A semi-active suspension anti-aiming control method integrating long and short time domain predictions

Abstract

This invention discloses a semi-active suspension anti-aiming control method integrating long and short time-domain predictions, comprising: designing a disturbance invariant set to provide feedback correction for uncertainties in long-time-domain anti-aiming information to ensure the robustness of the Tube-MPC controller; outputting the optimal suspension active force as the target force of the lower actuator; obtaining the target forces of the dual-chamber air spring and the CDC damper; and designing a dual-chamber air spring-CDC damper collaborative allocation and compensation control strategy based on short-time-domain anti-aiming information to control the switching of the dual-chamber switching solenoid valve in the dual-chamber air spring and the current of the CDC damper. This invention introduces long-time-domain anti-aiming information into vehicle attitude control and, addressing the uncertainty of this information, uses a disturbance invariant set of the Tube-MPC controller to provide feedback correction for the uncertainty, ensuring the robustness of the Tube-MPC controller in the face of uncertainty.

Description

Technical Field

[0001] This invention belongs to the field of automotive suspension control technology, specifically relating to a semi-active suspension anti-aiming control method that integrates long and short time domain predictions. Background Technology

[0002] Currently, semi-active suspension control systems typically employ a hierarchical control architecture. The upper-level controller calculates the global control objective based on the vehicle's dynamics model, generating target forces for each suspension element to optimize pitch, roll, and vertical motion. The lower-level controller directly controls the actuators to execute these target forces. Solutions exemplified by dual-actuator suspensions (e.g., dual-chamber air springs with continuous damping control (CDC dampers) combine the force adjustment capabilities of air springs with the damping control advantages of dampers, better handling dynamic loads and road disturbances) are particularly effective. Furthermore, intelligent control algorithms incorporating road surface anticipation information have been considered an effective way to further optimize suspension system performance in recent years. Existing research on anticipation control methods for suspension systems includes, for example, Chinese invention patent application number CN202110623048. .8, entitled "A Pre-aiming Control Method for Automotive Electronic Suspension", achieves effective identification and detection of different road surface types and optimal suspension parameter matching control for different road surface types; Chinese invention patent application number CN202010677973.4, entitled "Active Suspension Pre-aiming Control Method Based on Camera Sensor Road Surface Information Identification", utilizes a camera sensor to acquire road surface pre-aiming information, combined with H∞ optimal control, to improve the time lag phenomenon in the active suspension damping parameter adjustment process and achieve balanced control of multiple performance indicators of the active suspension; Chinese invention patent application number CN202410102300.4, entitled "MPC Pre-aiming Suspension Control Method and System Based on Neurodynamic Model", uses a neural network to establish a seven-degree-of-freedom suspension motion mathematical model and integrates pre-aiming MPC to achieve control.

[0003] However, the existing control strategies mentioned above have the following problems:

[0004] First, in terms of the preview time domain, existing studies generally use a single preview time domain information and do not consider integrating the preview information of the long time domain and the short time domain into the controller design respectively. Therefore, it is difficult to simultaneously take into account the global vehicle attitude control performance and the local actuator response performance, which restricts the control effect of semi-active suspension under complex road conditions.

[0005] Second, regarding the hierarchical control architecture of suspension, existing research mainly focuses on upper-level vehicle attitude anti-aiming control, lacking research on lower-level actuator anti-aiming control. Furthermore, although both upper and lower level controllers can benefit from anti-aiming information, their information application methods differ significantly due to their different functional roles within the hierarchical control architecture.

[0006] Third, in terms of the fusion of long and short time domain preview information with upper and lower level control, existing research often overlooks the fact that the upper level long time domain preview information usually has uncertainties that affect control accuracy. Meanwhile, the lower level actuator, such as the dual-chamber air spring, has a time lag in air filling and discharging during the adjustment mode switching process. Theoretically, this lag can be compensated by short time domain preview to achieve more efficient collaborative control between the air spring and the CDC damper.

[0007] Therefore, in-depth research into integrating long-time and short-time domain preview information into controller design, exploring how to effectively reduce uncertainty in long-time domain preview and improve the hysteresis characteristics of actuators through short-time domain preview technology, has important theoretical significance and application value for further improving the performance of suspension systems. Summary of the Invention

[0008] In view of the shortcomings of the prior art, the purpose of this invention is to provide a semi-active suspension anti-aiming control method that integrates long and short time domain predictions, so as to solve the problems of uncertainty in long time domain anti-aiming information and serious time delay between lower actuators in the prior art.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] The present invention provides a semi-active suspension anti-aiming control method that integrates long and short time domain predictions. The method is based on a semi-active suspension system, which includes: a dual-chamber air spring, a CDC damper, a sensor assembly, and an electronic control unit.

[0011] Both the dual-chamber air spring and the CDC damper are installed between the vehicle body and the wheel. One end of each is connected to the vehicle body via the top of the tower, and the other end is connected to the wheel via the lower control arm. The dual-chamber air spring is used to provide support force and variable stiffness. The CDC damper is used for dynamic damping adjustment.

[0012] The sensor assembly includes: a vehicle body acceleration sensor, a suspension travel sensor, an inertial measurement unit, and a camera;

[0013] The vehicle body acceleration sensor is used to collect the vibration acceleration of the vehicle body in the vertical direction;

[0014] Suspension travel sensor, used to collect the compression or tension of the suspension;

[0015] Inertial measurement unit, used to collect vehicle roll rate and pitch rate;

[0016] The camera is used to collect data on road surface interference in front of the vehicle to provide long-term and short-term preview information.

[0017] The electronic control unit is electrically connected to the solenoid valve in the CDC damper, the dual-chamber switching solenoid valve in the dual-chamber air spring, and the sensor assembly. It calculates based on the data collected by the sensor assembly to obtain output control commands, thereby realizing real-time adjustment of the stiffness of the dual-chamber air spring and the damping force of the CDC damper.

[0018] The steps are as follows:

[0019] 1) Based on the seven-degree-of-freedom suspension model of the whole vehicle, long-term domain preview information is used as road input to build a Tube-MPC controller. Combined with vehicle vibration acceleration, vehicle roll rate, and vehicle pitch rate, the vehicle attitude is controlled. A disturbance invariant set is designed to provide feedback correction for the uncertainty of long-term domain preview information to ensure the robustness of the Tube-MPC controller. The optimal suspension active force is output as the target force of the lower actuator.

[0020] 2) Based on the target force of the lower actuator in step 1), the target force of the lower actuator is separated in the frequency domain to obtain the target forces of the dual-chamber air spring and the CDC damper respectively;

[0021] 3) Based on the target forces of the dual-chamber air spring and CDC damper obtained in step 2), analyze the mapping relationship between different time delays and short-preview time domains between the lower actuators, and design a dual-chamber air spring-CDC damper collaborative allocation and compensation control strategy based on short-time domain preview information to control the switching of the dual-chamber switching solenoid valve in the dual-chamber air spring and the current of the CDC damper.

[0022] Further, step 1) specifically includes:

[0023] 11) Set the long-term time-domain aiming distance to l p Then the time domain is pre-aimed. The distance of the vehicle's forward aiming l p The road surface disturbance at the location containing uncertainty is w k w k =w(t)+Δw, then the road disturbance involving uncertainty encountered by the vehicle is w k (t)=w(tt p )+Δw, where Δw is the uncertainty contained in the pre-aiming information, v is the vehicle speed, t is time, w(t) is the road disturbance without uncertainty at time t, and w(tt) is the road disturbance without uncertainty at time t. p ) for tt p The road surface is free from uncertainties at all times;

[0024] w k (t) Discretization yields discrete road surface disturbance w k * (t), the expression is:

[0025]

[0026] In the formula, w k+1 (t) represents the road disturbance at time t, step k+1, w k (t) represents the road disturbance at time t, step k. k+1 (t p ) represents t p Road surface disturbance at time k+1, w k+1 (t p -t s ) represents t p -t s Road surface disturbance at time k+1;

[0027] 12) Based on the discrete road surface disturbance w obtained in step 11), k * (t) Construct a Tube-MPC controller and design a disturbance invariant set; the Tube-MPC controller includes a nominal system and an error system. Design a minimum disturbance invariant set Z to tighten the constraints on the state and control variables of the nominal system. for Where X and U are the set of real system state variables and the set of control variables, respectively;

[0028] The vehicle body and wheel state quantities are obtained based on data collected by the vehicle vibration acceleration sensor and suspension travel sensor. The constraint range is (z b -z w ) min ≤z b -z w ≤(z b -z w ) max To ensure that the semi-active suspension system operates within its working stroke, among which Indicates the vehicle body vibration acceleration. The acceleration due to wheel vibration, z b The z-axis represents the vertical displacement of the vehicle body. w This represents the vertical displacement of the wheel; the nominal system and error system of the Tube-MPC controller are designed as follows:

[0029]

[0030]

[0031] In the formula, The nominal system state variable at time k+1; These are the nominal system state variables and control variables at time k, respectively. These are the nominal system state variable set and the control variable set, respectively; e k+1 Let e ​​be the error between the real system and the nominal system at time k+1. k Let k be the error between the real system and the nominal system at time k, i.e. Defined as the error system control quantity; Let A be the control variable of the error system at time k; d B is the state transition matrix of the discrete system; d Let W be the control matrix of the discrete system; W is a convex subset covering all possible values ​​of Δw and including the origin.

[0032] The control quantity u of the Tube-MPC controller k It is divided into nominal system control variables and error system state feedback control variables, and the expressions are as follows:

[0033]

[0034] In the formula, K is the state feedback matrix, Ke k This is the error system state feedback control quantity;

[0035] Get e k+1 =A K e k +Δw, where the closed-loop system matrix A includes state feedback. K =A d +B d K; K is set to exist to guarantee A K If it is stable, then the error set is bounded. Prove that there exists a set Z satisfying... Z is the perturbation-invariant set of the Tube-MPC controller; if the perturbation sequence Δw∈W, then the nominal system state variables at time k deviate from the actual system state variables, if the following conditions are met: The error at the next time step lies within the perturbation-invariant set.

[0036] Constraint spectral radius ρ(A) k To ensure the stability of the Tube-MPC controller, the disturbance invariant set formula is designed as follows:

[0037]

[0038] In the formula, Z(t) p ) for Tube-MPC controller and preview time domain t p The relevant perturbation invariant set;

[0039] 13) Based on the constructed disturbance invariant set, further solve the Tube-MPC controller, transforming the problem of solving the nominal system into finding the optimal control problem, and outputting the optimal suspension force as the target force of the lower actuator; at the same time, considering the Lyapunov stability of the system, the objective function and constraints are designed as follows:

[0040]

[0041] In the formula, J is the objective function; x represents the value of the nominal system state variable predicted at time k in the future at time k+i; ref|k+i|k Let the expected values ​​of the actual system state variables be at future time k+i. Let X be the value of the nominal system control variable change at time k+i in the future; R is the weight matrix of the nominal system control variable change; P is the weight matrix of the terminal state; Np is the prediction time domain; X f For terminal constraints; To predict the nominal system state variables of the time-domain terminal; Q1, Q2, and Q3 correspond to the outputs of the Tube-MPC controller, including the weights of vehicle body vibration acceleration, roll rate, and pitch rate, respectively.

[0042] Based on historical vehicle data, real-time vehicle vibration acceleration data, suspension travel data, roll rate data, pitch rate data, and future road disturbance data collected by sensor components, the future output response of the Tube-MPC controller is predicted. The real system in the Tube-MPC controller is discretized, and the discrete real system is represented as:

[0043] x k+1 =A d x k +B d u k

[0044] In the formula, x k+1 x represents the actual system state at time k+1; k Let k be the actual system state variables at time k, including vehicle body vibration acceleration. Wheel vibration acceleration Suspension travel z b -z w ;u k Let K be the actual system control quantity at time k, and the control quantity is the optimal suspension force.

[0045] Combining the state variables and control variables of the real system, the expanded-dimensional discrete real system representation is obtained as follows:

[0046]

[0047] In the formula, u represents the state error vector of the real system after dimension expansion; k-1 The actual system control quantity at time k-1; extended dimension matrix. Extended dimension matrix Extended dimension matrix η k Let I be the state vector of the real system at time k after dimension expansion; Nu Let I be an identity matrix of dimension Nu, representing the control input; Nx Let Δu be an identity matrix of dimension Nx, representing the state variables; k This represents the change of the actual system control quantity at time k and subsequent time points;

[0048] Obtain the predicted output y of the real system after dimension expansion in the prediction time domain. k+1∣k :

[0049] y k+1∣k =S x ξ k +S u ΔU k

[0050] In the formula,

[0051]

[0052] N c To control the time domain, It is a sequence of control quantity changes, with the optimal suspension active force serving as the target force for the lower-level actuator.

[0053] Furthermore, step 2) specifically includes:

[0054] The target force of the lower-level actuator obtained in step 1) is allocated, and the time-domain signal is converted into a frequency-domain signal using Fourier transform. Frequency domain separation is then performed, dividing the target force into low-frequency active force and high-frequency active force. Given a time-domain signal x(t), its frequency-domain expression X(f) is as follows:

[0055]

[0056] In the formula, f is the frequency in the frequency domain and t is the time in the time domain;

[0057] Low-frequency active force F is extracted using a low-pass filter. low (t) is the target force of the dual-chamber air spring, which determines the transfer function H of the first-order low-pass filter. low (jω) and low-frequency active force F low (t) is represented as:

[0058]

[0059] In the formula, X c R is the reactance of capacitor C, R is the resistance in the filter, ω is the target force angular frequency of the lower actuator, C is the capacitance of the filter, and F is the capacitance of the filter. Tube-MPC (t) represents the actuator target force output by the Tube-MPC controller, where j is the imaginary unit;

[0060] High-frequency active force F is extracted using a low-pass filter. high (t) serves as the target force for the CDC damper, and the transfer function H of the first-order high-pass filter is... high (jω) and high-frequency active force F high (t) is represented as:

[0061]

[0062] Furthermore, step 3) specifically includes:

[0063] 31) Based on the target force obtained in step 2) of the dual-chamber air spring and CDC damper, analyze the specific required short pre-aiming time domain T. preview The mapping relationship between different time delays between the lower actuators was established; a series of simulations were designed and run, and system response data under different time delays were collected by parameter scanning. Specifically, the simulation scenarios included various road disturbance characteristics at different vehicle speeds. The different time delays between the lower actuators were simulated by adjusting the inflation and deflation time delay of the dual-chamber air spring and the response time of the CDC damper. The preview time domain was dynamically adjusted in each simulation, and the accuracy of the system response was recorded.

[0064] Generalized regression neural networks are used to analyze the specific short preview time domain T required. preview The mapping relationship between the lower-level actuators and the different time delays is as follows:

[0065] Radial basis functions are used to measure the similarity between the input data and the training samples, and the activation function is calculated as follows:

[0066]

[0067] In the formula, φ i D is the output of the i-th neuron; i The Euclidean distance between the input data and the training samples, where the input data includes time delay t. delay Vehicle speed v and road surface disturbance characteristics r; σ is the smoothing factor of the radial basis function;

[0068] Calculate normalized weights Among them W i The normalized weights for the i-th training sample are given; N is the number of training samples; the final output, i.e., the short preview time domain, is calculated. Y iThis refers to the short preview time domain corresponding to the i-th training sample;

[0069] 32) Based on the short preview time domain T obtained in step 31), preview The following is a design strategy for coordinated distribution and compensation control of a dual-chamber air spring-CDC damper based on short-time domain preview information:

[0070] According to the low-frequency active force F low (t) controls the mode switching of the dual-chamber air spring to adapt to low-frequency vibrations of different amplitudes. The mode switching logic is as follows:

[0071]

[0072] In the formula, u valve This indicates the on / off state of the solenoid valve in the dual-chamber air spring, with 1 representing open and 0 representing closed; F thresh The threshold value is the main power threshold for the solenoid valve switching in the dual-chamber air spring. In order to improve the stability of the dual-chamber air spring and avoid the adverse effects caused by frequent mode switching, the threshold value needs to be controlled within a reasonable range during the design process.

[0073] The output force F is based on the on / off state of the solenoid valve in the dual-chamber air spring. AS (t), the main force output by the dual-chamber air spring in a certain stiffness mode, is expressed as follows:

[0074]

[0075] In the formula, F open (t) represents the main power force when the valve is opened, F closed (t) represents the main power when the valve is closed;

[0076] Controlling the CDC damper in the high-frequency range by tracking the target damping force F high (t) Absorb vibration, and at the same time use the CDC damper to compensate for the insufficient active force F of the dual-chamber air spring in the low-frequency range of the next time period. low (t+T preview )-F AS (t+T preview Meanwhile, the high-frequency damping control at time t is maintained; the final target damping force F of the CDC damper is calculated. CDC (t) represents the superposition of damping force in the high-frequency range and compensation force in the low-frequency range, expressed as follows:

[0077] F CDC (t)=F high (t)+F low (t+T preview )-F AS (t+T preview )

[0078] Sliding mode control is used to achieve the damping force T of the CDC damper at the final target. CDC For tracking (t), the sliding surface s is defined as the combination of the error between the final target damping force and the actual damping force and its derivative, as follows:

[0079]

[0080] In the formula, γ is the sliding surface weighting coefficient, used to adjust the system's response speed; F error =e high (t)+e low (t+T preview Error e in the high-frequency range high (t) represents the actual damping force of the CDC damper in the high-frequency range and F. high The error between (t) and the error e in the low-frequency range. low (t+T preview ) = F low (t+T preview )-F AS (t+T preview );

[0081] The sliding mode control law is designed as follows:

[0082]

[0083] In the formula, ε is the constant velocity approaching rate, κ is the coefficient of the exponential approaching term, and δ is the boundary layer thickness.

[0084] The beneficial effects of this invention are:

[0085] This invention introduces long-time domain anticipation information into vehicle body attitude control. To address the uncertainty issue in long-time domain anticipation information, the invention uses the disturbance invariant set of the Tube-MPC controller to provide feedback correction for the uncertainty, thereby ensuring the robustness of the Tube-MPC controller in the face of uncertainty.

[0086] This invention addresses the severe time delay problem between the dual-chamber air spring and CDC damper in the lower actuator. It proposes a collaborative allocation and compensation control strategy for the dual-chamber air spring-CDC damper based on short-time-domain preview information. The strategy uses a generalized regression neural network model to fit the mapping relationship between the short preview time domain and the different time delays between the lower actuators. By using the short-time-domain preview information to compensate for the time delay between the lower actuators, the accuracy of the system response is improved. Attached Figure Description

[0087] Figure 1 This is a flowchart illustrating the principle of the method of the present invention. Detailed Implementation

[0088] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0089] The present invention provides a semi-active suspension anti-aiming control method that integrates long and short time domain predictions. The method is based on a semi-active suspension system, which includes: a dual-chamber air spring, a CDC damper, a sensor assembly, and an electronic control unit.

[0090] Both the dual-chamber air spring and the CDC damper are installed between the vehicle body and the wheel. One end of each is connected to the vehicle body via the top of the tower, and the other end is connected to the wheel via the lower control arm. The dual-chamber air spring is used to provide support force and variable stiffness. The CDC damper is used for dynamic damping adjustment.

[0091] The sensor assembly includes: a vehicle body acceleration sensor, a suspension travel sensor, an inertial measurement unit, and a camera;

[0092] The vehicle body acceleration sensor is used to collect the vibration acceleration of the vehicle body in the vertical direction;

[0093] Suspension travel sensor, used to collect the compression or tension of the suspension;

[0094] Inertial measurement unit, used to collect vehicle roll rate and pitch rate;

[0095] The camera is used to collect data on road surface interference in front of the vehicle to provide long-term and short-term preview information.

[0096] The electronic control unit is electrically connected to the solenoid valve in the CDC damper, the dual-chamber switching solenoid valve in the dual-chamber air spring, and the sensor assembly. It calculates based on the data collected by the sensor assembly to obtain output control commands, thereby realizing real-time adjustment of the stiffness of the dual-chamber air spring and the damping force of the CDC damper.

[0097] Reference Figure 1 As shown, the method steps are as follows:

[0098] 1) Based on a seven-DOF suspension model of the whole vehicle, a Tube-MPC controller is built using long-time domain preview information as road surface input. This controller combines vehicle vibration acceleration, vehicle roll rate, and vehicle pitch rate to control vehicle attitude. An invariant perturbation set is designed to provide feedback correction for uncertainties in the long-time domain preview information, ensuring the robustness of the Tube-MPC controller. The optimal suspension active force is output as the target force for the lower-level actuators. Specifically, this includes:

[0099] 11) Set the long-term time-domain aiming distance to l p Then the time domain is pre-aimed. The distance of the vehicle's forward aiming l p The road surface disturbance at the location containing uncertainty is w k w k =w(t)+Δw, then the road disturbance involving uncertainty encountered by the vehicle is w k (t)=w(tt p )+Δw, where Δw is the uncertainty contained in the pre-aiming information, v is the vehicle speed, t is time, w(t) is the road disturbance without uncertainty at time t, and w(tt) is the road disturbance without uncertainty at time t. p ) for tt p The road surface is free from uncertainties at all times;

[0100] w k (t) Discretization yields discrete road surface disturbance w k * (t), the expression is:

[0101]

[0102] In the formula, w k+1 (t) represents the road disturbance at time t, step k+1, w k (t) represents the road disturbance at time t, step k. k+1 (t p ) represents t p Road surface disturbance at time k+1, w k+1 (t p -t s ) represents t p -t s Road surface disturbance at time k+1;

[0103] 12) Based on the discrete road surface disturbance w obtained in step 11), k * (t) Construct a Tube-MPC controller and design a disturbance invariant set; the Tube-MPC controller includes a nominal system and an error system. Design a minimum disturbance invariant set Z to tighten the constraints on the state and control variables of the nominal system. for Where X and U are the set of real system state variables and the set of control variables, respectively;

[0104] The vehicle body and wheel state quantities are obtained based on data collected by the vehicle vibration acceleration sensor and suspension travel sensor. The constraint range (set X) is (z b -z w ) min ≤z b -z w ≤(z b -zw ) max To ensure that the semi-active suspension system operates within its working stroke, among which Indicates the vehicle body vibration acceleration. The acceleration due to wheel vibration, z b The z-axis represents the vertical displacement of the vehicle body. w This represents the vertical displacement of the wheel; the nominal system and error system of the Tube-MPC controller are designed as follows:

[0105]

[0106] In the formula, The nominal system state variable at time k+1; These are the nominal system state variables and control variables at time k, respectively. These are the nominal system state variable set and the control variable set, respectively; e k+1 Let e ​​be the error between the real system and the nominal system at time k+1. k Let k be the error between the real system and the nominal system at time k, i.e. Defined as the error system control quantity; Let A be the control variable of the error system at time k; d B is the state transition matrix of the discrete system; d Let W be the control matrix of the discrete system; W is a convex subset covering all possible values ​​of Δw and including the origin.

[0107] The control quantity u of the Tube-MPC controller k It is divided into nominal system control variables and error system state feedback control variables, and the expressions are as follows:

[0108]

[0109] In the formula, K is the state feedback matrix, Ke k This is the error system state feedback control quantity;

[0110] Get e k+1 =A K e k +Δw, where the closed-loop system matrix A includes state feedback. K =A d +B d K; K is set to exist to guarantee A K If it is stable, then the error set is bounded. Prove that there exists a set Z satisfying... Z is the perturbation-invariant set of the Tube-MPC controller; if the perturbation sequence Δw∈W, then the nominal system state variables at time k deviate from the actual system state variables, if the following conditions are met: The error at the next time step lies within the perturbation-invariant set.

[0111] Constraint spectral radius ρ(A) k To ensure the stability of the Tube-MPC controller, the disturbance invariant set formula is designed as follows:

[0112]

[0113] In the formula, Z(t) p ) for Tube-MPC controller and preview time domain t p The relevant perturbation invariant set;

[0114] 13) Based on the constructed disturbance invariant set, further solve the Tube-MPC controller, transforming the problem of solving the nominal system into finding the optimal control problem, and outputting the optimal suspension force as the target force of the lower actuator; at the same time, considering the Lyapunov stability of the system, the objective function and constraints are designed as follows:

[0115]

[0116] In the formula, J is the objective function; x represents the value of the nominal system state variable predicted at time k in the future at time k+i; ref|k+i|k Let the expected values ​​of the actual system state variables be at future time k+i. R is the value of the nominal system control variable change at time k+i in the future; P is the weight matrix of the nominal system control variable change; N is the weight matrix of the terminal state; p For prediction of the time domain X f For terminal constraints; To predict the nominal system state variables of the time-domain terminal; Q1, Q2, and Q3 correspond to the outputs of the Tube-MPC controller, including the weights of vehicle body vibration acceleration, roll rate, and pitch rate, respectively.

[0117] Based on historical vehicle data, real-time vehicle vibration acceleration data, suspension travel data, roll rate data, pitch rate data, and future road disturbance data collected by sensor components, the future output response of the Tube-MPC controller is predicted. The real system in the Tube-MPC controller is discretized, and the discrete real system is represented as:

[0118] x k+1 =A d x k +B d u k

[0119] In the formula, x k+1x represents the actual system state at time k+1; k Let k be the actual system state variables at time k, including vehicle body vibration acceleration. Wheel vibration acceleration Suspension travel z b -z w ;u k Let K be the actual system control quantity at time k, and the control quantity is the optimal suspension force.

[0120] Combining the state variables and control variables of the real system, the expanded-dimensional discrete real system representation is obtained as follows:

[0121]

[0122] In the formula, u represents the state error vector of the real system after dimension expansion; k-1 The actual system control quantity at time k-1; extended dimension matrix. Extended dimension matrix Extended dimension matrix η k Let I be the state vector of the real system at time k after dimension expansion; Nu Let I be an identity matrix of dimension Nu, representing the control input; Nx Let Δu be an identity matrix of dimension Nx, representing the state variables; k This represents the change of the actual system control quantity at time k and subsequent time points;

[0123] Obtain the predicted output y of the real system after dimension expansion in the prediction time domain. k+1∣k :

[0124] y k+1∣k =S x ξ k +S u ΔU k

[0125] In the formula,

[0126]

[0127] N c To control the time domain, It is a sequence of control quantity changes, with the optimal suspension active force serving as the target force for the lower-level actuator.

[0128] 2) Based on the target force of the lower actuator in step 1), perform frequency domain separation on the target force of the lower actuator to obtain the target forces of the dual-chamber air spring and the CDC damper respectively; specifically including:

[0129] The target force of the lower-level actuator obtained in step 1) is allocated, and the time-domain signal is converted into a frequency-domain signal using Fourier transform. Frequency domain separation is then performed, dividing the target force into low-frequency active force and high-frequency active force. Given a time-domain signal x(t), its frequency-domain expression X(f) is as follows:

[0130]

[0131] In the formula, f is the frequency in the frequency domain and t is the time in the time domain;

[0132] Low-frequency active force F is extracted using a low-pass filter. low (t) is the target force of the dual-chamber air spring, which determines the transfer function H of the first-order low-pass filter. low (jω) and low-frequency active force F low (t) is represented as:

[0133]

[0134] In the formula, X c R is the reactance of capacitor C, R is the resistance in the filter, ω is the target force angular frequency of the lower actuator, C is the capacitance of the filter, and F is the capacitance of the filter. Tube-MPC (t) represents the actuator target force output by the Tube-MPC controller, where j is the imaginary unit;

[0135] High-frequency active force F is extracted using a low-pass filter. high (t) serves as the target force for the CDC damper, and the transfer function H of the first-order high-pass filter is... high (jω) and high-frequency active force F high (t) is represented as:

[0136]

[0137] 3) Based on the target forces of the dual-chamber air spring and CDC damper obtained in step 2), analyze the mapping relationship between different time delays and short-preview time domains between the lower-level actuators, and design a dual-chamber air spring-CDC damper collaborative allocation and compensation control strategy based on short-time domain preview information to control the switching of the dual-chamber switching solenoid valve in the dual-chamber air spring and the current of the CDC damper; specifically including:

[0138] 31) Based on the target force obtained in step 2) of the dual-chamber air spring and CDC damper, analyze the specific required short pre-aiming time domain T. previewMapping relationships between different time delays between lower-level actuators; design and run a series of simulations, and collect system response data under different time delays through parameter scanning. Specifically, the simulation scenarios include various road disturbance characteristics at different vehicle speeds. Different time delays between lower-level actuators (such as 100ms, 300ms, and 500ms) are simulated by adjusting the inflation and deflation time delay of the dual-chamber air spring and the response time of the CDC damper. In each simulation, the pre-aiming time domain is dynamically adjusted, and the accuracy of the system response is recorded.

[0139] The General Regression Neural Network (GRNN) is used to analyze the specific short preview time domain T required. preview The mapping relationship between the lower-level actuators and the different time delays is as follows:

[0140] Radial basis functions (Gaussian kernels) are used to measure the similarity between the input data and the training samples, and the activation function is calculated as follows:

[0141]

[0142] In the formula, φ i D is the output of the i-th neuron; i The Euclidean distance between the input data and the training samples, where the input data includes time delay t. delay Vehicle speed v and road surface disturbance characteristics r; σ is the smoothing factor of the radial basis function;

[0143] Calculate normalized weights Among them W i The normalized weights for the i-th training sample are given; N is the number of training samples; the final output, i.e., the short preview time domain, is calculated. Y i This refers to the short preview time domain corresponding to the i-th training sample;

[0144] 32) Based on the short preview time domain T obtained in step 31), preview The following is a design strategy for coordinated distribution and compensation control of a dual-chamber air spring-CDC damper based on short-time domain preview information:

[0145] According to the low-frequency active force F low (t) controls the mode switching of the dual-chamber air spring (the switching of the solenoid valve in the dual-chamber air spring) to adapt to low-frequency vibrations of different amplitudes. The mode switching logic is as follows:

[0146]

[0147] In the formula, u valve This indicates the on / off state of the solenoid valve in the dual-chamber air spring, with 1 representing open and 0 representing closed; Fthresh The threshold value is the main power threshold for the solenoid valve switching in the dual-chamber air spring. In order to improve the stability of the dual-chamber air spring and avoid the adverse effects caused by frequent mode switching, the threshold value needs to be controlled within a reasonable range during the design process.

[0148] The output force F is based on the on / off state of the solenoid valve in the dual-chamber air spring. AS (t), the main force output by the dual-chamber air spring in a certain stiffness mode, is expressed as follows:

[0149]

[0150] In the formula, F open (t) represents the main power force when the valve is opened, F closed (t) represents the main power when the valve is closed;

[0151] Controlling the CDC damper in the high-frequency range by tracking the target damping force F high (t) Absorb vibration, and at the same time use the CDC damper to compensate for the insufficient active force F of the dual-chamber air spring in the low-frequency range of the next time period (short time domain preview time domain). low (t+T preview )-F AS (t+T preview Meanwhile, the high-frequency damping control at time t is maintained; the final target damping force F of the CDC damper is calculated. CDC (t) represents the superposition of damping force in the high-frequency range and compensation force in the low-frequency range, expressed as follows:

[0152] F CDC (t)=F high (t)+F low (t+T preview )-F AS (t+T preview )

[0153] Sliding mode control (SMC) is used to achieve the damping force F of the CDC damper at the final target. CDC For tracking (t), the sliding surface s is defined as the combination of the error between the final target damping force and the actual damping force and its derivative, as follows:

[0154]

[0155] In the formula, γ is the sliding surface weighting coefficient, used to adjust the system's response speed; F error =e high (t)+e low (t+T preview Error e in the high-frequency range high (t) represents the actual damping force of the CDC damper in the high-frequency range and F.high The error between (t) and the error e in the low-frequency range. low (t+T preview ) = F low (t+T preview )-F AS (t+T preview );

[0156] The sliding mode control law is designed as follows:

[0157]

[0158] In the formula, ε is the constant velocity approaching rate, κ is the coefficient of the exponential approaching term, and δ is the boundary layer thickness.

[0159] This invention has many specific applications. The above description is only a preferred embodiment of this invention. It should be noted that for those skilled in the art, several improvements can be made without departing from the principle of this invention, and these improvements should also be considered within the scope of protection of this invention.

Claims

1. A semi-active suspension anti-aiming control method integrating long and short time domain predictions, based on a semi-active suspension system, the system comprising: Dual-chamber air spring, CDC damper, sensor assembly, and electronic control unit; Both the dual-chamber air spring and the CDC damper are installed between the vehicle body and the wheel. One end of each is connected to the vehicle body via the top of the tower, and the other end is connected to the wheel via the lower control arm. The dual-chamber air spring is used to provide support force and variable stiffness. The CDC damper is used for dynamic damping adjustment; The sensor assembly includes: a vehicle body acceleration sensor, a suspension travel sensor, an inertial measurement unit, and a camera; The vehicle body acceleration sensor is used to collect the vibration acceleration of the vehicle body in the vertical direction; Suspension travel sensor, used to collect the compression or tension of the suspension; Inertial measurement unit, used to collect vehicle roll rate and pitch rate; The camera is used to collect data on road surface interference in front of the vehicle to provide long-term and short-term preview information. The electronic control unit is electrically connected to the solenoid valve in the CDC damper, the dual-chamber switching solenoid valve in the dual-chamber air spring, and the sensor assembly. It calculates based on the data collected by the sensor assembly to obtain output control commands, thereby realizing real-time adjustment of the stiffness of the dual-chamber air spring and the damping force of the CDC damper. The method is characterized by the following steps: 1) Based on the seven-degree-of-freedom suspension model of the whole vehicle, long-term domain preview information is used as road input to build a Tube-MPC controller. Combined with vehicle vibration acceleration, vehicle roll rate, and vehicle pitch rate, the vehicle attitude is controlled. A disturbance invariant set is designed to provide feedback correction for the uncertainty of long-term domain preview information to ensure the robustness of the Tube-MPC controller. The optimal suspension active force is output as the target force of the lower actuator. 2) Based on the target force of the lower actuator in step 1), the target force of the lower actuator is separated in the frequency domain to obtain the target forces of the dual-chamber air spring and the CDC damper respectively; 3) Based on the target forces of the dual-chamber air spring and CDC damper obtained in step 2), analyze the mapping relationship between different time delays and short-preview time domains between the lower actuators, and design a dual-chamber air spring-CDC damper collaborative allocation and compensation control strategy based on short-time domain preview information to control the switching of the dual-chamber switching solenoid valve in the dual-chamber air spring and the current of the CDC damper.

2. The semi-active suspension anti-aiming control method based on the fusion of long and short time domain predictions according to claim 1, characterized in that, Step 1) specifically includes: 11) Set the long-term time-domain aiming distance to l p Then the time domain is pre-aimed. The distance of the vehicle's forward aiming l p The road surface disturbance at the location containing uncertainty is w k w k =w(t)+Δw, then the road disturbance involving uncertainty encountered by the vehicle is w k (t)=w(tt p )+Δw, where Δw is the uncertainty contained in the pre-aiming information, v is the vehicle speed, t is time, w(t) is the road disturbance without uncertainty at time t, and w(tt) is the road disturbance without uncertainty at time t. p ) for tt p The road surface is free from uncertainties at all times; w k (t) Discretization yields discrete road surface disturbance w k * (t), the expression is: In the formula, w k+1 (t) represents the road disturbance at time t, step k+1, w k (t) represents the road disturbance at time t, step k. k+1 (t p ) represents t p Road surface disturbance at time k+1, w k+1 (t p -t s ) represents t p -t s Road surface disturbance at time k+1; 12) Based on the discrete road surface disturbance w obtained in step 11), k * (t) Construct a Tube-MPC controller and design a disturbance invariant set; the Tube-MPC controller includes a nominal system and an error system. Design a minimum disturbance invariant set Z to tighten the constraints on the state and control variables of the nominal system. for Where X and U are the set of real system state variables and the set of control variables, respectively; The vehicle body and wheel state quantities are obtained based on data collected by the vehicle vibration acceleration sensor and suspension travel sensor. The constraint range is (z b -z w ) min ≤z b -z w ≤(z b -z w ) max To ensure that the semi-active suspension system operates within its working stroke, among which Indicates the vehicle body vibration acceleration. The z-axis represents the acceleration due to wheel vibration. b The z-axis represents the vertical displacement of the vehicle body. w This represents the vertical displacement of the wheel; the nominal system and error system of the Tube-MPC controller are designed as follows: In the formula, The nominal system state variable at time k+1; These are the nominal system state variables and control variables at time k, respectively. These are the nominal system state variable set and the control variable set, respectively; e k+1 Let e ​​be the error between the real system and the nominal system at time k+1. k Let k be the error between the real system and the nominal system at time k, i.e. Defined as the error system control quantity; Let A be the control input of the error system at time k; d B is the state transition matrix of the discrete system; d Let W be the control matrix of the discrete system; W is a convex subset covering all possible values ​​of Δw and including the origin. The control quantity u of the Tube-MPC controller k It is divided into nominal system control variables and error system state feedback control variables, and the expressions are as follows: In the formula, K is the state feedback matrix, Ke k This is the error system state feedback control quantity; Get e k+1 =A K e k +Δw, where the closed-loop system matrix A includes state feedback. K =A d +B d K; K is set to exist to guarantee A K If it is stable, then the error set is bounded. Prove that there exists a set Z satisfying... Z is the perturbation-invariant set of the Tube-MPC controller; if the perturbation sequence Δw∈W, then the nominal system state variables at time k deviate from the actual system state variables, if the following conditions are met: The error at the next time step lies within the perturbation-invariant set. Constraint spectral radius ρ(A) k To ensure the stability of the Tube-MPC controller, the disturbance invariant set formula is designed as follows: In the formula, Z(t) p ) for Tube-MPC controller and preview time domain t p The relevant perturbation invariant set; 13) Based on the constructed disturbance invariant set, further solve the Tube-MPC controller, transforming the problem of solving the nominal system into finding the optimal control problem, and outputting the optimal suspension force as the target force of the lower actuator; at the same time, considering the Lyapunov stability of the system, the objective function and constraints are designed as follows: In the formula, J is the objective function; x represents the value of the nominal system state variable predicted at time k in the future at time k+i; ref|k+i|k Let the expected values ​​of the actual system state variables be at future time k+i. R is the value of the nominal system control variable change at time k+i in the future; P is the weight matrix of the nominal system control variable change; N is the weight matrix of the terminal state; p For prediction in the time domain; X f For terminal constraints; To predict the nominal system state variables of the time-domain terminal; Q1, Q2, and Q3 correspond to the outputs of the Tube-MPC controller, including the weights of vehicle body vibration acceleration, roll rate, and pitch rate, respectively. Based on historical vehicle data, real-time vehicle vibration acceleration data, suspension travel data, roll rate data, pitch rate data, and future road disturbance data collected by sensor components, the future output response of the Tube-MPC controller is predicted. The real system in the Tube-MPC controller is discretized, and the discrete real system is represented as: x k+1 =A d x k +B d u k In the formula, x k+1 x represents the actual system state at time k+1; k Let k be the actual system state variables at time k, including vehicle body vibration acceleration. Wheel vibration acceleration Suspension travel z b -z w ;u k Let K be the actual system control quantity at time k, and the control quantity is the optimal suspension force. Combining the state variables and control variables of the real system, the expanded-dimensional discrete real system representation is obtained as follows: In the formula, u represents the state error vector of the real system after dimension expansion; k-1 The actual system control quantity at time k-1; extended dimension matrix. Extended dimension matrix Extended dimension matrix η k Let I be the state vector of the real system at time k after dimension expansion; Nu Let I be an identity matrix of dimension Nu, representing the control input; Nx Let Δu be an identity matrix of dimension Nx, representing the state variables; k This represents the change of the actual system control quantity at time k and subsequent time points; Obtain the predicted output y of the real system after dimension expansion in the prediction time domain. k+1|k : y k+1|k =S x x k +S u D.U. k In the formula, Nc represents the control time domain. It is a sequence of control quantity changes, with the optimal suspension active force serving as the target force for the lower-level actuator.

3. The semi-active suspension anti-aiming control method based on the fusion of long and short time domain predictions according to claim 2, characterized in that, Step 2) specifically includes: The target force of the lower-level actuator obtained in step 1) is allocated, and the time-domain signal is converted into a frequency-domain signal using Fourier transform. Frequency domain separation is then performed, dividing the target force into low-frequency active force and high-frequency active force. Given a time-domain signal x(t), its frequency-domain expression X(f) is as follows: In the formula, f is the frequency in the frequency domain and t is the time in the time domain; Low-frequency active force F is extracted using a low-pass filter. low (t) is the target force of the dual-chamber air spring, which determines the transfer function H of the first-order low-pass filter. low (jω) and low-frequency active force F low (t) is represented as: In the formula, X c R is the reactance of capacitor C, R is the resistance in the filter, ω is the target force angular frequency of the lower actuator, C is the capacitance of the filter, and F is the capacitance of the filter. Tube-MPC (t) represents the actuator target force output by the Tube-MPC controller, where j is the imaginary unit; High-frequency active force F is extracted using a low-pass filter. high (t) serves as the target force for the CDC damper, and the transfer function H of the first-order high-pass filter is... high (jω) and high-frequency active force F high (t) is represented as:

4. The semi-active suspension anti-aiming control method based on the fusion of long and short time domain predictions according to claim 1, characterized in that, Step 3) specifically includes: 31) Based on the target force obtained in step 2) of the dual-chamber air spring and CDC damper, analyze the specific required short pre-aiming time domain T. preview The mapping relationship between different time delays between the lower actuators was established; a series of simulations were designed and run, and system response data under different time delays were collected by parameter scanning. Specifically, the simulation scenarios included various road disturbance characteristics at different vehicle speeds. The different time delays between the lower actuators were simulated by adjusting the inflation and deflation time delay of the dual-chamber air spring and the response time of the CDC damper. The pre-aiming time domain was dynamically adjusted in each simulation, and the accuracy of the system response was recorded. Generalized regression neural networks are used to analyze the specific short preview time domain T required. preview The mapping relationship between the lower-level actuators and the different time delays is as follows: Radial basis functions are used to measure the similarity between the input data and the training samples, and the activation function is calculated as follows: In the formula, φ i D is the output of the i-th neuron; i The Euclidean distance between the input data and the training samples, where the input data includes time delay t. delay Vehicle speed v and road surface disturbance characteristics r; σ is the smoothing factor of the radial basis function; Calculate normalized weights Among them W i The normalized weights for the i-th training sample are given; N is the number of training samples; the final output, i.e., the short preview time domain, is calculated. Y i This refers to the short preview time domain corresponding to the i-th training sample; 32) Based on the short preview time domain T obtained in step 31), preview The following is a design strategy for coordinated distribution and compensation control of a dual-chamber air spring-CDC damper based on short-time domain preview information: According to the low-frequency active force F low (t) controls the mode switching of the dual-chamber air spring to adapt to low-frequency vibrations of different amplitudes. The mode switching logic is as follows: In the formula, u valve This indicates the on / off state of the solenoid valve in the dual-chamber air spring, with 1 representing open and 0 representing closed; F thresh The threshold value is the main power threshold for the solenoid valve switching in the dual-chamber air spring. In order to improve the stability of the dual-chamber air spring and avoid the adverse effects caused by frequent mode switching, the threshold value needs to be controlled within a reasonable range during the design process. The output force F is based on the on / off state of the solenoid valve in the dual-chamber air spring. AS (t), the main force output by the dual-chamber air spring in a certain stiffness mode, is expressed as follows: In the formula, F open (t) represents the main power force when the valve is opened, F closed (t) represents the main power when the valve is closed; Controlling the CDC damper in the high-frequency range by tracking the target damping force F high (t) Absorb vibration, and at the same time use the CDC damper to compensate for the insufficient active force F of the dual-chamber air spring in the low-frequency range of the next time period. low (t+T preview )-F AS (t+T preview Meanwhile, the high-frequency damping control at time t is maintained; the final target damping force F of the CDC damper is calculated. CDC (t) represents the superposition of damping force in the high-frequency range and compensation force in the low-frequency range, expressed as follows: F CDC (t)=F high (t)+F low (t+T preview )-F AS (t+T preview ) Sliding mode control is used to achieve the damping force F of the CDC damper at the final target. CDC For tracking (t), the sliding surface s is defined as the combination of the error between the final target damping force and the actual damping force and its derivative, as follows: In the formula, γ is the sliding surface weighting coefficient, used to adjust the system's response speed; F error =e high (t)+e low (t+T preview Error e in the high-frequency range high (t) represents the actual damping force of the CDC damper in the high-frequency range and F. high The error between (t) and the error e in the low-frequency range. low (t+T preview ) = F low (t+T preview )-F AS (t+T preview ); The sliding mode control law is designed as follows: In the formula, ε is the constant velocity approaching rate, κ is the coefficient of the exponential approaching term, and δ is the boundary layer thickness.

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