Active disturbance rejection steering control method and system for trucks in high-altitude and cold regions

Abstract

This invention discloses an active disturbance rejection steering control method and system for trucks in cold regions. Based on the problems faced by trucks on icy and snowy roads in cold regions, the artificial lemming optimization algorithm is improved to obtain an improved artificial lemming algorithm, and the fitness value of the improved artificial lemming algorithm is calculated. Based on the improved artificial lemming optimization algorithm, the optimal fitness is selected after iterative processing, and the optimal position coordinates corresponding to the optimal fitness are assigned to the parameters of the active disturbance rejection controller. The parameters of the active disturbance rejection controller are substituted into the active disturbance rejection controller and applied to a pre-established front-wheel hydraulic power steering and rear-wheel steer-by-wire system model. An active rear-wheel steering control strategy is designed, using LQR control to output the compensation angle of the rear-wheel steering, which is combined with the rear-wheel steering angle output by the steer-by-wire part to form a hierarchical control strategy. Simultaneously, the front wheels use hydraulic power steering to control the vehicle to complete the driving conditions on medium-icy roads in cold regions.

Description

Technical Field

[0001] This invention relates to the field of vehicle electronic control technology, specifically to an active disturbance rejection steering control method and system for trucks in cold regions. Background Technology

[0002] High-altitude and frigid regions face harsh environments year-round, characterized by extremely low temperatures, icy roads, and strong winds. Traditional trucks mostly employ hydraulic control systems. However, hydraulic oil viscosity increases at low temperatures, leading to delayed or stuck power steering, and even pump damage. Simultaneously, the system lacks dynamic compensation capabilities for reduced traction on icy roads and crosswind interference, resulting in increased vehicle yaw and a higher risk of accidents. Steer-by-wire systems replace traditional mechanical connections with electronic signals, eliminating hydraulic components and avoiding the problem of hydraulic fluid solidification at low temperatures. However, current commercial electric motors struggle to meet the extreme steering torque demands of fully loaded heavy-duty trucks, and the performance of sensors and motor drivers degrades in extreme low-temperature environments. Therefore, pure steer-by-wire systems cannot be directly applied to trucks in this scenario.

[0003] To address the above issues, a control scheme combining a hydraulic electronic steering system and a steer-by-wire system is considered. The front axle retains the mature hydraulic steering system, leveraging its ability to stably output high torque even in low-temperature environments to ensure the reliability of basic steering functions and avoid the risk of electronic component failure that may occur with pure steer-by-wire in extremely cold conditions. The rear axle employs steer-by-wire technology, controlling the rear wheel angle via electrical signals to improve vehicle stability on low-traction surfaces. However, traditional PID control struggles to meet the system's requirements for anti-interference and high robustness.

[0004] Active Disturbance Rejection Controllers (ADRCs) are better able to handle the complex dynamics and uncertainties of steer-by-wire systems due to their superior nonlinear control capabilities and disturbance rejection performance. However, nonlinear ADRCs have too many parameters, making parameter tuning difficult. Methods employed mainly include empirical methods, artificial intelligence methods, and time-scale-based methods. Swarm intelligence algorithms are widely used in controller parameter optimization, reducing tuning complexity. Existing artificial lemming optimization algorithms are prone to getting trapped in local optima, making them unsuitable for real-time system control. Summary of the Invention

[0005] The purpose of this invention is to provide a self-disturbance-resistant steering control method and system for trucks in cold regions, so as to effectively address the problems of steering assist failure and insufficient dynamic stability of traditional commercial vehicles under low road surface adhesion coefficient conditions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: An active disturbance rejection steering control method for trucks in high-altitude and cold regions includes the following steps: Step 1: Based on the problems faced by trucks on icy and snowy roads in high-altitude and cold regions, the artificial lemming optimization algorithm is improved to obtain the improved artificial lemming algorithm, and the fitness value of the improved artificial lemming algorithm is calculated. Step 2: Based on the improved artificial lemming optimization algorithm, the optimal fitness is selected after iterative processing, and the best position coordinates corresponding to the optimal fitness are assigned to the parameters of the active disturbance rejection controller. Step 3: Substitute the parameters of the active disturbance rejection controller into the active disturbance rejection controller and apply them to the pre-established front-wheel hydraulic electric power steering and rear-wheel steer-by-wire system models; Step 4: Design an active rear-wheel steering control strategy. Utilize LQR control to output the compensated steering angle of the rear wheels. Combine this with the rear-wheel steering angle output by the steer-by-wire part in Step 3 to form a layered control strategy. At the same time, use hydraulic electronic power steering for the front wheels to control the vehicle to complete the driving conditions on icy roads in cold regions.

[0007] Furthermore, the specific models of the front-wheel hydraulic electronic power steering and rear-wheel steer-by-wire system are as follows: (1) (2) (3) (4) (5) (6) (7) Among them, formula (1) is the equation for the equilibrium of transverse forces, where, m For vehicle quality; v x This refers to the longitudinal speed of the vehicle. v y This refers to the lateral speed. r This refers to the yaw rate; F y1 , F y2 These are the lateral forces on the front and rear axle tires, respectively. F wind The crosswind interference force; formula (2) is the yaw moment balance equation, where, I z The moment of inertia is about the Z-axis; a 1. a 2 represents the longitudinal distance from the front and rear axles to the center of mass; M windThe crosswind yaw moment; formulas (3), (4), and (5) are models of the steering actuator, where T m This refers to the torque of the steering motor; J m The moment of inertia of the steering motor shaft; B m This refers to the damping coefficient of the steering motor shaft; K m For the torsional stiffness of the steering motor shaft; θ m , m 、 m These are the steering motor shaft rotation angle, angular velocity, and angular acceleration, respectively. G m This refers to the reduction ratio of the steering motor reducer; R p The radius of the small gear at the pitch circle of the steering gear; X r This represents the rack displacement; T fm This is the equivalent frictional torque of the steering motor; u m For steering actuator motor armature voltage; i m , m These are the armature current and current change rate of the steering actuator motor, respectively. R m Resistance of the armature winding of the steering actuator motor; L m The armature winding inductance of the steering actuator motor; K b The proportional coefficient of the back EMF of the steering actuator motor; C m The electromagnetic torque coefficient of the steering actuator motor is given by equation (6), where the hydraulic assist torque is given by equation (6). K p For hydraulic cylinder pressure-torque gain; P L For load pressure; θ cmd The steering wheel command indicates the angle of rotation; θ w This is the actual steering angle of the front wheels; K s For the steering column stiffness, equation (7) is the dynamic equation for the front wheel steering angle, where, J w This refers to the inertia of the front wheel steering system. b w This refers to the steering damping coefficient; Ttire This is the tire return torque.

[0008] Furthermore, the artificial lemming optimization algorithm includes the following steps: Step 1.1: Randomly initialize the artificial lemming population; Step 1.2: Determine whether to conduct global development or local search of the artificially broodstock population based on energy factors; Step 1.3: During the global development process, randomly select long-distance migration behavior or burrowing behavior to update the population location; during the local search process, randomly select foraging behavior or predator evasion behavior to update the population location. Step 1.4: Calculate the fitness values ​​of all artificial lemming individuals after location updates, and select the optimal fitness value; Step 1.5: Determine if the maximum number of iterations has been reached. If it has, output the location coordinates and fitness of the optimal lemming individual; if not, return to step 2.2.

[0009] Furthermore, in step 1.2, the formula for calculating the energy factor is as follows:

[0010] In the formula, T max The maximum number of iterations, t The current iteration number is given, and rand is a random number that follows a uniform distribution within the range [0,1]. In step 1.3, when overpopulation leads to food shortages, lemmings begin long-distance migration, as shown in the mathematical model below:

[0011] In the formula, Z ( t () represents the lemming's current location. Z ( t +1) represents the updated position. Z best ( t () represents the optimal position from initialization to the current search. Z a For lemming individuals randomly selected from the population, a The integers are in the range [1, n], where n is the population size. t This represents the current iteration number. BM To characterize the random number vector of Brownian motion, the step size of standard Brownian motion is obtained from the probability density function of a normal distribution with variance of 1 and mean of 0. R Let be a vector of size 1×Dim, whose elements are random numbers uniformly distributed in the interval [-1,1]. FA marker to change the search direction;

[0012] In the formula, The floor function for rounding down;

[0013]

[0014] In the formula, Dim is the dimension of the problem space, and rand(1,Dim) is a random number that follows a uniform distribution within the range (1,Dim). The mathematical model for digging behavior is as follows:

[0015] In the formula, L A random number related to the current iteration number. Z b Let b be an individual lemming randomly selected from the population, where b is an integer in the range [1, n]. L The expression is as follows:

[0016] The mathematical model for foraging behavior is as follows:

[0017] In the formula, spiral The spiral shape representing the random search during foraging is calculated by the following formula:

[0018]

[0019] In the formula, radius The radius represents the foraging range, i.e., the Euclidean distance between the current position and the optimal solution. Z best,k (t) indicates that in t In the nth iteration, the optimal solution is... k The value in dimension Z i,k (t) indicates that in t In the next iteration, the individual i No. k Values ​​in the dimension; The mathematical model for predator evasion behavior is as follows:

[0020] In the formula, G Let be the escape coefficient, representing the lemming's escape ability. It decreases as the number of iterations increases, and its expression is: , Tmax The maximum number of iterations is given by 'Lev'(Dim), which represents Levy's flight and is used to simulate the deceptive movements of a lemming when it tries to escape.

[0021] Furthermore, the improvement of the artificial lemming optimization algorithm to obtain the improved artificial lemming algorithm specifically includes the following steps: Step 2.1: Use the lemming's seven-dimensional coordinates as the values ​​of the seven parameters of the active disturbance rejection controller; Step 2.2: Design an energy-driven role switching strategy, and determine whether to perform global development or local search. If it is global development, proceed to step 2.3; if it is local search, proceed to step 2.4. Step 2.3: Randomly select long-distance migration behavior or cooperative burrowing strategy to update individual location; Step 2.4: Randomly select either foraging behavior or sentry escape mechanism to update individual location; Step 2.5: After each lemming individual updates its location, calculate the fitness of the lemming population and select the optimal lemming individual based on the fitness value; Step 2.6: Determine if the maximum number of iterations has been reached. If yes, output the location coordinates and fitness value of the optimal lemming individual. If no, return to step 2.2.

[0022] Furthermore, the energy-driven role switching strategy designed in step 2.2 has the following role division formula:

[0023] In the formula, O ( t ) represents the proportion of scouts in the current iteration, K is the adjustment slope to control the transition speed, and E0 is the energy threshold to determine the critical point for role switching.

[0024] Furthermore, the specific formula for the collaborative hole-digging strategy designed in step 2.3 is as follows:

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] In the formula, Z Nbest Individual radius rt The optimal solution within, ρ , ω For dynamic weights, It is the best individual in the neighborhood. The optimal individual position within the neighborhood. f ( Z j (for individuals) j fitness value, N i For individuals i The neighborhood set of a given value, i.e., the set of values ​​whose distance is within a certain range. rt An individual that is not itself d ij Z represents the distance between individuals. j,k For individuals j No. k The value of dimension Z i,k For individuals i No. k The value of the dimension.

[0033] Furthermore, the specific formula for the sentry escape mechanism designed in step 2.4 is as follows:

[0034] In the formula, Danger( i () is a Boolean value, marking an individual. i Whether it gets stuck in a local optimum.

[0035] Furthermore, in step 4, an active rear-wheel steering control strategy is designed, employing a hierarchical control architecture, including an upper decision layer and a lower stability layer. Specifically, the upper layer generates a reference rear-wheel steering angle through a CES-ALA-ADRC controller, compensates for external disturbances using active disturbance rejection control, and optimizes parameters through an improved artificial lemming algorithm to adapt to cold environments. The lower layer uses an LQR controller, which outputs a stability compensation steering angle based on the errors in yaw rate and centroid sideslip angle. Finally, the reference steering angle and the compensation value are superimposed and input to the rear-wheel drive actuator. While preserving the driver's steering intention on the front wheels, the active rear-wheel steering significantly improves the vehicle's driving stability under complex conditions such as icy roads and crosswinds.

[0036] An active disturbance rejection steering control system for trucks in cold-weather regions includes: Algorithm Improvement Module: Based on the problems faced by trucks on icy and snowy roads in high-altitude and cold regions, the artificial lemming optimization algorithm is improved to obtain the improved artificial lemming algorithm, and the fitness value of the improved artificial lemming algorithm is calculated. Iterative assignment module: Based on the improved artificial lemming optimization algorithm, the optimal fitness is selected after iterative processing, and the best position coordinates corresponding to the optimal fitness are assigned to the parameters of the active disturbance rejection controller. Parameter Substitution Module: Substitute the parameters of the active disturbance rejection controller into the active disturbance rejection controller and apply them to the pre-established front-wheel hydraulic electric power steering and rear-wheel steer-by-wire system models; Control Module: An active rear-wheel steering control strategy is designed, utilizing LQR control to output the compensation angle of the rear wheel steering. This angle is combined with the rear wheel steering angle output by the steer-by-wire system to form a layered control strategy. Simultaneously, the front wheels use hydraulic electronic power steering to control the vehicle to complete the driving conditions on icy roads in high-altitude and cold regions.

[0037] Compared with the prior art, the present invention has the following beneficial technical effects: This invention applies an ADRC controller optimized by swarm intelligence to a rear-wheel steer-by-wire control system, and incorporates an active rear-wheel steering control strategy with LQR control, forming a hierarchical control architecture that enhances the stability and anti-interference capabilities of the vehicle during driving.

[0038] This invention designs a collaborative hole-digging strategy, which uses neighborhood optima to guide local development and improves the efficiency of local development of the algorithm; it introduces a sentinel escape mechanism to detect dangerous individuals and implement targeted escape to escape from local optima in a timely manner; and it sets an energy-driven role switching strategy, using the Sigmoid function to smoothly transition between the global search and local convergence process, making the algorithm convergence more stable.

[0039] Compared with the traditional artificial lemming algorithm, this invention has a faster convergence speed, better convergence accuracy, and higher operating accuracy for vehicles on icy roads. Attached Figure Description

[0040] The accompanying drawings are provided to further understand the invention and constitute a part of this invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0041] Figure 1 This is a flowchart illustrating the present invention; Figure 2 This is a flowchart of the improved artificial lemming algorithm of the present invention; Figure 3 The following is a simulation result diagram of the steering wheel angle sinusoidal input under the working condition of ice road surface according to the present invention. Among them, (a) is the error comparison curve of the actual rear wheel angle and the ideal rear wheel angle of different controllers, (b) is the error comparison curve of the actual yaw rate and the ideal yaw rate of different controllers, (c) is the error comparison curve of the actual center of gravity sideslip angle and the ideal center of gravity sideslip angle of different controllers, and (d) is the rear wheel angle comparison curve of different control methods. Figure 4 The figure shows the simulation results of the steering wheel angle sinusoidal input under the working conditions of ice road surface. (a) is a comparison curve of the yaw rate error with and without the active rear wheel steering strategy, and (b) is a comparison curve of the center of gravity sideslip angle error with and without the active rear wheel steering strategy. Detailed Implementation

[0042] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 See Figure 1 The flowchart of this invention shows that the basic artificial lemming algorithm has been optimized and improved, and a cooperative artificial lemming algorithm with energy-driven role switching and sentinel escape (CES-ALA) is proposed. Figure 2 The active disturbance rejection controller was optimized. First, models of the front-wheel hydraulic power steering and rear-wheel steer-by-wire systems were established. Second, the original artificial lemming algorithm was improved through three strategies: a cooperative burrowing strategy was designed to guide local exploration using neighborhood optima, improving the efficiency of local exploration; a sentry escape mechanism was introduced to detect dangerous individuals and implement directional escape, promptly escaping local optima; and an energy-driven role-switching strategy was set up, using the sigmoid function to smoothly transition between global search and local convergence, making the algorithm convergence more stable. Finally, it was applied to a hierarchical control system for trucks. Figure 3 It can be seen that the improved artificial lemming algorithm provides better control of trucks when driving on icy roads compared to the original algorithm.

[0043] The specific steps are as follows: Step 1: Model the front-wheel hydraulic power steering and rear-wheel steer-by-wire systems as shown below:

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] The first formula is the equation for the equilibrium of lateral forces, where... m For vehicle quality; v x This refers to the longitudinal speed of the vehicle. v y This refers to the lateral speed. r This refers to the yaw rate; F y1 , F y2 These are the lateral forces on the front and rear axle tires, respectively. F wind The first formula represents the crosswind interference force; the second formula is the yaw moment balance equation, where... I z The moment of inertia is about the Z-axis; a 1. a 2 represents the longitudinal distance from the front and rear axles to the center of mass; M wind The crosswind yaw moment; the third, fourth, and fifth formulas are for the steering actuator model, where... T m This refers to the torque of the steering motor; J m The moment of inertia of the steering motor shaft; B m This refers to the damping coefficient of the steering motor shaft; K m For the torsional stiffness of the steering motor shaft; θ m , m 、 m These are the steering motor shaft rotation angle, angular velocity, and angular acceleration, respectively. G m This refers to the reduction ratio of the steering motor reducer; R p The radius of the small gear at the pitch circle of the steering gear; X r This represents the rack displacement; T fm This is the equivalent frictional torque of the steering motor; u m For steering actuator motor armature voltage; i m , m These are the armature current and current change rate of the steering actuator motor, respectively. R m Resistance of the armature winding of the steering actuator motor; L m The armature winding inductance of the steering actuator motor;K b The proportional coefficient of the back EMF of the steering actuator motor; C m The sixth formula represents the electromagnetic torque coefficient of the steering actuator motor; the sixth formula represents the hydraulic power assist torque, where... K p For hydraulic cylinder pressure-torque gain; P L For load pressure; θ cmd The steering wheel command indicates the angle of rotation; θ w This is the actual steering angle of the front wheels; K s For steering column stiffness, the seventh formula is the dynamic equation for front wheel steering angle, where, J w This refers to the inertia of the front wheel steering system. b w This refers to the steering damping coefficient; T tire This is the tire return torque.

[0051] Step 2: The basic artificial lemming optimization algorithm consists of the following steps: Based on the energy factor used to determine lemming behavior, an energy factor was designed to maintain a balance between exploration and extraction. E This reduces the energy factor during the iteration process. When energy is plentiful, lemmings will selectively migrate or dig burrows; otherwise, they will forage for food and avoid predators. The formula for calculating the energy factor is as follows:

[0052] In the formula, T max The maximum number of iterations, t The current iteration number is , and rand is a random number that follows a uniform distribution within the range [0,1].

[0053] Lemmings exhibit long-distance migration when overpopulation leads to food shortages. Due to the population surge, some lemmings migrate to new territories in search of more resources and space. The mathematical model is as follows:

[0054] In the formula, Z ( t () represents the lemming's current location. Z ( t +1) represents the updated position. Z best To determine the optimal position from initialization to the current search. Z aFor lemming individuals randomly selected from the population, a The integers are in the range [1, n], where n is the population size. t This represents the current iteration number. F The flag used to change the search direction is expressed as follows: BM To characterize the random number vector of Brownian motion, the step size of standard Brownian motion is obtained from the probability density function of a normal distribution with variance of 1 and mean of 0. R Let be a vector of size 1×Dim, whose elements are random numbers uniformly distributed in the interval [-1, 1]. Its expression is as follows:

[0055] In the formula, This is the floor function for rounding down.

[0056]

[0057]

[0058] In the formula, Dim is the dimension of the problem space, and rand(1,Dim) is a random number that follows a uniform distribution within the range (1,Dim).

[0059] Burrowing behavior refers to the lemmings digging burrows in their habitat, forming complex tunnels to provide safe shelter and food storage. The mathematical model for this behavior is as follows:

[0060] In the formula, L A random number related to the current iteration number. Z b Let b be an individual lemming randomly selected from the population, where b is an integer in the range [1, n]. L The expression is as follows:

[0061] Foraging behavior refers to the extensive and random movement of lemmings within their burrows, relying on their keen sense of smell and hearing to locate food sources. The mathematical model for this behavior is as follows:

[0062] In the formula, spiral The spiral shape representing the random search during foraging is calculated using the following formula:

[0063]

[0064] In the formula, radiusThe radius represents the foraging range, i.e., the Euclidean distance between the current position and the optimal solution. Z best,k (t) indicates that in t In the nth iteration, the optimal solution is... k The value in dimension Z i,k (t) indicates that in t In the next iteration, the individual i No. k Values ​​in the dimension.

[0065] Lemmings exhibit predator evasion behavior. When encountering a predator, they quickly flee back to their burrows, simultaneously employing deceptive maneuvers to escape pursuit. The mathematical model for this is as follows:

[0066] In the formula, G Let be the escape coefficient, representing the lemming's escape ability. It decreases as the number of iterations increases, and its expression is as follows: T max The maximum number of iterations is given by 'Lev'(Dim), which represents Levy's flight and is used to simulate the deceptive movements of a lemming when it tries to escape.

[0067]

[0068] Step 3: Improve the artificial lemming optimization algorithm to obtain the improved artificial lemming algorithm.

[0069] First, design an energy-driven role switching strategy, as shown in the following formula:

[0070] In the formula, O ( t ) represents the proportion of scouts in the current iteration, K is the adjustment slope to control the transition speed, and E0 is the energy threshold to determine the critical point for role switching.

[0071] Energy Factor E The algorithm directly controls the switching of the ALA search strategy. When the energy fluctuates around the threshold E0, the strategy changes frequently, causing the convergence curve to exhibit a sawtooth-like decline. Moreover, relying solely on a single threshold as the judgment criterion means that even small deviations in the energy threshold can trigger drastic fluctuations in convergence behavior, reducing the algorithm's stability and convergence performance.

[0072] To avoid the aforementioned problems, this invention designs an energy-driven role-switching strategy. This strategy simulates the role division characteristics within a lemming population, assigning scouts and caretakers. Scouts mimic the lemmings' dispersal behavior, primarily responsible for global exploration; caretakers guard the burrows, mainly responsible for local development. While retaining the energy factor calculation method of the original algorithm, a sigmoid function is introduced to achieve a smooth role transition, better meeting the continuous search requirements of optimization problems. This role division mechanism effectively overcomes the limitations of traditional methods where "the entire population blindly explores at high energy levels and collectively develops local areas at low energy levels," thus significantly improving the algorithm's search efficiency.

[0073] Secondly, the specific formula for designing a collaborative hole-digging strategy is as follows:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] In the formula, Z Nbest Individual radius rt The optimal solution within, ρ , ω For dynamic weights, It is the best individual in the neighborhood. The optimal individual position within the neighborhood. f ( Z j (for individuals) j fitness value, N i For individuals i The neighborhood set of a given value, i.e., the set of values ​​whose distance is within a certain range. rt An individual that is not itself d ij Z represents the distance between individuals. j,k For individuals j No. k The value of dimension Z i,k For individuals i No. k The value of the dimension.

[0082] During the burrowing phase, lemmings randomly choose their digging direction based on their current burrow location and random individual positions. The algorithm's individual position updates rely solely on the global optimum and random perturbations, lacking information exchange among the group, which is detrimental to later local development. Therefore, a cooperative burrowing strategy is introduced. By simulating the information sharing and collaborative digging behavior of lemming groups during burrowing, the algorithm's search efficiency is improved.

[0083] Finally, the location is updated through the designed sentry escape mechanism, with the specific formula as follows:

[0084] In the formula, Danger( i () is a Boolean value, marking an individual. i Whether it gets stuck in a local optimum.

[0085] In traditional ALA (Alternating Range Alignment) systems, lemmings rely solely on blind individual perturbations to escape predators, potentially missing high-quality solution regions and struggling to escape premature convergence, resulting in inefficient escapes from local optima. Therefore, a sentinel escape mechanism is designed, allowing dangerous individuals to escape in a targeted manner. This improves the targeting of escapes from local optima while preserving random perturbations and maintaining exploration diversity.

[0086] When engaging in group activities, individual lemmings act as "sentinels," standing and observing their surroundings, immediately fleeing upon detecting danger. This strategy mainly consists of two parts: sentinel detection and directional escape. In the sentinel detection part, the fitness changes of individuals in the population are periodically assessed, and individuals with stagnant fitness and fitness worse than the median are marked as dangerous individuals, i.e., individuals prone to getting trapped in local optima. The directional escape part involves all dangerous individuals immediately fleeing, combining random perturbation and a global optimal direction to escape local optima.

[0087] Figure 3 The simulation results are shown for an icy road surface. The vehicle speed is 90 km / h, and the steering wheel input is sinusoidal. The rear wheel steering angle, yaw rate, and sideslip angle obtained by different control methods are compared and analyzed. (a) shows the comparison between the actual and ideal rear wheel steering angle errors; (b) shows the comparison between the actual and ideal yaw rate; (c) shows the comparison between the actual and ideal sideslip angle; and (d) shows the comparison between the actual rear wheel steering angle. Figure 3 It can be seen that after the improved artificial lemming algorithm is applied to the ADRC controller, the rear wheel steering angle error is minimized when the vehicle is on an icy road, and it has better operating performance compared to other methods.

[0088] Figure 4The simulation results are shown for an icy road surface, with a vehicle speed of 90 km / h and a sinusoidal steering input. The yaw rate and sideslip angle errors obtained with and without an active rear-wheel steering strategy are compared and analyzed. (a) shows the comparison curve between the actual yaw rate and the ideal value, and (b) shows the comparison curve between the actual sideslip angle and the ideal value. Figure 4 It can be seen that by adding the active rear-wheel steering strategy to the vehicle steering system, the yaw rate and the center of gravity sideslip angle error are both smaller than those of the steering system without the active rear-wheel steering control strategy, proving that the vehicle of the present invention can maintain better stability on icy road conditions.

[0089] Example 2 An active disturbance rejection steering control system for trucks in cold-weather regions includes: Algorithm Improvement Module: Based on the problems faced by trucks on icy and snowy roads in high-altitude and cold regions, the artificial lemming optimization algorithm is improved to obtain the improved artificial lemming algorithm, and the fitness value of the improved artificial lemming algorithm is calculated. Iterative assignment module: Based on the improved artificial lemming optimization algorithm, the optimal fitness is selected after iterative processing, and the best position coordinates corresponding to the optimal fitness are assigned to the parameters of the active disturbance rejection controller. Parameter Substitution Module: Substitute the parameters of the active disturbance rejection controller into the active disturbance rejection controller and apply them to the pre-established front-wheel hydraulic electric power steering and rear-wheel steer-by-wire system models; Control Module: An active rear-wheel steering control strategy is designed, utilizing LQR control to output the compensation angle of the rear wheel steering. This angle is combined with the rear wheel steering angle output by the steer-by-wire system to form a layered control strategy. Simultaneously, the front wheels use hydraulic electronic power steering to control the vehicle to complete the driving conditions on icy roads in high-altitude and cold regions.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A method for active disturbance rejection steering control of trucks in cold regions, characterized in that, Includes the following steps: Step 1: Based on the problems faced by trucks on icy and snowy roads in high-altitude and cold regions, the artificial lemming optimization algorithm is improved to obtain the improved artificial lemming algorithm, and the fitness value of the improved artificial lemming algorithm is calculated. The improvement of the artificial lemming optimization algorithm, resulting in the improved artificial lemming algorithm, specifically includes the following steps: Step 2.1: Use the lemming's seven-dimensional coordinates as the values ​​of the seven parameters of the active disturbance rejection controller; Step 2.2: Design an energy-driven role switching strategy to determine whether to perform global development or local search. If it is global development, proceed to step 2.3; if it is local search, proceed to step 2.

4. The role division formula for the designed energy-driven role switching strategy is as follows: In the formula, O ( t The percentage of scouts in the current iteration is represented by , K is the adjustment slope that controls the transition speed, and E0 is the energy threshold that determines the critical point for role switching. Energy factor; Step 2.3: Randomly select long-distance migration behavior or cooperative burrowing strategy to update individual location; Step 2.4: Randomly select either foraging behavior or sentry escape mechanism to update individual location; Step 2.5: After each lemming individual updates its location, calculate the fitness of the lemming population and select the optimal lemming individual based on the fitness value; Step 2.6: Determine if the maximum number of iterations has been reached. If yes, output the location coordinates and fitness value of the optimal lemming individual. If no, return to step 2.

2. Step 2: Based on the improved artificial lemming optimization algorithm, the optimal fitness is selected after iterative processing, and the best position coordinates corresponding to the optimal fitness are assigned to the parameters of the active disturbance rejection controller. Step 3: Substitute the parameters of the active disturbance rejection controller into the active disturbance rejection controller and apply them to the pre-established front-wheel hydraulic electric power steering and rear-wheel steer-by-wire system models; Step 4: Design an active rear-wheel steering control strategy. Utilize LQR control to output the compensated steering angle of the rear wheels. Combine this with the rear-wheel steering angle output by the steer-by-wire part in Step 3 to form a layered control strategy. At the same time, use hydraulic electronic power steering for the front wheels to control the vehicle to complete the driving conditions on icy roads in cold regions.

2. The active disturbance rejection steering control method for trucks in cold regions according to claim 1, characterized in that, The models of the front-wheel hydraulic electronic power steering and the rear-wheel steer-by-wire system are as follows: （1） （2） （3） （4） （5） （6） （7） Among them, formula (1) is the equation for the equilibrium of transverse forces, where, m For vehicle quality; v x This refers to the longitudinal speed of the vehicle. v y This refers to the lateral speed. r This refers to the yaw rate; F y1 , F y2 These are the lateral forces on the front and rear axle tires, respectively. F wind The crosswind interference force; formula (2) is the yaw moment balance equation, where, I z The moment of inertia is about the Z-axis; a 1. a 2 represents the longitudinal distance from the front and rear axles to the center of mass; M wind The crosswind yaw moment; formulas (3), (4), and (5) are models of the steering actuator, where T m This refers to the torque of the steering motor; J m The moment of inertia of the steering motor shaft; B m This refers to the damping coefficient of the steering motor shaft; K m For the torsional stiffness of the steering motor shaft; θ m , m 、 m These are the steering motor shaft rotation angle, angular velocity, and angular acceleration, respectively. G m This refers to the reduction ratio of the steering motor reducer; R p The radius of the small gear at the pitch circle of the steering gear; X r This represents the rack displacement; T fm This is the equivalent frictional torque of the steering motor; u m For steering actuator motor armature voltage; i m , m These are the armature current and current change rate of the steering actuator motor, respectively. R m Resistance of the armature winding of the steering actuator motor; L m The armature winding inductance of the steering actuator motor; K b The proportional coefficient of the back EMF of the steering actuator motor; C m The electromagnetic torque coefficient of the steering actuator motor is given by equation (6), where the hydraulic assist torque is given by equation (6). K p For hydraulic cylinder pressure-torque gain; P L For load pressure; θ cmd The steering wheel command indicates the angle of rotation; θ w This is the actual steering angle of the front wheels; K s For the steering column stiffness, equation (7) is the dynamic equation for the front wheel steering angle, where, J w This refers to the inertia of the front wheel steering system. b w This refers to the steering damping coefficient; T tire This is the tire return torque.

3. The active disturbance rejection steering control method for trucks in cold regions according to claim 1, characterized in that, The specific formula for the collaborative hole-digging strategy designed in step 2.3 is as follows: In the formula, Z i ( t (for individuals) i Z's current position i ( t +1) for individuals i The updated location Z best ( t () represents the optimal position from initialization to the current search. T max The maximum number of iterations, t This represents the current iteration number. F A marker to change the search direction. L A random number related to the current iteration number. Z Nbest Individual radius rt The optimal solution within, ρ , ω For dynamic weights, It is the best individual in the neighborhood. The optimal individual position within the neighborhood. f ( Z j (for individuals) j fitness value, N i For individuals i The neighborhood set of a given value, i.e., the set of values ​​whose distance is within a certain range. rt An individual that is not itself d ij Z represents the distance between individuals. j,k For individuals j No. k The value of dimension Z i,k For individuals i No. k The value of dimensionality, where Dim is the dimension of the problem space.

4. The active disturbance rejection steering control method for trucks in cold regions according to claim 1, characterized in that, The specific formula for the sentry escape mechanism designed in step 2.4 is as follows: In the formula, Z ( t () represents the lemming's current location. Z ( t +1) represents the updated position. Z best ( t () represents the optimal position from initialization to the current search. G The escape coefficient, F To indicate a change in search direction, Levy(Dim) represents Levy's flight path, and R is a uniformly distributed interval [ ]. Random numbers within the range [1, 1], Danger( i () is a Boolean value, marking an individual. i Whether it gets stuck in a local optimum.

5. The active disturbance rejection steering control method for trucks in cold regions according to claim 1, characterized in that, Step 4 involves designing an active rear-wheel steering control strategy, employing a hierarchical control architecture consisting of an upper decision layer and a lower stability layer. Specifically, the upper layer generates a reference rear-wheel steering angle using a CES-ALA-ADRC controller, compensates for external disturbances using active disturbance rejection control, and optimizes parameters through an improved artificial lemming algorithm to adapt to cold-weather environments. The lower layer uses an LQR controller, which outputs a stability compensation steering angle based on the errors in yaw rate and sideslip angle. Finally, the reference steering angle and the compensation value are superimposed and input to the rear-wheel drive actuator. While preserving the driver's steering intention on the front wheels, the active rear-wheel steering significantly improves the vehicle's driving stability under complex conditions such as icy roads and crosswinds.

6. An active disturbance rejection steering control system for trucks in cold-weather regions, used to implement the active disturbance rejection steering control method for trucks in cold-weather regions as described in any one of claims 1-5, characterized in that, include: Algorithm Improvement Module: Based on the problems faced by trucks on icy and snowy roads in high-altitude and cold regions, the artificial lemming optimization algorithm is improved to obtain the improved artificial lemming algorithm, and the fitness value of the improved artificial lemming algorithm is calculated. Iterative assignment module: Based on the improved artificial lemming optimization algorithm, the optimal fitness is selected after iterative processing, and the best position coordinates corresponding to the optimal fitness are assigned to the parameters of the active disturbance rejection controller. Parameter Substitution Module: Substitute the parameters of the active disturbance rejection controller into the active disturbance rejection controller and apply them to the pre-established front-wheel hydraulic electric power steering and rear-wheel steer-by-wire system models; Control Module: An active rear-wheel steering control strategy is designed, utilizing LQR control to output the compensation angle of the rear wheel steering. This angle is combined with the rear wheel steering angle output by the steer-by-wire system to form a layered control strategy. Simultaneously, the front wheels use hydraulic electronic power steering to control the vehicle to complete the driving conditions on icy roads in high-altitude and cold regions.

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