CN109334378B - Vehicle ISD suspension active control method based on single neuron PID control - Google Patents
Vehicle ISD suspension active control method based on single neuron PID control Download PDFInfo
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- CN109334378B CN109334378B CN201811091503.9A CN201811091503A CN109334378B CN 109334378 B CN109334378 B CN 109334378B CN 201811091503 A CN201811091503 A CN 201811091503A CN 109334378 B CN109334378 B CN 109334378B
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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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G2400/00—Indexing codes relating to detected, measured or calculated conditions or factors
- B60G2400/10—Acceleration; Deceleration
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- 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
- B60G2600/1878—Neural Networks
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Abstract
The invention discloses a vehicle ISD suspension active control method based on single neuron PID control, which comprises the following steps: (1) establishing an ISD suspension quarter model; (2) determining the vertical displacement of the road surface; (3) determining vehicle suspension performance evaluation indexes; (4) and determining a neuron learning rule and a learning algorithm. The method can simply and easily adapt the ISD suspension system to environmental changes and improve the vehicle performance, and provides a new research idea and direction for the design and application of the vehicle ISD suspension, in particular to the research on the controllable ISD suspension.
Description
Technical Field
The invention belongs to the field of vehicle suspension system control, and particularly relates to vehicle ISD (inertial-Spring-Damper) suspension system control applying an Inerter-Spring technology. The invention relates to a control method of an ISD suspension of a vehicle, in particular to a control method of a hydro-electric coupling type ISD suspension of the vehicle.
Background
Vehicle suspension is a generic term for all force-transmitting connections between the vehicle body and the wheels, the components of which usually include springs, dampers and guides. The main function of the device is to transmit force and moment between a vehicle body and wheels, and buffer impact transmitted to the vehicle body from an uneven road surface so as to ensure smooth running of the vehicle and riding comfort of passengers, and the device is one of important points of vehicle system dynamics research.
Smith, scholars of cambridge university, in 2003, put forward the idea of the inertial container, and designed a rack-and-pinion type inertial container and a ball screw type inertial container to realize strict correspondence between a mechanical network and a circuit network, wherein the inertial container has a function similar to a capacitor, namely, can block the transmission of low-frequency signals. The ISD suspension of the vehicle is a novel suspension formed by an Inerter-Spring-Damper (Damper), and the suspension introduces the Inerter into a suspension system, breaks through the structure of the Spring-Damper of the traditional suspension, and provides a new idea for the suspension vibration control research.
The single neuron is the most basic component unit of the neural network, and is combined with PID control to form the single neuron PID control, so that the control parameters adapt to the external environment.
At present, the research direction of the vehicle ISD suspension mainly comprises network synthesis of a passive ISD suspension and a controllable ISD suspension, the latter is a new hotspot of research in the field, is still in a semi-active coordination stage of element parameters, does not combine complex dynamic characteristics of the ISD suspension to carry out active coordination control, and the suspension performance needs to be further improved.
Disclosure of Invention
Based on the reasons, the invention provides the vehicle ISD suspension active control method based on the single neuron PID control, which can be used for carrying out dynamic analysis on multiple elements of a vehicle ISD suspension system, determining the optimal solution of control parameters to obtain the optimal output force, achieving the coordination control effect of impedance matching and effectively solving the problem of active coordination control of the controllable ISD suspension. The method can design the evaluation index of the vehicle suspension performance by self and weight the proper performance index according to the requirement.
In order to achieve the purpose, the technical scheme adopted by the invention is a vehicle ISD suspension active control method based on single neuron PID control, which comprises the following steps:
step 1), establishing an ISD suspension quarter model:
wherein z iss、zu、zbAnd zrRespectively the vertical displacement of the vehicle body, the tire, the upper damping end point and the road surface, u is the force at two ends of the hydro-electric coupling type inertia container, f is the actuating force of the linear motor, c is the damping coefficient, k is the suspension stiffness coefficient, b is the inertia mass coefficient, m is the damping coefficientsIs sprung mass, muIs an unsprung mass, ktIs the tire stiffness;
step 2) determining the vertical displacement z of the road surfacerThe formula is as follows:
wherein n is0W (t) white noise with a mean value of 0 for the reference spatial frequency, f0Is the lower cut-off frequency; gq(n0) The coefficient of road surface unevenness; v is the vehicle speed;
step 3), determining vehicle suspension performance evaluation indexes including at least one of vehicle body acceleration, tire dynamic load and suspension dynamic deflection;
step 4), determining that the neuron learning rule is a supervised Delta learning rule:
wherein r isj(k) In order to be able to output the desired output,
is the activation value of the neuron j,
to a desired output rj(k) And actual output
Difference of (a) wij(k) The weight coefficient variation between two neurons, d is the learning speed;
and then according to the determined learning rule, determining a specific learning algorithm:
wherein e (k) is an acceleration error signal, xi(k) (i ═ 1,2,3) is an input signal of a neuron obtained by conversion of an acceleration error, wi(k) Inputting x for the corresponding neuroni(k) Weight of (d)i(i is 1,2,3) is wi(i ═ 1,2,3) learning rate, K is neuron gain coefficient, and u (K) is output signal of single neuron PID control；
The control flow of the single neuron PID control is as follows: the desired vehicle body acceleration r (k) is used as an input signal, the vehicle body acceleration is used as a feedback signal y (k), e (k) is used as an acceleration error signal, namely e (k) r (k) -y (k), xi(k) (i ═ 1,2,3) is an input signal of a neuron obtained by conversion of an acceleration error, wi(k) Inputting x for the corresponding neuroni(k) The weight coefficient is adjusted on line through a determined learning rule and a learning algorithm, and u (k) is an output signal controlled by a single neuron PID and used for controlling the output force of the linear motor.
Further, the learning process of the learning algorithm of the step 4) is as follows: 4.1) determining the initial weight coefficient wi(0) (ii) a 4.2) calculating the error between the actual output and the expected output at the current moment; 4.3) if the error is less than the given value, ending, otherwise, continuing; 4.4) updating the weight coefficient through a learning rule; 4.5) return to step 4.2).
The invention has the beneficial effects that: the invention designs the single-neuron PID controller of the suspension system based on the single-neuron theory, optimizes control parameters by applying a multi-objective genetic algorithm, can actively adjust the suspension control parameters according to different inputs of a random road surface, achieves the coordination control effect of impedance matching, enables the performance of the ISD suspension to be in an optimal state, improves the processing nonlinearity and the adaptability to the environment of the ISD suspension system, and effectively improves the vehicle performance. Compared with other control methods, the single neuron PID control has the advantages of strong self-adaptability, good robustness, simple and feasible algorithm, high reliability and self-organization self-adaption function of a neural network. The method provides a new research idea and direction for the design and application of the vehicle ISD suspension, and particularly provides active coordination control for the controllable ISD suspension.
Drawings
FIG. 1 is a flow chart of a method for actively controlling an ISD suspension of a vehicle based on single-neuron PID control;
FIG. 2 is a view showing a structure of a suspension employed in the example;
FIG. 3 is a schematic diagram of a vehicle ISD suspension quarter model of the configuration of FIG. 2;
FIG. 4 is a schematic diagram of single neuron PID control of the suspension in the example;
FIG. 5 is a graph comparing vehicle active controllable ISD suspension performance with conventional passive suspension performance using the method;
wherein, (a) is a vehicle body acceleration time domain diagram, (b) is a suspension dynamic deflection time domain diagram, and (c) is a tire dynamic load time domain diagram.
Detailed Description
The invention will be further described with reference to the drawings and the specific examples, but the scope of the invention is not limited thereto.
Fig. 1 is a flowchart of a method for actively controlling an ISD suspension of a vehicle based on single-neuron PID control, and fig. 2 is a suspension structure in an example of the method.
The inerter in fig. 2 is preferably a hydro-electric coupling type vehicle suspension impedance control device disclosed in chinese patent CN 204526713U. Taking this as an example, research on single-neuron PID active control of the ISD suspension system is carried out.
Referring to fig. 1, the active control method for the ISD suspension of the vehicle based on the single-neuron PID control of the present invention comprises: step 1): establishing a suspension system model; step 2): determining road surface input and suspension basic parameters; step 3): determining a performance evaluation index; step 4): a single neuron PID controller is determined.
Wherein, the step 1) is specifically as follows: according to the suspension structure shown in fig. 2, a corresponding suspension quarter model is established, as shown in fig. 3, and the dynamic equation is as follows:
wherein z iss、zu、zbAnd zrRespectively the vertical displacement of the vehicle body, the tire, the upper damping end point and the road surface, u is the force at two ends of the hydro-electric coupling type inertia container, f is the actuating force of the linear motor, c is the damping coefficient, k is the suspension stiffness coefficient, b is the inertia mass coefficient, m is the damping coefficientsIs sprung mass, muIs an unsprung mass, ktIs the tire stiffness.
Wherein, the step 2) is specifically as follows: determining the vertical displacement z of the road surfacerAnd suspension basic parameters, as shown in table 1.
The road surface input is as follows:
wherein n is0For reference spatial frequency, w (t) is white noise with a mean value of 0.
Table 1 road surface parameters and suspension basic parameters table:
wherein, the step 3) is specifically as follows: according to the research purpose, vehicle suspension performance evaluation indexes including vehicle body acceleration, tire dynamic load and suspension dynamic deflection are determined.
Wherein, the step 4) is specifically as follows: determining the neuron learning rule as a supervised Delta learning rule:
wherein r isj(k) In order to be able to output the desired output,
is the activation value of the neuron j,
to a desired output rj(k) And actual output
Difference of (a) wij(k) Is the weight coefficient variation between two neurons, and d is the learning speed.
From the determined learning rules, a specific learning algorithm can be determined:
wherein e (k) is an acceleration error signal, xi(k) (i ═ 1,2,3) is an input signal of a neuron obtained by conversion of an acceleration error, wi(k) Inputting x for the corresponding neuroni(k) Weight of (d)i(i is 1,2,3) is wi(i is 1,2 and 3), K is a neuron gain coefficient, u (K) is an output signal controlled by a single neuron PID, the initial value is arbitrarily selected in a small range (0.1-0.9) according to the empirical weight, the system response is not greatly influenced, and w is repeatedly simulated and debugged1(0)、w2(0) And w3(0) Are all taken to be 0.03.
The learning process of the learning algorithm comprises the following steps: 4.1) determining an initial weight coefficient wi (0); 4.2) calculating the error between the actual output and the expected output at the current moment; 4.3) if the error is less than the given value, ending, otherwise, continuing; 4.4) updating the weight coefficient through a learning rule; 4.5) return to step 4.2).
As shown in fig. 4, the schematic diagram of the single-neuron PID controller takes a desired vehicle body acceleration r (k) as an input signal, a vehicle body acceleration as a feedback signal y (k), and e (k) is an acceleration error signal, i.e., e (k) r (k) -y (k), xi(k) (i ═ 1,2,3) is an input signal of a neuron obtained by conversion of an acceleration error, wi(k) The weight coefficient of xi (K) is input to a corresponding neuron, online adjustment is carried out through a certain learning rule, the dynamic control effect of a single neuron PID controller is achieved, K is a neuron gain coefficient, u (K) is an output signal controlled by the single neuron PID, the output force of a linear motor is the output force of the linear motor, the output force u (K) of the linear motor can change along with the change of pavement input, the purpose of adjusting the electrical impedance is achieved, the total impedance of the liquid-electric coupling type inertia container can also change along with the change of the electrical impedance, and when the total impedance is matched with the input impedance, the performance of an ISD suspension frame can be greatly improved.
Simulation verification is performed as follows:
the root mean square of the three performance indexes of the electrohydraulic coupling type ISD suspension is respectively subjected to ratio with the root mean square of the performance indexes of the traditional passive suspension, then the obtained three ratios are subjected to weighted combination, and the three ratios are directly summed to be used as a fitness function of an optimization target:
wherein BA (X), SWS (X) and DTL (X) are the root mean square of the vehicle body acceleration, the suspension dynamic deflection and the tire dynamic load corresponding to the liquid-electricity coupling type ISD suspension; BApass、SWSpassAnd DTLpassThe root mean square value, BA, of the acceleration of the vehicle body, the dynamic deflection of the suspension and the dynamic load of the tire of the traditional passive suspension<BApass，SWS<SWSpass，DTL<DTLpass。
X＝(K,d1,d2,d3) As a set of control parameters, wherein di(i is 1,2,3) is wi(i ═ 1,2,3) learning rate, K is neuron gain coefficient, LB<X<UB, UB and LB are the upper and lower control parameter limits, respectively.
minJ is the minimum value of a self-adaptive function, and represents the ratio and the minimum of the root mean square values of three performance indexes of the ISD suspension and the passive suspension when the parameter individual X is at a specific value, namely the ISD suspension controlled by the method has the optimal performance at the parameter.
A vehicle single-wheel suspension model is established in Matlab/Simulink, an m file of a fitness function and a genetic algorithm is compiled according to the control method, and the individual sequentially assigns values to output an optimal fitness function value, as shown in Table 2.
TABLE 2 optimum response function value table
And simulating the liquid-electricity coupling type vehicle ISD suspension under random road surface input to obtain a performance curve of the suspension. The performance curve is compared with the traditional passive suspension, and the influence of the control method on the suspension performance is analyzed.
Fig. 5 is a graph comparing the performance of the vehicle active controllable ISD according to the embodiment of the present invention with the performance of the conventional passive suspension, wherein (a) is a time domain graph of the vehicle body acceleration, (b) is a time domain graph of the suspension dynamic deflection, and (c) is a time domain graph of the tire dynamic load.
TABLE 3 root-mean-square value table of suspension performance indexes
The results show that the method has a remarkable effect of improving the suspension performance. The acceleration of the vehicle body and the dynamic deflection of the suspension are obviously reduced, and the running smoothness of the vehicle is further improved.
The examples are preferred embodiments of the present invention, but the present invention is not limited to the embodiments, and modifications, variations and substitutions by those skilled in the art may be made without departing from the spirit of the present invention.
Claims (2)
1. A vehicle ISD suspension active control method based on single-neuron PID control is characterized by comprising the following steps:
step 1), establishing an ISD suspension quarter model:
wherein z iss、zu、zbAnd zrRespectively the vertical displacement of the vehicle body, the tire, the upper damping end point and the road surface, u is the force at two ends of the hydro-electric coupling type inertia container, f is the actuating force of the linear motor, c is the damping coefficient, k is the suspension stiffness coefficient, b is the inertia mass coefficient, m is the damping coefficientsIs sprung mass, muIs an unsprung mass, ktIs the tire stiffness;
step 2) determining the vertical displacement z of the road surfacerThe formula is as follows:
wherein n is0W (t) white noise with a mean value of 0 for the reference spatial frequency, f0Is the lower cut-off frequency; gq(n0) The coefficient of road surface unevenness; v is the vehicle speed;
step 3), determining vehicle suspension performance evaluation indexes including at least one of vehicle body acceleration, tire dynamic load and suspension dynamic deflection;
step 4), determining that the neuron learning rule is a supervised Delta learning rule:
wherein r isj(k) In order to be able to output the desired output,
is the activation value of the neuron j,
to a desired output rj(k) And actual output
Difference of (a) Δ wij(k) The weight coefficient variation between two neurons, d is the learning speed;
and then according to the determined learning rule, determining a specific learning algorithm:
wherein e (k) is an acceleration error signal, xi(k) (i ═ 1,2,3) is an input signal of a neuron obtained by conversion of an acceleration error, wi(k) Inputting x for the corresponding neuroni(k) Weight coefficient of (d)i(i is 1,2,3) is wi(i ═ 1,2,3) learning rate, K is neuron gain coefficient, u (K) is output signal of single neuron PID control;
the control flow of the single neuron PID control is as follows: the method comprises the steps of taking expected vehicle body acceleration r (k) as an input signal, taking the vehicle body acceleration as a feedback signal y (k), taking e (k) as an acceleration error signal, namely e (k) r (k) -y (k), carrying out online adjustment through a determined learning rule and a learning algorithm, and taking u (k) as an output signal of single-neuron PID control and used for controlling the output force of the linear motor.
2. The method for actively controlling the ISD suspension of the vehicle based on the single-neuron PID control as claimed in claim 1, wherein the learning process of the learning algorithm of the step 4) is as follows: 4.1) determining the initial weight coefficient wi(0) (ii) a 4.2) calculating the error between the actual output and the expected output at the current moment; 4.3) if the error is less than the given value, ending, otherwise, continuing; 4.4) updating the weight coefficient through a learning rule; 4.5) return to step 4.2).
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| CN110001336B (en) * | 2019-03-12 | 2021-11-23 | 江苏大学 | Vehicle ISD suspension first-order ideal model based on ADD real network optimization |
| CN109932907B (en) * | 2019-03-14 | 2023-01-17 | 江苏大学 | Vehicle ISD suspension active control method based on RBF sliding mode variable structure control |
| CN109849932B (en) * | 2019-03-27 | 2020-07-28 | 哈尔滨工业大学 | Road surface self-adaptive wheel dynamic load estimation method |
| CN111016566B (en) * | 2019-12-05 | 2023-07-18 | 江苏大学 | Inertial-energy and damping double-ceiling suspension system and control method thereof |
| CN115298045A (en) * | 2020-03-18 | 2022-11-04 | 日立安斯泰莫株式会社 | Vehicle control device, vehicle control method, and vehicle control system |
| CN111439087B (en) * | 2020-03-27 | 2022-08-23 | 江苏大学 | Vehicle ISD suspension active control method based on model reference adaptive control |
| CN113183709B (en) * | 2021-06-04 | 2022-09-27 | 合肥工业大学 | Preview control method for automobile electric control suspension |
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