CN113022247A - Adaptive fixed time event trigger fuzzy control method for active vehicle suspension system - Google Patents

Adaptive fixed time event trigger fuzzy control method for active vehicle suspension system Download PDF

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CN113022247A
CN113022247A CN202110255254.8A CN202110255254A CN113022247A CN 113022247 A CN113022247 A CN 113022247A CN 202110255254 A CN202110255254 A CN 202110255254A CN 113022247 A CN113022247 A CN 113022247A
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vehicle suspension
active vehicle
suspension system
fixed time
fuzzy control
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CN113022247B (en
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潘英男
梁洪晶
薛红
贾婷寒
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Bohai University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
    • B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
    • B60G17/018—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2600/00—Indexing codes relating to particular elements, systems or processes used on suspension systems or suspension control systems
    • B60G2600/18—Automatic control means
    • B60G2600/187—Digital Controller Details and Signal Treatment
    • B60G2600/1879—Fuzzy Logic Control

Abstract

The disclosure provides a self-adaptive fixed time event triggered fuzzy control method for an active vehicle suspension system, which comprises the steps of obtaining state data of the active vehicle suspension system; inputting the acquired state data into a preset adaptive fuzzy control model based on events, and controlling an active vehicle suspension to track a given track in real time according to the output control quantity; in fixed time, all signals of the active vehicle suspension system are semi-globally consistent and finally bounded, and the vertical displacement of a vehicle body is limited in an upper boundary and a lower boundary which change along with time; the control strategy disclosed by the disclosure is irrelevant to the initial state information, the convergence performance of fixed time is realized, and the limited communication resources of an active vehicle suspension system are saved; time-varying displacement constraints can be guaranteed in active vehicle suspension systems to increase passenger ride comfort.

Description

Adaptive fixed time event trigger fuzzy control method for active vehicle suspension system
Technical Field
The disclosure relates to the technical field of active vehicle suspension control, in particular to a self-adaptive fixed-time event triggered fuzzy control method for an active vehicle suspension system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Suspension systems are roughly classified into passive vehicle suspension systems, semi-active vehicle suspension systems, and active vehicle suspension systems according to different control forms. In contrast to passive and semi-active vehicle suspension systems, active vehicle suspension systems have not only elastically visible and adjustable damping elements, but also force actuators which are able to reduce the influence of road disturbances on the vehicle and to maintain the stability of the vehicle.
Researchers have constructed a barrier lyapunov function for uncertain stochastic nonlinear systems to ensure that the output trajectory is limited to a predetermined range, but the above approach considers a constrained range of fixed values, which is limited and impractical in some practical applications; meanwhile, a communication network is gradually applied to an active vehicle suspension system, in the conventional time-triggered control, a control signal of the active vehicle suspension system needs to be transmitted in real time, and under the control scheme, data is continuously transmitted regardless of whether the data needs to be transmitted, which results in unnecessary transmission of communication resources.
Disclosure of Invention
In order to solve the defects of the prior art, the self-adaptive fixed time event triggered fuzzy control method for the active vehicle suspension system is provided, a control strategy is irrelevant to initial state information, the convergence performance of fixed time is realized, and limited communication resources of the active vehicle suspension system are saved; time-varying displacement constraints can be guaranteed in active vehicle suspension systems to increase passenger ride comfort.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
a first aspect of the present disclosure provides an adaptive fixed-time event-triggered fuzzy control method for an active vehicle suspension system.
An adaptive fixed time event triggered fuzzy control method for an active vehicle suspension system comprises the following steps:
acquiring state data of an active vehicle suspension system;
inputting the acquired state data into a preset adaptive fuzzy control model based on events, and controlling an active vehicle suspension to track a given track in real time according to the output control quantity;
in fixed time, all signals of the active vehicle suspension system are semi-globally consistent and finally bounded, and the vertical displacement of the vehicle body is limited in an upper boundary and a lower boundary which change along with time.
A second aspect of the present disclosure provides an adaptive fixed-time event-triggered fuzzy control system for an active vehicle suspension system.
An adaptive fixed-time event-triggered fuzzy control system for an active vehicle suspension system, comprising:
a data acquisition module configured to; acquiring state data of an active vehicle suspension system;
a fuzzy control module configured to: inputting the acquired state data into a preset adaptive fuzzy control model based on events, and controlling an active vehicle suspension to track a given track in real time according to the output control quantity;
in fixed time, all signals of the active vehicle suspension system are semi-globally consistent and finally bounded, and the vertical displacement of the vehicle body is limited in an upper boundary and a lower boundary which change along with time.
A third aspect of the present disclosure provides a computer readable storage medium having stored thereon a program which, when executed by a processor, implements the steps in the method of adaptive fixed-time event-triggered fuzzy control for an active vehicle suspension system according to the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the steps in the method for adaptive fixed-time event-triggered fuzzy control of an active vehicle suspension system according to the first aspect of the present disclosure when executing the program.
Compared with the prior art, the beneficial effect of this disclosure is:
1. according to the method, the system, the medium or the electronic equipment, the control strategy is irrelevant to the initial state information, the convergence performance of fixed time is realized, and the limited communication resources of an active vehicle suspension system are saved; time-varying displacement constraints can be guaranteed in active vehicle suspension systems to increase passenger ride comfort, and are more common than constant constraints in practical applications.
2. The method, the system, the medium or the electronic equipment establish a new event-based self-adaptive fixed time control strategy, the fuzzy logic system is used for approximating a middle control function containing unknown vehicle body mass, a time-varying barrier Lyapunov function technology is adopted to meet a predefined time-varying constraint boundary, and the stability and the safety of an active vehicle suspension system are guaranteed.
3. The method, system, medium or electronic device disclosed by the disclosure is based on the Lyapunov function stability theory and the fixed time theory, and proves that all signals of the obtained closed-loop system are bounded, and the tracking error is kept in a small tight concentration in a fixed time, so that the overall control precision of the vehicle suspension system is improved.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a schematic diagram of an active vehicle suspension system model of a quarter-car provided in embodiment 1 of the present disclosure.
Fig. 2 is a schematic diagram of a trajectory of a system output and a given reference trajectory provided in embodiment 1 of the present disclosure.
Fig. 3 is a schematic diagram of a curve of an event trigger controller and a time trigger controller provided in embodiment 1 of the present disclosure.
Fig. 4 is a schematic diagram of time intervals of event triggering provided in embodiment 1 of the present disclosure.
Fig. 5 is a schematic diagram of a fixed time scheme provided in embodiment 1 of the present disclosure.
Fig. 6 is a schematic diagram of a non-fixed time scheme provided in example 1 of the present disclosure.
FIG. 7 is a trajectory diagram of a bounded adaptive function provided in embodiment 1 of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
the embodiment 1 of the present disclosure provides an adaptive fixed-time event triggered fuzzy control method for an active vehicle suspension system, which includes the following steps:
acquiring state data of an active vehicle suspension system;
inputting the acquired state data into a preset adaptive fuzzy control model based on events, and controlling an active vehicle suspension to track a given track in real time according to the output control quantity;
in fixed time, all signals of the active vehicle suspension system are semi-globally consistent and finally bounded, and the vertical displacement of the vehicle body is limited in an upper boundary and a lower boundary which change along with time.
Specifically, the method comprises the following steps:
s1: system description and problem formulation
S1.1: problem formulation and preparation
Based on mechanical analysis, the kinetic equation of the quarter car's active vehicle suspension system model, as shown in fig. 1, can be described as:
Figure BDA0002967984900000051
wherein m isbAnd musRepresenting the body mass and unsprung mass, respectively, (1) the force is generated by the stiffness and damping of some physical structure, which can be expressed as:
Figure BDA0002967984900000052
Figure BDA0002967984900000053
wherein DsAnd DwRespectively, refers to the displacement of the body mass and the unsprung mass. DrIndicating a road disturbance input. k is a radical ofaAnd ktRepresents the stiffness coefficient, caAnd ctRepresenting the damping coefficient. u is the dynamic input to the active vehicle suspension system. x is the number of1＝DsWhich is representative of the displacement of the vehicle body,
Figure BDA0002967984900000054
representing the speed of the vehicle body, x3＝DwWhich represents the displacement under the spring,
Figure BDA0002967984900000055
representing the unsprung velocity.
Equation of motion (1) can be defined as the following equation:
Figure BDA0002967984900000061
Figure BDA0002967984900000062
in this embodiment, the vertical displacement D is setsLimited to | Ds|≤k1(t) wherein k1(t) is a known time-varying function.
The main objective of the present embodiment is to provide an event-based adaptive fixed-time control strategy for an active vehicle suspension system, and to achieve the following control performance:
1) the displacement of the sprung mass is constrained within a specified time-varying tight set in terms of passenger comfort.
2) In terms of driving safety, the dynamic load of the tire should be limited to | Fw+Fr|≤(mb+mus) g, wherein g represents the acceleration of gravity.
3) To achieve overall system stability, the suspension span must not exceed the limit of suspension dynamic displacement, i.e. | Ds-Dw|≤Dmax。
4) A controller based on an event trigger mechanism is designed to output a desired reference trajectory on tracking, and all signals in a closed loop system are semi-globally consistent and finally bounded for a fixed time.
To achieve the control objective, the following assumptions and reasoning are provided.
Assume that 1: due to limitations in vehicle structure and practical physical properties, mbTo receive mbmin<mb<mbmaxWherein m isbminAnd mbmaxIs a normal number.
Assume 2: presence of normal number
Figure BDA0002967984900000063
And
Figure BDA0002967984900000064
so that
Figure BDA0002967984900000065
And
Figure BDA0002967984900000066
this is true.
Assume that 3: presence of normal number
Figure BDA0002967984900000067
And
Figure BDA0002967984900000068
so that the inequality
Figure BDA0002967984900000069
Wherein y isdIs a reference track and, at the same time, defines
Figure BDA00029679849000000610
Wherein
Figure BDA00029679849000000611
And alpha1Is a virtual controller to be designed later.
Introduction 1: for any real number x1,x2,…,xnAnd b is 0. ltoreq. b.ltoreq.1, has (| x)1|+…+|xn|)b≤|x1|b+…+|xn|b。
2, leading: if r ∈ (0,1), the following inequality holds:
Figure BDA0002967984900000071
and 3, introduction: given having x (0) ═ x0，x∈RnAnd f is Rn×Rn→RnOf (2) a
Figure BDA0002967984900000072
Assuming the origin is the equilibrium point, V (x) represents a smooth function of V (x) > 0.
If the following inequality holds:
Figure BDA0002967984900000073
where α, β, p and q are positive real numbers, where p ∈ (1, + ∞) and q ∈ (0, 1). Then, it can be said that the origin of (1) is fixed-time stable, and the convergence time can be derived:
Figure BDA0002967984900000074
wherein T isfdIs the convergence time of the corresponding system.
And (4) introduction: h (x) is any continuous function defined over a tight set Ω, for any constant δ > 0, there is a fuzzy logic system ΦTP (x) satisfies:
Figure BDA0002967984900000075
s1.2: fuzzy logic system
Knowledge base of fuzzy logic system is composed of RrThe rule is formed as follows: if x1Is F1 rAnd x2Is composed of
Figure BDA0002967984900000076
...，xnIs composed of
Figure BDA0002967984900000077
Then y is Br(r ═ 1,2, …, m) where x ═ x1,…,xn]T∈RnAnd y ∈ R are the input and output, respectively, of the fuzzy system.
Figure BDA0002967984900000078
And
Figure BDA0002967984900000079
relative to Fi rAnd Br。
The output of the fuzzy system can be obtained by using the strategies of single-point fuzzification, center-average defuzzification and product inferenceIs expressed as
Figure BDA00029679849000000710
Wherein
Figure BDA00029679849000000711
The fuzzy basis function may be defined as:
Figure BDA0002967984900000081
the fuzzy logic system can be rewritten as:
y(x)＝ΦTP(x)。
s2: controller design and stability analysis
S2.1: event-based adaptive fixed-time fuzzy controller design
An adaptive fuzzy fixed time control scheme is established for active vehicle suspension systems. In the reverse control design, the intermediate function h (x) containing the unknown body weight will be approximated by a fuzzy logic system. To achieve this goal, the unknown constant is defined as η ═ Φm||2. Is provided with
Figure BDA0002967984900000082
Is an estimate of η. Then, for any given bounded initial condition η (t)0)≥0，
Figure BDA0002967984900000083
Eta (t) is not less than 0
Figure BDA0002967984900000084
According to the coordinate transformation: z is a radical of1＝x1-yd(t), giving:
Figure BDA0002967984900000085
the following Lyapunov function was constructed:
Figure BDA0002967984900000086
wherein omegaz1＝{z1:|z1|<ka(t) }, and
Figure BDA0002967984900000087
by calculating V1The derivative of (c) is:
Figure BDA0002967984900000088
the virtual controller is designed to:
Figure BDA0002967984900000089
wherein Ki>0(i — 1,2) is a parameter to be designed, and the time-varying gain λ (t) is:
Figure BDA0002967984900000091
(β＞0)
Figure BDA0002967984900000092
by means of inequalities
Figure BDA0002967984900000093
Wherein
Figure BDA0002967984900000094
And e1(> 0), equation (5) can be rewritten as:
Figure BDA0002967984900000095
the case (7) can be further developed as follows:
Figure BDA0002967984900000096
based on coordinate transformation z2＝x2-α1The method comprises the following steps:
Figure BDA0002967984900000097
wherein the content of the first and second substances,
Figure BDA0002967984900000098
the following Lyapunov functions were selected:
Figure BDA0002967984900000101
by combining (8), V is obtained2The derivative of (c) is:
Figure BDA0002967984900000102
wherein
Figure BDA0002967984900000103
H (X) contains an unknown term mbThis means that it cannot be used to design a practical controller. Thus, according to lemma 4, there is a fuzzy logic system ΦTP (X) such that:
H(X)＝ΦTP(X)+ε
wherein the content of the first and second substances,
Figure BDA0002967984900000104
ε is the bounded approximation error and there is a normal number
Figure BDA0002967984900000105
Satisfy the requirement of
Figure BDA0002967984900000106
Using young's inequality, etc., one can obtain:
Figure BDA0002967984900000107
wherein a represents a normal number.
Substituting (11) into (10) to obtain:
Figure BDA0002967984900000108
the adaptive event trigger controller is designed as follows:
Figure BDA0002967984900000109
and, the adaptive fixed-time controller u is designed to:
Figure BDA00029679849000001010
wherein, Kj> 0(j ═ 3,4) is the parameter to be designed.
Adaptive parameters
Figure BDA00029679849000001011
Given as:
Figure BDA0002967984900000111
wherein σ1And
Figure BDA0002967984900000112
is a normal number.
The event trigger mechanism is defined as:
Figure BDA0002967984900000113
wherein, 0<δu<1, d and
Figure BDA0002967984900000114
is a positive parameter, the measurement error between control strategies with and without event-triggered methods is:
Figure BDA0002967984900000115
at time intervals tk,tk+1) In, the control signal is kept as
Figure BDA0002967984900000116
Until the next trigger condition (14) is met and the control input u (t) will be updated to
Figure BDA0002967984900000117
According to the trigger condition (14), there are
Figure BDA0002967984900000118
Wherein k isu1(t) and ku2(t) is two time-varying parameters, satisfying | ku1(t) less than or equal to 1 and ku2(t)|≤1。
Thus, the following results:
Figure BDA0002967984900000119
then, V can be converted into2The derivative of (d) is calculated as:
Figure BDA00029679849000001110
due to | ku1(t)|<1，|ku2(t)|<1，0<δu(t)<1 and e3> 0 has z2w/(1+ku1(t)δu)≤z2w/(1+δu) And | ku2(t)d/(1+ku1(t)δu)|≤d/(1-δu) Then, we obtain:
Figure BDA0002967984900000121
by substituting (13) into (16), we obtain:
Figure BDA0002967984900000122
wherein the content of the first and second substances,
Figure BDA0002967984900000123
theorem 1: a tracking error z is based on assumptions 1-3 for a non-linear active vehicle suspension system (2) and design of an adaptive event trigger controller (12) and a parametric adaptation law (13)1A small neighborhood that can converge to near the zero point at a fixed time TfdAll signals of the active vehicle suspension system (i.e.
Figure BDA0002967984900000124
u,
Figure BDA0002967984900000125
) Are all semi-globally consistent and finally bounded, and are vertically displaced by x1Can be limited to upper and lower boundaries, i.e. k, which vary with time1(t) and-k1(t)。
And (3) proving that: for those in (17)
Figure BDA0002967984900000126
Terms, based on young's inequality, yield:
Figure BDA0002967984900000127
furthermore, by selecting σaAnd > 0, establishing the following inequality:
Figure BDA0002967984900000128
then, the following were obtained:
Figure BDA0002967984900000131
Figure BDA0002967984900000132
Figure BDA0002967984900000133
thus, it is possible to obtain:
Figure BDA0002967984900000134
wherein the content of the first and second substances,
Figure BDA0002967984900000135
based on (18), the case (17) can be rewritten as:
Figure BDA0002967984900000136
wherein C ═ C1+C2。
According to lemma 2, expression (19) can be further written as:
Figure BDA0002967984900000137
defining:
μ1＝min{K1,K2,K3,K4,σa}，
Figure BDA0002967984900000138
by using the arguments 1-3, one can further obtain:
Figure BDA0002967984900000139
thus, due to the inequality
Figure BDA00029679849000001310
And is
Figure BDA00029679849000001311
Is established, therefore V can be obtained2Is limited. Further, it can be appreciated that all signals are bounded. Note that when
Figure BDA0002967984900000141
For the
Figure BDA0002967984900000142
When there is
Figure BDA0002967984900000143
According to (21), there are obtained:
Figure BDA0002967984900000144
according to the introduction of 3, V2Can converge to the set within a fixed time
Figure BDA0002967984900000145
And the convergence time can be estimated as:
Figure BDA0002967984900000146
obtaining according to (9):
Figure BDA0002967984900000147
Figure BDA0002967984900000148
according to the definitions of (22) and (23), there is | zi|<U (i ═ 1,2), wherein,
Figure BDA0002967984900000149
thus, the error signal z can be obtained in the following formi。
Υ＝{zi∈R||zi|<U},i＝1,2。
In compact γ, due to x1＝z1+yd(t)，
Figure BDA00029679849000001410
And is
Figure BDA00029679849000001411
Can be derived from
Figure BDA00029679849000001412
Then, it can be concluded that state x1Satisfying a predetermined time-varying constraint boundary-k1(t) and k1(t)。
S2.2: zero dynamic analysis
As previously described, the stability of the first two order dynamics of the original system (1) of a quarter car active vehicle suspension system with fourth order error dynamics was demonstrated. Next, the remaining second order zero dynamics states will be designed according to theorem 1.
Substituting (15) into (3) can result in kinetics:
Figure BDA0002967984900000151
wherein:
Figure BDA0002967984900000152
Figure BDA0002967984900000153
Figure BDA0002967984900000154
comprising:
Figure BDA0002967984900000155
since v is bounded, assume that
Figure BDA0002967984900000156
Selecting V ═ XTBX is taken as the Lyapunov function, where B is a symmetric matrix and B > 0. Next, we obtain:
Figure BDA0002967984900000157
it is easy to prove that the real part of the eigenvalue of A is negative, and thus ATB + BA ═ Q, where Q is a symmetric matrix, Q > 0, note that:
2XTBΥ≤τΥTΥ+XT(ATB+BA)X/τ
where τ is a positive parameter. Furthermore, the time derivative of V can be written as:
Figure BDA0002967984900000158
also, there is λ*And ξ, such that:
λ*≤-λmin(B-1/2QB-1/2)+τ-1λmin(B),ξ≥τγTγ。
the derivative of V can be further written as:
Figure BDA0002967984900000161
thus, the following results:
Figure BDA0002967984900000162
wherein the content of the first and second substances,
Figure BDA0002967984900000163
can obtain
Figure BDA0002967984900000164
And the following performance constraints:
Figure BDA0002967984900000165
next, if the parameters τ and P are appropriately adjusted, the control targets (25) and (26) can be secured.
In order to achieve vehicle safety, it is necessary to limit the forces within the following constraints:
|Fw+Fr|≤|Fw|+|Fr|≤(mb+mus)g (25)
in order to maintain the stability of the vehicle, the following suspension space conditions should be satisfied by selecting initial values:
Figure BDA0002967984900000166
from the above analysis, it can be concluded that all of the control objectives herein can be achieved by appropriate adjustment of the parameters.
Finally, to avoid the phenomenon of sesame, it is necessary to prove that there is a t*> 0 satisfy
Figure BDA0002967984900000167
According to measurement error
Figure BDA0002967984900000168
Comprises the following steps:
Figure BDA0002967984900000169
based on (12), the adaptive event triggers the controller
Figure BDA00029679849000001610
Can be written again as:
Figure BDA0002967984900000171
due to the fact that
Figure BDA0002967984900000172
All signals in
Figure BDA0002967984900000173
Is bounded, knows
Figure BDA0002967984900000174
Is a continuous function. Thus, there is a constant ζ > 0 that satisfies
Figure BDA0002967984900000175
Due to e (t)k) Is equal to 0 and
Figure BDA0002967984900000176
the execution interval t can be derived*Satisfies t*Not less than (| u (t) | + d)/ζ, therefore, the glossy ganoderma is successfully eliminatedThe phenomenon is not shown.
S3: simulation example
To verify the effectiveness of the proposed control method for an active vehicle suspension system, the following parameters were chosen: k is a radical ofa＝18000N/m，ca＝2400％Ns/m，kt＝150000N/m，ct＝1000Ns/m，mb＝320kg，mbmin＝300kg，mbmax1000kg and mus50 kg. The periodic disturbance signal of the road surface can be defined as Dr0.02sin (10 π t). The reference track is chosen to be yd(t) ═ sin (0.5t) +0.5sin (t). The time varying displacement constraint is k1(t) ═ 0.05sin (1.5t) + 1.5. Initial conditions are x1(0)＝0.11，x2(0)＝x3(0)＝x4(0) 0 and
Figure BDA0002967984900000177
the simulation results are shown in fig. 2-6. The trajectory of the system output y and a given reference trajectory yd(t) is shown in FIG. 2, which shows that good tracking performance can be ensured, and the vehicle body x1Does not violate a predefined constraint-k1(t) and k1(t)。
FIG. 3 shows an event triggered controller
Figure BDA0002967984900000178
And the profile of the time-triggered controller u (t). The time intervals for event triggering are plotted in fig. 4. To show the effectiveness of the proposed scheme, a fixed time scheme and a fixed time-free scheme are compared in fig. 5 and 6. As can be seen from fig. 5 and 6, the proposed fixed time control strategy has better tracking performance. FIG. 7 illustrates a bounded adaptive function
Figure BDA0002967984900000179
The trajectory of (2). From fig. 2 to 7, the effectiveness of the designed control method can be confirmed.
Example 2:
the embodiment 2 of the present disclosure provides an adaptive fixed-time event triggered fuzzy control system for an active vehicle suspension system, including:
a data acquisition module configured to; acquiring state data of an active vehicle suspension system;
a fuzzy control module configured to: inputting the acquired state data into a preset adaptive fuzzy control model based on events, and controlling an active vehicle suspension to track a given track in real time according to the output control quantity;
in fixed time, all signals of the active vehicle suspension system are semi-globally consistent and finally bounded, and the vertical displacement of the vehicle body is limited in an upper boundary and a lower boundary which change along with time.
The working method of the system is the same as the adaptive fixed-time event-triggered fuzzy control method of the active vehicle suspension system provided in embodiment 1, and details are not repeated here.
Example 3:
the embodiment 3 of the present disclosure provides a computer-readable storage medium, on which a program is stored, which when executed by a processor, implements the steps in the adaptive fixed-time event-triggered fuzzy control method for an active vehicle suspension system according to the embodiment 1 of the present disclosure.
Example 4:
the embodiment 4 of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and executable on the processor, and when the processor executes the program, the processor implements the steps in the adaptive fixed-time event-triggered fuzzy control method for an active vehicle suspension system according to embodiment 1 of the present disclosure.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims (10)

1. An adaptive fixed time event triggered fuzzy control method for an active vehicle suspension system is characterized by comprising the following steps: the method comprises the following steps:
acquiring state data of an active vehicle suspension system;
inputting the acquired state data into a preset adaptive fuzzy control model based on events, and controlling an active vehicle suspension to track a given track in real time according to the output control quantity;
in fixed time, all signals of the active vehicle suspension system are semi-globally consistent and finally bounded, and the vertical displacement of the vehicle body is limited in an upper boundary and a lower boundary which change along with time.
2. The active vehicle suspension system adaptive fixed time event triggered fuzzy control method of claim 1 characterized by:
the tracking error converges to a preset neighborhood on the zero point side.
3. The active vehicle suspension system adaptive fixed time event triggered fuzzy control method of claim 1 characterized by:
the displacement of the sprung mass of the active vehicle suspension system is constrained within a specified time-varying tight set.
4. The active vehicle suspension system adaptive fixed time event triggered fuzzy control method of claim 1 characterized by:
the absolute value of the dynamic load of the active vehicle suspension system is less than or equal to the product of the sum of the body mass and the unsprung mass and the gravitational acceleration.
5. The active vehicle suspension system adaptive fixed time event triggered fuzzy control method of claim 1 characterized by:
the suspension span of the active vehicle suspension system is less than or equal to a limit value of suspension dynamic displacement.
6. The active vehicle suspension system adaptive fixed time event triggered fuzzy control method of claim 1 characterized by:
and in the time period from the current moment to a certain future moment, the control signal is kept as the control signal of the adaptive event trigger controller, and when the next trigger condition is met, the control signal is updated to the control signal of the adaptive event trigger controller at the next moment.
7. The active vehicle suspension system adaptive fixed time event triggered fuzzy control method of claim 1 characterized by:
a time-varying barrier lyapunov function is used to define a predefined time-varying constraint boundary.
8. An adaptive fixed time event triggered fuzzy control system for an active vehicle suspension system, characterized by: the method comprises the following steps:
a data acquisition module configured to; acquiring state data of an active vehicle suspension system;
a fuzzy control module configured to: inputting the acquired state data into a preset adaptive fuzzy control model based on events, and controlling an active vehicle suspension to track a given track in real time according to the output control quantity;
in fixed time, all signals of the active vehicle suspension system are semi-globally consistent and finally bounded, and the vertical displacement of the vehicle body is limited in an upper boundary and a lower boundary which change along with time.
9. A computer readable storage medium having a program stored thereon, which program, when executed by a processor, performs the steps in the method of adaptive fixed time event triggered fuzzy control for an active vehicle suspension system as claimed in any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in the method of adaptive fixed time event triggered fuzzy control for an active vehicle suspension system of any of claims 1-7.
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