CN110007605B - Robust prediction control method of repelling magnetic suspension device - Google Patents

Robust prediction control method of repelling magnetic suspension device Download PDF

Info

Publication number
CN110007605B
CN110007605B CN201910419115.7A CN201910419115A CN110007605B CN 110007605 B CN110007605 B CN 110007605B CN 201910419115 A CN201910419115 A CN 201910419115A CN 110007605 B CN110007605 B CN 110007605B
Authority
CN
China
Prior art keywords
magnetic suspension
suspension device
model
gamma
repulsion type
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.)
Active
Application number
CN201910419115.7A
Other languages
Chinese (zh)
Other versions
CN110007605A (en
Inventor
周锋
朱培栋
谢明华
陈俊东
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Changsha University
Original Assignee
Changsha University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Changsha University filed Critical Changsha University
Priority to CN201910419115.7A priority Critical patent/CN110007605B/en
Publication of CN110007605A publication Critical patent/CN110007605A/en
Application granted granted Critical
Publication of CN110007605B publication Critical patent/CN110007605B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02NELECTRIC MACHINES NOT OTHERWISE PROVIDED FOR
    • H02N15/00Holding or levitation devices using magnetic attraction or repulsion, not otherwise provided for

Abstract

The invention discloses a robust prediction control method of a repulsion type magnetic suspension device, which comprises the steps of collecting historical data N groups of input voltage and output distance of the repulsion type magnetic suspension device, and establishing a nonlinear model; establishing a convex polyhedron state space model of the repelling magnetic suspension device based on the nonlinear model; and obtaining an optimized objective function for the robust control of the repelling magnetic suspension device based on the convex polyhedron state space model, and solving the objective function to obtain an input voltage value acting on a winding of the repelling magnetic suspension device at the moment t. The invention considers the influence of system modeling error and uncertain interference in the controller design, and is a control method with stable robustness and strong applicability.

Description

Robust prediction control method of repelling magnetic suspension device
Technical Field
The invention relates to the field of automatic control, in particular to a robust prediction control method for a repulsive magnetic suspension device.
Background
The magnetic suspension technology is an electromechanical integration technology, eddy current is formed on the surface of metal by utilizing a high-frequency magnetic field, and then Lorentz magnetic force is generated to suspend metal equipment, so that the contact between the equipment is effectively avoided, the mutual friction is reduced, and the application prospect is wide. The magnetic suspension system is a complex nonlinear system integrating control of electric magnetic force, air gap, current and the like, and an accurate mathematical model of the magnetic suspension system is difficult to obtain in practical application. The identification modeling method based on data driving is a mathematical modeling method independent of system physical mechanism, only uses input and output data of an object to perform modeling, and is widely applied to modeling of a complex nonlinear system.
The PID controller has a simple control algorithm structure and does not depend on an accurate mathematical model of a controlled object, so that the PID controller is widely applied to magnetic suspension control. However, the global control characteristic of the PID controller for a complex nonlinear object is poor, and particularly for a magnetic levitation ball control system with extremely high stability requirement, the situation of instantaneous uncontrolled falling of a levitation ball is very easy to occur in a large range near a boundary. The linear quadratic regulator is a control algorithm based on a controlled object state space model, and is widely applied to control of complex systems. But the robustness and stability of the control still need to be improved because the control is highly dependent on the accuracy of the controlled object model. Model predictive control is an advanced computer control algorithm generated in industrial process control practice and is widely used in the control of complex industrial systems. Through the search of the existing documents, the patent of 'a wind power magnetic suspension yaw motor control method based on model predictive control' (application number: 201810076334.5) provides a predictive control method based on the physical mechanism model design of a magnetic suspension system. The patent "a magnetic levitation ball position control method" (application number: 201510180614.7) proposes a predictive control method based on an autoregressive model with function weight coefficients. However, the two methods do not consider the influence of system modeling errors and uncertain interferences in the design process of the predictive controller, and the stability and robustness of the algorithm cannot be effectively guaranteed. Meanwhile, in the process of establishing a system state space model for subsequent predictive controller design, the patent "201510180614.7" directly uses the state quantity of the system at the current moment to approximate and replace the future state quantity of the system, and performs direct single-point linearization approximation processing on the future state space model of the system, and the method itself can greatly affect the accuracy of the model.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a robust prediction control method of a repulsive magnetic suspension device aiming at the defects of the prior art, consider the influence of system modeling errors and uncertain interference, and improve the robustness and applicability of the control method.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a robust prediction control method of a repulsive magnetic suspension device comprises the following steps:
1) acquiring historical data N groups of input voltage and output distance of the repulsion type magnetic suspension device, and establishing a nonlinear model as follows:
wherein y (t) is the distance between the bottom of a globe of the repulsion type magnetic suspension device and the infrared reflection position sensor at the time t, namely the output quantity of the repulsion type magnetic suspension device, u (t) is the voltage applied to a winding by a control board of the repulsion type magnetic suspension device at the time t, namely the input quantity of the repulsion type magnetic suspension device, ξ (t +1) is a term containing modeling error and uncertain disturbance, and | ξ (t +1) | is less than or equal to 1; { a%0,t,a1,t,b1,t,a2,t,b2,tIs an inverse quadratic time-varying coefficient with respect to y (t), and | L | · | | survival of the electrically non-woven hair2Representing a two-norm operation; relevant parameters of non-linear modelAre obtained by optimization calculation through an R-SNPOM optimization method;
2) establishing a convex polyhedron state space model of the repelling magnetic suspension device based on the nonlinear model;
3) and obtaining an optimized objective function for the robust control of the repelling magnetic suspension device based on the convex polyhedron state space model, and solving the objective function to obtain an input voltage value acting on a winding of the repelling magnetic suspension device at the moment t.
The specific implementation process of the step 2) comprises the following steps:
1) the input increment and the output increment of the repulsive magnetic levitation device are defined as follows:
wherein y (t + j) is the output of the repulsive magnetic suspension device at the moment t + j; y issetThe expected value of the repulsive magnetic suspension device at the time t is shown; u (t + j) is the input of the repulsion type magnetic suspension device at the moment t + j, and u (t + j-1) is the input of the repulsion type magnetic suspension device at the moment t + j-1; j is an integer less than or equal to zero;
2) one-step forward prediction polynomial model of repelling magnetic suspension deviceThe structure is as follows:
wherein θ (t) is derivedξ (t +1| t) is the quantity containing system modeling error and uncertain disturbance, and | ξ (t +1| t) | is less than or equal to 1;
3) based on the one-step forward prediction polynomial model and the definition of the input increment and the output increment of the system, the state space model corresponding to the polynomial model of the repulsive magnetic suspension device is deduced as follows:
wherein, the coefficient matrix A of the one-step forward prediction state vector X (t +1| t) of the repulsion type magnetic suspension devicet,BtAnd X (t | t) are respectively a parameter and a state calculated by the nonlinear model at the time t;is the input increment of the repulsion type magnetic suspension device at the time t; xi (t) in a vectorAndto (c) to (d); a. thet+g|t,Bt+g|tAnd predicting a coefficient matrix of a state vector X (t + g +1| t) for the repelling magnetic suspension device in the future g steps.
The coefficient matrix At+g|t,Bt+g|tThe variation range is within the following convex polyhedron range:
wherein, { lambda ]t+g|t,μ1,2,3,4 is the linear coefficient of the convex polyhedron; the 4 vertexes of the convex polyhedron are (A)1,B1),(A2,B2),(A3,B3) And (A)4,B4) And, and:
wherein the content of the first and second substances,andare functions relating to y (t), respectivelyMaximum and minimum values of;andare functions relating to y (t), respectivelyMaximum and minimum values of.
In step 3), the optimization objective function is designed as follows:
wherein the content of the first and second substances,I2is a unit array; x (t + g | t) is the system state quantity of the step t + g predicted by the model at the moment t;inputting a control increment for the t + g step repulsion type magnetic suspension device predicted at the t moment;g≥1,Ftthe future feedback control rate of the repulsion type magnetic suspension device at the time t.
Solving the optimized objective function by the following set of inequalities:
wherein, the symbol represents the symmetric structure of the matrix; { Qμ1,2,3,4 is an intermediate matrix variable generated by solving the inequality set, namely the convex optimization problem; gamma ray0+ gamma is the optimized target value { (A) of the convex optimization problemμ,Bμ) 1,2,3,4 is the vertex of the convex polyhedron model; gamma, gamma0、{Y,G,Qμ1,2,3,4 |, andare all the minimized variable gamma0+ gamma solving intermediate variables obtained in the process; in solving the minimization problemThen, the optimization function automatically searches for the gamma satisfying the constraint conditions of the inequality set0+ gamma minimum intermediate variables gamma, gamma0、{Y,G,Qμ1,2,3,4 |, andwhen the inequality group has feasible solution, the optimization process is ended, and the obtained result isI.e. the value of the input voltage acting on the windings of the magnetic levitation system at time t.
Compared with the prior art, the invention has the beneficial effects that: the invention takes the influence of external interference which cannot be avoided by an actual magnetic suspension system into consideration, the invention utilizes a time-varying coefficient quasi-linear regression structure model based on data driving to model the magnetic suspension system, and the influence of modeling error and external uncertain interference of the system is taken into consideration in the modeling process. In order to overcome the defect that the stability and robustness of an algorithm are difficult to ensure by a common prediction control method, the invention provides a robust prediction control method which can be realized by solving a linear matrix inequality set based on an established time-varying coefficient regression model of a magnetic suspension system, and the influence of system modeling errors and uncertain interference is considered in the design of a controller, so that the method is a control method with stable robustness and strong applicability.
Drawings
Fig. 1 is a structural view of a repulsive magnetic levitation apparatus to which the present invention is directed.
Detailed Description
The structure of the repulsive magnetic suspension device to which the invention is directed is shown in fig. 1, wherein: the globe comprises a globe shell (the radius is 20 cm), a square cylindrical magnet 3, a support between the globe shell 1 and the square cylindrical magnet 3, an infrared reflection position sensor 4 (the model is ST178H), an iron core 5 (the section radius is 2.5 cm), a winding 6 (the number of turns is 3500), and a control circuit board 7 based on a single chip microcomputer. The system controls the distance (the control distance range is 5 cm-25 cm) between the bottom of the globe 2 and the infrared reflection position sensor 4 by adjusting the voltage applied to the winding 6 by the control board 7 (the output voltage range is 0V-20V). The specific embodiment of the robust control method of the repulsive magnetic suspension device comprises the following steps of:
step 1: for the repulsive magnetic levitation device shown in fig. 1, a historical data 2500 group of input voltage and output distance of the system is acquired, and the following nonlinear model of the system is established:
in the above formula, y (t) is the distance between the bottom of the globe 2 and the infrared reflection position sensor 4 at the time t, namely the output quantity of the system, u (t) is the voltage applied to the winding 6 by the control board 7 at the time t, namely the input quantity of the system, ξ (t +1) is a term containing modeling error and uncertain disturbance, and | ξ (t +1) | is less than or equal to 1, and { a +1 ≦ is0,t,a1,t,b1,t,a2,t,b2,tIs an inverse quadratic time-varying coefficient with respect to y (t), and | L | · | | survival of the electrically non-woven hair2Representing a two-norm operation; relevant parameters of the modelAre obtained by optimization calculation through an R-SNPOM optimization method (the R-SNPOM optimization method is detailed in the documents of Zeng X, Peng H, Zhou F, 2018, A customized SNPOM for standing parameter estimation of RBF-AR (X) model, IEEE Transactions on Neural networks and Learning Systems,29, No.4, 779-:
step 2: based on the magnetic suspension system inverse quadratic function type time-varying coefficient regression model (1) established in the step 1, a convex polyhedron state space model of the system is established, and the specific process is as follows:
the input increment and the output increment of the magnetic levitation system are defined as follows:
in the above formula, y (t + j) is the output of the system at the time t + j; y isset∈[5,25]The expected value of the system at the moment t; u (t + j) is the input of the system at the moment t + j, and u (t + j-1) is the input of the system at the moment t + j-1; j is 0, -1, -2. From the above definitions, one step forward prediction of the model can be derivedThe structure is as follows:
in the above formula, θ (t) is derivedThe intermediate quantities that are generated in the process,ξ (t +1| t) is the amount of uncertainty interference and system modeling error involved, and | ξ (t +1| t) | ≦ 1.
The state vector of the magnetic levitation system is defined as follows:
based on the above definition of the one-step forward prediction polynomial model and the input/output deviation amount of the system, the state space model corresponding to the polynomial model of the system can be derived as follows:
in the above equation, the coefficient matrix A of the one-step forward prediction state vector X (t +1| t) of the systemt,BtAnd X (t | t) are the parameters and states respectively calculated by the model identified in step S1 at time t;the xi (t) cannot be accurately obtained at the moment t because the unknown interference term ξ (t +1| t) of the system is contained in the xi (t), but the change range of the xi (t) is less than or equal to 1 in the vector quantity because ξ (t +1| t) |Andto (c) to (d); coefficient matrix A of forward predicted state vector X (t + g +1| t) for g steps in the future of the systemt+g|t,Bt+g|tThe variation range of the method cannot be directly calculated at the time t, but is within the following convex polyhedron range:
in the above formula, { λt+g|t,μ1,2,3,4 is a linear coefficient of the polyhedron; the 4 vertexes of the polyhedron are (A)1,B1),(A2,B2),(A3,B3) And (A)4,B4) And is and
wherein the content of the first and second substances, because y (t) e [5,25]Then, then Thus, 4 vertices (A) of the convex polyhedron set (9) can be calculated1,B1),(A2,B2),(A3,B3) And (A)4,B4)。
And step 3: based on the system convex polyhedron state space model (7-8) established in the step 2, the optimized objective function design of the robust control method for the repulsive magnetic suspension device is as follows:
in the above formula, the first and second carbon atoms are,i is a unit array; x (t + g | t) is the system state quantity of the step t + g predicted by the model at the moment t;and inputting a control increment for the system at the step t + g predicted at the moment t. The future control rate structure design of the robust predictive controller is as follows:g≥1,Ftthe future feedback control rate of the system at the time t.
Based on the designed controller optimization objective function, the optimal control rate of the robust control method for the repulsive magnetic levitation device is obtained by solving the following linear matrix inequality set:
in the above formula, the symbol represents a symmetric structure of the matrix; ft=YG-1The future feedback control rate of the system at the moment t; { Qμ1,2,3,4 is an intermediate matrix variable generated by solving the convex optimization problem; gamma ray0+ gamma is the optimized target value of the convex optimization problem (12), and gamma are simultaneously0Also an intermediate quantity generated in the above optimization process; coefficient matrix At、BtX (t | t) is a parameter matrix known at time t; { (A)μ,Bμ) And |. mu. ═ 1,2,3,4} is a vertex of the system polyhedron model (8) in the step 2. Gamma, gamma0、{Y,G,Qμ1,2,3,4 |, andare all the minimized variable gamma0The intermediate variables obtained in the + gamma solution process. When solving the minimization problem (12), the optimization function automatically searches for gamma which meets the requirement according to the inequality constraint conditions (13-16)0+ gamma minimum intermediate variables gamma, gamma0、{Y,G,Qμ1,2,3,4 |, andwhen the above is mentionedWhen the optimization problem (12-16) has a feasible solution, the optimization process is ended. At this time, obtainedI.e. the value of the input voltage acting on the windings of the magnetic levitation system at time t.

Claims (1)

1. A robust prediction control method of a repulsive magnetic suspension device is characterized by comprising the following steps:
1) acquiring historical data N groups of input voltage and output distance of the repulsion type magnetic suspension device, and establishing a nonlinear model as follows:
wherein y (t) is the distance between the bottom of a globe of the repulsion type magnetic suspension device and the infrared reflection position sensor at the time t, namely the output quantity of the repulsion type magnetic suspension device, u (t) is the voltage applied to a winding by a control board of the repulsion type magnetic suspension device at the time t, namely the input quantity of the repulsion type magnetic suspension device, ξ (t +1) is a term containing modeling error and uncertain disturbance, and | ξ (t +1) | is less than or equal to 1; { a%0,t,a1,t,b1,t,a2,t,b2,tIs an inverse quadratic time-varying coefficient with respect to y (t), and | L | · | | survival of the electrically non-woven hair2Representing a two-norm operation; relevant parameters of non-linear modelAre obtained by optimization calculation through an R-SNPOM optimization method;
2) establishing a convex polyhedron state space model of the repelling magnetic suspension device based on the nonlinear model;
3) based on the convex polyhedron state space model, obtaining an optimized objective function for robust control of the repelling magnetic suspension device, and solving the optimized objective function to obtain an input voltage value acting on a winding of the repelling magnetic suspension device at the moment t;
the specific implementation process of the step 2) comprises the following steps:
(1) the input increment and the output increment of the repulsive magnetic levitation device are defined as follows:
wherein y (t + j) is the output of the repulsive magnetic suspension device at the moment t + j; y issetThe expected value of the repulsive magnetic suspension device at the time t is shown; u (t + j) is the input of the repulsion type magnetic suspension device at the moment t + j, and u (t + j-1) is the input of the repulsion type magnetic suspension device at the moment t + j-1; j is an integer less than or equal to zero;
(2) one-step forward prediction polynomial model of repelling magnetic suspension deviceThe structure is as follows:
wherein the content of the first and second substances,to deriveξ (t +1| t) is the quantity containing system modeling error and uncertain disturbance, and | ξ (t +1| t) | is less than or equal to 1;
(3) based on the one-step forward prediction polynomial model and the definition of the input increment and the output increment of the system, the state space model corresponding to the polynomial model of the repulsive magnetic suspension device is deduced as follows:
wherein, the coefficient matrix A of the one-step forward prediction state vector X (t +1| t) of the repulsion type magnetic suspension devicet,BtAnd X (t | t) are respectively a parameter and a state calculated by the nonlinear model at the time t;is the input increment of the repulsion type magnetic suspension device at the time t; xi (t) in a vectorAndto (c) to (d); a. thet+g|t,Bt+g|tPredicting a coefficient matrix of a state vector X (t + g +1| t) for the repelling magnetic suspension device in the future g steps;
the coefficient matrix At+g|t,Bt+g|tThe variation range is within the following convex polyhedron range:
wherein, { lambda ]t+g|t,μ1,2,3,4 is the linear coefficient of the convex polyhedron; the 4 vertexes of the convex polyhedron are (A)1,B1),(A2,B2),(A3,B3) And (A)4,B4) And, and:
wherein the content of the first and second substances,andare functions relating to y (t), respectivelyMaximum and minimum values of;andare functions relating to y (t), respectivelyMaximum and minimum values of;
in step 3), the optimization objective function is designed as follows:
wherein the content of the first and second substances,I2is a unit array; x (t + g | t) is the system state quantity of the step t + g predicted by the model at the moment t;t + g step rejection for prediction at time tInputting control increment by a magnetic suspension device;Ftfor the future feedback control rate of the repulsion type magnetic suspension device at the time t,
solving the optimized objective function by the following set of inequalities:
wherein, the symbol represents the symmetric structure of the matrix; { Qμ1,2,3,4 is an intermediate matrix variable generated by solving the inequality set, namely the convex optimization problem; gamma ray0+ gamma is the optimized target value { (A) of the convex optimization problemμ,Bμ) 1,2,3,4 is the vertex of the convex polyhedron model; gamma, gamma0、{Y,G,Qμ1,2,3,4 |, andare all the minimized variable gamma0+ gamma solving intermediate variables obtained in the process; in solving the minimization problemThe optimization function is based on the above inequalityFormula group constraint condition automatic search for gamma satisfying0+ gamma minimum intermediate variables gamma, gamma0、{Y,G,Qμ1,2,3,4 |, andwhen the inequality group has feasible solution, the optimization process is ended, and the obtained result isI.e. the value of the input voltage acting on the windings of the magnetic levitation system at time t.
CN201910419115.7A 2019-05-20 2019-05-20 Robust prediction control method of repelling magnetic suspension device Active CN110007605B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910419115.7A CN110007605B (en) 2019-05-20 2019-05-20 Robust prediction control method of repelling magnetic suspension device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910419115.7A CN110007605B (en) 2019-05-20 2019-05-20 Robust prediction control method of repelling magnetic suspension device

Publications (2)

Publication Number Publication Date
CN110007605A CN110007605A (en) 2019-07-12
CN110007605B true CN110007605B (en) 2020-03-24

Family

ID=67177452

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910419115.7A Active CN110007605B (en) 2019-05-20 2019-05-20 Robust prediction control method of repelling magnetic suspension device

Country Status (1)

Country Link
CN (1) CN110007605B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI724888B (en) * 2020-05-05 2021-04-11 崑山科技大學 Deep learning proportional derivative control method for magnetic levitation system
CN112286054A (en) * 2020-10-20 2021-01-29 江苏科技大学 Prediction control method based on magnetic suspension damping device

Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000337434A (en) * 1999-05-25 2000-12-05 Delta Tooling Co Ltd Vibration mechanism
WO2007065608A1 (en) * 2005-12-08 2007-06-14 Eth Zurich Magnetic levitation system
CN102508433A (en) * 2011-11-06 2012-06-20 北京航空航天大学 Method for compensating digital control delay of magnetic bearing switch power amplifier
CN103940392A (en) * 2014-04-17 2014-07-23 江苏大学 Rotor position/displacement self-detection method of magnetic-levitation switched reluctance motor
CN104793645A (en) * 2015-04-16 2015-07-22 中南大学 Magnetic levitation ball position control method
CN105893654A (en) * 2016-03-11 2016-08-24 中南大学 Robust predictive control method for first-order continuous stirred tank reactor (CSTR)
CN106933107A (en) * 2017-05-15 2017-07-07 中南大学 A kind of output tracking Robust Predictive Control method based on the design of multifreedom controlling amount
CN107450352A (en) * 2017-09-18 2017-12-08 江苏海事职业技术学院 The simulation control method of non-linear Backstepping Controller based on Matlab
CN107589666A (en) * 2017-08-30 2018-01-16 湖北工业大学 A kind of maglev train system control method of the sliding formwork control based on power Reaching Law
CN107748543A (en) * 2017-09-21 2018-03-02 中南大学 A kind of nonlinear system modeling method based on DBN ARX models
CN108183650A (en) * 2018-01-26 2018-06-19 曲阜师范大学 A kind of wind-powered electricity generation magnetic suspension yaw motor control method based on Model Predictive Control
CN108681255A (en) * 2018-05-16 2018-10-19 江苏大学 A method of the weakening magnetically levitated flywheel based on Sliding mode variable structure control is buffeted
CN109062115A (en) * 2018-09-11 2018-12-21 长沙学院 A kind of spin control method based on double-closed-loop control
CN109491248A (en) * 2018-11-20 2019-03-19 中南大学 Magnetic levitation ball position prediction control method based on RBF-ARX model and laguerre function
CN109507882A (en) * 2018-11-20 2019-03-22 中南大学 A kind of fast robust forecast Control Algorithm based on RBF-ARX model

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013188725A1 (en) * 2012-06-14 2013-12-19 President And Fellows Of Harvard College Levitation of materials in paramagnetic ionic liquids
FI126506B (en) * 2015-06-26 2017-01-13 Lappeenrannan Teknillinen Yliopisto Control device and method for controlling magnetic support and torque generation

Patent Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000337434A (en) * 1999-05-25 2000-12-05 Delta Tooling Co Ltd Vibration mechanism
WO2007065608A1 (en) * 2005-12-08 2007-06-14 Eth Zurich Magnetic levitation system
CN102508433A (en) * 2011-11-06 2012-06-20 北京航空航天大学 Method for compensating digital control delay of magnetic bearing switch power amplifier
CN103940392A (en) * 2014-04-17 2014-07-23 江苏大学 Rotor position/displacement self-detection method of magnetic-levitation switched reluctance motor
CN104793645A (en) * 2015-04-16 2015-07-22 中南大学 Magnetic levitation ball position control method
CN105893654A (en) * 2016-03-11 2016-08-24 中南大学 Robust predictive control method for first-order continuous stirred tank reactor (CSTR)
CN106933107A (en) * 2017-05-15 2017-07-07 中南大学 A kind of output tracking Robust Predictive Control method based on the design of multifreedom controlling amount
CN107589666A (en) * 2017-08-30 2018-01-16 湖北工业大学 A kind of maglev train system control method of the sliding formwork control based on power Reaching Law
CN107450352A (en) * 2017-09-18 2017-12-08 江苏海事职业技术学院 The simulation control method of non-linear Backstepping Controller based on Matlab
CN107748543A (en) * 2017-09-21 2018-03-02 中南大学 A kind of nonlinear system modeling method based on DBN ARX models
CN108183650A (en) * 2018-01-26 2018-06-19 曲阜师范大学 A kind of wind-powered electricity generation magnetic suspension yaw motor control method based on Model Predictive Control
CN108681255A (en) * 2018-05-16 2018-10-19 江苏大学 A method of the weakening magnetically levitated flywheel based on Sliding mode variable structure control is buffeted
CN109062115A (en) * 2018-09-11 2018-12-21 长沙学院 A kind of spin control method based on double-closed-loop control
CN109491248A (en) * 2018-11-20 2019-03-19 中南大学 Magnetic levitation ball position prediction control method based on RBF-ARX model and laguerre function
CN109507882A (en) * 2018-11-20 2019-03-22 中南大学 A kind of fast robust forecast Control Algorithm based on RBF-ARX model

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
.Nonlinear model predictive control of a magnetic levitation system.《Control Engineering Practice》.2013, *
RBF-ARX Model-based Robust MPC for Nonlinear Systems;彭辉等;《The 16th IFAC World Congress》;20051231;第1025-1030页 *
Thomas Bächle;Sebastian Hentzelt;Knut Graichen *
一种排斥式磁悬浮平台的磁场设计方法;余玲等;《科学技术与工程》;20101031;第7491-7493页 *
一种新的ARX 模型在磁悬浮系统建模中的应用;侯海良等;《计算机工程与应用》;20071231;第196-200、213页 *
基于线性函数型权重的RBF−ARX模型的磁悬浮球系统预测控制;覃业梅等;《中南大学学报(自然科学版)》;20160831;第2676-2684页 *

Also Published As

Publication number Publication date
CN110007605A (en) 2019-07-12

Similar Documents

Publication Publication Date Title
CN110007605B (en) Robust prediction control method of repelling magnetic suspension device
Verma et al. Indirect IMC‐PID controller design
CN109507882B (en) RBF-ARX model-based rapid robust prediction control method
Qi et al. Stable indirect adaptive control based on discrete-time T–S fuzzy model
Khandekar et al. Discrete sliding mode control for robust tracking of higher order delay time systems with experimental application
Sadek et al. Improved adaptive fuzzy backstepping control of a magnetic levitation system based on symbiotic organism search
Zhu et al. Controller dynamic linearisation‐based model‐free adaptive control framework for a class of non‐linear system
Hosseinzadeh et al. Robust adaptive passivity‐based control of open‐loop unstable affine non‐linear systems subject to actuator saturation
Gouta et al. Model-based predictive and backstepping controllers for a state coupled four-tank system with bounded control inputs: A comparative study
Sun et al. A modified dynamic surface approach for control of nonlinear systems with unknown input dead zone
Demirtas DSP-based sliding mode speed control of induction motor using neuro-genetic structure
Morales et al. Adaptive control based on fast online algebraic identification and GPI control for magnetic levitation systems with time-varying input gain
Li et al. State/model-free variable-gain discrete sliding mode control for an ultraprecision wafer stage
Hsu et al. Emotional fuzzy sliding-mode control for unknown nonlinear systems
CN109491248A (en) Magnetic levitation ball position prediction control method based on RBF-ARX model and laguerre function
Wanfeng et al. Adaptive PID controller based on online LSSVM identification
Alanis et al. Real-time discrete neural control applied to a Linear Induction Motor
Xu Adaptive integral terminal third-order finite-time sliding-mode strategy for robust nanopositioning control
Song et al. Fractional order modeling and nonlinear fractional order pi‐type control for PMLSM system
Meng et al. Disturbance observer-based integral backstepping control for a two-tank liquid level system subject to external disturbances
Martin et al. Achieving an equilibrium position of pendubot via swing-up and stabilizing model predictive control
Gao et al. A recursive modified partial least square aided data-driven predictive control with application to continuous stirred tank heater
Yousuf et al. PSO based single and two interconnected area predictive automatic generation control
Toshani et al. Constrained generalised minimum variance controller design using projection‐based recurrent neural network
Mahapatro Control algorithms for a two tank liquid level system: An experimental study

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
GR01 Patent grant
GR01 Patent grant