Automatic driving longitudinal control method, device, equipment and storage medium

Abstract

The present application discloses a method, apparatus, device, and storage medium for autonomous driving longitudinal control. The method comprises obtaining the vehicle's current longitudinal speed and desired speed, calculating the current speed error based on the current longitudinal speed and desired speed; designing an AFF-ILC controller; using the current speed error as the input to the AFF-ILC controller, and controlling the vehicle's longitudinal speed for the next iteration using the current traction / braking force input output by the AFF-ILC controller. Based on the existing PD-type ILC controller, the present invention constructs the AFF-ILC controller by introducing a time-domain error feedback term and an iteration-domain error integral term. This enables the AFF-ILC controller to accurately track the desired longitudinal speed curve, thereby improving the control accuracy of the bus's longitudinal speed.

Description

Technical Field

[0001] The present application relates to the field of vehicle autonomous driving technology, and specifically to a method, device, equipment and storage medium for autonomous driving longitudinal control of a bus based on adaptive iterative learning. Background Art

[0002] With the continuous development of social economy, the general improvement of people's living standards and the rapid growth of motor vehicles, the saturation of urban traffic road networks will continue to increase, and traffic congestion problems will become increasingly serious. Vigorously developing public transportation is an important means to alleviate traffic congestion. The application of bus autonomous driving technology and the upgrading of intelligent systems play a vital role in improving the service quality of the bus system and improving the efficiency of bus operations.

[0003] Control execution technology is the process of controlling a vehicle's driving behavior based on the outputs of its intelligent decision-making to ensure its safe arrival at the predetermined destination. Public bus control execution technology includes both lateral and longitudinal control. Lateral control primarily controls the vehicle's steering while ensuring safety and comfort, while longitudinal control primarily controls the vehicle's longitudinal velocity (i.e., driving speed) to ensure it reaches its destination safely and on schedule. The longitudinal and lateral motions of a bus are coupled, and the use of integrated lateral and longitudinal control methods requires precise mathematical models, which complicates the control of driverless buses.

[0004] At low speeds, separate controllers for longitudinal and lateral control can also achieve good tracking results. When operating in certain environments, such as dedicated lanes, buses often ignore the interference of pedestrians, other vehicles, and unexpected obstacles. Compared to lateral control and other influencing factors, longitudinal speed motion control becomes the primary factor in the bus's normal operation.

[0005] In most bus operation studies, the establishment of the longitudinal dynamic process of the bus involving longitudinal velocity changes usually uses time as the independent variable and position and longitudinal velocity as the dependent variables. During the driving process of the bus, the factors affecting the longitudinal velocity are mainly the various forces acting on the vehicle in the longitudinal direction. According to the relevant knowledge of automobile principles, the vehicle is mainly affected by air resistance, rolling resistance and slope resistance during driving. The longitudinal force analysis is as follows: Figure 1 shown.

[0006] According to Newton's second law of classical mechanics, the mechanical equation of longitudinal force can be expressed as:

[0007]

[0008] In formula (1), m is the mass of the bus, F A(k) is the traction force that the engine's output torque finally exerts on the tire through the transmission mechanism, is the vehicle longitudinal acceleration, F C (k) is the air resistance, F B (k) is the rolling resistance, and F D (k) is the slope resistance. The traction force calculation formula is:

[0009]

[0010] In formula (2), r represents the radius of the bus tire; i g is the transmission ratio, which is related to the gear selection; u is the traction / braking force input information of the bus engine, η k is the mechanical efficiency of the transmission mechanism, and i0 is the transmission ratio of the main reducer.

[0011] The rolling resistance calculation formula is:

[0012] F B (k) = f b mgcosα (3)

[0013] In formula (3), f b is the wheel rolling resistance coefficient; α is the angle between the road surface and the horizontal plane; g is the acceleration due to gravity.

[0014] The formula for calculating air resistance is:

[0015]

[0016] In formula (4), C D is the air resistance coefficient, A is the cross-sectional area of ​​the vehicle, ρ is the air density, and v is the longitudinal velocity of the vehicle.

[0017] According to equations (1) to (4), the longitudinal dynamic equation can be obtained as follows:

[0018]

[0019] Further deduction of formula (5) yields

[0020]

[0021] Discretize equation (6) and obtain the corresponding mathematical model as follows:

[0022]

[0023] In formula (7), p is the sampling time, u(k) is the traction / braking force input information of the vehicle engine at the kth moment, v(k) is the longitudinal speed of the vehicle at the kth moment, and v(k+1) is the longitudinal speed of the vehicle at the k+1th moment.

[0024] Analyzing the above equation, we find that once the engine torque reaches the tire's limit, the bus will begin to move. When the bus starts and needs to accelerate, the accelerator pedal is pressed, increasing the engine torque. At this point, the bus's traction exceeds the sum of air resistance and slope resistance, resulting in a positive longitudinal acceleration, which accelerates the bus forward. If pedestrians or obstacles appear ahead of the bus, the bus needs to slow down. By releasing the accelerator pedal, the engine torque decreases, and the traction becomes less than the sum of external resistance, causing the bus's longitudinal speed to decrease.

[0025] Equation (7) represents the discrete dynamics relationship for a public bus. However, all of these studies on the longitudinal control of unmanned public buses are conducted under the premise that the controlled vehicle is in an ideal state. Considering that the longitudinal motion of an unmanned public bus is affected by various resistances and frictions, in actual driving, it is difficult to accurately describe the longitudinal dynamics model of the bus using Equation (7). If the various influencing factors such as resistance and friction are fully considered, the established precise mathematical model is extremely complex, and therefore, it is impossible to achieve precise control of the longitudinal velocity.

[0026] The content of this background technology description is only for facilitating understanding of the relevant technology in this field and is not regarded as an admission of the prior art. Summary of the Invention

[0027] Therefore, the embodiments of the present invention intend to provide a method, device, equipment and storage medium for longitudinal control of automatic driving of a bus to solve the problem that the existing mathematical model of longitudinal dynamics of a bus cannot achieve accurate control of longitudinal speed.

[0028] In a first aspect, an embodiment of the present invention provides a method for longitudinal control of an automatic driving bus, the method comprising the following steps:

[0029] Obtain the vehicle's current longitudinal speed and expected speed, and calculate the current longitudinal speed error based on the current longitudinal speed and the expected speed;

[0030] An AFF-ILC controller (Adaptive Feedforward-Feedback Iterative Learning Control) is designed. The control expression of the AFF-ILC controller is:

[0031] u(i,k)=u(i-1,k)+α d Δe(i,k-1)+(α p +β(i,k))e(i-1,k)

[0032] β(i,k)=β(i-1,k)+qe(i-1,k) 2

[0033] Among them, u(i,k) is the traction / braking force input of the vehicle at the current moment in this iteration, u(i-1,k) is the traction / braking force input of the vehicle at the current moment in the previous iteration, α d is the gain coefficient of the differential link, Δe(i,k-1) is the difference of the longitudinal velocity error of the vehicle at the previous moment in this iteration, α p is the gain coefficient of the proportional link, β(i,k) is the adaptive learning gain at the current moment in this iteration, β(i-1,k) is the adaptive learning gain at the current moment in the previous iteration, e(i-1,k)=y d (k)-y(i-1,k), e(i-1,k) is the longitudinal velocity error of the vehicle at the current moment in the previous iteration, y d (k) is the desired speed of the vehicle at the current moment, y(i-1,k) is the longitudinal speed of the vehicle at the current moment in the previous iteration, and q is the error adaptive learning gain;

[0034] The longitudinal speed error at the current moment in the previous iteration is used as the input of the AFF-ILC controller, and the traction / braking force input at the current moment in the previous iteration output by the AFF-ILC controller is used to control the longitudinal speed of the vehicle at the current moment in this iteration.

[0035] Furthermore, the expected speed y at the current moment d The specific expression of (k) is:

[0036]

[0037] Among them, T s is the sampling duration, k is the sampling time, and L is the delay factor.

[0038] Furthermore, the specific design process of the AFF-ILC controller is as follows:

[0039] The iteration axis i is introduced, and the nonlinear discrete-time dynamic equation of the vehicle longitudinal motion is defined as:

[0040] y(i+1,k)=f(y(i,k),...,y(in y ,k),u(i,k),...,u(in u ,k))

[0041] Where y(i,k) is the longitudinal velocity of the vehicle at the current moment in this iteration, y(i+1,k) is the longitudinal velocity of the vehicle at the current moment in the next iteration (i.e., the predicted longitudinal velocity), f(·) is an unknown nonlinear function, k represents the sampling time, and n y and n u are two unknown positive integers;

[0042] Construct a PD type iterative learning controller, the specific expression is:

[0043] u(i,k)=u(i-1,k)+α d Δe(i,k-1)+α p e(i-1,k)

[0044] Based on the PD-type iterative learning controller, a time-domain error feedback term and an iteration-domain error integral term are introduced to construct the AFF-ILC controller in this iteration.

[0045] Furthermore, a saturation constraint is added to the AFF-ILC controller, and its control expression is:

[0046]

[0047] in, is the saturated traction / braking force input of the vehicle at the current moment in this iteration, u * Indicates the maximum constraint value of traction / braking force, u t * is the maximum traction force of the vehicle, is the maximum braking force of the vehicle, is the estimated value of β(i,k), for The initial value of β * for K is the upper limit of the controller running time, and q is the error adaptive learning gain.

[0048] Furthermore, the magnitude of the error adaptive learning gain q depends on the maximum total mass of the vehicle;

[0049] When the maximum total mass of the vehicle is 1.8×10 4 kg, the value range of the error adaptive learning gain q is: 1×10 -5 ~1×10 -9 .

[0050] Furthermore, the specific implementation process of using the AFF-ILC controller to control the longitudinal speed of the vehicle at the current moment in this iteration includes:

[0051] If the longitudinal measured velocity y at the current moment in the previous iteration 测 (i-1,k) and the expected speed y at the current moment d (k) are not equal, the longitudinal velocity error e(i-1,k) at the current moment in the previous iteration is transmitted to the AFF-ILC controller;

[0052] The traction / braking force input u(i,k) at the current moment in this iteration output by the AFF-ILC controller is transmitted to the accelerator / brake system to control the speed adjustment number of the accelerator / brake system and to energize the engine to generate the actual longitudinal speed output signal y(i,k);

[0053] Compare y(i,k) with the speed measurement y 测 (i, k) is compared to obtain the longitudinal velocity error e(i, k) at the current moment in this iteration, which is used for the next iterative learning of the AFF-ILC controller to achieve longitudinal velocity adjustment.

[0054] Furthermore, the specific implementation process of controlling the longitudinal speed of the vehicle at the current moment in this iteration using the AFF-ILC controller further includes:

[0055] If the longitudinal velocity y(i,k) at the current moment in this iteration is equal to the expected velocity y d (k) If they are equal, a normal message is sent to the accelerator / brake system;

[0056] If acceleration is required, an acceleration message is sent to the throttle / brake system;

[0057] If deceleration is required, a deceleration message is sent to the accelerator / brake system.

[0058] In a second aspect, an embodiment of the present invention provides a longitudinal control device for automatic driving of a bus, the device comprising:

[0059] an acquisition and calculation unit configured to acquire the longitudinal speed and the desired speed of the vehicle at a current moment, and calculate the speed error at a current moment based on the longitudinal speed and the desired speed at the current moment;

[0060] The AFF-ILC controller is configured to calculate the traction / braking force input at the current moment in this iteration based on the longitudinal velocity error of the vehicle at the current moment in the previous iteration. The specific control expression is:

[0061] u(i,k)=u(i-1,k)+α d Δe(i,k-1)+(α p +β(i,k))e(i-1,k)

[0062] β(i,k)=β(i-1,k)+qe(i-1,k) 2

[0063] Among them, u(i,k) is the traction / braking force input of the vehicle at the current moment in this iteration, u(i-1,k) is the traction / braking force input of the vehicle at the current moment in the previous iteration, α d is the gain coefficient of the differential link, Δe(i,k-1) is the difference of the longitudinal velocity error of the vehicle at the previous moment in this iteration, α p is the gain coefficient of the proportional link, β(i,k) is the adaptive learning gain at the current moment in this iteration, β(i-1,k) is the adaptive learning gain at the current moment in the previous iteration, e(i-1,k)=y d (k)-y(i-1,k), e(i-1,k) is the longitudinal velocity error of the vehicle at the current moment in the previous iteration, y d (k) is the desired speed of the vehicle at the current moment, y(i-1,k) is the longitudinal speed of the vehicle at the current moment in the previous iteration, and q is the error adaptive learning gain;

[0064] The control unit is configured to control the longitudinal speed of the vehicle at the current moment in this iteration according to the traction / braking force input at the current moment in the previous iteration output by the AFF-ILC controller and the longitudinal speed error at the current moment.

[0065] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for longitudinal control of automatic driving of a bus according to any embodiment of the present invention is implemented.

[0066] In a fourth aspect, an embodiment of the present invention provides an electronic device comprising: a processor and a memory storing a computer program, wherein the processor is configured to execute any of the bus automatic driving longitudinal control methods of the present invention when running the computer program.

[0067] In the bus automatic driving longitudinal control method used in the embodiment of the present invention, on the basis of the original PD-type ILC controller (PD-type iterative learning controller), the AFF-ILC controller is constructed by introducing the time domain error feedback term and the iterative domain error integral term, so that the AFF-ILC controller makes corresponding adaptive changes as the error feedback information changes. Combined with the adaptively adjusted PD parameters, the feedforward and feedback learning capabilities of the adaptive feedforward-feedback iterative learning control algorithm can be retained at the same time, so that the AFF-ILC controller can achieve accurate tracking of the required longitudinal speed curve, thereby improving the control accuracy of the bus longitudinal speed.

[0068] Other optional features and technical effects of the embodiments of the present invention are partially described below, and partially can be understood by reading this document. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. The elements shown are not limited to the scale shown in the drawings. The same or similar reference numerals in the drawings represent the same or similar elements, wherein:

[0070] Figure 1 It shows the longitudinal force diagram of the vehicle according to the background technology of the present invention;

[0071] Figure 2 An example flow chart of a method for longitudinal control of an automatic driving bus according to an embodiment of the present invention is shown;

[0072] Figure 3 A block diagram of a control system according to an embodiment of the present invention is shown;

[0073] Figure 4 The AFF-ILC curves under different error adaptive learning gains q according to an embodiment of the present invention are shown;

[0074] Figure 5 The AFF-ILC error curves under different error adaptive learning gains q according to an embodiment of the present invention are shown;

[0075] Figure 6 A comparison diagram of acceleration and deceleration process algorithms of three methods, namely AFF-ILC, PID, and PD-ILC, according to an embodiment of the present invention is shown;

[0076] Figure 7 shows a comparison of tracking errors between PD-ILC and AFF-ILC control according to an embodiment of the present invention;

[0077] Figure 8 shows the traction force input curve of the AFF-ILC algorithm under saturation constraint conditions according to an embodiment of the present invention;

[0078] Figure 9 An exemplary structural diagram of a longitudinal control device for automatic driving of a bus according to an embodiment of the present invention is shown;

[0079] Figure 10 A schematic diagram of an exemplary structure of an electronic device capable of implementing the method according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with specific embodiments and accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0081] As used herein, the term "including" and its variations represent open inclusion, i.e., "including but not limited to." Unless otherwise stated, the term "or" means "and / or." The term "based on" means "based at least in part on." The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment." The term "another embodiment" means "at least one additional embodiment." The terms "first," "second," etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0082] To address the longitudinal speed control issues of autonomous buses, this paper proposes an improved adaptive iterative learning control method. This method utilizes a PD-type ILC controller architecture. By introducing a time-domain error feedback term and an iterative domain error integral term, combined with adaptively adjusted PD parameters, the AFF-ILC controller's feedforward and feedback learning capabilities are preserved. Furthermore, considering factors such as bus overspeed protection, the AFF-ILC controller design process also considers saturation constraints on control inputs and controller parameters, minimizing the nonlinear effects of aerodynamics and traction / braking force constraints.

[0083] Based on the analysis of background technology, the longitudinal motion process of the driverless bus is represented by a general nonlinear discrete-time dynamic equation:

[0084] y(k+1)=f(y(k),...,y(kn y ),u(k),...,u(kn u )) (8)

[0085] Among them, y(k+1) represents the longitudinal velocity of the vehicle at the next moment (i.e., the velocity output), f(·) is an unknown nonlinear function, y(k) represents the longitudinal velocity of the vehicle at the current moment, and y(kn y ) represents the vehicle kn y The longitudinal velocity at the moment, k represents the sampling time, n y and n u are two unknown positive integers, and u(k) represents the traction / braking force input of the vehicle at the current moment.

[0086] The control object of the present invention is an unmanned bus. Compared with a small unmanned vehicle, the difference is that in actual driving, the route of a small unmanned vehicle is mainly determined by the driver himself or the navigation system, which has a large uncertainty. For an unmanned bus, due to the social demand for buses, the bus must strictly follow a fixed route every day and has a certain repetitive operation characteristic. This difference provides a prerequisite for the use of the iterative learning control method of the present invention.

[0087] To address the problem that traditional bus longitudinal dynamics mathematical models cannot accurately control longitudinal velocity, this paper designs an AFF-ILC controller (Adaptive Feedforward-Feedback Iterative Learning Control) to achieve precise tracking control of the bus's longitudinal velocity. The specific design steps of the AFF-ILC controller are as follows:

[0088] Step a: Introducing the iteration axis i, equation (8) can be expressed as follows:

[0089] y(i+1,k) =f(y(i,k),...,y(in y ,k),u(i,k),...,u(in u ,k)) (9)

[0090] Where y(i, k) is the longitudinal velocity of the vehicle at the current moment in this iteration, y(i+1, k) is the longitudinal velocity of the vehicle at the current moment in the next iteration, and u(i, k) is the traction / braking force input of the vehicle at the current moment in this iteration.

[0091] Step b: Construct a PD-type iterative learning controller (i.e., PD-ILC controller), the specific expression is:

[0092] u(i,k)=u(i-1,k)+α d Δe(i,k-1)+α p e(i-1,k) (10)

[0093] Among them, u(i-1,k) is the traction / braking force input of the vehicle at the current moment in the previous iteration, α d is the gain coefficient of the differential link, Δe(i,k-1) is the difference of the longitudinal velocity error of the vehicle at the previous moment in this iteration, α p is the gain coefficient of the proportional link, e(i-1,k)=y d (k)-y(i-1,k), e(i-1,k) is the longitudinal velocity error of the vehicle at the current moment in the previous iteration, y d(k) is the expected speed of the vehicle at the current moment, and y(i-1,k) is the longitudinal speed of the vehicle at the current moment in the previous iteration.

[0094] Step c: Construct AFF-ILC controller

[0095] The parameters of the PD-ILC controller are fixed. In order to improve the adaptability of the controller, a time-domain error feedback term and an iterative-domain error integral term are introduced on the basis of the PD-ILC controller. Combined with the adaptively adjusted PD parameters, the feedforward and feedback learning capabilities of the AFF-ILC controller can be retained at the same time.

[0096] In the i-th iteration (i.e., this iteration), the expression of the constructed AFF-ILC controller is:

[0097] u(i,k)=u(i-1,k)+α d Δe(i,k-1)+(α p +β(i,k))e(i-1,k) (11)

[0098] β(i,k)=β(i-1,k)+qe(i-1,k) 2 (12)

[0099] Among them, β(i,k) is the adaptive learning gain at the current moment in this iteration, β(i-1,k) is the adaptive learning gain at the current moment in the previous iteration, and q is the error adaptive learning gain.

[0100] The adaptive learning gain β(i,k) plays the main role in the AFF-ILC controller: it makes corresponding adaptive changes as the longitudinal velocity error information changes, so that the iterative domain error integral term in the AFF-ILC controller can be effectively adjusted as the vehicle moves, further enhancing the control performance of the AFF-ILC controller.

[0101] In formula (12), each update of β(i,k) uses the historical information of the previous iteration, specifically including the previous adaptive learning gain β(i-1,k) and longitudinal velocity error e(i-1,k). Therefore, the adaptive learning gain β(i,k) will make corresponding adaptive changes as the longitudinal velocity error changes.

[0102] Step d: Construct the AFF-ILC controller under saturation constraints

[0103] Considering that unmanned buses, as a practical engineering system, are inevitably affected by the nonlinear effects of aerodynamics and traction / braking force constraints, the design of an AFF-ILC controller under saturation constraints has certain research significance.

[0104] An input saturation constraint is added to the AFF-ILC controller constructed in step c, so that the AFF-ILC controller design can cope with the situation where the maximum longitudinal speed of the unmanned bus is limited.

[0105] In the i-th iteration, the AFF-ILC controller with saturation constraint is constructed as follows:

[0106] u(i,k)=sat(u(i-1,k),u * )+α d Δe(i,k-1))+(α p +β(i,k))e(i-1,k) (13)

[0107] The parameter update law for β(k) is designed in this iteration as:

[0108]

[0109]

[0110] in, is the estimated value of β(i,k), is the adaptive learning gain at the current moment in the last iteration under the saturation constraint, for The initial value of β * for The upper limit of β * >0, K is the upper limit of the controller running time, and q>0 is the error adaptive learning gain that can be freely selected.

[0111] In addition, the AFF-ILC controller reference equations (14) and (15) for buses under input constraints can be further expressed as:

[0112]

[0113] in, is the saturated bus tractive effort input, and

[0114]

[0115] in, is the saturated traction / braking force input of the vehicle at the current moment in this iteration, u * Indicates the maximum constraint value of traction / braking force, u t * is the maximum traction force of the vehicle, The maximum braking force of the vehicle.

[0116] The mechanism proposed in Equation (11) includes a nonlinear feedback term and a parameter estimation term. Therefore, the AFF-ILC control strategy is a combination of the PD-ILC control strategy and the adaptive control strategy. This combination provides a possible solution to the nonlinear uncertain dynamics of buses in the iteration domain. Furthermore, AFF-ILC is essentially a model-free control method, requiring less prior knowledge of bus dynamics for controller design and parameter updating. Furthermore, this helps improve the control performance of the closed-loop system through proportional and differential actions, achieving good tracking performance in both the iteration and time domains.

[0117] In order to limit the estimation of parameters, the present invention uses the fully saturated learning parameter update law (14), resulting in an estimated value of β(i,k) Limited to upper β * ,Right now For example, as shown in (14), the parameter estimates Is non-degradable and will not update β after reaching the upper limit * Therefore, β * The update rate of will directly affect the convergence rate of AFF-ILC in Equation (16). By introducing the parameter estimation method and selecting the appropriate update law gain q, the control performance of the closed-loop system can be improved in the time domain. In fact, the exact value of the adaptive learning parameter β(t) is within 1×10 -5 ~1×10 -7 The update law gain q does not affect the convergence of the proposed control strategy in the iteration domain.

[0118] Based on the designed AFF-ILC controller, the embodiment of the present invention provides a longitudinal control method for autonomous driving of a bus, such as Figure 2 As shown, the method includes the following steps:

[0119] Step 1100: Obtain the current longitudinal speed and the desired speed of the vehicle, and calculate the current longitudinal speed error based on the current longitudinal speed and the desired speed;

[0120] Step 1200: Design an AFF-ILC controller, the AFF-ILC controller is shown in equations (11) to (12) or equations (14) to (17);

[0121] Step 1300: Use the longitudinal velocity error of the previous iteration at the current moment as the input of the AFF-ILC controller, and use the traction / braking force input at the current moment in the previous iteration output by the AFF-ILC controller to control the longitudinal velocity of the vehicle at the current moment in this iteration.

[0122] In step 1300, if the longitudinal velocity y(i-1,k) at the current moment in the previous iteration is equal to the expected velocity y d If (k) is not equal, the longitudinal speed error e(i-1,k) at the current moment in the previous iteration is transmitted to the AFF-ILC controller, and the traction / braking force input u(i,k) at the current moment in this iteration output by the AFF-ILC controller is transmitted to the throttle / brake system to control the speed adjustment times of the throttle / brake system, and the engine is powered to generate the actual longitudinal speed output signal y(i,k), and y(i,k) is compared with the speed measurement value y 测 (i, k) is compared to obtain the longitudinal velocity error e(i, k) at the current moment in this iteration, which is used for the next iterative learning of the AFF-ILC controller to achieve longitudinal velocity adjustment.

[0123] In actual operation, the longitudinal velocity value y(i,k) transmitted by the engine must be measured by the vehicle-mounted sensor to form y 测 (i-1,k) can be compared with the expected speed y d (i, k) is compared to form the longitudinal velocity error value e(i, k), so here e(i-1, k)=yd(k)-y 测 (i-1, k), which is different from e(i-1, k) = yd(k) - y(i-1, k) in the controller design. Here y(i-1, k) is the theoretical longitudinal velocity value of the AFF-ILC controller, which does not exist in practical applications.

[0124] If the longitudinal velocity y(i,k) at the current moment in this iteration is equal to the expected velocity y d (k) are equal, a normal message is sent to the throttle / brake system; if acceleration is required, an acceleration message is sent to the throttle / brake system; if deceleration is required, a deceleration message is sent to the throttle / brake system.

[0125] like Figure 3 As shown, the AFF-ILC controller, the traction / brake execution module, the acceleration / deceleration execution module, the throttle / brake system, and the engine are connected in sequence. The signal output by the AFF-ILC controller is sent to the traction / brake execution module to start the acceleration / deceleration execution module, generate traction / braking force input information u(i,k), which is used to control the longitudinal speed adjustment number of the throttle / brake system and enable the generator to generate the actual speed output signal y(i,k). y(i,k) is compared with the speed measurement value y 测 (i, k) is compared to obtain the longitudinal velocity error e(i, k) at the current moment in this iteration, which is used for the next iterative learning of the AFF-ILC controller to achieve longitudinal velocity adjustment.

[0126] During the actual operation of the bus, the AFF-ILC controller designed by the present invention takes the vehicle torque as the control input, which is equivalent to the actual vehicle throttle, and the longitudinal speed of the bus as the output. The AFF-ILC controller compares the actual speed value generated by each iteration of the bus with the expected speed value, and the generated longitudinal speed error information is used for the next iterative learning process. The adaptive learning gain β(i,k) in the AFF-ILC controller can make adaptive changes as the error feedback information changes, thereby retaining the feedforward and feedback learning capabilities of the adaptive feedforward-feedback iterative learning control algorithm, which is used to achieve the desired longitudinal speed. Accurate tracking of the speed curve; overspeed protection design under the torque traction constraint of the bus, which is used for the driving conditions of the bus on the road speed limit and the vehicle in front to avoid collision and rear-end collision; the AFF-ILC controller, traction / brake execution module, acceleration and deceleration execution module, throttle / brake braking system, and engine are connected in sequence, and the signal output by the AFF-ILC controller is sent to the traction / brake execution module to start the acceleration and deceleration execution module, generate traction / braking force input information u(i,k), which is used to control the number of longitudinal speed adjustments of the throttle / brake braking system, and energize the generator to generate the actual speed output signal y(i,k), and compare y(i,k) with the speed measurement value y 测 (i, k) is compared to obtain the longitudinal velocity error e(i, k) at the current moment in this iteration, which is used for the next iterative learning of the AFF-ILC controller to achieve longitudinal velocity adjustment.

[0127] In order to verify the effectiveness of the method proposed in the present invention, a numerical simulation is performed to verify the method of the present invention, specifically including:

[0128] (1) Setting of parameters related to simulation object and AFF-ILC controller

[0129] Based on the relevant equipment parameters of the selected experimental buses and the actual longitudinal speed requirements that unmanned buses need to achieve on the road, the selected scenario only studies the arrival scenario between two stops. The bus travel time from one stop to another is set to 3 minutes, so the simulation duration of this example is 180 seconds. According to the relevant provisions of the "Road Traffic Safety Law of the People's Republic of China", the maximum speed limit for buses on urban roads is 54.5 km / h. After calculation, the selected route length is approximately 2.5 to 3 kilometers. The dynamic model used in this example is based on the relevant data of the Beiqi Foton BJ6105EVCA-49 bus, as shown in Table 1.

[0130] Table 1 BJ6105EVCA-49 bus related data

[0131]

[0132] The input-output relationship of the dynamic model used in the simulation is described in Equation (7). Substituting the relevant data in Table 1 into Equation (7), assuming that the angle α between the road surface and the horizontal plane is 0°, the nonlinear relationship of the longitudinal motion of the bus is obtained as follows:

[0133]

[0134] It should be noted that the model in Equation (18) is only used to generate the input and output data relationship of the unmanned bus, and the model structure and parameter information are not involved in the design of the AFF-ILC controller.

[0135] The reference bus expected speed trajectory is expressed as follows:

[0136]

[0137] Among them, T s is the sampling duration, k is the sampling time, L is the delay factor, and the introduction of the delay factor is to make the expected velocity trajectory y d The speed change of (k) is smoother, thus meeting the actual speed changes of buses in daily driving. The expected speed trajectory includes two acceleration phases, four driving phases, and three braking phases of the bus.

[0138] (2) AFF-ILC controller parameter analysis

[0139] This example analyzes the control effect of the AFF-ILC control strategy and verifies the effectiveness of the adaptive iterative algorithm. The traction input information u1(0)=1 in the first iteration and the traction input information u2(0)=0 in the second iteration. The total number of iterations in this example is 100, and the 100 iteration simulation diagram is selected as the number of iterations for this AFF-ILC controller parameter analysis. The controller parameters are set as: α d =α p =6×10 -6 The error adaptive learning gain q in the AFF-ILC control strategy has a significant effect on the control. Therefore, this example mainly discusses the effect of different q parameters on the AFF-ILC control strategy. The values ​​of q are set to q1 = 1 × 10 -5 ,q2=1×10 -7 ,q3=1×10 -9 , simulate these three parameters, the effect is as follows Figure 4 and 5 shown.

[0140] according to Figure 4 The simulation of error adaptive learning gain q is shown. When q is only 1×10 -5 Under the same conditions, compared with q2=1×10-7 At the moment of sudden change in longitudinal velocity, there is a phenomenon of lagging behind the expected velocity; q3 = 1×10 -9 In the subsequent iteration time, the expected longitudinal velocity curve cannot be tracked. -5 When the value of is set, the desired longitudinal velocity is quickly converged at the initial stage of iteration, and the consistent tracking performance is maintained during the subsequent simulation time.

[0141] Depend on Figure 4 and Figure 5 The simulation results show that in this bus longitudinal control system, as the error adaptive learning gain q decreases, the longitudinal speed tracking effect becomes worse and worse. If the value of q is lower than 1×10 -5 , it will be difficult to track the desired longitudinal velocity, as shown in Figure 4. -9 When the value of q exceeds 1×10 -5 When , the AFF-ILC control strategy will be in a divergent state and cannot effectively control the system. Therefore, the value of the error adaptive learning gain q in this example is set to 1×10 -5 .

[0142] The magnitude of the value of q depends mainly on the mass parameters of the bus. In the simulation part of this example, the nonlinear relationship of the longitudinal motion of the bus is obtained, and the maximum total mass of the bus is 1.8×10 4 kg, so the corresponding error adaptive learning gain q is also maintained at 1×10 -5 ~1×10 -9 within the order of magnitude.

[0143] (3) Simulation analysis of PD-ILC, AFF-ILC and PID control strategies

[0144] This example considers three comparative control strategies, namely, PD-ILC scheme, improved adaptive feedforward-feedback iterative learning control (AFF-ILC), and PID control. In order to further verify the effectiveness of the method proposed in this invention, this example compares the method proposed in this invention with PD-ILC and PID control. The specific settings of AFF-ILC are shown in point (1) of this example. The structures of PD-ILC and PID controllers are described as follows:

[0145] PD type iterative learning controller:

[0146] u1(i,k)=u1(i-1,k)+α d Δe(i,k-1)+α p e(i-1,k) (20)

[0147] Among them, u1(i,k) is the traction / braking force input of the PD type iterative learning controller, α d =0.02,α p =0.001.

[0148] PID controller:

[0149] u2(k)=K P (e(k)+ee / Ti+T D (e(k)-e(k-1))) (21)

[0150] Where u2(k) is the traction / braking force input of the PID controller, e(k) and e(k-1) are the error values ​​of the PID controller at this moment and the previous moment respectively, and e(k)=y d (k)-y(k), y(k) is the actual longitudinal velocity output at this moment, and the controller proportional link learning gain K P =0.0018, integral link learning gain T i =64.3077, differential link learning gain T D =18.0202, ee is the cumulative value of the error value generated each time in the PID controller.

[0151] According to the relevant regulations on road safety for large motor vehicles, the maximum longitudinal speed warning value of the bus is set to 54.5 km / h (i.e. the maximum speed limit mentioned in (1)), and the result is Figure 6 Comparison of the algorithm effects of AFF-ILC, PD-ILC, and PID algorithms in the acceleration and deceleration processes with 100 iterations.

[0152] pass Figure 6 The process of a bus accelerating from 0 to 55 km / h and then decelerating back to 0 shows that the PD-ILC and PID algorithms can track the desired longitudinal velocity under normal circumstances. However, when the vehicle accelerates or decelerates suddenly, the PID control algorithm lags behind and exceeds the maximum longitudinal velocity warning curve during the deceleration phase between 1400 and 1600 seconds. If an unexpected situation occurs ahead of the vehicle, requiring rapid acceleration or deceleration, using the PID and PD-ILC control algorithms will prevent the vehicle from accurately controlling to the desired speed, which is extremely dangerous in a real-world autonomous bus. Although the AFF-ILC control algorithm may experience a momentary jitter when the longitudinal velocity suddenly changes, it quickly returns to a stable state and tracks the corresponding longitudinal velocity. Overall, the performance is better than that of PID control, thus verifying the effectiveness of the AFF-ILC control algorithm for longitudinal vehicle control.

[0153] Figure 7Table 2 shows the comparison of tracking speed errors of PD-ILC and AFF-ILC in the iteration domain. The performance comparison is expressed by the MAE mean absolute error evaluation index function, where the MAE function can be expressed by the following formula and is used for error calculation here:

[0154]

[0155] Where t(i,k) represents the absolute error obtained in each iteration, and n represents the number of iterations. It can be seen that compared with the method proposed in this invention, PD-ILC control only has feedback control based on tracking error and lacks an adaptive mechanism. Therefore, PD-ILC has a slow convergence speed and low tracking accuracy. On the other hand, AFF-ILC has better tracking performance for the expected trajectory, which is manifested in a faster convergence speed and higher tracking accuracy. According to Figure 7 The comparison simulation diagram of the two algorithms is shown. The biggest advantage of the AFF-ILC control algorithm is its faster response in the early stage of iteration. Figure 7 The tracking error data obtained in Table 2 also show that when the number of iterations is 20 to 90, the AFF-ILC control algorithm has higher longitudinal speed tracking accuracy. When the number of bus operation iterations is 90, the speed tracking error reaches 0.036 km / h, which also proves that the algorithm can achieve accurate tracking of the required trajectory.

[0156] Table 2 Comparison of longitudinal velocity tracking errors of the two algorithms

[0157]

[0158] (4) Analysis of AFF-ILC traction force under saturation constraint conditions

[0159] In point (3), a comparative analysis of three control methods was conducted to address the longitudinal speed control problem with a maximum longitudinal speed limit. However, simply being able to accurately track the longitudinal speed requirement is not enough to demonstrate the actual simulation of the AFF-ILC control algorithm. The following analysis results of the AFF-ILC control algorithm are given in terms of the traction change of the unmanned bus under saturation constraints.

[0160] Figure 8 The simulation shows the change of the AFF-ILC traction / braking force proposed by the present invention. This simulation is based on the bus with a speed limit of 54.5 km / h, where the bus's maximum traction force u * The relationship between the longitudinal velocity and y is approximately as follows:

[0161]

[0162] Among them, u *is the maximum traction force under the bus speed saturation constraint, L max is the maximum traction power of the bus.

[0163] from Figure 8 It can be seen that, considering that the traction or braking force is limited by the maximum speed, the control signal exceeds the maximum traction limit u in the second iteration. * However, as the number of iterations increases, it can be gradually adjusted below the expected limit, which to a certain extent reflects that the AFF-ILC algorithm can maintain precise control under the longitudinal velocity saturation constraint.

[0164] Numerical simulations validated the effectiveness of the proposed method, explored the impact of different q parameters on the AFF-ILC control algorithm, compared the proposed method with PD-ILC and PID, and analyzed the AFF-ILC control algorithm's traction changes under saturation constraints. This method maintains vehicle control even in extreme overspeed emergencies, effectively adjusting autonomous bus operation and preventing accidents.

[0165] Figure 9 A structural block diagram of a longitudinal control device for automatic driving of a bus provided by an embodiment of the present invention is shown.

[0166] like Figure 9 As shown, an embodiment of the present invention provides a bus automatic driving longitudinal control device 2000 including an acquisition and calculation unit 2100, an AFF-ILC controller 2200 and a control unit 2300.

[0167] The acquisition and calculation unit 2100 is configured to acquire the longitudinal speed and the desired speed of the vehicle at a current moment, and calculate the speed error at a current moment based on the longitudinal speed and the desired speed at a current moment;

[0168] The AFF-ILC controller 2200 is configured to calculate the traction / braking force input at the current moment based on the vehicle longitudinal speed error at the current moment. The specific control expressions are shown in equations (11) to (12) or (14) to (17);

[0169] The control unit 2300 is configured to control the longitudinal speed of the vehicle at the current moment in this iteration according to the traction / braking force input at the current moment in the previous iteration output by the AFF-ILC controller and the longitudinal speed error at the current moment.

[0170] In the bus automatic driving longitudinal control device used in the embodiment of the present invention, on the basis of the original PD-type ILC controller (PD-type iterative learning controller), the AFF-ILC controller is constructed by introducing the time domain error feedback term and the iterative domain error integral term, so that the AFF-ILC controller makes corresponding adaptive changes as the error feedback information changes. Combined with the adaptively adjusted PD parameters, the feedforward and feedback learning capabilities of the adaptive feedforward-feedback iterative learning control algorithm can be retained at the same time, so that the AFF-ILC controller can achieve accurate tracking of the required longitudinal speed curve, thereby improving the control accuracy of the bus longitudinal speed.

[0171] In some embodiments, the bus autonomous driving longitudinal control device can be combined with the method features of the bus autonomous driving longitudinal control method of any embodiment, and vice versa, which will not be repeated here.

[0172] In an embodiment of the present invention, an electronic device is provided, comprising: a processor and a memory storing a computer program, wherein the processor is configured to execute any of the methods for longitudinal control of automatic driving for a bus according to the embodiments of the present invention when running the computer program.

[0173] Figure 10 A schematic diagram of an electronic device 3000 that can implement a method or implement an embodiment of the present invention is shown. In some embodiments, the method may include more or fewer electronic devices than shown. In some embodiments, the method may be implemented using a single electronic device or multiple electronic devices. In some embodiments, the method may be implemented using cloud-based or distributed electronic devices.

[0174] like Figure 10 As shown, the electronic device 3000 includes a processor 3001, which can perform various appropriate operations and processes according to the programs and / or data stored in the read-only memory (ROM) 3002 or the programs and / or data loaded from the storage part 1008 into the random access memory (RAM) 3003. The processor 3001 can be a multi-core processor or can include multiple processors. In some embodiments, the processor 3001 can include a general-purpose main processor and one or more special coprocessors, such as a central processing unit (CPU), a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), etc. In the RAM 3003, various programs and data required for the operation of the electronic device 3000 are also stored. The processor 3001, ROM 3002 and RAM 3003 are connected to each other via a bus 3004. An input / output (I / O) interface 3005 is also connected to the bus 3004.

[0175] The processor and memory are used together to execute the program stored in the memory. When the program is executed by the computer, the methods, steps or functions described in the above embodiments can be implemented.

[0176] The following components are connected to the I / O interface 3005: an input section 3006 including a keyboard, a mouse, a touch screen, and the like; an output section 3007 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 3008 including a hard disk and the like; and a communication section 3009 including a network interface card such as a LAN card or a modem. The communication section 3009 performs communication processing via a network such as the Internet. A drive 3010 is also connected to the I / O interface 3005 as needed. A removable medium 3011, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 3010 as needed, so that computer programs read therefrom can be installed into the storage section 3008 as needed. Figure 10 Only some components are shown schematically, which does not mean that the computer system 3000 only includes Figure 10 Components shown.

[0177] The systems, devices, modules, or units described in the above embodiments may be implemented by a computer or its associated components. The computer may be, for example, a mobile terminal, a smartphone, a personal computer, a laptop computer, an in-vehicle human-computer interaction device, a personal digital assistant, a media player, a navigation device, a game console, a tablet computer, a wearable device, a smart TV, an Internet of Things system, a smart home, an industrial computer, a server, or a combination thereof.

[0178] Although not shown, in an embodiment of the present invention, a storage medium is provided, wherein the storage medium stores a computer program, and the computer program is configured to execute any file difference-based compilation method according to any embodiment of the present invention when executed.

[0179] Storage media in embodiments of the present invention include permanent and non-permanent, removable and non-removable items that can be used to store information using any method or technology. Examples of storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0180] The methods, programs, systems, and apparatuses of the embodiments of the present invention may be executed or implemented in a single or multiple networked computers, or may be practiced in a distributed computing environment. In the embodiments of this specification, in these distributed computing environments, tasks may be performed by remote processing devices connected via a communication network.

[0181] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, those skilled in the art will appreciate that the functional modules / units or controllers and related method steps described in the above embodiments may be implemented using software, hardware, or a combination of software / hardware.

[0182] Unless explicitly stated, the actions or steps of the methods, procedures, and methods described in accordance with the embodiments of the present invention do not have to be performed in a specific order and can still achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0183] In this document, multiple embodiments of the present invention are described, but for the sake of brevity, the description of each embodiment is not exhaustive, and the same or similar features or parts between the embodiments may be omitted. In this document, "one embodiment", "some embodiments", "example", "specific example", or "some examples" are intended to apply to at least one embodiment or example according to the present invention, but not all embodiments. The above terms do not necessarily mean to refer to the same embodiment or example. Those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually contradictory.

[0184] While the exemplary systems and methods of the present invention have been specifically shown and described with reference to the foregoing embodiments, these are merely examples of the best modes for implementing the present systems and methods. Those skilled in the art will appreciate that various changes may be made to the embodiments of the systems and methods described herein when implementing the present systems and / or methods without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A method for longitudinal control of an automatic driving bus, characterized in that: The method comprises the following steps: Obtain the vehicle's current longitudinal speed and expected speed, and calculate the current longitudinal speed error based on the current longitudinal speed and the expected speed; Design an AFF-ILC controller, the control expression of the AFF-ILC controller is: u(i,k)=u(i-1,k)+α d Δe(i,k-1)+(α p +β(i,k))e(i-1,k) β(i,k)=β(i-1,k)+qe(i-1,k) 2 Among them, u(i,k) is the traction / braking force input of the vehicle at the current moment in this iteration, u(i-1,k) is the traction / braking force input of the vehicle at the current moment in the previous iteration, α d is the gain coefficient of the differential link, Δe(i,k-1) is the difference of the longitudinal velocity error of the vehicle at the previous moment in this iteration, α p is the gain coefficient of the proportional link, β(i,k) is the adaptive learning gain at the current moment in this iteration, β(i-1,k) is the adaptive learning gain at the current moment in the previous iteration, e(i-1,k)=y d (k)-y(i-1,k), e(i-1,k) is the longitudinal velocity error of the vehicle at the current moment in the previous iteration, y d (k) is the desired speed of the vehicle at the current moment, y(i-1,k) is the longitudinal speed of the vehicle at the current moment in the previous iteration, and q is the error adaptive learning gain; The longitudinal speed error at the current moment in the previous iteration is used as the input of the AFF-ILC controller, and the traction / braking force input at the current moment in the previous iteration output by the AFF-ILC controller is used to control the longitudinal speed of the vehicle at the current moment in this iteration.

2. The method for longitudinal control of an automatic driving bus according to claim 1, characterized in that: The expected speed y at the current moment d The specific expression of (k) is: Among them, T s is the sampling duration, k is the sampling time, and L is the delay factor.

3. The method for longitudinal control of automatic driving of a bus according to claim 1, characterized in that: The specific design process of the AFF-ILC controller is as follows: The iteration axis i is introduced, and the nonlinear discrete-time dynamic equation of the vehicle longitudinal motion is defined as: y(i+1,k)=f(y(i,k),...,y(i-n y ,k),u(i,k),...,u(i-n u ,k)) Where y(i,k) is the longitudinal velocity of the vehicle at the current moment in this iteration, y(i+1,k) is the longitudinal velocity of the vehicle at the current moment in the next iteration, f(·) is an unknown nonlinear function, k represents the sampling time, and n y and n u are two unknown positive integers; Construct a PD type iterative learning controller, the specific expression is: u(i,k)=u(i-1,k)+α d Δe(i,k-1)+α p e(i-1,k) Based on the PD-type iterative learning controller, a time-domain error feedback term and an iteration-domain error integral term are introduced to construct the AFF-ILC controller in this iteration.

4. The method for longitudinal control of an automatic driving bus according to any one of claims 1 to 3, characterized in that: Adding a saturation constraint condition to the AFF-ILC controller, the control expression is: in, is the saturated traction / braking force input of the vehicle at the current moment in this iteration, u * Indicates the maximum constraint value of traction / braking force, u t * is the maximum traction force of the vehicle, is the maximum braking force of the vehicle, is the estimated value of β(i,k), for The initial value of β * for The upper limit of the controller operation time is K.

5. The method for longitudinal control of an automatic driving bus according to any one of claims 1 to 3, characterized in that: The magnitude of the error adaptive learning gain q depends on the maximum total mass of the vehicle; When the maximum total mass of the vehicle is 1.8×10 4 kg, the value range of the error adaptive learning gain q is: 1×10 -5 ~1×10 -9 .

6. The method for longitudinal control of an automatic driving bus according to any one of claims 1 to 3, characterized in that: In actual operation, the specific implementation process of using the AFF-ILC controller to control the longitudinal speed of the vehicle at the current moment in this iteration includes: If the longitudinal measured velocity y at the current moment in the previous iteration 测 (i-1,k) and the expected speed y at the current moment d (k) are not equal, the longitudinal velocity error e(i-1,k) at the current moment in the previous iteration is transmitted to the AFF-ILC controller; The traction / braking force input u(i,k) at the current moment in this iteration output by the AFF-ILC controller is transmitted to the accelerator / brake system to control the speed adjustment number of the accelerator / brake system and to energize the engine to generate the actual longitudinal speed output signal y(i,k); Compare y(i,k) with the speed measurement y 测 (i, k) is compared to obtain the longitudinal velocity error e(i, k) at the current moment in this iteration, which is used for the next iterative learning of the AFF-ILC controller to achieve longitudinal velocity adjustment.

7. The method for longitudinal control of automatic driving of a bus according to claim 6, characterized in that: The specific implementation process of controlling the longitudinal speed of the vehicle at the current moment in this iteration using the AFF-ILC controller further includes: If the longitudinal velocity y(i,k) at the current moment in this iteration is equal to the expected velocity y d (k) If they are equal, a normal message is sent to the accelerator / brake system; If acceleration is required, an acceleration message is sent to the throttle / brake system; If deceleration is required, a deceleration message is sent to the accelerator / brake system.

8. A longitudinal control device for automatic driving of a bus, characterized in that: The device comprises: an acquisition and calculation unit configured to acquire the longitudinal speed and the desired speed of the vehicle at a current moment, and calculate the speed error at a current moment based on the longitudinal speed and the desired speed at the current moment; The AFF-ILC controller is configured to calculate the traction / braking force input at the current moment in this iteration based on the longitudinal velocity error of the vehicle at the current moment in the previous iteration. The specific control expression is: u(i,k)=u(i-1,k)+α d Δe(i,k-1)+(α p +β(i,k))e(i-1,k) β(i,k)=β(i-1,k)+qe(i-1,k) 2 Among them, u(i,k) is the traction / braking force input of the vehicle at the current moment in this iteration, u(i-1,k) is the traction / braking force input of the vehicle at the current moment in the previous iteration, α d is the gain coefficient of the differential link, Δe(i,k-1) is the difference of the longitudinal velocity error of the vehicle at the previous moment in this iteration, α p is the gain coefficient of the proportional link, β(i,k) is the adaptive learning gain at the current moment in this iteration, β(i-1,k) is the adaptive learning gain at the current moment in the previous iteration, e(i-1,k)=y d (k)-y(i-1,k), e(i-1,k) is the longitudinal velocity error of the vehicle at the current moment in the previous iteration, y d (k) is the desired speed of the vehicle at the current moment, y(i-1,k) is the longitudinal speed of the vehicle at the current moment in the previous iteration, and q is the error adaptive learning gain; The control unit is configured to control the longitudinal speed of the vehicle at the current moment in this iteration according to the traction / braking force input at the current moment in the previous iteration output by the AFF-ILC controller and the longitudinal speed error at the current moment.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, wherein when the program is executed by a processor, the method for longitudinal control of automatic driving of a bus as claimed in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The electronic device includes: a processor and a memory storing a computer program, and the processor is configured to execute the bus automatic driving longitudinal control method according to any one of claims 1 to 7 when running the computer program.

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