Multi-point preview model prediction trajectory tracking control method and device and vehicle

Abstract

The invention discloses a multi-point preview model prediction trajectory tracking control method and device and a vehicle, and belongs to the technical field of prediction trajectory tracking, and the method comprises the steps: obtaining the current position of the vehicle, the attitude information of the vehicle and prediction parameters in response to a prediction trajectory request instruction; based on the current position of the vehicle, the attitude information of the vehicle and the prediction parameters, determining a prediction model with time delay extension and a multi-point preview reference sequence; determining an optimal control increment sequence at the current sampling moment based on the prediction model with time delay extension and the multi-point preview reference sequence; based on the optimal control increment sequence at the current sampling moment, a control instruction set is generated, and the control instruction set is used for controlling a steering execution mechanism of the articulated vehicle. According to the invention, effective compensation of the input time delay is realized, trajectory tracking errors under the time delay condition are significantly reduced, and the stability and robustness of the system are improved.

Description

Technical Field

[0001] This invention discloses a multi-point pre-aiming model prediction trajectory tracking control method, device, and vehicle, belonging to the field of prediction trajectory tracking technology. Background Technology

[0002] Articulated loaders, articulated mining trucks, and other engineering vehicles need to travel precisely along a given path in narrow or complex working conditions to avoid collisions and improve work efficiency. Traditional path tracking methods often employ proportional-integral-derivative (PID) control or control strategies based on simplified kinematic models, which struggle to simultaneously account for geometric errors, vehicle constraints, and actuator time delays, resulting in limited tracking accuracy and stability.

[0003] Model predictive control (MPC) can explicitly consider input and state constraints within a finite prediction time domain and has been increasingly applied to vehicle trajectory tracking. However, existing MPC control methods for articulated vehicles generally suffer from the following problems:

[0004] Most methods rely on single-point or short-range reference points, making it difficult to utilize the curvature information of the entire multi-point reference trajectory, resulting in insufficient tracking performance on sharp curves and S-shaped paths. Many methods employ fixed linear models or single-operating-point linearization, failing to adapt to changes in vehicle motion state through time-varying linearization, thus affecting model accuracy. The inherent time delay of actuators is often not explicitly modeled and compensated, easily leading to additional phase lag. Furthermore, model uncertainties, linearization errors, and external disturbances can cause steady-state residual errors, affecting path tracking accuracy. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a multi-point pre-aiming model predictive trajectory tracking control method, device, and vehicle. This method achieves high-precision and robust tracking of complex trajectories while considering actuator delays, and simultaneously improves the numerical stability and engineering feasibility of the model predictive control optimization problem.

[0006] The technical solution of the present invention is as follows:

[0007] According to a first aspect of the present invention, a multi-point pre-aiming model predicted trajectory tracking control method is provided, comprising:

[0008] In response to the trajectory prediction request command, obtain the vehicle's current position, vehicle attitude information, and prediction parameters;

[0009] Based on the vehicle's current position, attitude information, and prediction parameters, a prediction model with time delay extension and a multi-point aiming reference sequence are determined.

[0010] Based on the prediction model with time delay and the multi-point aiming reference sequence, the optimal control increment sequence at the current sampling time is determined;

[0011] Based on the optimal control increment sequence at the current sampling time, a control instruction set is generated, which is used to control the steering actuator of the articulated vehicle.

[0012] Furthermore, based on the vehicle's current position, attitude information, and prediction parameters, a prediction model with time delay extension and a multi-point aiming reference sequence are determined, including:

[0013] Based on the target working environment and working points in the prediction parameters, the initial prediction trajectory is determined, where the working points include: the starting point, the ending point, and intermediate key working points;

[0014] Based on the initial predicted trajectory, the vehicle's current position, the vehicle's attitude information, and the number of steps corresponding to the time delay in the prediction parameters, a multi-point pre-aiming reference sequence is determined.

[0015] The prediction model with time delay extension is determined by considering the sampling period, the number of steps corresponding to the time delay, and the nonlinear system of the controlled articulated vehicle in the prediction parameters.

[0016] Furthermore, based on the initial predicted trajectory, the vehicle's current position, the vehicle's attitude information, and the number of steps corresponding to the time delay in the prediction parameters, a multi-point pre-aiming reference sequence is determined, including:

[0017] Based on the initial predicted trajectory, determine the horizontal and vertical coordinates of multiple path points in the global coordinate system;

[0018] Obtain the x and y coordinates of three consecutive path points from multiple path points to determine the curvature and turning radius;

[0019] Based on curvature and turning radius, determine the reference heading angle and / or reference articulation angle;

[0020] Angle expansion and continuum processing are performed on the reference heading angle and / or reference hinge angle to obtain the processed predicted trajectory;

[0021] Based on the processed predicted trajectory, the vehicle's current position, the vehicle's attitude information, and the number of steps corresponding to the time delay in the predicted parameters, a multi-point pre-aiming reference sequence is determined.

[0022] Furthermore, based on the time-delayed extended prediction model and the multi-point aiming reference sequence, the optimal control increment sequence at the current sampling time is determined, including:

[0023] Based on the time-delayed extended prediction model, historical control variables, and control variables that will take effect, a time-delayed extended prediction model is used to determine the extended state.

[0024] The optimal control increment sequence is determined based on the extended state with time-delayed extended prediction model and multi-point aiming reference sequence.

[0025] Furthermore, based on the optimal control increment sequence at the current sampling time, a control instruction set is generated, including:

[0026] Based on the optimal control increment sequence at the current sampling time, determine the first control increment;

[0027] Based on the control increment from the first step, a control instruction set is generated.

[0028] According to a second aspect of the present invention, a multi-point pre-aiming model prediction trajectory tracking control device is provided, comprising:

[0029] The acquisition module is used to acquire the vehicle's current position, vehicle attitude information, and prediction parameters in response to the prediction trajectory request command.

[0030] The determination module is used to determine the prediction model with time delay spread and the multi-point aiming reference sequence based on the vehicle's current position, vehicle attitude information and prediction parameters;

[0031] The optimal module, based on a prediction model with time delay and a multi-point aiming reference sequence, determines the optimal control increment sequence at the current sampling time.

[0032] The generation module generates a control instruction set based on the optimal control increment sequence at the current sampling time. The control instruction set is used to control the steering actuator of the articulated vehicle.

[0033] Furthermore, the determination module also includes:

[0034] Based on the target working environment and working points in the prediction parameters, the initial prediction trajectory is determined, where the working points include: the starting point, the ending point, and intermediate key working points;

[0035] Based on the initial predicted trajectory, the vehicle's current position, the vehicle's attitude information, and the number of steps corresponding to the time delay in the prediction parameters, a multi-point pre-aiming reference sequence is determined.

[0036] The prediction model with time delay extension is determined by considering the sampling period, the number of steps corresponding to the time delay, and the nonlinear system of the controlled articulated vehicle in the prediction parameters.

[0037] According to a third aspect of the present invention, a vehicle is provided, comprising:

[0038] One or more processors;

[0039] Memory for storing the one or more processor-executable instructions;

[0040] Wherein, the one or more processors are configured as follows:

[0041] Perform the method described in the first aspect of the embodiments of the present invention.

[0042] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein when instructions in the storage medium are executed by a processor of a terminal, the terminal is enabled to perform the method described in the first aspect of the present invention.

[0043] According to a fifth aspect of the present invention, an application product is provided that, when the application product is running on a terminal, causes the terminal to execute the method described in the first aspect of the present invention.

[0044] The beneficial effects of this invention are as follows:

[0045] This invention provides a multi-point pre-aiming model-based trajectory tracking control method, device, and vehicle. In response to a trajectory prediction request command, the method acquires the vehicle's current position, attitude information, and prediction parameters. Based on these parameters, it determines a time-delay-extended prediction model and a multi-point pre-aiming reference sequence. Then, based on this model and reference sequence, it determines the optimal control increment sequence for the current sampling time. Finally, it generates a control instruction set to control the steering actuator of the articulated vehicle. This effectively compensates for input time delay, significantly reduces trajectory tracking errors under time delay conditions, and improves system stability and robustness.

[0046] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating a multi-point preview model prediction trajectory tracking control method according to an exemplary embodiment.

[0048] Figure 2 This is a flowchart illustrating a multi-point preview model prediction trajectory tracking control method according to an exemplary embodiment.

[0049] Figure 3 This is a system structure block diagram of the model prediction controller and the controlled object in a multi-point aiming model prediction trajectory tracking control method according to an exemplary embodiment.

[0050] Figure 4 This is a schematic diagram of the sampling timing of discrete output / control quantities with time delay in a multi-point preview model prediction trajectory tracking control method according to an exemplary embodiment.

[0051] Figure 5This is a schematic diagram illustrating the kinematic geometry and turning radius of an articulated engineering vehicle in a multi-point pre-aiming model predictive trajectory tracking control method according to an exemplary embodiment.

[0052] Figure 6 This is a schematic block diagram illustrating the structure of a multi-point pre-aiming model predictive trajectory tracking control device according to an exemplary embodiment.

[0053] Figure 7 This is a schematic block diagram of a vehicle structure according to an exemplary embodiment. Detailed Implementation

[0054] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] In the description of this invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0056] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0057] This invention provides a multi-point aiming model prediction trajectory tracking control method implemented by a terminal, which includes at least a CPU.

[0058] Example 1: Figure 1 and 2 This is an exemplary embodiment illustrating a multi-point preview model-predicted trajectory tracking control method, comprising:

[0059] Step 101: In response to the predicted trajectory request command, obtain the vehicle's current position, vehicle attitude information, and prediction parameters;

[0060] In step 101, in response to the predicted trajectory request command, the vehicle's current position, vehicle attitude information, and predicted parameters are obtained, specifically including: sampling period Ts; prediction time domain length Np and control time domain length Nc; state dimension Nx and control input dimension Nu, where the state is taken as... These correspond to the vehicle's center of gravity in the global coordinate system, along with its horizontal and vertical coordinates, heading angle, and body hinge angle, respectively; the control input is set to... These correspond to the vehicle's longitudinal velocity and the body articulation angular velocity, respectively; the length of the articulated vehicle's front body. and rear body length State error weight matrix Q, terminal weight matrix F, and control increment weight matrix R; Absolute control constraints: upper and lower limits of longitudinal velocity. Control Incremental Constraints: Total latency between the line mechanism and the communication link Set the delay compensation steps A control instruction queue of length d is established to implement control output delay compensation.

[0061] Step 102: Based on the vehicle's current position, attitude information, and prediction parameters, determine the prediction model with time delay extension and the multi-point aiming reference sequence.

[0062] like Figures 3-5 As shown, in step 102, based on the target work environment and work points in the prediction parameters, the initial prediction trajectory is determined. The work points include: the start point, the end point, and intermediate key work points. The specific steps are as follows:

[0063] First, a planar workspace model is established based on the target operating environment (e.g., mining roads, loader yard operating area). Combining the start and end points of the operation with key intermediate points, a predicted trajectory that satisfies vehicle accessibility and safety constraints is generated using a path planning module. This path planning module can be a manually interactive path editing tool or any existing path planning algorithm, and its output is a set of discrete path points that are monotonic along the arc length direction.

[0064] Then, based on the initial predicted trajectory, the vehicle's current position, the vehicle's attitude information, and the number of steps corresponding to the time delay in the prediction parameters, a multi-point pre-aiming reference sequence is determined, specifically including:

[0065] Based on the initial predicted trajectory, the x and y coordinates of multiple path points in the global coordinate system are determined. These path points are denoted as follows: .

[0066] To determine the curvature and turning radius, the specific steps include: Obtaining the x and y coordinates of three consecutive path points from multiple path points;

[0067] The body hinge angle is calculated based on the three-point geometric relationship. The curvature and turning radius are calculated using three continuous reference points, and then the hinge angle is obtained from the turning radius and geometric parameters.

[0068] On the discrete reference trajectory, arbitrarily select three consecutive path points:

[0069]

[0070] In the formula These are three consecutive points on the trajectory.

[0071] The vector of a line segment formed by pairwise combinations of adjacent elements is calculated as follows:

[0072]

[0073] triangle The directed area is:

[0074] (1)

[0075] The lengths of the three sides are as follows:

[0076] (2)

[0077] (3)

[0078] (4)

[0079] The curvature can be determined from the geometric rule that three points determine a circle:

[0080] (5)

[0081] In the formula, κ is the curvature.

[0082] When the curvature is 0, it is taken as 0.00001. When the curvature is not 0, the original value is taken.

[0083] The value of the turning radius R is obtained as follows:

[0084] (6)

[0085] In the formula, R is the turning radius.

[0086] Based on the curvature and turning radius, the reference hinge angle is determined. According to the geometric relationship in the figure:

[0087] (7)

[0088] In the formula, δref is the reference hinge angle, φ1 and φ2 are the intermediate angles in the geometric derivation, R is the turning radius, and Lf and Lr are the lengths of the front and rear vehicle bodies, respectively.

[0089] When the curvature is greater than 0, the hinge angle is positive; otherwise, it is negative.

[0090] Angle expansion and continuum processing are performed on the reference heading angle and / or reference articulation angle to obtain the processed predicted trajectory, which includes:

[0091] The reference angle is processed to be continuous. Since the heading angle is in + and- There are often instances where the reference sequence is lost, so it is necessary to make the reference sequence continuous:

[0092] For adjacent heading angles, in In the following circumstances:

[0093] when hour, ;

[0094] when hour, k [1, n]. n is the number of parameters; This represents the k-th reference heading angle, in radians.

[0095] In the formula, Let k be the heading angle at time k. Let be the heading angle at time k-1.

[0096] Based on the processed predicted trajectory, the vehicle's current position, the vehicle's attitude information, and the number of steps corresponding to the time delay in the predicted parameters, a multi-point pre-aiming reference sequence is determined, including:

[0097] At each sampling time k, the current state of the vehicle is obtained. Based on this state, search for the index of the next nearest point in the global reference trajectory. This ensures that the vehicle continues to move forward during the tracking process, avoids tracking failure due to the closest point behind, and constructs a multi-point pre-aiming reference window with time delay compensation capability, i.e., a multi-point pre-aiming reference sequence.

[0098] The starting point of the window after delay compensation is set as follows:

[0099] (8)

[0100] The preview window is R [ , + ].

[0101] In the formula, d is the index, representing the "next path point index of the nearest point" obtained by searching the global reference trajectory based on the vehicle's current state at sampling time k, and d is the number of steps corresponding to the time delay. Np is the prediction time domain.

[0102] The sampling period, the number of steps corresponding to the time delay, and the nonlinear system of the controlled articulated vehicle in the prediction parameters are used to determine the prediction model with time delay extension. The specific steps include:

[0103] The following assumptions are made: Sampling period: Delay: ,

[0104] Nonlinear systems: However, there is a delay in input.

[0105] Continuous nonlinearity + input delay: starting point

[0106] (9)

[0107]

[0108]

[0109]

[0110] Linearization is performed along a reference trajectory (or the trajectory predicted in the previous step). Assume there is a reference / equilibrium point at discrete time k:

[0111]

[0112] Taylor expansion in continuous time (with time delay)

[0113] right At point Perform a first-order Taylor expansion:

[0114]

[0115] (10)

[0116] In the formula

[0117]

[0118]

[0119]

[0120]

[0121] Substituting Taylor expansion into discrete form

[0122] Substituting the above approximation into the Taylor expansion:

[0123]

[0124] (11)

[0125] Expand and put ( , The items are collected together:

[0126] (12)

[0127]

[0128] Thus, we obtain the discrete linear time-varying + affine term + input delay model:

[0129] (14)

[0130] In the formula

[0131]

[0132]

[0133]

[0134]

[0135]

[0136] Step 103: Based on the time-delayed extended prediction model and the multi-point pre-aiming reference sequence, determine the optimal control increment sequence at the current sampling time;

[0137] In step 103, based on the time-delayed extended prediction model, historical control variables, and the control variables to be implemented, the time-delayed extended prediction model for the extended state is determined. The specific steps are as follows:

[0138] Input queue Definition

[0139] Define the input queue:

[0140] (15) The input that is actually affecting the system is the end of the queue. , can be written as

[0141] (16)

[0142] In the formula,

[0143] Substitute it into the above equation:

[0144] (18)

[0145] The queue's "move one position to the right" update:

[0146] Each step you calculate a new (u) k After that, the queue is updated as follows:

[0147] (19)

[0148] This can be written as a linear relationship:

[0149]

[0150] in

[0151] (20)

[0152] (twenty one)

[0153] Construct augmented states:

[0154] (twenty two)

[0155] Combining the above formulas, we get:

[0156] (twenty three)

[0157] Right now:

[0158] (twenty four)

[0159] Expressed using an input queue

[0160] Define the input queue:

[0161] (25)

[0162] Define the selection matrix

[0163] (26)

[0164] but

[0165] (27)

[0166] then

[0167]

[0168] (28)

[0169] Bundle Substitute into the augmented model:

[0170] Therefore, at the single-step prediction level, we obtain a result regarding... Affine system:

[0171]

[0172] In the formula

[0173]

[0174]

[0175] Multi-step prediction + multi-point reference

[0176] Rolling forward Np steps further can be written in matrix form:

[0177] (30)

[0178] in

[0179]

[0180]

[0181] Output:

[0182] (31)

[0183] (32)

[0184] Amplitude constraints and rate of change constraints are set for the control quantity and control increment to limit the output range and change speed of the steering actuator and avoid the impact caused by saturation and excessively rapid changes.

[0185] Based on the extended state-delayed extended prediction model and the multi-point aiming reference sequence, the optimal control increment sequence is determined. Specific details include:

[0186] Based on the extended state with time delay extended prediction model and multi-point pre-aiming reference sequence, rolling prediction is performed on the extended state in each future step to obtain the multi-point tracking error between the predicted state and the corresponding reference state. The multi-point tracking error includes at least one or more of position error and attitude error.

[0187] Furthermore, a performance index function is constructed with multi-point tracking error and control increment as weighting terms, for example:

[0188] (33)

[0189] In the formula, To predict the length of the time domain, To control the length of the time domain, Let be the tracking error vector at the (k+i)th prediction time. and These are the error weighting matrix and the control increment weighting matrix.

[0190] Furthermore, under the premise of satisfying the state constraints, input constraints, and control queue structure constraints, the problem of minimizing the performance index function is transformed into a constrained quadratic programming optimization problem, and the optimal control increment sequence at the current sampling time is obtained by using a quadratic programming solver.

[0191] Right now:

[0192] The cost function remains:

[0193] (34)

[0194] Rewriting the above equation in matrix quadratic form

[0195]

[0196] In the formula, , , ,

[0197] Q is the weight matrix for the state / output tracking error, and R is the weight matrix for the control increment Δu.

[0198] Obtain the standard QP:

[0199] (35)

[0200] In the formula, ,

[0201] Control input constraints:

[0202]

[0203] Control Incremental Constraints:

[0204]

[0205] State constraints:

[0206]

[0207] Step 104: Based on the optimal control increment sequence at the current sampling time, generate a control instruction set, which is used to control the steering actuator of the articulated vehicle.

[0208] In step 104, the preceding Model Predictive Control (MPC) problem is practically applied to vehicle control. Through execution and optimization processes, the control system achieves real-time control of the vehicle and adapts to changes in the environment and system state through continuous rolling optimization.

[0209] In step four, we obtained the optimal control increment sequence through MPC optimization: (36)

[0210] In the formula, ΔUk: the optimal control increment sequence / optimal increment vector at time k (the optimal solution of the decision variables of QP). Δuk: the optimal single-step control increment obtained at time k and planned to be applied in the i-th future step.

[0211] Based on this control increment sequence, we only take The first step is to control the increment and update the control quantity from the previous moment using the increment. To obtain the control commands at the current moment

[0212] (37)

[0213] In the formula, It is the "nominal control quantity" output by the controller.

[0214] Because of the input delay, the control quantity actually applied to the vehicle at the current moment is still the command from the historical control queue, i.e. The oldest control command in .

[0215] Within the framework of latency compensation, the controller achieves input latency compensation by updating the control queue. (Control queue) It stores the control commands from the past d steps, arranged in chronological order.

[0216] Each time a control command is output, the end of the queue contains the control command at the current moment. The process of updating the control queue is as follows: (38) The updated queue contains the control variables currently being calculated. And historical control commands, which will come into play in the coming steps.

[0217] Although the control quantity at the current moment is Due to input delay, the actual control input applied to the vehicle is the control command at the very end of the historical queue. This is consistent with the delay compensation section in steps three and four, whereby the control commands output by the controller only affect the vehicle after a certain time delay.

[0218] In actual vehicle control systems, data is transmitted via network or CAN bus, etc. The control input is then sent to the vehicle's actuators (such as the steering system and drive motor). This is the feedback loop in the control system: the control input is progressively passed to the vehicle's actuators, and the vehicle executes the corresponding actions based on the current control command.

[0219] Under the current control command, the vehicle will evolve into a new state. The vehicle's motion state (such as position, heading angle, speed, etc.) can be measured and estimated using sensors (such as GPS, IMU, vision sensors, etc.). At this point, we feed back the vehicle's current position and attitude information to the control system.

[0220] Update the linearized operating point: redetermine the current operating point based on the vehicle's feedback status.

[0221] Update the reference trajectory and multi-point preview reference sequence: Based on the vehicle's new state, update the multi-point preview reference sequence. This reference sequence will be provided as input to the optimization problem to generate the control input for the next sampling period.

[0222] At this point, the control system returns to the above process, re-modeling, predicting, optimizing, and executing to form a multi-point preview model predictive closed-loop control based on time delay compensation.

[0223] During each sampling period, when the controller executes, the system will enter a rolling optimization process:

[0224] Within each sampling period, the controller recalculates a new control increment based on the current state and the control output of the previous period.

[0225] During optimization, the controller continuously predicts the state and control inputs for the next few cycles and guides the optimization process through a multi-point preview reference sequence to ensure that the vehicle can follow the predetermined path as closely as possible.

[0226] As each cycle progresses, the contents of the control queue are continuously updated to ensure that input latency is effectively compensated and that each step is performed under the guidance of the optimal control increment.

[0227] This application models the input delay queue and introduces extended states, explicitly incorporating the hysteresis control quantity that actually acts on the controlled object into the prediction model. Combined with the control increment as the optimization decision variable, it accurately characterizes the fixed step delay of the actuator and / or communication link during the model prediction process, thereby achieving effective compensation for the input delay, significantly reducing the trajectory tracking error under delay conditions, and improving the system stability and robustness.

[0228] This application introduces a multi-point pre-aiming reference sequence in the model prediction time domain, enabling the controller to consider the trajectory shape and attitude changes at multiple future moments simultaneously in one optimization. Compared with control methods based solely on a single nearest point, it can achieve higher tracking accuracy and smoother control output under complex conditions such as large curvature changes, S-shaped curves, or U-turns.

[0229] This application eliminates the angle variation by performing angle expansion and continuity processing on the reference heading angle and / or reference hinge angle sequence. .

[0230] The numerical jumps in the vicinity ensure the continuity and differentiability of the tracking error calculation and linearization process, significantly improving the numerical conditions of the quadratic programming optimization problem, which is conducive to improving the stability and convergence speed of the optimization solution.

[0231] This application uses the control increment as the optimization variable and applies physical constraints to the control quantity and its increment, so that the output control command meets the amplitude and rate of change limits of the actuator, avoids saturation and strong impact, and is easy to integrate with existing vehicle control systems and actuators. It has good engineering application prospects and promotion value.

[0232] Example 2: Figure 6 A multi-point pre-aiming model predictive trajectory tracking control device, as illustrated in an exemplary embodiment, includes:

[0233] The acquisition module 210 is used to acquire the vehicle's current position, vehicle attitude information, and prediction parameters in response to the prediction trajectory request command.

[0234] The determination module 220 is used to determine the prediction model with time delay extension and the multi-point aiming reference sequence based on the vehicle's current position, the vehicle's attitude information and prediction parameters.

[0235] The optimal module 230, based on the prediction model with time delay and the multi-point aiming reference sequence, determines the optimal control increment sequence at the current sampling time;

[0236] The generation module 240 generates a control instruction set based on the optimal control increment sequence at the current sampling time. The control instruction set is used to control the steering actuator of the articulated vehicle.

[0237] Furthermore, the determining module 220 also includes:

[0238] Based on the target working environment and working points in the prediction parameters, the initial prediction trajectory is determined, where the working points include: the starting point, the ending point, and intermediate key working points;

[0239] Based on the initial predicted trajectory, the vehicle's current position, the vehicle's attitude information, and the number of steps corresponding to the time delay in the prediction parameters, a multi-point pre-aiming reference sequence is determined.

[0240] The prediction model with time delay extension is determined by considering the sampling period, the number of steps corresponding to the time delay, and the nonlinear system of the controlled articulated vehicle in the prediction parameters.

[0241] Example 3: Figure 7 This is a block diagram of a vehicle 300 provided in an embodiment of this application. For example, vehicle 300 can be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. Vehicle 300 can be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle. Vehicle 300 can also be equipped with a brake-by-wire system.

[0242] Reference Figure 7 The vehicle 600 may include various subsystems, such as an infotainment system 310, a perception system 320, a decision control system 330, a drive system 340, and a computing platform 350. The vehicle 300 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and component of the vehicle 300 can be interconnected via wired or wireless means.

[0243] In some embodiments, the infotainment system 310 may include a communication system, an entertainment system, and a navigation system, etc.

[0244] The perception system 320 may include several sensors for sensing information about the environment surrounding the vehicle 300. For example, the perception system 320 may include a global positioning system (which may be a GPS system, a BeiDou system, or another positioning system), an inertial measurement unit (IMU), a lidar, a millimeter-wave radar, an ultrasonic radar, and a camera device.

[0245] The decision control system 330 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0246] The drive system 340 may include components that provide powered motion to the vehicle 300. In one embodiment, the drive system 340 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0247] Some or all of the functions of the vehicle 300 are controlled by a computing platform 350. The computing platform 350 may include at least one processor 351 and a memory 352, the processor 351 being able to execute instructions 353 stored in the memory 352.

[0248] Processor 351 can be any conventional processor, such as a commercially available CPU. Processors may also include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), system-on-chips (SoCs), application-specific integrated circuits (ASICs), or combinations thereof.

[0249] The memory 352 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0250] In addition to instruction 353, memory 352 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in memory 352 can be used by computing platform 350.

[0251] In this embodiment of the disclosure, the processor 351 may execute instruction 353 to complete all or part of the steps of the above-described multi-point pre-aiming model prediction trajectory tracking control method.

[0252] Example 4: In an exemplary embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements a multi-point aiming model prediction trajectory tracking control method as provided in all embodiments of the present application.

[0253] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0254] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0255] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0256] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0257] Example 5: In an exemplary embodiment, an application product is also provided, including one or more instructions, which can be executed by the processor 351 of the above-described device to complete the above-described multi-point pre-aiming model prediction trajectory tracking control method.

[0258] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Other modifications can be readily made by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. A multi-point pre-aiming model-predicted trajectory tracking control method, characterized in that, include: In response to the trajectory prediction request command, obtain the vehicle's current position, vehicle attitude information, and prediction parameters; Based on the vehicle's current position, the vehicle's attitude information, and the prediction parameters, a prediction model with time delay extension and a multi-point aiming reference sequence are determined. Based on the time-delayed spread prediction model and the multi-point pre-aiming reference sequence, the optimal control increment sequence at the current sampling time is determined; Based on the optimal control increment sequence at the current sampling time, a control instruction set is generated, which is used to control the steering actuator of the articulated vehicle.

2. The multi-point pre-aiming model predicted trajectory tracking control method according to claim 1, characterized in that, Based on the vehicle's current position, the vehicle's attitude information, and the prediction parameters, the time-delayed extended prediction model and the multi-point pre-aiming reference sequence are determined, including: Based on the target working environment and working points in the prediction parameters, an initial prediction trajectory is determined, wherein the working points include: the starting point, the ending point, and intermediate key working points; Based on the initial predicted trajectory, the current position of the vehicle, the attitude information of the vehicle, and the number of steps corresponding to the time delay in the prediction parameters, a multi-point pre-aiming reference sequence is determined. The sampling period, the number of steps corresponding to the time delay, and the nonlinear system of the controlled articulated vehicle in the prediction parameters are used to determine the prediction model with time delay extension.

3. The multi-point pre-aiming model predicted trajectory tracking control method according to claim 2, characterized in that, Based on the initial predicted trajectory, the vehicle's current position, the vehicle's attitude information, and the number of steps corresponding to the time delay in the prediction parameters, a multi-point pre-aiming reference sequence is determined, including: Based on the initial predicted trajectory, determine the horizontal and vertical coordinates of multiple path points in the global coordinate system; Obtain the x and y coordinates of three consecutive path points from the multiple path points to determine the curvature and turning radius; Based on the curvature and the turning radius, determine the reference heading angle and / or the reference hinge angle; The reference heading angle and / or reference hinge angle are subjected to angle expansion and continuum processing to obtain the processed predicted trajectory; Based on the processed predicted trajectory, the vehicle's current position, the vehicle's attitude information, and the number of steps corresponding to the time delay in the prediction parameters, a multi-point pre-aiming reference sequence is determined.

4. The multi-point pre-aiming model predicted trajectory tracking control method according to claim 1, characterized in that, Based on the time-delayed spread prediction model and the multi-point pre-aiming reference sequence, the optimal control increment sequence for the current sampling time is determined, including: Based on the aforementioned delay-based extended prediction model, historical control variables, and control variables that will take effect, the delay-based extended prediction model for the extended state is determined. Based on the extended state with time-delayed extended prediction model and the multi-point pre-aiming reference sequence, the optimal control increment sequence is determined.

5. The multi-point pre-aiming model predicted trajectory tracking control method according to claim 1, characterized in that, Based on the optimal control increment sequence at the current sampling time, the control instruction set is generated, including: Based on the optimal control increment sequence at the current sampling time, determine the first step of the control increment; Based on the control increment described in the first step, the control instruction set is generated.

6. A multi-point pre-aiming model predicted trajectory tracking control device, characterized in that, include: The acquisition module is used to acquire the vehicle's current position, vehicle attitude information, and prediction parameters in response to the prediction trajectory request command. The determination module is used to determine a prediction model with time delay spread and a multi-point aiming reference sequence based on the current position of the vehicle, the attitude information of the vehicle, and the prediction parameters. The optimal module determines the optimal control increment sequence at the current sampling time based on the time-delayed extended prediction model and the multi-point pre-aiming reference sequence. The generation module generates a control instruction set based on the optimal control increment sequence at the current sampling time. The control instruction set is used to control the steering actuator of the articulated vehicle.

7. The multi-point pre-aiming model prediction trajectory tracking control device according to claim 1, characterized in that, The determining module further includes: Based on the target working environment and working points in the prediction parameters, an initial prediction trajectory is determined, wherein the working points include: the starting point, the ending point, and intermediate key working points; Based on the initial predicted trajectory, the current position of the vehicle, the attitude information of the vehicle, and the number of steps corresponding to the time delay in the prediction parameters, a multi-point pre-aiming reference sequence is determined. The sampling period, the number of steps corresponding to the time delay, and the nonlinear system of the controlled articulated vehicle in the prediction parameters are used to determine the prediction model with time delay extension.

8. A vehicle, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to perform the steps of the multi-point preview model prediction trajectory tracking control method according to any one of claims 1 to 5.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When executed by the processor, the program instructions implement the steps of the multi-point preview model prediction trajectory tracking control method according to any one of claims 1 to 5.

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