Method and system for controlling optimal acceleration of high-order cruise

Abstract

The invention relates to a high-order cruise optimal acceleration control method and system, and the method comprises the steps: determining prospective road conditions based on a navigation map, and the road conditions comprise the slope and curvature of a road; constructing a function of a first acceleration curve in a set look-ahead time domain based on the look-ahead road condition, the engine power and the vehicle running speed; defining a sliding mode surface by taking the speed error and integral and differential of the speed error as state variables, determining switching gain and boundary layer thickness of a sliding mode controller of the sliding mode surface based on road conditions, and outputting a function of a second acceleration curve in a set look-ahead time domain through a control law of the sliding mode surface; performing peak filtering on the higher values of the first acceleration curve and the second acceleration curve at the same moment to obtain the estimated acceleration of the vehicle; through deep fusion of navigation map data, a forward-looking sliding mode controller with parameters capable of being adaptively adjusted is designed, so that the high-order cruise process has smoothness, economical efficiency, safety and high robustness.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, and in particular to a control method and system for high-order cruise optimal acceleration. Background Technology

[0002] Currently, mainstream CNOA (Cruise Control Alert) systems primarily rely on onboard sensors (such as radar and cameras) to detect vehicles or obstacles ahead, and employ classic control algorithms (such as PID control) or some modern control algorithms (such as Model Predictive Control, MPC) or LQR (Linear Quadratic Regulator) to adjust the vehicle's speed to maintain a safe following distance. However, existing technologies have the following limitations: Limited sensing range and lack of foresight: Relying solely on onboard sensors, the sensing distance is short, making it impossible to "foresee" static or quasi-static information such as road curvature, slope, traffic flow density changes, and toll booths in the distance. This leads to delayed vehicle control response, frequent sudden acceleration or braking, resulting in poor ride comfort and energy waste.

[0003] Traditional PID controllers suffer from limited control strategies and poor adaptability, making parameter tuning difficult and robust under complex and variable operating conditions. While MPC offers superior performance, it requires significant computation and places high demands on the onboard controller hardware.

[0004] Poor energy efficiency: Existing CNOA systems primarily focus on following the vehicle and maintaining a set speed, rarely considering energy efficiency as a core optimization indicator. In particular, they fail to utilize driving techniques such as coasting downhill or pre-coasting uphill.

[0005] Insufficient model accuracy: Many control algorithms are based on simple vehicle dynamics models and do not fully consider higher-order dynamic characteristics (such as transmission system delay, nonlinear changes in air resistance, etc.), which leads to a decrease in control accuracy under high-speed and other operating conditions. Summary of the Invention

[0006] This invention addresses the technical problems existing in the prior art by providing a control method and system for optimal acceleration during high-order cruise. By deeply integrating navigation map data, a forward-looking sliding mode controller with adaptively adjustable parameters is designed, enabling the high-order cruise process to have smoothness, economy, safety and strong robustness.

[0007] According to a first aspect of the present invention, a method for controlling high-order cruise optimal acceleration is provided, comprising: Step 1: Determine the forward road conditions based on the navigation map, including the road's slope and curvature; construct a function for the first acceleration curve within the set forward time domain based on the forward road conditions, engine power, and vehicle speed. Step 2: Define the sliding surface as a state variable using the speed error and its integral and derivative. Determine the switching gain and boundary layer thickness of the sliding mode controller for the sliding surface based on the road conditions. Output a function that sets the second acceleration curve in the look-ahead time domain through the control law of the sliding surface. Step 3: The estimated acceleration of the vehicle is obtained by performing peak filtering on the higher value of the first acceleration curve and the second acceleration curve at the same moment.

[0008] Based on the above technical solution, the present invention can also be improved as follows.

[0009] Optionally, step 1 includes: Step 101: Construct the state equation based on velocity, acceleration, and jerk. Step 102: Construct an objective function based on acceleration, jerk, and transmitter power, and construct constraints including: state boundary constraints, acceleration control constraints, and path constraints; Step 103: Based on the optimality condition of Pontryagin's minimum principle, calculate the acceleration when the objective function is minimized under the constraints to obtain the first acceleration curve.

[0010] Optionally, the state equation in step 101 is: (t) = [v(t), a(t), j(t)] T Control input u(t) = j(t); Where t represents time. Let v represent the state, a represent the velocity, and j represent the acceleration.

[0011] Optionally, in step 102, the objective function is constructed: ; Where J represents the objective function, [t0, tf] represents the set look-ahead time domain, P_engine represents the engine power, and w1, w2 and 3 are weighting coefficients; The path constraints include: velocity constraints, acceleration constraints, and road constraints; The speed constraint and acceleration constraint are constraints on the speed range and acceleration range dynamically set based on forward-looking road conditions, respectively; the road constraint is a constraint on the road segment curvature range and slope range dynamically set based on forward-looking road conditions.

[0012] Optionally, step 103 includes: Step 10301: Construct the Hamiltonian function for calculating the first acceleration curve. : ; Wherein, λ1, λ2 and λ3 are costate variables, corresponding to the dual variables of displacement, velocity and acceleration, respectively; Step 10302, construct the costate variable equation as follows: λ1=- H / s=-w3 P_engine / ; λ2=- H / v=-w3 P_engine / v-λ1; λ3=- H / a=-2w1 a-w3 P_engine / a-λ2; Step 10303: Set the terminal conditions as follows: if tf is fixed, then λ1(tf)=λ2(tf)=λ1(tf)=0; if tf is free, then H(tf)=0. Step 10304, calculate the optimality condition j (t)=argmin j H; When the aforementioned constraints are met, H / j=2w2 j+λ3=0, thus obtaining j =-λ3 / (2w2), which must satisfy j_min≤j ≤j_max; When the constraint condition is exceeded, j takes the boundary value that satisfies the constraint condition.

[0013] Optionally, step 2 includes: Step 201, construct the sliding surface S= _v+λ1 e_v+λ2 ∫e_vdt; Where e_v is the velocity error, _v and ∫e_vdt are the derivative and integral of the velocity error e_v, respectively, and λ1 and λ2 are the weighting coefficients; Step 202: Preset a rule table for dynamically adjusting the switching gain η and boundary layer thickness Φ of the sliding mode controller; the rule table adjusts the switching gain η and boundary layer thickness Φ according to the curvature k and slope θ of the forward-looking road. Step 203, determine the control law output u of the sliding surface as: u = u_eq - η(k, θ) sat(S / Φ(k, θ)); Where u is the output acceleration or torque command, u_eq is the equivalent control term calculated from the high-order vehicle model, and sat() is the saturation function used to replace the sign function to smooth the control output; Step 204: Determine the second acceleration curve based on the control law output u of the sliding surface.

[0014] Optionally, during the peak filtering process in step 3, the set filtering threshold is changed over time by increasing, maintaining, and decreasing.

[0015] According to a second aspect of the present invention, a high-order cruise optimal acceleration control system is provided, comprising: a navigation and positioning module, a cruise optimal acceleration planner, a threshold adjustment sliding mode controller, and a vehicle actuator; The navigation and positioning module is used to determine forward road conditions based on the navigation map, the road conditions including the road's slope and curvature; The cruise optimal acceleration planner is a function used to construct the first acceleration curve in the set forward time domain with the forward road conditions, engine power and vehicle speed as constraints. The threshold-adjustable sliding mode controller is used to define the sliding surface as a state variable with the speed error and the integral and derivative of the speed error, determine the switching gain and boundary layer thickness of the sliding mode controller of the sliding surface based on the road conditions, and output a function that sets the second acceleration curve in the look-ahead time domain through the control law of the sliding surface. The vehicle actuator is used to perform peak filtering on the higher value of the first acceleration curve and the second acceleration curve at the same moment to obtain the estimated acceleration of the vehicle, and to control the vehicle based on the estimated acceleration.

[0016] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the processor is configured to execute a computer management program stored in the memory to implement a control method for high-order cruise optimal acceleration.

[0017] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer management class program is stored, wherein when the computer management class program is executed by a processor, the steps of a control method for high-order cruise optimal acceleration are implemented.

[0018] This invention provides a high-order cruise optimal acceleration control method, system, electronic device, and storage medium. By deeply integrating navigation map data, it designs a forward-looking sliding mode controller with adaptively adjustable parameters, significantly reducing unnecessary acceleration and braking and improving ride comfort. By optimizing the acceleration curve, it achieves efficient use of fuel or electric energy. Based on more comprehensive environmental information, it makes earlier and more reasonable decisions. The sliding mode control combined with a high-order vehicle model has a strong ability to suppress internal and external disturbances of the system. Attached Figure Description

[0019] Figure 1 A flowchart of a high-order cruise optimal acceleration control method provided by the present invention; Figure 2 A flowchart illustrating a method for controlling vehicle acceleration based on the TSMC principle, provided as an embodiment of the present invention; Figure 3 A velocity-time curve of a look-ahead coasting control method provided in an embodiment of the present invention; Figure 4 An acceleration-time curve of a look-ahead coasting control method provided in an embodiment of the present invention; Figure 5 A schematic diagram of a possible hardware structure of an electronic device provided by the present invention; Figure 6 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0020] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0021] Figure 1 A flowchart of a high-order cruise optimal acceleration control method provided by the present invention is shown below. Figure 1 As shown, the control method includes: Step 1: Determine the forward road conditions based on the navigation map, including the road's slope and curvature; construct a function for the first acceleration curve within the set forward time domain based on the forward road conditions, engine power, and vehicle speed.

[0022] Step 2: Define the sliding surface as a state variable using the velocity error and its integral and derivative. Determine the switching gain and boundary layer thickness of the sliding mode controller based on road conditions. Output the function of the control law of the sliding surface to set the second acceleration curve in the look-ahead time domain.

[0023] Step 3: After peak filtering of the higher values ​​of the first acceleration curve and the second acceleration curve at the same moment, the estimated acceleration of the vehicle is obtained.

[0024] This invention provides a control method for optimal acceleration during high-order cruise. By deeply fusing navigation map data, a forward-looking sliding mode controller with adaptively adjustable parameters is designed, enabling the high-order cruise process to have smoothness, economy, safety and strong robustness.

[0025] Example 1 Embodiment 1 provided by this invention is an embodiment of a high-order cruise optimal acceleration control method provided by this invention, combined with... Figure 1 It can be seen that embodiments of this control method include: Step 1: Determine the forward road conditions based on the navigation map, including the road's slope and curvature; construct a function for the first acceleration curve within the set forward time domain based on the forward road conditions, engine power, and vehicle speed.

[0026] In one possible embodiment, step 1 includes: Step 101: Construct the state equation based on velocity, acceleration, and jerk.

[0027] In one possible embodiment, the state equation in step 101 is: (t) = [v(t), a(t), j(t)] T Control input u(t) = j(t).

[0028] Where t represents time. Let v represent the state, a represent the velocity, and j represent the acceleration.

[0029] In practice, the state vector is defined as x(t) = [s(t), v(t), a(t)]. T s represents displacement, and j = jerk = jerk, which is a derivative of the control input. The control input u(t) = j(t) directly controls the jerk and indirectly constrains the acceleration / velocity to meet comfort requirements.

[0030] Step 102: Construct an objective function based on acceleration, jerk, and transmitter power, and construct constraints including: state boundary constraints, acceleration control constraints, and path constraints.

[0031] In one possible embodiment, the objective function is constructed in step 102: .

[0032] Where J represents the objective function, [t0, tf] represents the look-ahead time domain, P_engine represents the engine power, and w1, w2 and 3 are weighting coefficients.

[0033] In practice, engine power can be modeled as: P_engine = F(t)·v(t) / η.

[0034] Where F is the driving force, F=m·a(t)+f_r·m·g·cosθ+m·g·sinθ+0.5ρC_dA·v²(t); η is the transmission efficiency, θ is the road slope, f_r is the rolling resistance coefficient, ρ is the air density, C_d is the wind resistance coefficient, and A is the frontal area.

[0035] By using jerk j to represent the comfort index and engine power P_engine to represent the economy index, an objective function J considering vehicle longitudinal dynamics, energy consumption, and comfort is established.

[0036] Path constraints include: velocity constraints, acceleration constraints, and road constraints.

[0037] The speed constraint and acceleration constraint are constraints on the speed range and acceleration range dynamically set based on forward-looking road conditions, respectively; the road constraint is the constraint on the road segment curvature range and slope range dynamically set based on forward-looking road conditions.

[0038] In practice, constraints are constructed by integrating road conditions with physical limits.

[0039] The boundary constraints are: initial state x(t0) = [s0, v0, a0], terminal state x(tf) = [sf, vf, af].

[0040] This boundary state needs to meet preset requirements, such as the forward end velocity / acceleration target.

[0041] The control constraints are: j_min≤j(t)≤j_max, accelerometer physical limit, and comfort level.

[0042] Path constraints include: Speed ​​constraint: v_min(t)≤v(t)≤v_max(t); Dynamic speed limit, which varies with road curvature: the greater the curvature, the smaller v_max, such as v_max(κ)=√(μ·g / κ), where κ is the curvature and μ is the road surface adhesion coefficient.

[0043] Acceleration constraint: a_min≤a(t)≤a_max; physical limit, which can be adjusted according to the slope: a_max decreases when going uphill.

[0044] Road constraints: κ(t)∈[κ_min,κ_max] (curvature of the forward road segment is known), θ(t)∈[θ_min,θ_max] (slope of the forward road segment is known).

[0045] Step 103: Based on the optimality condition of Pontryagin's minimum principle, calculate the acceleration when the objective function is minimized under the constraint conditions to obtain the first acceleration curve.

[0046] In one possible embodiment, step 103 includes: Step 10301: Construct the Hamiltonian function for calculating the first acceleration curve. : .

[0047] Here, λ1, λ2, and λ3 are costate variables, corresponding to the dual variables of displacement, velocity, and acceleration, respectively.

[0048] Step 10302, construct the costate variable equation as follows: λ1=- H / s=-w3 P_engine / .

[0049] λ2=- H / v=-w3 P_engine / v-λ1.

[0050] λ3=- H / a=-2w1 a-w3 P_engine / a-λ2.

[0051] λ1 corresponds to the dual variable of displacement. When the slope / curvature changes with displacement, P_engine is related to s.

[0052] Step 10303: Set the terminal conditions as follows: if tf is fixed, then λ1(tf)=λ2(tf)=λ1(tf)=0; if tf is free, then H(tf)=0.

[0053] Step 10304, calculate the optimality condition j (t)=argmin j H.

[0054] When the constraints are met, H / j=2w2 j+λ3=0, thus obtaining j =-λ3 / (2w2), which must satisfy j_min≤j ≤j_max; When the constraint is exceeded, j takes the boundary value that satisfies the constraint.

[0055] By using road curvature, gradient, and speed limit as constraints, and employing the Pontryagin minimum principle, the optimal acceleration curve a_opt(t) and optimal velocity curve v_opt(t) in the look-ahead time domain are obtained. This is the cruise optimal acceleration programming.

[0056] Step 2: Define the sliding surface as a state variable using the velocity error and its integral and derivative. Determine the switching gain and boundary layer thickness of the sliding mode controller based on road conditions. Output the function of the control law of the sliding surface to set the second acceleration curve in the look-ahead time domain.

[0057] like Figure 2 The diagram shows a flowchart illustrating a method for controlling vehicle acceleration based on the TSMC (Terminal Sliding Mode Control) principle, as provided in an embodiment of the present invention. Figure 2 It is understood that, in one possible embodiment, step 2 includes: Step 201, construct the sliding surface S= _v+λ1 e_v+λ2 ∫e_vdt.

[0058] Where e_v is the velocity error, _v and ∫e_vdt are the derivative and integral of the velocity error e_v, respectively, and λ1 and λ2 are the weighting coefficients.

[0059] In specific implementation, unlike the traditional first-order sliding surface, the embodiments of the present invention provide a high-order sliding surface, using the velocity error e_v = v_opt - v_actual and its integral and derivative as state variables to suppress chattering.

[0060] Step 202: Preset a rule table for dynamically adjusting the switching gain η and boundary layer thickness Φ of the sliding mode controller; the rule table adjusts the switching gain η and boundary layer thickness Φ according to the curvature k and slope θ of the forward-looking road.

[0061] In practice, the switching gain η and boundary layer thickness Φ of the sliding mode controller are no longer fixed values. Instead, they are dynamically adjusted based on the forward road curvature κ and slope θ provided by the navigation map, using fuzzy logic or a pre-defined lookup table. When approaching a sharp curve or steep descent: increase the switching gain η and decrease the boundary layer thickness Φ to enable the system to quickly and accurately track the planned speed, ensuring safety. When on a straight road or gentle slope: decrease the switching gain η and increase the boundary layer thickness Φ to allow for smooth speed changes within a small range, prioritizing comfort and economy while effectively suppressing chatter.

[0062] Step 203, determine the control law output u of the sliding surface as: u = u_eq - η(k, θ) sat(S / Φ(k, θ)).

[0063] Where u is the output acceleration or torque command, u_eq is the equivalent control term calculated from the high-order vehicle model, and sat() is the saturation function used to replace the sign function to smooth the control output.

[0064] Step 204: Determine the second acceleration curve based on the control law output u of the sliding surface.

[0065] Step 3: After peak filtering of the higher values ​​of the first acceleration curve and the second acceleration curve at the same moment, the estimated acceleration of the vehicle is obtained.

[0066] In one possible embodiment, during the peak filtering process in step 3, the set filtering threshold is changed over time by increasing, maintaining, and decreasing.

[0067] Step 3 generates an optimized acceleration curve based on the first acceleration curve generated in Step 1 and the second acceleration curve generated in Step 2, increasing comfort and generalization ability. By pre-integrating map information, the precision of the gliding control is anticipated, and a gliding strategy is used during the transition phase of uphill and downhill processes. With the map information available, control is directly implemented in advance. Figure 3 and Figure 4 The figures shown are velocity-time curves and acceleration-time curves of a look-ahead coasting control method provided in an embodiment of the present invention. Figure 3 and Figure 4 In the model, 0-t1: acceleration phase, t1-t2: uniform acceleration phase, t2-t3: deceleration phase, t3-t4: uniform speed phase, t4-t5: acceleration / deceleration phase, t5-t6: acceleration / deceleration phase, t6-t7: deceleration / deceleration phase, and a typical road condition model of "uphill - hilltop (flat road) - downhill" are used to demonstrate that by applying the forward-looking ideas of this invention, we can better serve the sliding film control theory, output more comfortable acceleration, acceleration / deceleration switching, and deceleration, making the entire control process more flexible and multi-processable.

[0068] This invention provides a high-order cruise optimal acceleration control method. By deeply fusing navigation map data, it designs a forward-looking, adaptively adjustable sliding mode controller, thereby achieving: Smoothness: Significantly reduces unnecessary acceleration and braking, improving ride comfort. Economy: Achieves efficient use of fuel or electricity by optimizing the acceleration curve. Safety: Makes earlier and more rational decisions based on more comprehensive environmental information. Strong robustness: The sliding mode control combined with a high-order vehicle model has a strong ability to suppress internal and external disturbances to the system.

[0069] Example 2 Embodiment 2 of the present invention is an embodiment of a high-order cruise optimal acceleration control system provided by the present invention. The embodiment of the control system includes: a navigation and positioning module, a cruise optimal acceleration planner, a threshold adjustment sliding mode controller, and a vehicle actuator.

[0070] The navigation and positioning module is used to determine forward road conditions based on the navigation map, including road gradient and curvature.

[0071] The navigation map and positioning module provides the vehicle's real-time location and extracts road information within a set distance range ahead, including: curvature k(s), slope angle θ(s), speed limit v_lim(s), and the location of specific event points (such as toll booths and ramps). This set distance range can be 0-1000 meters.

[0072] In a specific implementation, the high-order cruise optimal acceleration control system provided by the present invention also includes an environmental perception module, which uses sensors such as radar and cameras to detect the status of vehicles ahead in real time (distance D_rel, relative speed V_rel).

[0073] The cruise optimal acceleration planner is a function used to construct the first acceleration curve within a given look-ahead time domain, with constraints including forward road conditions, engine power, and vehicle speed.

[0074] A threshold-adjustable sliding mode controller is used to define the sliding surface as state variables with velocity error and its integral and derivative. The switching gain and boundary layer thickness of the sliding mode controller are determined based on road conditions. The control law output of the sliding surface sets the function of the second acceleration curve in the look-ahead time domain.

[0075] The vehicle actuator is used to obtain the vehicle's estimated acceleration by performing peak filtering on the higher value of the first acceleration curve and the second acceleration curve at the same moment, and to control the vehicle based on the estimated acceleration.

[0076] It is understood that the high-order cruise optimal acceleration control system provided by the present invention corresponds to the high-order cruise optimal acceleration control method provided in the foregoing embodiments. The relevant technical features of the high-order cruise optimal acceleration control system can be referred to the relevant technical features of the high-order cruise optimal acceleration control method, and will not be repeated here.

[0077] Please see Figure 5 , Figure 5 A schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 5 As shown, this embodiment of the invention provides an electronic device, including a memory 1310, a processor 1320, and a computer program 1311 stored in the memory 1310 and executable on the processor 1320. When the processor 1320 executes the computer program 1311, it performs the following steps: determining forward road conditions based on a navigation map, the road conditions including the road's slope and curvature; constructing a function for a first acceleration curve within a set forward time domain based on the forward road conditions, engine power, and vehicle speed; defining a sliding surface as a state variable using speed error and the integral and derivative of the speed error; determining the switching gain and boundary layer thickness of the sliding surface's sliding mode controller based on the road conditions; outputting a function for a second acceleration curve within the set forward time domain through the control law of the sliding surface; and obtaining the vehicle's estimated acceleration by performing peak filtering on the higher values ​​of the first and second acceleration curves at the same moment.

[0078] Please see Figure 6 , Figure 6 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by the present invention. (See diagram below.) Figure 6 As shown, this embodiment provides a computer-readable storage medium 1400, on which a computer program 1411 is stored. When the computer program 1411 is executed by a processor, it performs the following steps: determining forward road conditions based on a navigation map, the road conditions including the road's slope and curvature; constructing a function for a first acceleration curve within a set forward time domain based on the forward road conditions, engine power, and vehicle speed; defining a sliding surface as a state variable using speed error and the integral and derivative of the speed error; determining the switching gain and boundary layer thickness of the sliding surface's sliding mode controller based on the road conditions; outputting a function for a second acceleration curve within a set forward time domain through the control law of the sliding surface; and obtaining the vehicle's estimated acceleration by performing peak filtering on the higher value of the first and second acceleration curves at the same moment.

[0079] The present invention provides a high-order cruise optimal acceleration control method, system, electronic device, and storage medium, the beneficial effects of which include: 1. Foresight and anticipation: By making full use of navigation map information, vehicle control is upgraded from "passive reaction" to "active planning", which fundamentally avoids control lag.

[0080] 2. Smoothness and energy saving: The CNOA planning generates the globally optimal speed / acceleration trajectory, and combined with threshold-adjusted sliding mode control for smooth tracking, which significantly reduces the number of sudden acceleration / deceleration and braking, improves comfort and reduces energy consumption.

[0081] 3. Strong robustness and high precision: Sliding mode control itself is insensitive to model uncertainties and external disturbances. Combined with high-order vehicle models, it further improves the accuracy and stability of control.

[0082] 4. Adaptive capability: The innovative threshold adjustment mechanism enables the controller to intelligently switch between driving mode and economy mode according to road conditions (curves, slopes), achieving the best balance between multiple objectives such as safety, comfort and economy.

[0083] 5. Chattering suppression: Through high-order sliding surface design and boundary layer-based dynamic adjustment, chattering inherent in traditional sliding mode control is effectively overcome.

[0084] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0085] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0090] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for controlling high-order cruise optimal acceleration, characterized in that, The control method includes: Step 1: Determine the forward road conditions based on the navigation map, including the road's slope and curvature; construct a function for the first acceleration curve within the set forward time domain based on the forward road conditions, engine power, and vehicle speed. Step 2: Define the sliding surface as a state variable using the speed error and its integral and derivative. Determine the switching gain and boundary layer thickness of the sliding mode controller for the sliding surface based on the road conditions. Output a function that sets the second acceleration curve in the look-ahead time domain through the control law of the sliding surface. Step 3: The estimated acceleration of the vehicle is obtained by performing peak filtering on the higher value of the first acceleration curve and the second acceleration curve at the same moment.

2. The control method according to claim 1, characterized in that, Step 1 includes: Step 101: Construct the state equation based on velocity, acceleration, and jerk. Step 102: Construct an objective function based on acceleration, jerk, and transmitter power, and construct constraints including: state boundary constraints, acceleration control constraints, and path constraints; Step 103: Based on the optimality condition of Pontryagin's minimum principle, calculate the acceleration when the objective function is minimized under the constraints to obtain the first acceleration curve.

3. The control method according to claim 2, characterized in that, The state equation in step 101 is: (t) = [v(t), a(t), j(t)] T Control input u(t) = j(t); Where t represents time. Let v represent the state, a represent the velocity, and j represent the acceleration.

4. The control method according to claim 3, characterized in that, In step 102, the objective function is constructed as follows: ； Where J represents the objective function, [t0, tf] represents the set look-ahead time domain, P_engine represents the engine power, and w1, w2 and 3 are weighting coefficients; The path constraints include: velocity constraints, acceleration constraints, and road constraints; The speed constraint and acceleration constraint are constraints on the speed range and acceleration range dynamically set based on forward-looking road conditions, respectively; the road constraint is a constraint on the road segment curvature range and slope range dynamically set based on forward-looking road conditions.

5. The control method according to claim 4, characterized in that, Step 103 includes: Step 10301: Construct the Hamiltonian function for calculating the first acceleration curve. : ； Wherein, λ1, λ2 and λ3 are costate variables, corresponding to the dual variables of displacement, velocity and acceleration, respectively; Step 10302, construct the costate variable equation as follows: λ1=- H / s=-w3 P_engine / ; λ2=- H / v=-w3 P_engine / v-λ1; λ3=- H / a=-2w1 a-w3 P_engine / a-λ2; Step 10303: Set the terminal conditions as follows: if tf is fixed, then λ1(tf)=λ2(tf)=λ1(tf)=0; if tf is free, then H(tf)=0. Step 10304, calculate the optimality condition j (t)=argmin j H; When the aforementioned constraints are met, H / j=2w2 j+λ3=0, thus obtaining j =-λ3 / (2w2), which must satisfy j_min≤j ≤j_max; When the constraint condition is exceeded, j takes the boundary value that satisfies the constraint condition.

6. The control method according to claim 1, characterized in that, Step 2 includes: Step 201, construct the sliding surface S= _v+λ1 e_v+λ2 ∫e_vdt; Where e_v is the velocity error, _v and ∫e_vdt are the derivative and integral of the velocity error e_v, respectively, and λ1 and λ2 are the weighting coefficients; Step 202: Preset a rule table for dynamically adjusting the switching gain η and boundary layer thickness Φ of the sliding mode controller; the rule table adjusts the switching gain η and boundary layer thickness Φ according to the curvature k and slope θ of the forward-looking road. Step 203, determine the control law output u of the sliding surface as: u=u_eq - η(k, θ) sat(S / Φ(k,θ)); Where u is the output acceleration or torque command, u_eq is the equivalent control term calculated from the high-order vehicle model, and sat() is the saturation function used to replace the sign function to smooth the control output; Step 204: Determine the second acceleration curve based on the control law output u of the sliding surface.

7. The control method according to claim 4, characterized in that, During the peak filtering process in step 3, the set filtering threshold is changed over time by increasing, maintaining, and decreasing.

8. A high-order cruise optimal acceleration control system, characterized in that, The control system includes: a navigation and positioning module, a cruise optimal acceleration planner, a threshold adjustment sliding mode controller, and a vehicle actuator; The navigation and positioning module is used to determine forward road conditions based on the navigation map, the road conditions including the road's slope and curvature; The cruise optimal acceleration planner is a function used to construct the first acceleration curve in the set forward time domain with the forward road conditions, engine power and vehicle speed as constraints. The threshold-adjustable sliding mode controller is used to define the sliding surface as a state variable with the speed error and the integral and derivative of the speed error, determine the switching gain and boundary layer thickness of the sliding mode controller of the sliding surface based on the road conditions, and output a function that sets the second acceleration curve in the look-ahead time domain through the control law of the sliding surface. The vehicle actuator is used to perform peak filtering on the higher value of the first acceleration curve and the second acceleration curve at the same moment to obtain the estimated acceleration of the vehicle, and to control the vehicle based on the estimated acceleration.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the processor is used to implement the steps of the high-order cruise optimal acceleration control method as described in any one of claims 1-7 when executing a computer management program stored in the memory.

10. A computer-readable storage medium, characterized in that, It stores a computer management program, which, when executed by a processor, implements the steps of the high-order cruise optimal acceleration control method as described in any one of claims 1-7.

Images