled based on the expected driving torque and a preset discrete nonlinear system for characterizing the longitudinal control of the unmanned vehicle;

[0147] The iterative calculation module is configured to, upon receiving an open-loop and closed-loop iterative learning control instruction, repeatedly perform an iterative calculation operation according to the open-loop and closed-loop iterative learning control instruction until a target driving torque is obtained;

[0148] The unmanned vehicle control module is configured to control the unmanned vehicle to be controlled according to the target driving torque;

[0149] The iterative calculation operation includes:

[0150] Determining a current actual motion velocity trajectory sequence of the unmanned vehicle to be controlled based on a current actual driving torque sequence and the discrete nonlinear system; wherein the actual driving torque sequence corresponding to the initial iterative calculation is generated based on a random driving torque;

[0151] Obtaining an error sequence and an iterative error index according to the expected motion speed trajectory sequence and the actual motion speed trajectory sequence;

[0152] forming an open-loop and closed-loop iterative learning controller according to a preset open-loop iterative learning control gain, a preset closed-loop iterative learning control gain, and the error sequence;

[0153] updating the actual driving torque sequence according to the error sequence, the open-loop iterative learning control gain, the closed-loop iterative learning control gain, and the open-loop and closed-loop iterative learning controller;

[0154] It is determined whether the iterative error index is less than a preset range. If so, the updated actual driving torque sequence is used as the target driving torque; if not, the next iterative calculation operation is performed according to the updated actual driving torque sequence.

[0155] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A longitudinal control method for an unmanned vehicle, characterized in that: include: Obtaining a desired driving torque of the unmanned vehicle to be controlled; Determining a desired motion velocity trajectory sequence of the unmanned vehicle to be controlled based on the desired driving torque and a preset discrete nonlinear system for characterizing longitudinal control of the unmanned vehicle; When receiving an open-loop and closed-loop iterative learning control instruction, repeatedly performing an iterative calculation operation according to the open-loop and closed-loop iterative learning control instruction until a target driving torque is obtained; controlling the unmanned vehicle to be controlled according to the target driving torque; The iterative calculation operation includes: Determining a current actual motion velocity trajectory sequence of the unmanned vehicle to be controlled based on a current actual driving torque sequence and the discrete nonlinear system; wherein the actual driving torque sequence corresponding to the initial iterative calculation is generated based on a random driving torque; Obtaining an error sequence and an iterative error index according to the expected motion speed trajectory sequence and the actual motion speed trajectory sequence; forming an open-loop and closed-loop iterative learning controller according to a preset open-loop iterative learning control gain, a preset closed-loop iterative learning control gain, and the error sequence; updating the actual driving torque sequence according to the error sequence, the open-loop iterative learning control gain, the closed-loop iterative learning control gain, and the open-loop and closed-loop iterative learning controller; It is determined whether the iterative error index is less than a preset range. If so, the updated actual driving torque sequence is used as the target driving torque; if not, the next iterative calculation operation is performed according to the updated actual driving torque sequence.

2. The longitudinal control method of an unmanned vehicle according to claim 1, wherein: The step of determining the desired motion velocity trajectory sequence of the unmanned vehicle to be controlled based on the desired driving torque and a preset discrete nonlinear system for characterizing the longitudinal control of the unmanned vehicle includes: The expected driving torque is sampled according to a preset sampling period and a preset iterative expected time length to obtain a discretized expected driving torque sequence; The discretized desired driving torque sequence is used as the input of the discrete nonlinear system, so that the discrete nonlinear system outputs the desired motion velocity trajectory sequence of the unmanned vehicle to be controlled.

3. The longitudinal control method of an unmanned vehicle according to claim 2, wherein: The method of using the discretized desired driving torque sequence as the input of the discrete nonlinear system so that the discrete nonlinear system outputs a desired motion velocity trajectory sequence of the unmanned vehicle to be controlled includes: The expected motion velocity trajectory sequence of the unmanned vehicle to be controlled is calculated according to the following formula: in, C = 1; is the expected state of the system; is the expected driving torque sequence; is the desired motion speed trajectory sequence; T r is the expected time length of the preset iteration, T s is the preset sampling period; m is the mass of the unmanned vehicle to be controlled; I w is the equivalent moment of inertia of the wheel; R w is the effective rolling radius of the wheel; ρ is the air density; C d is the air resistance coefficient; S is the vehicle's frontal area; V w is the wind speed.

4. The longitudinal control method of an unmanned vehicle according to claim 3, wherein: The discrete nonlinear system satisfies the following conditions: Among them, f is a Lipschitz constant greater than 0; is the initial state of the system at the jth iteration; x r (0)=0.

5. The longitudinal control method of an unmanned vehicle according to claim 4, wherein: Determining a current actual motion velocity trajectory sequence of the unmanned vehicle to be controlled according to the current actual driving torque sequence and the discrete nonlinear system includes: The actual motion velocity trajectory sequence of the unmanned vehicle to be controlled is calculated according to the following formula: in, C = 1; is the actual state of the system at the jth iteration; is the actual driving torque sequence of the jth iteration; is the actual motion velocity trajectory sequence of the jth iteration; T r is the expected time length of the preset iteration, T s is the preset sampling period; m is the mass of the unmanned vehicle to be controlled; I w is the equivalent moment of inertia of the wheel; R w is the effective rolling radius of the wheel; ρ is the air density; C d is the air resistance coefficient; S is the vehicle's frontal area; V w is the wind speed.

6. The longitudinal control method of an unmanned vehicle according to claim 5, wherein: Obtaining an error sequence and an iterative error index according to the desired motion speed trajectory sequence and the actual motion speed trajectory sequence, including: The error sequence is calculated according to the following formula: in, is the error sequence of the jth iteration; is the actual motion velocity trajectory sequence of the jth iteration; is the desired motion speed trajectory sequence; The iterative error index is calculated according to the following formula: Among them, SE j is the iterative error index of the jth iteration; T r The expected duration of the preset iteration; is the actual motion velocity trajectory sequence of the jth iteration; is the desired motion velocity trajectory sequence.

7. The longitudinal control method of an unmanned vehicle according to claim 6, wherein: The open-closed loop iterative learning controller is: Among them, G, H are open-loop control gains; M is closed-loop control gain; is the actual driving torque sequence of the jth iteration; is the actual driving torque sequence of the jth iteration of the open loop, is the actual driving torque sequence of the jth iteration of the closed loop; Updated actual driving torque sequence.

8. The longitudinal control method of an unmanned vehicle according to claim 7, wherein: The preset open-loop iterative learning control gain satisfies the following convergence conditions: Wherein, G is the preset open-loop iterative learning control gain; H is the preset open-loop iterative learning control gain.

9. A longitudinal control device for an unmanned vehicle, characterized in that: include: Expected driving torque acquisition module, expected motion speed trajectory sequence determination module, iterative calculation module and unmanned vehicle control module; The desired driving torque acquisition module is used to acquire the desired driving torque of the unmanned vehicle to be controlled; The expected motion speed trajectory sequence determination module is used to determine the expected motion speed trajectory sequence of the unmanned vehicle to be controlled based on the expected driving torque and a preset discrete nonlinear system for characterizing the longitudinal control of the unmanned vehicle; The iterative calculation module is configured to, upon receiving an open-loop and closed-loop iterative learning control instruction, repeatedly perform an iterative calculation operation according to the open-loop and closed-loop iterative learning control instruction until a target driving torque is obtained; The unmanned vehicle control module is configured to control the unmanned vehicle to be controlled according to the target driving torque; The iterative calculation operation includes: Determining a current actual motion velocity trajectory sequence of the unmanned vehicle to be controlled based on a current actual driving torque sequence and the discrete nonlinear system; wherein the actual driving torque sequence corresponding to the initial iterative calculation is generated based on a random driving torque; Obtaining an error sequence and an iterative error index according to the expected motion speed trajectory sequence and the actual motion speed trajectory sequence; forming an open-loop and closed-loop iterative learning controller according to a preset open-loop iterative learning control gain, a preset closed-loop iterative learning control gain, and the error sequence; updating the actual driving torque sequence according to the error sequence, the open-loop iterative learning control gain, the closed-loop iterative learning control gain, and the open-loop and closed-loop iterative learning controller; It is determined whether the iterative error index is less than a preset range. If so, the updated actual driving torque sequence is used as the target driving torque; if not, the next iterative calculation operation is performed according to the updated actual driving torque sequence.

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