CN111806246A - Suspension system control method for magnetic suspension train - Google Patents
Suspension system control method for magnetic suspension train Download PDFInfo
- Publication number
- CN111806246A CN111806246A CN202010561052.1A CN202010561052A CN111806246A CN 111806246 A CN111806246 A CN 111806246A CN 202010561052 A CN202010561052 A CN 202010561052A CN 111806246 A CN111806246 A CN 111806246A
- Authority
- CN
- China
- Prior art keywords
- suspension
- sliding mode
- controller
- control
- levitation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000000725 suspension Substances 0.000 title claims abstract description 83
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000013528 artificial neural network Methods 0.000 claims abstract description 23
- 230000002093 peripheral effect Effects 0.000 claims abstract description 9
- 238000012549 training Methods 0.000 claims abstract description 5
- 238000005339 levitation Methods 0.000 claims description 36
- 230000006870 function Effects 0.000 claims description 18
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 claims description 13
- 238000013178 mathematical model Methods 0.000 claims description 13
- 238000004804 winding Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 238000013461 design Methods 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 230000001133 acceleration Effects 0.000 claims description 3
- 230000035699 permeability Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 3
- 238000009795 derivation Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 210000001258 synovial membrane Anatomy 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L13/00—Electric propulsion for monorail vehicles, suspension vehicles or rack railways; Magnetic suspension or levitation for vehicles
- B60L13/04—Magnetic suspension or levitation for vehicles
- B60L13/06—Means to sense or control vehicle position or attitude with respect to railway
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Feedback Control In General (AREA)
Abstract
The invention relates to a control method of a suspension system for a magnetic suspension train, which specifically comprises the following steps: constructing a second-order sliding mode surface based on a suspension control dynamics model of a maglev train, and introducing a nonlinear bounded function which is related to a positioning error signal and is approximated by an online real-time training neural network to obtain a final sliding mode variable structure control law model which is used for constructing a maglev controller of a suspension system; inputting set physical parameters of a suspension system in a magnetic suspension controller; the magnetic suspension controller acquires gap data between the track and the train body in real time and outputs a control signal; and peripheral hardware of the suspension system receives the control signal and then drives the suspension electromagnet to move to the target position within a limited time. Compared with the prior art, the method can realize random approximation of uncertain working model parameters in practice, improve the adaptability of the controller to various environments, and finally improve the stability of the control of the suspension system.
Description
Technical Field
The invention relates to the field of magnetic suspension trains, in particular to a suspension system control method for a magnetic suspension train.
Background
Maglev trains are a modern mode of transportation with contactless electromagnetic levitation, guidance and drive systems. It relies on electromagnetic attraction or electrical repulsion to suspend the train in the air to achieve no mechanical contact between the train and the track and is driven by a linear motor. Magnetic levitation trains are ideal vehicles due to their high speed, low energy consumption, comfortable ride and low noise. At present, magnetic Suspension trains are generally divided into two categories according to different Suspension principles and modes adopted by magnetic Suspension vehicles, wherein one category is electric Suspension (EDS type for short); one type is Electromagnetic Suspension (EMS) type for short. The EDS type magnetic levitation system suspends the vehicle above the track by using electromagnetic repulsion force, while the EMS type magnetic levitation system lifts the vehicle by using attraction force generated by an electromagnet located below the track to ensure that the vehicle does not contact the track. The EDS type magnetic levitation system can stably suspend without applying control, and the EMS type magnetic levitation system can ensure the stable suspension of the system by applying active control. At present, the commercial operation is an EMS type magnetic suspension train.
For EMS magnetic levitation trains, the levitation system is the key and core of the magnetic levitation train. However, suspension systems have strong non-linearity and open-loop instability. Furthermore, the system parameters have uncertainties and the system is subject to external disturbances during operation. Therefore, the design of a high-performance levitation controller poses a high challenge.
The most urgent problems currently faced by the levitation systems of EMS type maglev trains are model uncertainty and extraneous disturbances, such as uncertainty of passenger weight, disturbances of wind, etc. Currently, most maglev vehicle levitation controllers are linear PID controllers. When parameters are changed or external interference is large, under the action of the current linear controller, the control performance of the system is reduced, and the stability of the system is reduced or even loses stability.
Disclosure of Invention
The present invention is directed to overcoming the above-mentioned deficiencies of the prior art and providing a method for controlling a levitation system for a magnetic levitation vehicle.
The purpose of the invention can be realized by the following technical scheme:
a suspension system control method for a magnetic suspension train is characterized by comprising the following steps:
s1, constructing a second-order sliding mode surface based on a suspension control dynamics model of the maglev train, and introducing a nonlinear bounded function which is related to a positioning error signal and is approximated by an online real-time training neural network to obtain a final sliding mode variable structure control law model for constructing a maglev controller of a suspension system;
s2, inputting set physical parameters of the suspension system in the magnetic suspension controller;
s3, the magnetic suspension controller acquires gap data between the track and the train body in real time and outputs a control signal;
and S4, after receiving the control signal, the peripheral hardware of the suspension system drives the suspension electromagnet to move to the target position within a limited time and keeps within the position error limit range.
Further, in step S1, the expression of the final sliding mode variable structure control law model is:
wherein sgn (·) is a sign function, s is a dynamic sliding mode surface, e is a system error, c1、c2Eta and mu are control gain parameters,
and
neural network approximations of unknown nonlinear bounded functions f (-) and g (-) respectively, x being the network input, j being the jth hidden layer node, W*,L*Is the ideal network weight, h, of f (-) and g (-) inf(x) And hg(x) A Koski equation of a neural network, wherein r is an ideal tracking instruction;
according to the minimum parameter learning method, the self-adaptive rate of f (-) and g (-) is defined as a single parameter
And
wherein the parameter gamma1,γ2,Ω1,Ω2∈R+。
Further, the method for constructing the final sliding mode variable structure control law model in step S1 includes:
s11, establishing an affine nonlinear mathematical model;
s12, carrying out sliding mode control law design on the sliding mode controller according to the affine nonlinear mathematical model to obtain a sliding mode variable structure control law model and two nonlinear bounded functions f (·) and g (·);
s13, approximating nonlinear bounded functions f (cndot.) and g (cndot.) in the sliding mode variable structure control law model through RBF neural network online learning to obtain the final sliding mode variable structure control law model.
Further, in step S11, the expression of the affine nonlinear mathematical model is as follows:
wherein,
in the formula, z1Denotes the air gap spacing, z2Representing the speed of change of the air gap distance, z3Represents the air gap acceleration, m is the vehicle body mass, mu0Is a vacuum permeability, NmNumber of coil windings, AmIs the cross-sectional area of the magnet, RmRepresenting the electromagnet winding resistance.
Further, in step S12, the expression of the sliding mode variable structure control law model is:
in the formula uSMC(x, t) is the control output, η, μ ∈ R+Respectively representing a constant arrival coefficient and an exponential arrival coefficient, r being an ideal trace instruction, c1,c2∈R+Is the positive position control gain, e represents the system error and s represents the dynamic sliding mode surface.
Furthermore, the suspension system comprises a gap sensor, a chopper, a magnetic suspension controller and an electromagnet, wherein the gap sensor is connected with the magnetic suspension controller through the chopper, the electromagnet is connected with the magnetic suspension controller through peripheral hardware, and the gap sensor is installed on the electromagnet.
Furthermore, the gap sensor is connected with the chopper through the gap processing plate, the control plate and the interface conversion plate.
Further, the magnetic suspension controller comprises computer hardware and algorithm software, and is used for executing the operation of the neural network approximation algorithm, acquiring the input set physical parameters of the suspension system, acquiring the gap data between the track and the vehicle body in real time and outputting a control signal.
Compared with the prior art, the invention has the following advantages:
according to the method, the magnetic suspension controller of the suspension system is reestablished, a second-order sliding mode surface is constructed based on the suspension control dynamics model of the magnetic suspension train, and a nonlinear bounded function which is related to the positioning error signal and is approximated by an online real-time training neural network is introduced to obtain a final sliding mode variable structure control law model, so that any approximation to uncertain working model parameters in practice can be realized, the adaptability of the controller to various environments is improved, and the control stability of the suspension system is finally improved.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a schematic diagram of a maglev train levitation control dynamics model.
Fig. 3 is a schematic diagram of a closed-loop control system of a magnetic levitation controller.
Fig. 4 is a schematic structural diagram of the suspension system.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in fig. 1, the present embodiment provides a suspension system control method for a magnetic levitation train, which specifically includes the following steps:
s1, constructing a second-order sliding mode surface based on a suspension control dynamics model of the maglev train, and introducing a nonlinear bounded function which is related to a positioning error signal and is approximated by an online real-time training neural network to obtain a final sliding mode variable structure control law model for constructing a maglev controller of a suspension system;
step S2, inputting the set physical parameters of the suspension system in the magnetic suspension controller;
step S3, the magnetic suspension controller acquires the gap data between the track and the train body in real time and outputs a control signal;
and step S4, after receiving the control signal, the peripheral hardware of the suspension system drives the suspension electromagnet to move to the target position within a limited time, and the position is kept within the position error limit range, so that a stable and reliable suspension control effect is achieved.
In step S1, the method for constructing the final sliding mode variable structure control law model includes:
step S11, establishing an affine nonlinear mathematical model;
s12, carrying out sliding mode control law design on the sliding mode controller according to the affine nonlinear mathematical model to obtain a sliding mode variable structure control law model and two nonlinear bounded functions f (·) and g (·);
and step S13, performing online learning to approximate nonlinear bounded functions f (-) and g (-) in the sliding mode variable structure control law model through an RBF (radial basis function) neural network to obtain the final sliding mode variable structure control law model.
The specific development of the steps is as follows:
as shown in fig. 2, the suspension control dynamics model of a magnetic levitation train is a set of electromagnetic and mechanical equations established using newton's law and maxwell's equations:
in the above formula, the state quantity x1(t)＝xm(t) is the gap, state quantity
The speed of change of the gap distance, the state quantity x3(t)＝im(t) is the control input current, NmNumber of coil windings, AmIs the sectional area of the magnet, m is the vehicle body mass, g is the gravity constant, um(t) is a control voltage, fdTo a degree of interference, mu0For vacuum permeability, RmRepresenting the electromagnet winding resistance.
And step S11, establishing an affine nonlinear mathematical model.
In order to obtain an affine nonlinear mathematical model of the suspension system, according to the principle of nonlinear coordinate transformation, the nonlinear transformation coordinates are selected as follows:
η＝[z1z2z3]T∈Ωη，Ωη∈R3
wherein,
in the formula, z1Denotes the air gap spacing, z2Representing the speed of change of the air gap distance, z3Represents the air gap spacing acceleration; x is the number of1、x2、x3I.e. the state quantity x1(t)、x2(t)、x3(t)。
The derivation is performed on the above formula to obtain:
for simplicity of presentation, k and L are defined as follows:
according to maxwell's equations and Biot-Savar's theory,
it can also be expressed as:
thus, an affine nonlinear mathematical model is obtained:
therein, two non-linear bounded functions are defined:
and step S12, carrying out synovial membrane control law design according to the affine nonlinear mathematical model.
The state variable of the magnetic levitation system is selected to be z1＝x1＝xm(t),
Defining the system error as e and the error change rate as
Wherein e ═ z1-r, r is the desired position.
The slip form surface is designed as follows:
wherein, c1,c2∈R+Is a positive position control gain, having e ═ z1-r＝x1-r。
Derivation of the above equation yields:
wherein f (x) and g (x) are two nonlinear bounded functions f (x) and g (x) in the affine nonlinear mathematical model.
Thus, a sliding mode variable structure control law model is obtained as follows:
wherein u isSMC(x, t) is the controller output, η, μ ∈ R+Respectively representing a constant arrival coefficient and an exponential arrival coefficient, r being an ideal trace instruction.
Verifying a sliding mode variable structure control law model:
the lyapunov equation is defined as:
the equation is positive and semi-positive, and two sides are derived at the same time to obtain:
therefore, based on lyapunov theory, the control system is globally stable.
The sliding mode variable structure controls the law model, but f (-) and g (-) are uncertain and even unknown, so that the approximation of f (-) and g (-) needs to be learned online through an RBF neural network.
And S13, correcting the sliding mode variable structure control law model through RBF neural network online learning approximation f (-) and g (-) to obtain the final sliding mode variable structure control law model.
A schematic of the closed loop control system is shown in fig. 3.
The input and output of the RBF neural network are:
f(x)＝W*Thf(x)+f,g(x)＝L*Thg(x)+g
where x is the network input, j is the jth hidden layer node, and h ═ hj]TIs the output of the Gaussian equation, W, L are approximate network approximation weights, W*,L*Is f (-), g (-) the ideal network weight,f,grespectively being network approximation errorf|≤Mf,|g|≤MgC is the neural network node center vector, b is the Gaussian basis function width.
When x ═ x1x2x3]TDetermined, the output of the RBF neural network is:
wherein h isf(x) And hg(x) For the Koski equation for RBF neural networks,
and
are the estimated network weights of f (x) and g (x).
Neural network minimum parameter learning methods are used for f (-) and g (-) respectively. Make phi ═ W2Phi is a positive real number,
is an on-line estimate of phi, learning error
Make it
Is a positive real number and is,
is that
On-line estimation of (1), learning error of
Therefore, the final sliding mode variable structure control law model is expressed as follows:
wherein sgn (·) is a symbolic equation, s is a dynamic sliding mode surface, um(x, t) is the control output.
According to the least parameter learning method, the adaptation law of f (-) and g (-) is defined as a single parameter
And
wherein, γ1,γ2,Ω1,Ω2∈R+。
Verifying the final sliding mode variable structure control law model:
according to step S13, the equation can be obtained:
the lyapunov equation is defined as:
it can be known that: v is more than or equal to 0
Wherein,
the method comprises the following steps of (1) preparing,
in the same way, the method for preparing the composite material,
because of RBF neural network approximation errorfFor very small real numbers, the rewrite is as follows:
the comprehensive self-adaptive rate is as follows:
wherein,
the following can be obtained:
wherein,
solution inequality
The following can be obtained:
it can be seen that the system is ultimately uniformly bounded. Thus, the system is demonstrated for both bounciness and convergence.
The embodiment is realized by the cooperation of software and hardware of the suspension system.
As shown in fig. 4, the levitation system includes a gap sensor, a chopper, a magnetic levitation controller, and a levitation electromagnet. The gap sensor is connected with the magnetic suspension controller through a chopper, the suspension electromagnet is connected with the magnetic suspension controller through peripheral hardware, and the gap sensor is installed on the suspension electromagnet. Further, the gap sensor is connected with the chopper through the gap processing plate, the control plate and the interface conversion plate.
The magnetic suspension controller comprises computer hardware and algorithm software. And the algorithm software for programming the magnetic suspension controller is stored in computer hardware. When the magnetic suspension controller works, the magnetic suspension controller is responsible for executing the operation of a neural network approximation algorithm, acquiring the input set physical parameters of the suspension system, acquiring the clearance data between the track and the vehicle body in real time, calculating and controlling the output control signal.
The working principle of the embodiment is as follows:
the gap sensor continuously measures and collects air gap distance data at a high speed in real time, and transmits the air gap distance data to computer equipment bearing the magnetic suspension controller through a communication line after the air gap distance data is modulated by analog-to-digital conversion and filtering. And the computer equipment bearing the magnetic suspension controller outputs the control quantity obtained by the magnetic suspension controller to peripheral hardware to drive the suspension electromagnet to work so as to suspend the train. And the error between the air gap distance and the target position is calculated by the magnetic suspension controller to obtain a control output quantity. With the real-time uninterrupted work of the gap sensor, the computer equipment and the peripheral hardware of the suspension electromagnet, the train can move to a target position within a limited time and keep within the position error limit range, and a stable and reliable suspension control effect is achieved.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (8)
1. A suspension system control method for a magnetic suspension train is characterized by comprising the following steps:
s1, constructing a second-order sliding mode surface based on a suspension control dynamics model of the maglev train, and introducing a nonlinear bounded function which is related to a positioning error signal and is approximated by an online real-time training neural network to obtain a final sliding mode variable structure control law model for constructing a maglev controller of a suspension system;
s2, inputting set physical parameters of the suspension system in the magnetic suspension controller;
s3, the magnetic suspension controller acquires gap data between the track and the train body in real time and outputs a control signal;
and S4, after receiving the control signal, the peripheral hardware of the suspension system drives the suspension electromagnet to move to the target position within a limited time and keeps within the position error limit range.
2. The levitation system control method for magnetic levitation trains as recited in claim 1, wherein in step S1, the expression of the final sliding mode variable structure control law model is:
wherein sgn (·) is a sign function, s is a dynamic sliding mode surface, e is a system error, c1、c2Eta and mu are control gain parameters,
and
neural network approximations of unknown nonlinear bounded functions f (-) and g (-) respectively, x being the network input, j being the jth hidden layer node, W*,L*Is the ideal network weight, h, of f (-) and g (-) inf(x) And hg(x) A Koski equation of a neural network, wherein r is an ideal tracking instruction;
according to the minimum parameter learning method, the self-adaptive rate of f (-) and g (-) is defined as a single parameter
And
wherein the parameter gamma1,γ2,Ω1,Ω2∈R+。
3. The levitation system control method for magnetic levitation trains as recited in claim 1, wherein the final sliding mode variable structure control law model construction method in step S1 comprises:
s11, establishing an affine nonlinear mathematical model;
s12, carrying out sliding mode control law design on the sliding mode controller according to the affine nonlinear mathematical model to obtain a sliding mode variable structure control law model and two nonlinear bounded functions f (·) and g (·);
s13, approximating nonlinear bounded functions f (cndot.) and g (cndot.) in the sliding mode variable structure control law model through RBF neural network online learning to obtain the final sliding mode variable structure control law model.
4. A levitation system control method for magnetic levitation trains as recited in claim 3, wherein in said step S11, the expression of affine non-linear mathematical model is:
wherein,
in the formula, z1Denotes the air gap spacing, z2Representing the speed of change of the air gap distance, z3Represents the air gap acceleration, m is the vehicle body mass, mu0Is a vacuum permeability, NmNumber of coil windings, AmIs the cross-sectional area of the magnet, RmRepresenting the electromagnet winding resistance.
5. The method as claimed in claim 4, wherein in step S12, the expression of the sliding mode variable structure control law model is:
in the formula uSMC(x, t) is the control output, η, μ ∈ R+Respectively representing a constant arrival coefficient and an exponential arrival coefficient, r being an ideal trace instruction, c1,c2∈R+Is the positive position control gain, e represents the system error and s represents the dynamic sliding mode surface.
6. The method as claimed in claim 1, wherein the levitation system comprises a gap sensor, a chopper, a magnetic levitation controller and an electromagnet, the gap sensor is connected to the magnetic levitation controller through the chopper, the electromagnet is connected to the magnetic levitation controller through peripheral hardware, and the gap sensor is mounted on the electromagnet.
7. A levitation system control method as recited in claim 6, wherein the gap sensor is connected to the chopper through a gap processing board, a control board and an interface conversion board.
8. The levitation system control method as recited in claim 1, wherein the levitation controller comprises computer hardware and algorithm software for performing neural network approximation algorithm operation, obtaining input set levitation system physical parameters, and obtaining gap data between the track and the car body in real time and outputting control signals.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010200582 | 2020-03-20 | ||
| CN2020102005823 | 2020-03-20 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111806246A true CN111806246A (en) | 2020-10-23 |
Family
ID=72846360
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010561052.1A Pending CN111806246A (en) | 2020-03-20 | 2020-06-18 | Suspension system control method for magnetic suspension train |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111806246A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113263921A (en) * | 2021-05-28 | 2021-08-17 | 同济大学 | Dynamic suspension control method of magnetic-levitation train combined with vibration identification |
| CN114002953A (en) * | 2021-10-09 | 2022-02-01 | 福州大学 | Adaptive notch sliding mode differentiator method for maglev train signal processing |
| CN116774588A (en) * | 2023-07-25 | 2023-09-19 | 石家庄铁道大学 | EMS type magnetic suspension train suspension frame double cross coupling self-adaptive backstepping control method |
| CN118393967A (en) * | 2024-06-24 | 2024-07-26 | 华东交通大学 | Control method, device, medium and product for operation process of heavy-duty train |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20050059230A (en) * | 2002-10-01 | 2005-06-17 | 마그네모션, 인코포레이티드 | Suspending, guiding and propelling vehicles using magnetic forces |
| WO2016051145A1 (en) * | 2014-09-29 | 2016-04-07 | Special Products Limited | A suspension |
| CN105751916A (en) * | 2016-02-29 | 2016-07-13 | 同济大学 | Magnetic-levitation train levitation control method |
| US20180273304A1 (en) * | 2017-03-21 | 2018-09-27 | B&R Industrial Automation GmbH | Method for controlling the normal force of a transport unit of a long stator linear motor |
| CN108984815A (en) * | 2018-04-20 | 2018-12-11 | 江苏大学 | The control method of vehicle-mounted flying wheel battery radial suspension supporting system based on operating condition |
| CN109532509A (en) * | 2018-12-28 | 2019-03-29 | 西南交通大学 | A kind of magnetic floating train suspending control method based on Sliding mode variable structure control |
| CN110244558A (en) * | 2019-05-09 | 2019-09-17 | 重庆大学 | Method for Fuzzy Logic Controller Design based on EMS suspension system |
-
2020
- 2020-06-18 CN CN202010561052.1A patent/CN111806246A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20050059230A (en) * | 2002-10-01 | 2005-06-17 | 마그네모션, 인코포레이티드 | Suspending, guiding and propelling vehicles using magnetic forces |
| WO2016051145A1 (en) * | 2014-09-29 | 2016-04-07 | Special Products Limited | A suspension |
| CN105751916A (en) * | 2016-02-29 | 2016-07-13 | 同济大学 | Magnetic-levitation train levitation control method |
| US20180273304A1 (en) * | 2017-03-21 | 2018-09-27 | B&R Industrial Automation GmbH | Method for controlling the normal force of a transport unit of a long stator linear motor |
| CN108984815A (en) * | 2018-04-20 | 2018-12-11 | 江苏大学 | The control method of vehicle-mounted flying wheel battery radial suspension supporting system based on operating condition |
| CN109532509A (en) * | 2018-12-28 | 2019-03-29 | 西南交通大学 | A kind of magnetic floating train suspending control method based on Sliding mode variable structure control |
| CN110244558A (en) * | 2019-05-09 | 2019-09-17 | 重庆大学 | Method for Fuzzy Logic Controller Design based on EMS suspension system |
Non-Patent Citations (2)
| Title |
|---|
| CHEN CHEN: "Sliding Mode Robust Adaptive Control of Maglev Vehicle"s Nonlinear Suspension System Based on Flexible Track: Design and Experiment", 《IEEE ACCESS》 * |
| YOUGANG SUN: "Adaptive Neural-Fuzzy Robust Position Control Scheme for Maglev Train Systems With Experimental Verification", 《IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113263921A (en) * | 2021-05-28 | 2021-08-17 | 同济大学 | Dynamic suspension control method of magnetic-levitation train combined with vibration identification |
| CN113263921B (en) * | 2021-05-28 | 2022-07-15 | 同济大学 | Dynamic suspension control method of magnetic-levitation train combined with vibration identification |
| CN114002953A (en) * | 2021-10-09 | 2022-02-01 | 福州大学 | Adaptive notch sliding mode differentiator method for maglev train signal processing |
| CN114002953B (en) * | 2021-10-09 | 2023-12-08 | 福州大学 | Self-adaptive notch sliding mode differentiator method for magnetic levitation train signal processing |
| CN116774588A (en) * | 2023-07-25 | 2023-09-19 | 石家庄铁道大学 | EMS type magnetic suspension train suspension frame double cross coupling self-adaptive backstepping control method |
| CN116774588B (en) * | 2023-07-25 | 2024-01-23 | 石家庄铁道大学 | EMS type magnetic suspension train suspension frame double cross coupling self-adaptive backstepping control method |
| CN118393967A (en) * | 2024-06-24 | 2024-07-26 | 华东交通大学 | Control method, device, medium and product for operation process of heavy-duty train |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111806246A (en) | Suspension system control method for magnetic suspension train | |
| Wai et al. | Backstepping fuzzy-neural-network control design for hybrid maglev transportation system | |
| Sun et al. | Reinforcement learning-based optimal tracking control for levitation system of maglev vehicle with input time delay | |
| Fei et al. | Adaptive sliding mode control of dynamic systems using double loop recurrent neural network structure | |
| Sun et al. | RBF neural network-based supervisor control for maglev vehicles on an elastic track with network time delay | |
| He et al. | Neural network control of a rehabilitation robot by state and output feedback | |
| Pan et al. | Adaptive fuzzy control with guaranteed convergence of optimal approximation error | |
| Wai et al. | Adaptive fuzzy-neural-network control for maglev transportation system | |
| Wai et al. | Observer-based adaptive fuzzy-neural-network control for hybrid maglev transportation system | |
| Yong et al. | Balance control of two-wheeled self-balancing mobile robot based on TS fuzzy model | |
| CN110401378B (en) | Magnetic suspension yaw motor control method based on neural network-model predictive control | |
| Mu et al. | Formation control strategy for underactuated unmanned surface vehicles subject to unknown dynamics and external disturbances with input saturation | |
| Zhao et al. | Deterministic learning from adaptive neural network control for a 2-DOF helicopter system with unknown backlash and model uncertainty | |
| Wang et al. | Event driven model free control of quadrotor | |
| CN110805523A (en) | Suspension control method of wind power magnetic suspension yaw system based on neural network | |
| Dai et al. | Finite-time trajectory tracking for marine vessel by nonsingular backstepping controller with unknown external disturbance | |
| CN212289523U (en) | Magnetic suspension control system | |
| CN116604532A (en) | Intelligent control method for upper limb rehabilitation robot | |
| Chen et al. | Event-triggered adaptive robust control for a class of uncertain nonlinear systems with application to mechatronic system | |
| Khamari et al. | Linear parameter varying induction motor control with two-degree-of freedom controller | |
| Zhou et al. | Super-Twisting Algorithm-Based Fractional-Order Sliding-Mode Control of Nonlinear Systems With Mismatched Uncertainties | |
| CN116560375A (en) | Wheel type mobile robot track tracking control method based on neural network observer | |
| Chen et al. | High performance control of a permanent magnet synchronous motor for electric vehicle applications | |
| CN115320405A (en) | Electric vehicle speed control method with saturated input and state | |
| Bai et al. | Trajectory tracking control of hovercraft based on Shutting-Backstepping Sliding Model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20201023 |
|
| WD01 | Invention patent application deemed withdrawn after publication |