Active suspension low-computational-power preview control method considering actuator time lag and vehicle speed change

Abstract

The invention belongs to the technical field of automatic driving test, and particularly relates to an active suspension low-computational-power preview control method considering actuator time lag and vehicle speed change. Comprising the following steps: step 1, establishing a quarter vehicle active suspension model considering time lag and an active suspension continuous time state-space equation, and discretizing; 2, establishing a pavement model capable of changing along with the vehicle speed, acquiring spatial domain road elevation information in a preview range by using a perception sensor, and further converting the spatial domain road elevation information into time domain road elevation information through time domain conversion; and step 3, designing an active suspension preview control method based on the discretization state-space equation of the active suspension and the time domain road elevation information. According to the invention, the computing power of the control method can be effectively reduced on the premise of ensuring the control performance of the active suspension.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous driving testing, and specifically is a low-computing-power preview control method for an active suspension that takes actuator time lag and vehicle speed changes into consideration. Background Art

[0002] The suspension system is an important component of the vehicle chassis and a key component to ensure the vehicle's ride comfort and road holding performance. For vehicles equipped with traditional passive suspension and semi-active suspension, it is usually difficult to meet people's requirements for vehicle ride comfort while ensuring road holding performance. Therefore, how to effectively balance these two indicators on roads with varying degrees of bumps has become a current research hotspot. Compared with passive suspension and semi-active suspension without external power sources, active suspension has an actuator with fast response speed and large actuation force, and is considered to be an ideal solution to the performance contradiction of the suspension system.

[0003] At the same time, high-performance perception sensors such as laser radar, millimeter-wave radar and binocular cameras have improved the recognition accuracy of road profiles. Active suspension can use the elevation information of the road ahead observed in advance by the perception sensor for preview control, so that the vehicle has the best ride comfort and road keeping performance when passing the road ahead. Therefore, active suspension preview control has received extensive attention from many scholars. Model predictive control is considered to be one of the best solutions for active suspension preview control due to its excellent control effect and ability to handle multiple constraints, but current related research still has limitations in some aspects. First, the current control methods mostly ignore the nonlinear problem caused by the actuator time lag of the active suspension when establishing the suspension model, resulting in the inability to guarantee the vertical performance of the vehicle during the actual control process. Secondly, the current preview control uses the road elevation information within a fixed time. When the vehicle speed changes, the distance of the road elevation information required for control will also change. However, in practice, the perception sensor has a fixed preview distance, which means that the current preview method is only applicable to active suspension control at a constant vehicle speed and cannot adapt to the speed changes during actual driving. Finally, although the traditional model predictive control method can greatly improve the performance of the active suspension, the use of quadratic programming to solve the optimal control sequence will greatly increase the computing power, resulting in longer calculation time and making it difficult to actually apply it on the vehicle. Summary of the invention

[0004] To solve the above problems, the present invention provides a low-computing-power preview control method for an active suspension that takes into account actuator time lag and vehicle speed changes, which can effectively reduce the computing power of the control method while ensuring the control performance of the active suspension.

[0005] The technical solution of the present invention is described as follows in conjunction with the accompanying drawings:

[0006] A low-computation preview control method for an active suspension considering actuator time lag and vehicle speed change comprises the following steps:

[0007] Step 1: Establish a quarter vehicle active suspension model and active suspension continuous time state space equation considering time lag and discretize them;

[0008] Step 2: Establish a road surface model that can change with vehicle speed and use a perception sensor to obtain spatial domain road elevation information within the preview range, and then convert the spatial domain road elevation information into time domain road elevation information through time domain conversion;

[0009] Step 3: Based on the discretized state space equation of the active suspension and the road elevation information in the time domain, the active suspension preview control method is designed.

[0010] Furthermore, the specific method of step one is as follows:

[0011] 11) A quarter-vehicle active suspension model considering time lag is established, which includes four parts: sprung mass, active suspension, unsprung mass and wheels; the controller of the active suspension part outputs the ideal control force which is converted into the actual control force output to the suspension system after time lag;

[0012] 12) According to the quarter-vehicle active suspension model considering time lag and the vehicle dynamics principle, the quarter-vehicle active suspension dynamics equation considering time lag is obtained as follows:

[0013]

[0014] In the formula, m s is the sprung mass; m u is the unsprung mass; k s is the spring stiffness; c s is the suspension damping coefficient; F r is the actual force of the active suspension; F is the ideal force of the active suspension; c t is the vertical equivalent damping of the tire; k t is the vertical equivalent stiffness of the tire; s is the vertical displacement of the sprung mass; z u is the vertical displacement of the unsprung mass; z r is the road surface height; τ is the time constant of the actuator hysteresis characteristic;

[0015] 13) Select the sprung mass displacement z s (t), unsprung mass displacement z u (t), sprung mass displacement velocity Unsprung mass displacement velocity and the actual force F r(t) as the state variable; select the sprung mass acceleration Suspension travel z u (t)-z s (t) and tire dynamic deflection z r (t)-z u (t) is the output variable of the system; then select the ideal force F(t) and the road height z r are the input variables and disturbances of the system; the quarter-vehicle active suspension dynamics equation (1) is rewritten as the system state space equation under continuous time, as shown below:

[0016]

[0017] in,

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] Where x(t) is the state variable; is the derivative of the state variable; y(t) is the output variable; u(t) is the input variable; ω(t) is the disturbance variable; A is the suspension system matrix; B is the suspension input matrix; C is the suspension output matrix; D is the direct transfer matrix; E is the state disturbance matrix; L is the output disturbance matrix; z s (t) is the sprung mass displacement; z u (t) is the unsprung mass displacement; is the sprung mass displacement velocity; is the displacement velocity of the unsprung mass; F r (t) to be the actual driving force; r is the road surface height; m s is the sprung mass; m u is the unsprung mass; k s is the spring stiffness; c s is the suspension damping coefficient; c t is the vertical equivalent damping of the tire; k t is the vertical equivalent stiffness of the tire;

[0024] 14) Discretize the system state space equation under continuous time, use the zero-order hold discretization method to discretize the continuous time matrix in equation (2), and obtain the discretized system state space equation as shown below:

[0025]

[0026]

[0027] Where A is the suspension system matrix; B is the suspension input matrix; E is the state interference matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; T s is the sampling period of the active suspension control system; is the discrete suspension system matrix; Input matrix for discrete suspension; is the discrete state interference matrix; x(k) is the state variable of the kth period; y(k) is the output variable of the kth period; u(k) is the input variable of the kth period; ω(k) is the interference variable of the kth period.

[0028] Furthermore, the specific method of step 2 is as follows:

[0029] 21) The road surface in the preview area is evenly divided into multiple grids of length Δl by the perception sensor, so that the number of grids in the preview range in the spatial domain is:

[0030]

[0031] Where N is the number of grids in the preview range in the spatial domain; L p is the length of the preview area; Δl is the grid length;

[0032] 22) The spatial domain road elevation information is stored in the road elevation information set Ψ(t), expressed as:

[0033] Ψ(t)={h1(t),h2(t),...,h N (t)} (6)

[0034] Where Ψ(t) is the road elevation information set in the spatial domain; h i (t) is the height corresponding to the i-th grid point at time t;

[0035] 23) Set the sampling period of active suspension to T s , then the distance traveled by the vehicle in each sampling period and the number of grids passed in p sampling periods are:

[0036] l s =v×T s (7)

[0037]

[0038] Where v is the vehicle speed; T s is the sampling period of the active suspension; n(k,v) is the number of grids that the vehicle passes through at speed v within p sampling periods; l s is the distance traveled by the vehicle in each sampling period; Δl is the grid length;

[0039] 24) If the vehicle is in kT s The road surface height at is h i (kT s ), then when the time is (k+1)T s When the vehicle is on the road, the height is h i+n(1,v) (kT s ); Get the vehicle in kT s The road elevation information set Γ(k,v) in the time domain when the speed is v is:

[0040]

[0041] in,

[0042] H i (k,v)=h n(i-1,v) (kT s ),i=1,2,...,N tp (v) (10)

[0043]

[0044] Where v is the vehicle speed; T s is the sampling period of the active suspension control system; Γ(k,v) is the road elevation information set in the time domain when the vehicle travels at a speed of v for k sampling periods; H i (k,v) is kT s The height of the i-th sampling point at time; N tp (v) is the number of sampling points in the time domain when the vehicle speed is v; n(p,v) is the number of grids that the vehicle passes through at speed v within p sampling times; h i (kT s ) is the vehicle in kT s The road surface height at the i-th grid point obtained by the sensing sensor; L p is the length of the preview area.

[0045] Furthermore, the specific method of step three is as follows:

[0046] 31) Design the cost function for the active suspension system optimization problem:

[0047]

[0048] Where J is the cost function; y(k) is the output variable of the kth period; u(k) is the input variable of the kth period; Q is N tp ×N tp The positive definite output weight matrix of tp ×N tp The positive definite input weight matrix N tp is the number of sampling points;

[0049] The constraints are expressed as follows:

[0050]

[0051] Where u(k) is the input variable of the kth cycle; x1(k) is the displacement of the sprung mass of the kth cycle; x2(k) is the displacement of the unsprung mass of the kth cycle; f max is the maximum force that the active suspension can generate; max is the maximum compression of the active suspension;

[0052] 32) The Laguerre function is used to fit the optimal actuation force, and combined with the time domain road elevation information, a discrete model predictive control method that adapts to the speed is designed, as follows:

[0053] 321) Read the vehicle state x(k) and speed v in the kth cycle;

[0054] 322) Calculate the number of sampling points N tp and disturbance variables ω(k);

[0055] 323) Using Laguerre function to fit the optimal input sequence of active suspension;

[0056] 324) Input the fitted system into the sequence u(k) with the previous control step length N c The value is taken as the optimal control value U of the actual suspension opt Output;

[0057] 325) Repeat the whole process in the k+1th cycle.

[0058] Further, the specific method of step 323) is as follows:

[0059] 3231) The active suspension input sequence is expressed using a discrete Laguerre function. The discrete form of the Laguerre function is shown in the following formula:

[0060]

[0061] In the formula, ξ i(z) is a discrete Laguerre polynomial; 0≤α≤1 is the pole of the discrete polynomial; N is the number of terms of the discrete Laguerre function;

[0062] To simplify the analysis of active suspension discrete system, ξ i (z) Perform z-transformation to express the discrete Laguerre function as follows:

[0063] L(k)=[l1(k),l2(k),...,l N (k)] T (15)

[0064] In the formula, l i (k) is the discrete Laguerre polynomial ξ i (z) is the z-transform; L(k) is the Laguerre function of the designed active suspension;

[0065] Combining equations (14) and (15), the active suspension Laguerre function is recursively expressed as follows:

[0066] L(k+1)=A l L(k) (16)

[0067]

[0068]

[0069] Where L(k) is the Laguerre function of the active suspension; A l is the recursive matrix; α is the pole of the discrete polynomial;

[0070] The input u(k+i) of the active suspension at time k+i is described by the discrete Laguerre function:

[0071]

[0072] Where u(k) is the input variable of the kth period; η=[c1,c2,…,c N ] T is the Laguerre fitting coefficient; l j (i) is the discrete Laguerre polynomial; L(i) is the active suspension Laguerre function; N is the number of grids in the preview range in the spatial domain;

[0073] 3232) The input u(k) described by the discrete Laguerre function is used to rewrite the active suspension discrete state space equation and cost function; the active suspension system input u(k) is brought into the active suspension discrete state space equation, i.e., equation (3), and combined with equation (16), the following iterative derivation is performed to obtain the system state quantity and output quantity represented by the Laguerre function:

[0074]

[0075]

[0076] In the formula, is the discrete suspension system matrix; Input matrix for discrete suspension; is the discrete state interference matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; x(k) is the state variable of the kth period; y(k) is the output variable of the kth period; u(k) is the input variable of the kth period; ω(k) is the interference variable of the kth period; L(k) is the active suspension Laguerre function of the kth period; η is the Laguerre fitting coefficient;

[0077] The cost function (12) is rewritten as the Laguerre function:

[0078]

[0079]

[0080] In the formula, is the discrete suspension system matrix; is the discrete suspension input matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; x(k) is the state variable of the kth period; ω(k) is the interference variable of the kth period; L(k) is the active suspension Laguerre function of the kth period; η is the Laguerre fitting coefficient; Q is N tp ×N tp The positive definite output weight matrix of tp ×N tp The positive definite input weight matrix N tp is the number of sampling points in the time domain;

[0081] 3233) The optimal input sequence of the active suspension is obtained by taking the derivative of the Laguerre fitting coefficient η; the goal of active suspension control is to find a suitable input sequence to minimize the value of the established cost function; through the above conversion, the cost function has been described as a function of the Laguerre fitting coefficient η, so by taking the derivative of the cost function with respect to η to be equal to zero, the corresponding optimal Laguerre fitting coefficient η can be obtained. opt :

[0082]

[0083] η opt =-Ω -1 ×G (25)

[0084]

[0085] Where, J is the cost function; η is the Laguerre fitting coefficient; is the discrete suspension system matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; x(k) is the state variable of the kth period; ω(k) is the interference variable of the kth period; Q is N tp ×N tp The positive definite output weight matrix of tp ×N tp The positive definite input weight matrix of ; L(k) is the active suspension Laguerre function of the kth period; η opt is the optimal Laguerre fitting coefficient of the active suspension system; Ω and G are N×N and N×1 matrices respectively; N is the number of grids in the preview range in the spatial domain; N tp is the number of sampling points in the time domain;

[0086] Through the above derivation and calculation combined with formula (19), we can get the fitted input sequence u(k). Select the previous control step size N c The value is the optimal input sequence U of the active suspension system. opt .

[0087] The beneficial effects of the present invention are:

[0088] 1) The present invention establishes a quarter vehicle active suspension model that takes into account the actuator time lag, which can take into account the time lag of the actual actuator actuation, improve the robustness of the active suspension system, and improve the vertical performance of the vehicle;

[0089] 2) The present invention designs a road elevation information collection mechanism in the time domain, which can adapt the collected road elevation information to the change of vehicle speed at a fixed preview distance, and match the actual demand of the active suspension preview control method for road information;

[0090] 3) The present invention designs an active suspension model predictive control method based on Laguerre function, and uses Laguerre function fitting to replace the quadratic programming link of solving the optimal control sequence in the traditional model predictive control method, which can significantly reduce the computing power requirements of the control method. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0092] Figure 1A simplified architecture diagram of a low-computation preview control method for an active suspension taking into account actuator time lag and vehicle speed changes according to the present invention;

[0093] Figure 2 Schematic diagram of a quarter vehicle active suspension model considering time lag;

[0094] Figure 3 It is a schematic diagram of road elevation information in the spatial domain;

[0095] Figure 4 It is a schematic diagram of the road elevation information in the time domain that changes with vehicle speed;

[0096] Figure 5 It is a schematic diagram of the distribution of discrete road elevation information;

[0097] Figure 6 It is a schematic diagram of the comparison of sprung mass acceleration;

[0098] Figure 7 This is a schematic diagram for comparing algorithm running time. DETAILED DESCRIPTION

[0099] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0100] Embodiment 1

[0101] See also Figure 1 The present invention provides a low-computation preview control method for an active suspension considering actuator time lag and vehicle speed change, comprising the following steps:

[0102] Step 1: Establish a quarter-vehicle active suspension model and active suspension continuous-time state space equation considering time lag and discretize them as follows:

[0103] 11) Establish Figure 2 The quarter-vehicle active suspension model considering time lag shown in the figure includes four parts: sprung mass, active suspension, unsprung mass and wheels; wherein the controller of the active suspension part outputs the ideal control force which is converted into the actual control force output to the suspension system after time lag;

[0104] 12) According to the quarter-vehicle active suspension model considering time lag and the vehicle dynamics principle, the quarter-vehicle active suspension dynamics equation considering time lag is obtained as follows:

[0105]

[0106] In the formula, ms is the sprung mass; m u is the unsprung mass; k s is the spring stiffness; c s is the suspension damping coefficient; F r is the actual force of the active suspension; F is the ideal force of the active suspension; c t is the vertical equivalent damping of the tire; k t is the vertical equivalent stiffness of the tire; s is the vertical displacement of the sprung mass; z u is the vertical displacement of the unsprung mass; z r is the road height; τ is the time constant that characterizes the time lag characteristics of the actuator;

[0107] 13) Select the sprung mass displacement z s (t), unsprung mass displacement z u (t), sprung mass displacement velocity Unsprung mass displacement velocity and the actual force F r (t) as the state variable; select the sprung mass acceleration Suspension travel z u (t)-z s (t) and tire dynamic deflection z r (t)-z u (t) is the output variable of the system; then select the ideal force F(t) and the road height z r are the input variables and disturbances of the system; the quarter-vehicle active suspension dynamics equation (1) is rewritten as the system state space equation under continuous time, as shown below:

[0108]

[0109] in,

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] Where x(t) is the state variable; is the derivative of the state variable; y(t) is the output variable; u(t) is the input variable; ω(t) is the disturbance variable; A is the suspension system matrix; B is the suspension input matrix; C is the suspension output matrix; D is the direct transfer matrix; E is the state disturbance matrix; L is the output disturbance matrix; z s (t) is the displacement of the sprung mass; z u (t) is the unsprung mass displacement; is the sprung mass displacement velocity; is the displacement velocity of the unsprung mass; F r (t) to be the actual driving force; r is the road surface height; m s is the sprung mass; m u is the unsprung mass; k s is the spring stiffness; c s is the suspension damping coefficient; c t is the vertical equivalent damping of the tire; k t is the vertical equivalent stiffness of the tire;

[0116] 14) Model predictive control uses a discrete model to predict the future state of the controlled object, and obtains the optimal control quantity through rolling optimization. The system state space equation under continuous time is discretized, and the continuous time matrix in formula (2) is discretized using the zero-order hold discretization method, and the discretized system state space equation is obtained as shown below:

[0117]

[0118]

[0119] Where A is the suspension system matrix; B is the suspension input matrix; E is the state interference matrix; D is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; T s is the sampling period of the active suspension control system; is the discrete suspension system matrix; Input matrix for discrete suspension; is the discrete state interference matrix; x(k) is the state variable of the kth period; y(k) is the output variable of the kth period; u(k) is the input variable of the kth period; ω(k) is the interference variable of the kth period.

[0120] Step 2: Establish a road surface model that can change with vehicle speed and use a perception sensor to obtain spatial domain road elevation information within the preview range, and then convert the spatial domain road elevation information into time domain road elevation information through time domain conversion;

[0121] In the past, active suspension preview control methods always use road elevation information within a fixed time. When the vehicle speed changes, the distance of the road elevation information required for preview control will also change. However, in practice, the preview distance of the perception sensor is fixed, which means that the past methods are only applicable to active suspension preview control at a certain constant speed and cannot adapt to the speed changes during actual driving. Therefore, a road model within a fixed preview distance that can change with the vehicle speed should be established for active suspension preview control under variable speed, as follows:

[0122] 21) The road elevation information in the spatial domain obtained by the perception sensor is as follows: Figure 3 As shown. The road surface in the preview area is evenly divided into multiple grids of length Δl by the perception sensor, so that the number of grids in the preview range in the spatial domain is:

[0123]

[0124] Where N is the number of grids in the preview range in the spatial domain; L p is the length of the preview area; Δl is the grid length;

[0125] 22) The spatial domain road elevation information is stored in the road elevation information set Ψ(t), expressed as:

[0126] Ψ(t)={h1(t),h2(t),...,h N (t)} (6)

[0127] Where Ψ(t) is the road elevation information set in the spatial domain; h i (t) is the height corresponding to the i-th grid point at time t;

[0128] 23) Set the sampling period of active suspension to T s , then the distance traveled by the vehicle in each sampling period and the number of grids passed in p sampling periods are:

[0129] l s =v×T s (7)

[0130]

[0131] Where v is the vehicle speed; T s is the sampling period of the active suspension; n(k,v) is the number of grids that the vehicle passes through at speed v within p sampling periods; l s is the distance traveled by the vehicle in each sampling period; Δl is the grid length;

[0132] 24) If the vehicle is in kT s The road surface height at is hi (kT s ), then when the time is (k+1)T s When the vehicle is on the road, the height is h i+n(1,v) (kT s ); Get the vehicle in kT s The road elevation information set Γ(k,v) in the time domain when the speed is v is:

[0133]

[0134] in,

[0135] H i (k,v)=h n(i-1,v) (kT s ),i=1,2,...,N tp (v) (10)

[0136]

[0137] Where v is the vehicle speed; T s is the sampling period of the active suspension control system; Γ(k,v) is the road elevation information set in the time domain when the vehicle travels at a speed of v for k sampling periods; H i (k,v) is kT s The height of the i-th sampling point at time; N tp (v) is the number of sampling points in the time domain when the vehicle speed is v; n(p,v) is the number of grids that the vehicle passes through at speed v within p sampling times; h i (kT s ) is the vehicle in kT s The road surface height at the i-th grid point obtained by the sensing sensor; L p is the length of the preview area.

[0138] According to the above road model establishment method, the time domain road elevation information at different speeds can be obtained, such as Figure 4 As shown in the figure. The blue and red colors represent the time domain road elevation information at low and high speeds, respectively. It can be seen that when the preview distance remains unchanged, the higher the vehicle speed, the fewer the number of system sampling points, and the sparser the distribution of sampling points in the spatial domain, which is consistent with the actual sampling situation of the perception sensor changing with vehicle speed.

[0139] Step 3: Based on the discretized state space equation of the active suspension and the road elevation information in the time domain, the active suspension preview control method is designed;

[0140] Due to the constraints of the vehicle's energy consumption and performance requirements, the active suspension needs to minimize the actuator output while maximizing the vehicle's vertical performance. Therefore, the control objectives of the active suspension can be summarized as follows:

[0141] 1) The vehicle should ensure driving comfort, safety and grounding during driving, which correspond to the three output variables of the system respectively z u (t)-z s (t), z r (t)-z u (t); therefore, the values ​​of the three output variables should be minimized.

[0142] 2) The active actuators on the active suspension require external energy supply. Each movement consumes energy, and the greater the actuating force generated, the more energy is consumed. Therefore, in order to reduce energy consumption, it is necessary to reduce the actuating force F(t) output at each moment, as follows:

[0143] 31) According to the above control objectives, design the cost function of the active suspension system optimization problem:

[0144]

[0145] Where J is the cost function; y(k) is the output variable of the kth period; u(k) is the input variable of the kth period; Q is N tp ×N tp The positive definite output weight matrix of tp ×N tp The positive definite input weight matrix N tp is the number of sampling points;

[0146] 32) Since the actuating force generated by the actuator in the active suspension is limited, and the range of the actuating force has a great influence on the vibration reduction performance of the active suspension, the limitation of the actuating force should be considered when designing the active suspension control method; at the same time, in order to avoid breaking through the suspension and ensure vehicle safety, corresponding requirements should also be made for the maximum compression of the active suspension. The specific constraints can be expressed as follows:

[0147]

[0148] Where u(k) is the input variable of the kth cycle; x1(k) is the displacement of the sprung mass of the kth cycle; x2(k) is the displacement of the unsprung mass of the kth cycle; f max is the maximum force that the active suspension can generate; max is the maximum compression of the active suspension;

[0149] 32) For the traditional model predictive control method, the optimal actuation force sequence will be solved by quadratic programming; however, the quadratic programming method has high requirements on computing power and cannot meet the real-time requirements of vehicle driving. Therefore, the present invention uses Laguerre function to fit the optimal actuation force to replace the quadratic programming link in the model predictive control, reduce the computing power of the control method, and combine the time domain road elevation information. The present invention designs a discrete model predictive control method that adapts to speed, as follows:

[0150] 321) Read the vehicle state x(k) and speed v in the kth cycle;

[0151] 322) Calculate the number of sampling points N tp and disturbance variables ω(k);

[0152] 323) The Laguerre function is used to fit the optimal input sequence of the active suspension, as follows:

[0153] 3231) The active suspension input sequence is expressed using a discrete Laguerre function. The discrete form of the Laguerre function is shown in the following formula:

[0154]

[0155] In the formula, ξ i (z) is a discrete Laguerre polynomial; 0≤α≤1 is the pole of the discrete polynomial; N is the number of terms of the discrete Laguerre function;

[0156] To simplify the analysis of active suspension discrete system, ξ i (z) Perform z-transformation to express the discrete Laguerre function as follows:

[0157] L(k)=[l1(k),l2(k),...,l N (k)] T (15)

[0158] In the formula, l i (k) is the discrete Laguerre polynomial ξ i (z) is the z-transform; L(k) is the Laguerre function of the designed active suspension;

[0159] Combining equations (14) and (15), the active suspension Laguerre function is recursively expressed as follows:

[0160] L(k+1)=A l L(k) (16)

[0161]

[0162]

[0163] Where L(k) is the Laguerre function of the active suspension; A l is the recursive matrix; α is the pole of the discrete polynomial;

[0164] The input u(k+i) of the active suspension at time k+i is described by the discrete Laguerre function:

[0165]

[0166] Where u(k) is the input variable of the kth period; η=[c1,c2,…,c N ] T is the Laguerre fitting coefficient; l j (i) is the discrete Laguerre polynomial; L(i) is the active suspension Laguerre function; N is the number of grids in the preview range in the spatial domain;

[0167] 3232) The input u(k) described by the discrete Laguerre function is used to rewrite the active suspension discrete state space equation and cost function; the active suspension system input u(k) is brought into the active suspension discrete state space equation, i.e., equation (3), and combined with equation (16), the following iterative derivation is performed to obtain the system state quantity and output quantity represented by the Laguerre function:

[0168]

[0169]

[0170] In the formula, is the discrete suspension system matrix; Input matrix for discrete suspension; is the discrete state interference matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; x(k) is the state variable of the kth period; y(k) is the output variable of the kth period; u(k) is the input variable of the kth period; ω(k) is the interference variable of the kth period; L(k) is the active suspension Laguerre function of the kth period; η is the Laguerre fitting coefficient;

[0171] The cost function (12) is rewritten as the Laguerre function:

[0172]

[0173]

[0174] In the formula, is the discrete suspension system matrix; is the discrete suspension input matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; x(k) is the state variable of the kth period; ω(k) is the interference variable of the kth period; L(k) is the active suspension Laguerre function of the kth period; η is the Laguerre fitting coefficient; Q is N tp ×N tp The positive definite output weight matrix of tp ×N tp The positive definite input weight matrix N tp is the number of sampling points in the time domain;

[0175] 3233) The optimal input sequence of the active suspension is obtained by taking the derivative of the Laguerre fitting coefficient η; the goal of active suspension control is to find a suitable input sequence to minimize the value of the established cost function; through the above conversion, the cost function has been described as a function of the Laguerre fitting coefficient η, so by taking the derivative of the cost function with respect to η to be equal to zero, the corresponding optimal Laguerre fitting coefficient η can be obtained. opt :

[0176]

[0177] η opt =-Ω -1 ×G (25)

[0178]

[0179] Where, J is the cost function; η is the Laguerre fitting coefficient; is the discrete suspension system matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; x(k) is the state variable of the kth period; ω(k) is the interference variable of the kth period; Q is N tp ×N tp The positive definite output weight matrix of tp ×N tp The positive definite input weight matrix of ; L(k) is the active suspension Laguerre function of the kth period; η opt is the optimal Laguerre fitting coefficient of the active suspension system; Ω and G are N×N and N×1 matrices respectively; N is the number of grids in the preview range in the spatial domain; N tp is the number of sampling points in the time domain;

[0180] Through the above derivation and calculation combined with formula (19), we can get the fitted input sequence u(k). Select the previous control step size N c The value is the optimal input sequence U of the active suspension system. opt ;

[0181] 324) Input the fitted system into the sequence u(k) with the previous control step length Nc The value is taken as the optimal control value U of the actual suspension opt Output;

[0182] 325) Repeat the whole process in the k+1th cycle.

[0183] In summary, the present invention firstly builds a quarter-vehicle active suspension model considering time lag and discretizes the state space equation so that it can be used for model predictive control; secondly, a road surface model is built using the road surface elevation information obtained by the perception sensor. The spatial domain road surface elevation information is converted into the time domain road surface elevation information, which can adapt to the change of vehicle speed at a fixed preview distance, meeting the actual requirements of active suspension preview control for road surface information; finally, an active suspension preview control method is constructed, and the Laguerre function fitting is used to replace the link of solving the optimal control sequence by quadratic programming in traditional model predictive control, which significantly reduces the computing power requirements of the control method.

[0184] Embodiment 2

[0185] In order to verify the effectiveness and superiority of the method proposed in the invention, the following Figure 5 The discrete road surface shown in the figure is simulated and verified, and the vehicle speed is set to 5m / s and the simulation time is 6s. The comparison between the sprung mass acceleration and the algorithm running time is shown in Figure 6 and Figure 7 shown.

[0186] As can be seen from the figure, in terms of control effect, the proposed discrete model predictive control method with adaptive speed can well approximate the traditional model predictive control method and can significantly reduce the acceleration of the sprung mass. In terms of computing power, the proposed control method can significantly reduce the running time of the traditional model predictive control method and meet the computing power and real-time requirements of active suspension control.

[0187] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A low-computation preview control method for active suspension considering actuator time lag and vehicle speed change, characterized in that: The following steps are involved: Step 1: Establish a quarter vehicle active suspension model and active suspension continuous time state space equation considering time lag and discretize them; Step 2: Establish a road surface model that can change with vehicle speed and use a perception sensor to obtain spatial domain road elevation information within the preview range, and then convert the spatial domain road elevation information into time domain road elevation information through time domain conversion; Step 3: Based on the discretized state space equation of the active suspension and the road elevation information in the time domain, the active suspension preview control method is designed.

2. The low-computation preview control method for active suspension considering actuator time lag and vehicle speed change according to claim 1, characterized in that: The specific method of step one is as follows: 11) A quarter-vehicle active suspension model considering time lag is established, which includes four parts: sprung mass, active suspension, unsprung mass and wheels; the controller of the active suspension part outputs the ideal control force which is converted into the actual control force output to the suspension system after time lag; 12) According to the quarter-vehicle active suspension model considering time lag and the vehicle dynamics principle, the quarter-vehicle active suspension dynamics equation considering time lag is obtained as follows: In the formula, m s is the sprung mass; m u is the unsprung mass; k s is the spring stiffness; c s is the suspension damping coefficient; F r is the actual force of the active suspension; F is the ideal force of the active suspension; c t is the vertical equivalent damping of the tire; k t is the vertical equivalent stiffness of the tire; s is the vertical displacement of the sprung mass; z u is the vertical displacement of the unsprung mass; z r is the road height; τ is the time constant that characterizes the time lag characteristics of the actuator; 13) Select the sprung mass displacement z s (t), unsprung mass displacement z u (t), sprung mass displacement velocity Unsprung mass displacement velocity and the actual force F r (t) as the state variable; select the sprung mass acceleration Suspension travel z u (t)-z s (t) and tire dynamic deflection z r (t)-z u (t) is the output variable of the system; then select the ideal force F(t) and the road height z r are the input variables and disturbances of the system; the quarter-vehicle active suspension dynamics equation (1) is rewritten as the system state space equation under continuous time, as shown below: in, Where x(t) is the state variable; is the derivative of the state variable; y(t) is the output variable; u(t) is the input variable; ω(t) is the disturbance variable; A is the suspension system matrix; B is the suspension input matrix; C is the suspension output matrix; D is the direct transfer matrix; E is the state disturbance matrix; L is the output disturbance matrix; z s (t) is the displacement of the sprung mass; z u (t) is the unsprung mass displacement; is the sprung mass displacement velocity; is the displacement velocity of the unsprung mass; F r (t) Actual power; r is the road surface height; m s is the sprung mass; m u is the unsprung mass; k s is the spring stiffness; c s is the suspension damping coefficient; c t is the vertical equivalent damping of the tire; k t is the vertical equivalent stiffness of the tire; 14) Discretize the system state space equation under continuous time, use the zero-order hold discretization method to discretize the continuous time matrix in equation (2), and obtain the discretized system state space equation as shown below: Where A is the suspension system matrix; B is the suspension input matrix; E is the state interference matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; T s is the sampling period of the active suspension control system; is the discrete suspension system matrix; Input matrix for discrete suspension; is the discrete state interference matrix; x(k) is the state variable of the kth period; y(k) is the output variable of the kth period; u(k) is the input variable of the kth period; ω(k) is the interference variable of the kth period.

3. The low-computation preview control method for active suspension considering actuator time lag and vehicle speed change according to claim 1, characterized in that: The specific method of step 2 is as follows: 21) The road surface in the preview area is evenly divided into multiple grids of length Δl by the perception sensor, so that the number of grids in the preview range in the spatial domain is: Where N is the number of grids in the preview range in the spatial domain; L p is the length of the preview area; Δl is the grid length; 22) The spatial domain road elevation information is stored in the road elevation information set Ψ(t), expressed as: Ψ(t)={h1(t),h2(t),...,h N (t)} (6) Where Ψ(t) is the road elevation information set in the spatial domain; h i (t) is the height corresponding to the i-th grid point at time t; 23) Set the sampling period of active suspension to T s , then the distance traveled by the vehicle in each sampling period and the number of grids passed in p sampling periods are: l s =v×T s (7) Where v is the vehicle speed; T s is the sampling period of the active suspension; n(k,v) is the number of grids that the vehicle passes through at speed v within p sampling periods; l s is the distance traveled by the vehicle in each sampling period; Δl is the grid length; 24) If the vehicle is at kT s The road surface height at is h i (kT s ), then when the time is (k+1)T s When the vehicle is on the road, the height is h i+n(1,v) (kT s ); Get the vehicle in kT s The road elevation information set Γ(k,v) in the time domain when the speed is v is: in, H i (k,v)=h n(i-1,v) (kT s ),i=1,2,...,N tp (v) (10) Where v is the vehicle speed; T s is the sampling period of the active suspension control system; Γ(k,v) is the road elevation information set in the time domain when the vehicle travels at a speed of v for k sampling periods; Hi(k,v) is kT s The height of the i-th sampling point at time; N tp (v) is the number of sampling points in the time domain when the vehicle speed is v; n(p,v) is the number of grids that the vehicle passes through at speed v within p sampling times; h i (kT s ) is the vehicle in kT s The road surface height at the i-th grid point obtained by the sensing sensor; L p is the length of the preview area.

4. The low-computation preview control method for active suspension considering actuator time lag and vehicle speed change according to claim 2, characterized in that: The specific method of step three is as follows: 31) Design the cost function for the active suspension system optimization problem: Where J is the cost function; y(k) is the output variable of the kth period; u(k) is the input variable of the kth period; Q is N tp ×N tp The positive definite output weight matrix of tp ×N tp The positive definite input weight matrix N tp is the number of sampling points; The constraints are expressed as follows: Where u(k) is the input variable of the kth cycle; x1(k) is the displacement of the sprung mass of the kth cycle; x2(k) is the displacement of the unsprung mass of the kth cycle; f max is the maximum force that the active suspension can generate; max is the maximum compression of the active suspension; 32) The Laguerre function is used to fit the optimal actuation force, and combined with the time domain road elevation information, a discrete model predictive control method that adapts to the speed is designed, as follows: 321) Read the vehicle state x(k) and speed v in the kth cycle; 322) Calculate the number of sampling points N tp and disturbance variables ω(k); 323) Using Laguerre function to fit the optimal input sequence of active suspension; 324) Input the fitted system into the sequence u(k) with the previous control step length N c The value is taken as the optimal control value U of the actual suspension opt Output; 325) Repeat the whole process in the k+1th cycle.

5. The low-computation preview control method for active suspension considering actuator time lag and vehicle speed change according to claim 4, characterized in that: The specific method of step 323) is as follows: 3231) The active suspension input sequence is expressed using a discrete Laguerre function. The discrete form of the Laguerre function is shown in the following formula: In the formula, ξ i (z) is a discrete Laguerre polynomial; 0≤α≤1 is the pole of the discrete polynomial; N is the number of terms of the discrete Laguerre function; To simplify the analysis of active suspension discrete system, ξ i (z) Perform z-transformation to express the discrete Laguerre function as follows: L(k)=[l1(k),l2(k),...,l N (k)] T (15) In the formula, l i (k) is the discrete Laguerre polynomial ξ i (z) is the z-transform; L(k) is the Laguerre function of the designed active suspension; Combining equations (14) and (15), the active suspension Laguerre function is recursively expressed as follows: L(k+1)=A l L(k) (16) Where L(k) is the Laguerre function of the active suspension; A l is the recursive matrix; α is the pole of the discrete polynomial; The input u(k+i) of the active suspension at time k+i is described by the discrete Laguerre function: Where u(k) is the input variable of the kth period; η=[c1,c2,…,c N ] T is the Laguerre fitting coefficient; l j (i) is the discrete Laguerre polynomial; L(i) is the active suspension Laguerre function; N is the number of grids in the preview range in the spatial domain; 3232) The input u(k) described by the discrete Laguerre function is used to rewrite the active suspension discrete state space equation and cost function; the active suspension system input u(k) is brought into the active suspension discrete state space equation, i.e., equation (3), and combined with equation (16), the following iterative derivation is performed to obtain the system state quantity and output quantity represented by the Laguerre function: In the formula, is the discrete suspension system matrix; Input matrix for discrete suspension; is the discrete state interference matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; x(k) is the state variable of the kth period; y(k) is the output variable of the kth period; u(k) is the input variable of the kth period; ω(k) is the interference variable of the kth period; L(k) is the active suspension Laguerre function of the kth period; η is the Laguerre fitting coefficient; The cost function (12) is rewritten as the Laguerre function: In the formula, is the discrete suspension system matrix; is the discrete suspension input matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; x(k) is the state variable of the kth period; ω(k) is the interference variable of the kth period; L(k) is the active suspension Laguerre function of the kth period; η is the Laguerre fitting coefficient; Q is N tp ×N tp The positive definite output weight matrix of tp ×N tp The positive definite input weight matrix N tp is the number of sampling points in the time domain; 3233) The optimal input sequence of the active suspension is obtained by taking the derivative of the Laguerre fitting coefficient η; the goal of active suspension control is to find a suitable input sequence to minimize the value of the established cost function; through the above conversion, the cost function has been described as a function of the Laguerre fitting coefficient η, so by taking the derivative of the cost function with respect to η to be equal to zero, the corresponding optimal Laguerre fitting coefficient η can be obtained. opt : or opt =-Ω -1 ×G (25) Where, J is the cost function; η is the Laguerre fitting coefficient; is the discrete suspension system matrix; C is the suspension output matrix; D is the direct transfer matrix; L is the output interference matrix; x(k) is the state variable of the kth period; ω(k) is the interference variable of the kth period; Q is N tp ×N tp The positive definite output weight matrix of tp ×N tp The positive definite input weight matrix of ; L(k) is the active suspension Laguerre function of the kth period; η opt is the optimal Laguerre fitting coefficient of the active suspension system; Ω and G are N×N and N×1 matrices respectively; N is the number of grids in the preview range in the spatial domain; N tp is the number of sampling points in the time domain; Through the above derivation and calculation combined with formula (19), we can get the fitted input sequence u(k). Select the previous control step size N c The value is the optimal input sequence U of the active suspension system. opt .

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