An engineering vehicle adaptive control strategy optimization method

Abstract

The application discloses an engineering vehicle self-adaptive control strategy optimization method, comprising the following steps: collecting the running state data and environmental parameters of the engineering vehicle in real time through a multi-source sensor; constructing an optimization model based on a dynamic programming algorithm, taking the minimization of the working energy consumption of the engineering vehicle as an optimization target, inputting the running state data and environmental parameters into the optimization model for real-time calculation to generate a dynamic control strategy; and dynamically adjusting the control parameters of the driving system, braking system and steering system of the engineering vehicle according to the dynamic control strategy. The application realizes real-time state sensing, dynamic programming decision and self-adaptive precise control of the engineering vehicle, embodies multiple advantages of data accuracy, control decision real-time and execution coordination, and effectively solves the problems of insufficient adaptive control precision, excessively high energy consumption and weak dynamic response capability of the engineering vehicle in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering vehicle control and intelligent optimization, and particularly relates to an engineering vehicle adaptive control strategy optimization method. BACKGROUND

[0002] With the continuous improvement of the automation and intelligence of engineering construction machinery, the operation control strategy of engineering vehicles has gradually become one of the key technical fields for improving construction efficiency, reducing energy consumption and improving safety performance; the traditional engineering vehicle control method adjusts parameters based on preset parameters or experience models, lacks real-time sensing and feedback mechanisms for changes in the environment and changes in the state of the vehicle during operation, and is difficult to achieve fine control in a dynamic environment, which can easily lead to increased energy consumption, insufficient control accuracy and safety hazards.

[0003] In recent years, advanced intelligent control technologies such as fuzzy control, neural network control and simple feedback control have been gradually introduced in the field of engineering vehicles, but these methods usually only consider the local state or specific working conditions of the engineering vehicle, and it is difficult to fully achieve energy consumption optimization and high-precision adaptive control of the vehicle in a complex environment; at the same time, the operating environment of the engineering vehicle is complex and variable, such as changes in terrain, random appearance of obstacles, dynamic changes in load state and uncertainty factors of meteorological conditions, which puts higher real-time and adaptability requirements on the control accuracy and energy efficiency of the vehicle, and existing control methods are difficult to fully meet these needs.

[0004] At present, the existing dynamic programming control method ignores the fusion of real-time environmental data and vehicle operating state, fails to establish an accurate and real-time updated optimization model, and is difficult to truly achieve real-time and efficient energy consumption optimization of the engineering vehicle in a complex dynamic environment; in addition, the existing technology lacks a complete and unified control strategy implementation framework, making it difficult to coordinate the dynamic correlation between the drive, brake and steering systems, affecting the effectiveness of the overall control strategy.

[0005] In summary, the existing engineering vehicle control technology has problems of insufficient utilization of real-time multi-source information, insufficient adaptive control capability and lack of unified coordination mechanism for overall optimization strategy, and the present application provides an engineering vehicle adaptive control strategy optimization method, which solves the problems of incomplete optimization of engineering vehicle control parameters, weak real-time adaptive capability and high energy consumption in the prior art. SUMMARY

[0006] This section is intended to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments, and some simplifications or omissions may be made in this section and the abstract and title of the present application to avoid obscuring the purpose of this section, the abstract and the title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0007] In view of the above existing problems, the present application is proposed.

[0008] To solve the above technical problems, the present application provides the following technical solutions: collecting the running state data and environmental parameters of the engineering vehicle in real time through a multi-source sensor, the running state data including speed, acceleration, steering angle and load state, and the environmental parameters including terrain features, obstacle distribution and weather conditions;

[0009] An optimization model is constructed based on a dynamic programming algorithm, taking the minimization of the working energy consumption of the engineering vehicle as the optimization target, and the running state data and environmental parameters are input into the optimization model for real-time calculation to generate a dynamic control strategy;

[0010] According to the dynamic control strategy, the control parameters of the drive system, brake system and steering system of the engineering vehicle are dynamically adjusted.

[0011] As a preferred scheme of the engineering vehicle adaptive control strategy optimization method of the present application, the optimization model is constructed based on a dynamic programming algorithm, taking the minimization of the working energy consumption of the engineering vehicle as the optimization target, and includes:

[0012] The system state variables are defined to include speed v(t), acceleration a(t), steering angle θ(t), load state m(t) and terrain feature parameter p(t), and the control variables include drive torque τ(t), brake force F(t) and steering angle correction amount Δθ(t); terr drive brake

[0013] In a scheduling period T, the energy consumption is represented as E(t), and the total energy consumption is:

[0014]

[0015] The optimization target is min J;

[0016] The state transition equation is established, and the relationship between the speed, acceleration and steering angle with time evolution is represented as:

[0017] v(t+1) = f(v(t), a(t), τ(t), F(t), p(t)) drive brake terr

[0018] θ(t+1) = θ(t) + Δθ(t)

[0019] Wherein, the function f(·) is used to reflect the speed evolution process of the vehicle under the combined action of the current terrain, load and drive and brake;

[0020] ​​​​​​Discretize the scheduling period [0, T] into several time points t = 0, 1, 2, …, T, and search all control sequences at each discrete time point;

[0021] Let V(t, s) be the minimum cumulative energy consumption value from the current to the end of the period at time t and state s, then:

[0022] V(t, s) = min u(t) {E(s, u(t)) + V(t+1, s')}

[0023] Wherein, u(t) represents the selectable set of control variables, and s' represents the next time state calculated by the state transition equation;

[0024] Through backward iteration calculation, the control variable u(t) that can minimize the cumulative energy consumption is selected at each state, and the optimal control sequence is constructed; *

[0025] When the iteration is completed, based on the obtained optimal control sequence, the corresponding driving torque, braking force and steering angle correction amount are matched at each discrete time point;

[0026] The optimal control amount is mapped back to the engineering vehicle control instruction, and the dynamic control strategy is obtained.

[0027] As a preferred scheme of the engineering vehicle adaptive control strategy optimization method, when establishing the state transition equation, a constraint condition needs to be introduced for limitation, and the constraint condition includes:

[0028] Physical limitations of speed, acceleration and steering angle change:

[0029] 0 ≤ v(t) ≤ v max , 0 ≤ a(t) ≤ a max , |θ(t)| ≤ θ max

[0030] Wherein, v max , a max and θ max respectively represent the maximum speed, maximum acceleration and steering angle allowed by the vehicle;

[0031] Terrain and load restrictions:

[0032] p terr (t) ∈ P, m(t) ∈ [m min , m max ]

[0033] Wherein, P represents a pre-defined terrain feature set, m min and m max respectively represent the minimum and maximum load of the engineering vehicle; ​

[0034] Obstacle and safe driving limit:

[0035] d obs (t)≥d safe

[0036] wherein d obs (t) represents the distance between the vehicle and the obstacle, d safe is the safety distance threshold, if d obs (t) < d safe , the emergency braking is performed preferentially;

[0037] Influence of weather and road conditions on vehicle speed and braking:

[0038] v(t)≤f weather (weather condition)

[0039] F brake (t)≤f brake (coefficient of ground friction)

[0040] wherein f weather (·) and f brake (·) are functions for dynamically adjusting the maximum safe speed and braking force of the vehicle according to the weather condition and the coefficient of ground friction, respectively.

[0041] As a preferred scheme of the adaptive control strategy optimization method of the engineering vehicle according to the present application, the running state data and the environmental parameters are input into the optimization model for real-time calculation to generate a dynamic control strategy, which comprises:

[0042] The optimal estimated speed v(t), acceleration a(t), steering angle θ(t), load state m(t), and the quantitative information of the external environment are comprehensively combined to form a state vector S(t) at time t:

[0043] S(t)=[v(t),a(t),θ(t),m(t),p terr (t),d obs (t),…]

[0044] At each discrete time, the energy consumption of the vehicle in the subsequent time domain is re-evaluated according to the latest state vector S(t) and environmental parameters;

[0045] Different control sequences are traversed through a dynamic programming algorithm, and under the premise of meeting the requirements of safe driving and operation, the optimal control quantity u * (t) at the next time is calculated as the optimization objective of minimum energy consumption;

[0046] The optimal solution u *(t) as output, make the vehicle real-time perform corresponding driving torque, braking force and steering angle adjustment;

[0047] Repeat the above process to obtain the latest control amount and form a dynamic optimization control strategy at each new time.

[0048] As a preferred scheme of the engineering vehicle adaptive control strategy optimization method of the present application, the optimal estimation comprises:

[0049] The real-time acquired operating state data is subjected to Kalman filtering processing to remove sensor noise and random interference;

[0050] The collected environmental parameters are classified;

[0051] The terrain features are converted into numerical form that can be used in the dynamic programming model;

[0052] The relative distance and direction of the obstacle are subjected to data fusion to obtain d obs And the obstacle size and moving speed information;

[0053] The acquired meteorological parameters are input into the road surface friction function to form a complete environmental input vector;

[0054] The operating state data and the environmental parameters are fused by using the particle filtering method to obtain the optimal estimation of the current state of the vehicle.

[0055] As a preferred scheme of the engineering vehicle adaptive control strategy optimization method of the present application, the dynamic control strategy comprises a driving system output power distribution strategy, a braking system adjustment strategy and a steering system correction strategy.

[0056] As a preferred scheme of the engineering vehicle adaptive control strategy optimization method of the present application, the control parameters of the driving system, the braking system and the steering system of the engineering vehicle are dynamically adjusted according to the dynamic control strategy, wherein the dynamic adjustment of the driving system parameters comprises:

[0057] The target torque generated by the driving system output power distribution strategy is input to the driving system; As input;

[0058] If the engineering vehicle is a fuel engine, the fuel injection amount and the intake amount are adjusted according to the engine speed-torque map;

[0059] If the engineering vehicle is a hybrid vehicle or a pure electric vehicle, the motor current and the gear position are adjusted;

[0060] When the vehicle needs to climb a slope, the accelerator is increased or the motor output is increased;

[0061] When the vehicle is on a flat road section, the torque output is reduced, thereby achieving energy saving.

[0062] As a preferred scheme of the adaptive control strategy optimization method of the engineering vehicle, the dynamic adjustment of the braking system parameter scheduling comprises:

[0063] According to the braking system adjustment strategy, the current optimal braking force is obtained and is mapped as a brake master cylinder pressure and a brake pedal displacement.

[0064] If an emergency occurs, a high braking force is immediately called, and a vehicle electronic auxiliary system is enabled to ensure that the wheels are not locked.

[0065] If the deceleration is small, a small braking force is used, or regenerative braking is used to recover energy.

[0066] As a preferred scheme of the adaptive control strategy optimization method of the engineering vehicle, the dynamic adjustment of the steering system parameter scheduling comprises:

[0067] According to the steering system correction strategy, a steering angle correction amount Δθ is calculated * (t) is used to drive a steering actuator.

[0068] If the vehicle is in a high-speed working condition or on a wet road, the steering sensitivity is appropriately reduced, and the return torque is increased.

[0069] If the vehicle is in a low-speed working condition, the steering sensitivity is increased to quickly correct the path.

[0070] During the steering execution process, the vehicle yaw rate and wheel speed sensor data are continuously monitored, and if the steering is excessive or insufficient, the steering angle is corrected again.

[0071] The beneficial effects of the present application are:

[0072] 1. By obtaining real-time accurate state and environmental data of the engineering vehicle, reliable information support is provided for subsequent control strategy formulation, real-time monitoring of vehicle operating conditions and working environmental conditions is performed, the integrity and real-time performance of data acquisition are ensured, and a data foundation is laid for high-precision and high-safety adaptive control.

[0073] 2. Through real-time online optimization of the engineering vehicle control strategy, the control behavior of the vehicle is dynamically planned and optimized according to the actual environmental conditions, the real-time performance and efficiency of the optimization decision are improved, and the energy efficiency and working accuracy of the engineering vehicle under complex dynamic conditions are fundamentally improved.

[0074] 3. According to the generated dynamic control strategy, the control parameters of the drive system, the brake system and the steering system of the engineering vehicle are dynamically adjusted, the theoretical optimal control strategy generated by the optimization model is converted into an actual executable real-time control action, the accurate response and execution efficiency of the engineering vehicle control system are ensured, the control systems of the engineering vehicle are accurately coordinated, the engineering vehicle can respond and cooperate with the environmental changes and vehicle state changes in real time, the energy consumption of the vehicle is significantly reduced, the engineering operation safety is enhanced, and the vehicle control stability is improved. BRIEF DESCRIPTION OF DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0076] Figure 1 The flowchart of the engineering vehicle adaptive control strategy optimization method shown in the present application. DETAILED DESCRIPTION

[0077] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, not all embodiments.

[0078] Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0079] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0080] According to the embodiments of the present application, combined with the flowchart shown in Figure 1 An engineering vehicle adaptive control strategy optimization method, specifically comprising the following steps:

[0081] S1, real-time acquisition of the running state data and environmental parameters of the engineering vehicle through multi-source sensors. It should be noted that in this step:

[0082] A number of sensors are arranged on the vehicle body for collecting vehicle dynamic information and environmental information, the vehicle dynamic information sensors include but are not limited to: speed sensors (such as wheel speed sensors), acceleration sensors (such as inertial measurement units IMU), steering angle sensors (installed on the steering system), load detection sensors (detecting the current load mass or the center of gravity position of the vehicle);

[0083] The environmental information sensors include but are not limited to: laser radar (for detecting obstacle distribution), camera (recognizing terrain features and obstacle profiles), meteorological sensors (such as temperature, humidity, rainfall, wind speed detectors);

[0084] Further, in order to facilitate subsequent processing, data sampling is performed in discrete steps Δt on the time axis, each sampling produces a set of vehicle sensor data and environmental sensor data at time t, forming an original multi-source measurement data set.

[0085] As an example, the running state data includes:

[0086] Speed data: the driving speed of the vehicle at time t is obtained through the speed sensor;

[0087] Acceleration data: the acceleration components of the vehicle in three-dimensional directions are obtained through the inertial measurement unit (IMU), and the acceleration of the vehicle along the forward direction or in a specific reference coordinate system is comprehensively obtained;

[0088] Steering angle data: the current steering angle of the vehicle steering system is obtained through the steering angle sensor;

[0089] Load state: the current load mass or the center of gravity position is obtained according to the load sensor installed on the top of the vehicle or the chassis.

[0090] As an example, the environmental parameter data includes:

[0091] Terrain feature parameters: the road surface slope, undulation or road surface type (such as sand, mud, asphalt) is recognized by laser radar and camera, and is quantified as terrain difficulty level, slope value, friction coefficient;

[0092] Obstacle distribution information: the position and distance of the obstacle are recognized by the laser radar, and the relative distance between the vehicle and the obstacle and the obstacle shape / moving state are obtained;

[0093] Meteorological condition parameters: temperature, humidity, rainfall, wind speed and direction information are obtained by meteorological sensors, forming a meteorological state vector, which provides a basis for subsequent vehicle speed limit and braking safety distance.

[0094] S2. Construct an optimization model based on dynamic programming algorithm, with the goal of minimizing the operating energy consumption of the engineering vehicle. Input the operating status data and environmental parameters into the optimization model for real-time calculation to generate a dynamic control strategy. Note that the following points should be noted in this step:

[0095] The system state variables are defined as velocity v(t), acceleration a(t), steering angle θ(t), load state m(t), and terrain feature parameter p. terr (t), where the control variables include the drive torque τ drive (t), braking force F brake (t) and steering angle correction Δθ(t);

[0096] Within the scheduling period T, if the energy consumption is expressed as E(t), then the total energy consumption is:

[0097]

[0098] The optimization objective is to minimize J.

[0099] Establishing the state transition equations, the relationship between velocity, acceleration, and steering angle as a function of time can be expressed as:

[0100] v(t+1)=f(v(t),a(t),τ drive (t),F brake (t),p terr (t))

[0101] θ(t+1) = θ(t) + Δθ(t)

[0102] Among them, the function f(·) is used to reflect the speed evolution of the vehicle under the current terrain, load and combined driving and braking effects;

[0103] Furthermore, constraints are introduced, where:

[0104] Physical limitations on changes in velocity, acceleration, and steering angle:

[0105] 0≤v(t)≤v max ,0≤a(t)≤a max ,|θ(t)|≤θ max

[0106] Among them, v max a max and θ max These represent the vehicle's maximum permissible speed, maximum acceleration, and steering angle, respectively.

[0107] Terrain and load limitations:

[0108] p terr (t)∈P,m(t)∈[mmin ,m max ]

[0109] where P represents a predefined set of terrain features, m min and m max represent the minimum and maximum load capacity of the engineering vehicle, respectively;

[0110] Obstacles and safe driving limits:

[0111] d obs (t)≥d safe

[0112] where d obs (t) represents the distance between the vehicle and the obstacle, d safe is the safe distance threshold, and if d obs (t) < d safe , then emergency braking is prioritized;

[0113] Weather and road conditions impact on vehicle speed and braking:

[0114] v(t)≤f weather (weather conditions)

[0115] F brake (t)≤f brake (coefficient of friction of the road surface)

[0116] where f weather (·) and f brake (·) are functions that dynamically adjust the maximum safe speed of the vehicle and the braking force, respectively, according to weather conditions and the coefficient of friction of the ground;

[0117] Discretize the scheduling period [0, T] into several time instants t = 0, 1, 2, …, T, and search for all control sequences at each discrete time instant;

[0118] Let V(t, s) be the minimum cumulative energy consumption value from the current to the end of the period at time t and state s, then:

[0119] V(t, s) = min u(t) {E(s, u(t)) + V(t + 1, s')}

[0120] where u(t) represents the selectable set of control variables, and s' represents the next time state calculated by the state transition equation;

[0121] Through backward iteration, the control variable u * (t) that minimizes the cumulative energy consumption at each state is selected to form the optimal control sequence;

[0122] When the iteration is completed, the optimal control sequence is obtained, and the corresponding driving torque, braking force and steering angle correction are matched at each discrete time;

[0123] The optimal control quantity is mapped back to the engineering vehicle control instruction, i.e. the dynamic control strategy is obtained.

[0124] In an optional embodiment, the Kalman filtering process is performed on the real-time acquired operating state data to remove sensor noise and random interference;

[0125] The collected environmental parameters are classified;

[0126] The terrain features are converted into a numerical form that can be used in the dynamic programming model;

[0127] The relative distance and direction of the obstacle are data fused to obtain d obs and the size and moving speed information of the obstacle;

[0128] The acquired meteorological parameters are input into the road surface friction function to form a complete environmental input vector;

[0129] The particle filtering method is used to fuse the operating state data and the environmental parameters to obtain the optimal estimation of the current state of the vehicle;

[0130] The speed v(t), acceleration a(t), steering angle θ(t) and load state m(t) after optimal estimation, and the quantitative information of the external environment are comprehensively integrated to form the state vector S(t) at time t:

[0131] S(t) = [v(t), a(t), θ(t), m(t), p terr (t), d obs (t), …]

[0132] At each discrete time, the energy consumption of the vehicle in the subsequent time domain is re-evaluated according to the latest state vector S(t) and environmental parameters;

[0133] The dynamic programming algorithm is used to traverse different control sequences, and under the premise of meeting the requirements of safe driving and operation, the optimal control quantity u * (t) at the next time is calculated as the optimization objective of minimum energy consumption;

[0134] The optimal solution u * (t) at the current time is taken as the output, and the vehicle is made to execute the corresponding driving torque, braking force and steering angle adjustment in real time;

[0135] When each new time arrives, the above process is repeated to obtain the latest control quantity and form a dynamic optimization control strategy.

[0136] It is further needed to be explained that the dynamic control strategy includes a driving system output power distribution strategy, a braking system adjustment strategy and a steering system correction strategy.

[0137] In an optional embodiment, the driving system output power distribution strategy includes:

[0138] If it is detected that the vehicle is in a large slope terrain, the current speed v(t) is compared with the target speed v target :

[0139] If v(t) < v target , and the torque is insufficient to overcome the slope resistance, the driving torque τ drive (t) is increased;

[0140] If v(t) ≥ v target , the driving torque is reduced to avoid unnecessary energy consumption;

[0141] If it is detected that the vehicle is in a flat terrain, the driving torque is reduced to reduce energy consumption under the premise of meeting the work requirements.

[0142] In an optional embodiment, the braking system adjustment strategy includes:

[0143] When the real-time detected obstacle distance d obs (t) approaches the safety threshold d safe , and there is a risk of collision, the braking force F brake (t) is preferentially increased;

[0144] When there is sufficient safety distance, only a small braking force is applied or regenerative braking is used (if it is a hybrid or electric system) to achieve smooth deceleration to reduce energy consumption;

[0145] In an emergency, the ABS system is activated to ensure that the vehicle does not lose control.

[0146] In an optional embodiment, the steering system correction strategy includes:

[0147] After the relative position of the obstacle is obtained, the expected steering angle θ desired (t) is compared with the current steering angle θ(t):

[0148] If θ(t) ≠ θ desired (t), the steering angle correction amount is calculated:

[0149] Δθ(t) = θ desired (t) - θ(t)

[0150] If there is an obstacle nearby and obstacle avoidance is required, the vehicle is preferentially biased to the safe side to ensure that the vehicle does not collide with the obstacle;

[0151] In order to reduce unnecessary energy loss and reduce the steering impact, when the road is relatively flat and there are no obstacles, the steering angle correction amount is maintained in a small range.

[0152] Preferably, by defining the system state variables, control variables and objective functions, the real-time vehicle operating state data (such as speed, acceleration, steering angle, load state) and external environmental parameters (such as terrain characteristics, obstacle distribution, weather conditions) collected from step S1 are input into the optimization model constructed by the dynamic programming algorithm, and multi-step iteration and search are performed on the entire scheduling period to solve the dynamic control strategy that minimizes the energy consumption of the engineering vehicle operation. The dynamic control strategy is executed in step S3, thereby achieving the purpose of adaptively controlling the driving, braking and steering of the engineering vehicle.

[0153] It should be further noted that through the implementation of the above steps, the energy consumption optimization model constructed based on the dynamic programming algorithm can fully utilize the vehicle operating state data and environmental parameters, continuously update the control decision for the actual working condition, effectively eliminate noise and uncertainty by combining the filtering method, and further improve the decision reliability and accuracy. On this basis, the output dynamic control strategy can not only meet the safety limit, but also complete the task of the engineering vehicle in complex working environment with minimum energy consumption.

[0154] Preferably, by defining the state variables and control variables, establishing the objective function and state transition equation, setting multiple constraint conditions and iteratively solving the optimal control sequence, the minimization of energy consumption is realized under the premise of ensuring safe driving and operation requirements.

[0155] S3, dynamically adjusting the control parameters of the driving system, braking system and steering system of the engineering vehicle according to the dynamic control strategy. It should be noted that in this step:

[0156] (1) Dynamic adjustment of driving system parameters

[0157] The target torque generated by the driving system output power distribution strategy is input as an input;

[0158] If the engineering vehicle is a fuel engine, adjust the fuel injection amount and air intake amount according to the engine speed-torque map;

[0159] If the engineering vehicle is a hybrid / electric vehicle, adjust the motor current and gear position;

[0160] When the vehicle needs to climb a slope, increase the throttle / increase the motor output;

[0161] When the vehicle is on a flat road, reduce the torque output, thereby achieving energy saving.

[0162] (2) Dynamic adjustment of braking system parameters​

[0163] According to the brake system adjustment strategy, the current optimal braking force is obtained And map it to the brake master cylinder pressure, brake pedal displacement;

[0164] If an emergency occurs, immediately call for high braking force, and enable the vehicle electronic auxiliary system (such as ABS, EBD), to ensure that the wheels do not lock, and improve braking safety;

[0165] If the deceleration is small, use a smaller braking force, or use regenerative braking to recover energy.

[0166] (3) Dynamic adjustment of steering system parameters

[0167] According to the steering system correction strategy, the calculated steering angle correction amount Δθ * (t) for driving the steering actuator (such as electric power steering or hydraulic power steering);

[0168] If the vehicle is in high-speed operation or on a wet road, the steering sensitivity should be appropriately reduced, and the return torque should be increased;

[0169] If the vehicle is in a low-speed environment, increase the steering sensitivity to quickly correct the path;

[0170] During steering execution, the vehicle yaw rate and wheel speed sensor data are continuously monitored, and if the steering is excessive or insufficient, the steering angle is corrected again.

[0171] It should be noted that the adaptive dynamic adjustment of the drive system, brake system and steering system is the core link of the optimal control strategy generated by step S2 to the physical level of the vehicle, through linkage control and real-time correction, to ensure that the whole vehicle can balance the power demand, energy consumption performance and safety requirements in the changing environment and work tasks.

[0172] The aforementioned preprocessing and vector correction method of multi-source data can be performed using existing techniques and means, which will not be described in detail in this example.

[0173] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for optimizing an adaptive control strategy for an engineering vehicle, characterized in that, include: The operation status data and environmental parameters of the engineering vehicle are collected in real time by multi-source sensors. The operation status data includes speed, acceleration, steering angle and load status, and the environmental parameters include terrain features, obstacle distribution and weather conditions. An optimization model is constructed based on a dynamic programming algorithm. The optimization objective is to minimize the operating energy consumption of the engineering vehicle. The operating status data and environmental parameters are input into the optimization model for real-time calculation to generate a dynamic control strategy. include: Define system state variables including velocity acceleration Steering angle Load status and terrain feature parameters Control variables include drive torque Braking force and steering angle correction amount ; Within the scheduling period T, energy consumption is expressed as... Then the total energy consumption is: The optimization goal is ; Establishing the state transition equations, the relationship between velocity, acceleration, and steering angle as a function of time can be expressed as: Among them, the function It is used to reflect the speed evolution of a vehicle under current terrain, load, and combined driving and braking effects; scheduling period Discretized into several time periods At each discrete time step, all control sequences are searched; make Let be the minimum cumulative energy consumption from the current state to the end of the cycle at time t and state s. Then: in, Represents an optional set of control variables. This represents the state at the next time step, calculated from the state transition equation. By iteratively calculating from back to front, the control quantity that minimizes the cumulative energy consumption is selected in each state. This constitutes the optimal control sequence; After the iteration is completed, based on the obtained optimal control sequence, the corresponding drive torque, braking force and steering angle correction are matched at each discrete time. Mapping the optimal control quantity back to the control command of the engineering vehicle yields the dynamic control strategy; When establishing the state transition equation, constraints also need to be introduced for restriction. The constraints include: Physical limitations on changes in velocity, acceleration, and steering angle: in, , and These represent the vehicle's maximum permissible speed, maximum acceleration, and steering angle, respectively. Terrain and load limitations: Where P represents a predefined set of terrain features. and These represent the minimum and maximum load capacities of the engineering vehicle, respectively. Obstacles and safe driving restrictions: in, Indicates the distance between the vehicle and the obstacle. As a safe distance threshold, if In this case, emergency braking should be performed first. The impact of weather and road conditions on vehicle speed and braking: in, and These are functions that dynamically adjust the vehicle's maximum safe speed and braking force based on weather conditions and road surface friction coefficient; The operational status data and environmental parameters are input into the optimization model for real-time calculation to generate a dynamic control strategy, including: The optimally estimated speed acceleration Steering angle Load status Together with quantitative information about the external environment, they form the state vector at time t: At each discrete time, based on the latest state vector Based on environmental parameters, reassess the vehicle's energy consumption in subsequent time domains; By iterating through different control sequences using a dynamic programming algorithm, and with the goal of minimizing energy consumption while meeting the requirements for safe driving and operation, the optimal control quantity for the next time step is calculated. ; Take the optimal solution at the current moment As an output, the vehicle can perform corresponding adjustments to drive torque, braking force and steering angle in real time; At each new moment, the above process is repeated to obtain the latest control input and form a dynamically optimized control strategy. The control parameters of the driving system, braking system, and steering system of the engineering vehicle are dynamically adjusted according to the dynamic control strategy.

2. The method for optimizing the adaptive control strategy of engineering vehicles according to claim 1, characterized in that, The optimal estimate includes: Kalman filtering is applied to the real-time acquired operating status data to remove sensor noise and random interference; Classify the collected environmental parameters; Convert terrain features into a numerical form that can be used in dynamic programming models; Data fusion of relative distance and orientation of obstacles yields And information on obstacle size and movement speed; The acquired meteorological parameters are input into the road surface friction function to form a complete environmental input vector; By using the particle filtering method, the operating status data is fused with environmental parameters to obtain the optimal estimate of the vehicle's current state.

3. The method for optimizing the adaptive control strategy of engineering vehicles according to claim 1, characterized in that, The dynamic control strategy includes a drive system output power distribution strategy, a braking system adjustment strategy, and a steering system correction strategy.

4. The method for optimizing the adaptive control strategy of engineering vehicles according to claim 3, characterized in that, The control parameters of the engineering vehicle's drive system, braking system, and steering system are dynamically adjusted according to the dynamic control strategy, wherein the dynamic adjustment of the drive system parameters includes: The target torque generated by the output power distribution strategy of the drive system As input; If the engineering vehicle has a fuel engine, adjust the fuel injection quantity and intake air quantity according to the engine speed-torque diagram; If the engineering vehicle is a hybrid / pure electric vehicle, adjust the motor current and gear ratio; When the vehicle needs to climb a hill, increase the throttle / increase the motor output; When the vehicle is on a flat road, the torque output is reduced to achieve energy saving.

5. The method for optimizing the adaptive control strategy of engineering vehicles according to claim 4, characterized in that, The dynamic adjustment of the braking system parameters includes: Based on the braking system adjustment strategy, obtain the current optimal braking force. And map it to brake master cylinder pressure and brake pedal displacement; In case of an emergency, immediately apply high braking force and activate the vehicle's electronic assistance systems to ensure that the wheels do not lock up; If the deceleration is slight, a smaller braking force should be used, or regenerative braking should be used to recover energy.

6. The method for optimizing the adaptive control strategy of engineering vehicles according to claim 4, characterized in that, The dynamic adjustment of the steering system parameter scheduling includes: The steering angle correction amount is calculated based on the steering system correction strategy. Used to drive steering actuators; If the vehicle is operating at high speed or on a slippery road surface, reduce the steering sensitivity and increase the return torque accordingly. If the vehicle is operating at low speed, the steering sensitivity is increased to quickly correct the path. During the steering process, the vehicle's yaw rate and wheel speed sensor data are continuously monitored. If oversteering or understeering occurs, the steering angle is corrected again.

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