CN102790577B - Constructing method for suspended subsystem controller of bearingless permanent magnet synchronous motor - Google Patents
Constructing method for suspended subsystem controller of bearingless permanent magnet synchronous motor Download PDFInfo
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Abstract
The invention discloses a constructing method for a suspended subsystem controller of a bearingless permanent magnet synchronous motor. A complex controlled object is constructed by a Park inverse transformer, a Clark inverse transformer, a current tracking inverter and a suspended subsystem model of the bearingless permanent magnet synchronous motor as a whole; a fuzzy neural network inverter of the complex controlled object is constructed by one fuzzy neural network and four integrators; then the fuzzy neural network inverter is arranged in front of the complex controlled object to form a pseudo-linear system; and the pseudo-linear system is linearly decoupled into two single-input and single-output second-order displacement subsystems, two displacement controllers are designed for the two second-order integral displacement subsystems to construct a linear closed-loop controller, and a fuzzy neural network inverter controller is finally constructed by the linear closed-loop controller, the fuzzy neural network inverter, the Park inverse transformer, the Clark inverse transformer and the current tracking inverter. High-performance control on the suspended subsystem of the bearingless permanent magnet synchronous motor can be realized.
Description
Technical field
The present invention is a kind of building method of Suspension Subsystem of PM Type Bearingless Motors fuzzy neural network inverse controller, is applicable to the high performance control of Suspension Subsystem of PM Type Bearingless Motors, belongs to the technical field of electric drive control equipment.
Background technology
Bearing-free permanent magnet synchronous motor is according to the similitude of conventional motors structure and magnetic bearing structure, to the magnetic suspension bearing winding of radial load be produced together with permanent-magnetic synchronous motor stator winding lap wound, make rotor have rotation and suspending power simultaneously, realize the bearing-free of motor.Bearing-free permanent magnet synchronous motor not only has magnetic suspension bearing does not need the advantage such as lubrication, mechanical friction, nothing wearing and tearing, high speed and super precision, and have the advantages such as traditional permagnetic synchronous motor power factor is high, efficiency is high, volume is little, lightweight, control characteristic is good, thus it is made to be with a wide range of applications at special dimensions such as accurate digital control lathe, flywheel energy storage, biological medicine, semiconductor manufacturing, Aero-Space.
Be different from common alternating current machine, bearing-free permanent magnet synchronous motor be non-linear a, close coupling, multiple-input and multiple-output, time the complication system that becomes, conventional PID control method is usually attended to one thing and lose sight of another, gratifying control effects cannot be reached.Therefore, the control of bearing-free permanent magnet synchronous motor is a very stubborn problem, urgently explores new theory, new method is broken through.Particularly under different operating conditions, bearing-free permanent magnet synchronous motor is very large by the impact of load disturbance and Parameters variation, realize its stable suspersion and controllable rotating, dynamic Decoupling Control of Load Torque must be carried out to the Suspension Subsystem of bearing-free permanent magnet synchronous motor, find its control law under different radial positions.
In order to inherently improve bearing-free permanent magnet synchronous motor to the robustness of load disturbance and parameter time varying and adaptability, realize the dynamic Decoupling Control of Load Torque of Suspension Subsystem under different radial position, thus improve the suspension operation performance of bearing-free permanent magnet synchronous motor, realize its high-quality to run, new control strategy must be adopted.
Summary of the invention
The object of this invention is to provide one can make Suspension Subsystem of PM Type Bearingless Motors radial load under different radial position carry out Nonlinear Decoupling control, make it have good quiet, dynamic control performance, and the building method of the Suspension Subsystem of PM Type Bearingless Motors fuzzy neural network inverse controller of Systematical control can be simplified again.
The technical solution used in the present invention adopts following steps successively: 1) compose in series composite controlled object successively by Park inverse transformation, Clark inverse transformation, current track inverter and controlled Suspension Subsystem of PM Type Bearingless Motors; 2) Mathematical Modeling of composite controlled object is set up, with having 6 input nodes, the fuzzy neural network of 2 output nodes adds 4 integrators and forms the fuzzy neural network inverse with 2 input nodes, 2 output nodes, wherein, first of fuzzy neural network inverse inputs first input as fuzzy neural network, and it is through an integrator s
-1output be second input of fuzzy neural network, then through second integrator s
-1output be the 3rd input of fuzzy neural network; Second of fuzzy neural network inverse inputs the 4th input as fuzzy neural network, and it is through an integrator s
-1output be the 5th input of fuzzy neural network, then through second integrator s
-1output be the 6th input of fuzzy neural network, the output of fuzzy neural network is the output of fuzzy neural network inverse; 3) adjust and determine parameter and the weight coefficient of fuzzy neural network, fuzzy neural network inverse is made to realize the inverse system function of composite controlled object, form pseudo-linear system before fuzzy neural network inverse is series at composite controlled object, the linearized decoupling zero of pseudo-linear system is two single-input single-output displacement Second Order Integral subsystems; 4) two corresponding to two displacement Second Order Integral subsystem design respectively displacement controllers form linear closed-loop controller; 5), before linear closed-loop controller is serially connected in fuzzy neural network inverse, fuzzy neural network inverse controller is jointly formed by linear closed-loop controller, fuzzy neural network inverse, Park inverse transformation, Clark inverse transformation and current track inverter.
The invention has the beneficial effects as follows:
1, by the Linearized Decoupling feature of method of inverse, combine with the approximation capability of fuzzy neural network to non linear system and the adaptive capacity to system parameter variations, The present invention gives Suspension Subsystem of PM Type Bearingless Motors fuzzy neural network inverse controller and building method, thus solve traditional parsing inverse system in actual applications inversion model be difficult to obtain bottleneck problem.
2, by structure fuzzy neural network inverse, this multivariable of Suspension Subsystem of PM Type Bearingless Motors, control that is non-linear, close coupling time-varying system will be converted into the control to two simple displacement Second Order Integral subsystems, the method design closed loop controller of conventional linear control theory can be used easily, thus realize the high performance control to Suspension Subsystem of PM Type Bearingless Motors.
Accompanying drawing explanation
Fig. 1 forms the schematic diagram of composite controlled object 15 by Park inverse transformation 11, Clark inverse transformation 12, current track inverter 13 and controlled Suspension Subsystem of PM Type Bearingless Motors 14;
Fig. 2 is the structural representation of the fuzzy neural network inverse 22 be made up of 5 layers of fuzzy neural network 21 of 6 input nodes, 2 output nodes and 4 integrators;
Fig. 3 is schematic diagram and the isoboles thereof of the pseudo-linear system 3 that fuzzy neural network inverse 22 is formed with composite controlled object 15 compound;
Fig. 4 is the closed-loop control system structure chart be made up of linear closed-loop controller 4 and pseudo-linear system 3;
Fig. 5 is Suspension Subsystem of PM Type Bearingless Motors fuzzy neural network inverse controller 5 theory diagram.
Embodiment
Embodiment of the present invention are: first form composite controlled object by Park inverse transformation, Clark inverse transformation, current track inverter and controlled Suspension Subsystem of PM Type Bearingless Motors, this composite controlled object is equivalent to 4 rank Differential Equation Models under two-phase rotating coordinate system, the Relative order of systematic vector is { 2,2}.Adopt the fuzzy neural network (5 layer network) of 6 input nodes, 2 output nodes and 4 linear element to form to have the fuzzy neural network inverse of the composite controlled object of 2 input nodes, 2 output nodes.And make fuzzy neural network inverse realize the inverse system function of composite controlled object by the parameter and weight coefficient adjusting fuzzy neural network.Form pseudo-linear system before fuzzy neural network inverse is series at composite controlled object, the linearized decoupling zero of pseudo-linear system is two single-input single-output integration subsystems, is respectively two displacement Second Order Integral subsystems.On this basis, linear closed-loop controller is formed for two Second Order Integral subsystem design, two displacement controllers respectively.Finally linear closed-loop controller, fuzzy neural network inverse, Park inverse transformation, Clark inverse transformation and current track inverter are formed fuzzy neural network inverse controller jointly and Dynamic Nonlinear Decoupling control is carried out to Suspension Subsystem of PM Type Bearingless Motors.
Concrete enforcement divides following 7 steps:
1, composite controlled object 15 is formed.Be connected in series successively by Park inverse transformation 11, Clark inverse transformation 12, current track inverter 13 and controlled Suspension Subsystem of PM Type Bearingless Motors 14, composite controlled object 15 is formed, as shown in Figure 1 by Park inverse transformation 11, Clark inverse transformation 12, current track inverter 13 and controlled Suspension Subsystem of PM Type Bearingless Motors 14.This composite controlled object 15 with
,
two current signals as input, using rotor radial displacement x, y as output.
2, by analyzing, equivalence and derivation, be the basis on the structure of fuzzy neural network inverse 22 and learning training supplying method.First based on bearing-free permanent magnet synchronous motor operation principle, set up the Mathematical Modeling of Suspension Subsystem of PM Type Bearingless Motors, through coordinate transform and Linear Amplifer, obtain the Mathematical Modeling of composite controlled object 15, the i.e. 4 rank differential equations under two-phase rotating coordinate system, its vector relative degree is { 2,2}.Can prove that this 4 rank differential equation is reversible through deriving, namely inverse system exists, and can determine that 2 of its inverse system are input as
,
, 2 outputs are
,
.Thus fuzzy neural network inverse 22 can be constructed, provide the basis in method for learning training, as shown in Figure 2.
3, fuzzy neural network 21 and 4 integrators are adopted to construct fuzzy neural network inverse 22, fuzzy neural network 21 adopts 5 layer self-adapting neural fuzzy inference systems (referred to as fuzzy neural network), input number of nodes is 6, output layer nodes is 2, error criterion chooses the mean square error of sample, the membership function of input and output variable all adopts bell shaped function, and 15 membership functions are got in each input, output function type is linear, and the parameter of fuzzy neural network 21 and weight coefficient are determined in next step off-line learning.Then adopt there are 6 input nodes, the fuzzy neural network 21 of 2 output nodes adds the fuzzy neural network inverse 22 that 4 linear elements there is 2 input nodes, 2 output nodes, wherein: first input of fuzzy neural network inverse 22
as first input of fuzzy neural network 21, it is through an integrator s
-1output be
, be second input of fuzzy neural network 21, then through second integrator s
-1output be
, be the 3rd input of fuzzy neural network 21; Second input of fuzzy neural network inverse 22
as the 4th input of fuzzy neural network 21, it is through an integrator s
-1output be
, be the 5th input of fuzzy neural network 21, then through second integrator s
-1output be
, be the 6th input of fuzzy neural network 21.Fuzzy neural network 21 forms fuzzy neural network inverse 22 together with 4 integrators, and the output of fuzzy neural network 21 is exactly the output of fuzzy neural network inverse 22.
4, determine parameter and the weight coefficient of fuzzy neural network 21: 1) by step excitation signal
,
be added to the input of composite controlled object 15, gather rotor radial displacement x, the y of Suspension Subsystem of PM Type Bearingless Motors radial position.2) two rotor displacement x, y off-lines are asked its single order, second dervative respectively, and standardization processing are done to signal, composition fuzzy neural network 21 training sample set
,
,
,
,
,
,
,
.3) adopt hybrid algorithm to train fuzzy neural network 21, through about 750 times training, fuzzy neural network 21 exports mean square error and is less than 0.001, meets the demands, thus determines parameters and the weight coefficient of fuzzy neural network 21.
5, two displacement Second Order Integral subsystems are formed.Fuzzy neural network inverse 22 is formed by the fuzzy neural network 21 and 4 integrators of determining parameters and weight coefficient, fuzzy neural network inverse 22 and composite controlled object 15 are contacted and are formed pseudo-linear system 3, the linearized decoupling zero of this pseudo-linear system 3 is two single-input single-output displacement Second Order Integral subsystems, be made up of two displacement Second Order Integral subsystems, thus realize the Nonlinear Decoupling of Suspension Subsystem of PM Type Bearingless Motors under different radial position between radial load, the control of Complex Nonlinear System is converted into the control of simple two Single-Input/Single-Output Linear Systems, as shown in Figure 3.
6, linear closed loop controller 4 is designed.For two radial positions of two Suspension Subsystem of PM Type Bearingless Motors, two displacement controllers 41,42 corresponding to two displacement Second Order Integral subsystem design respectively, form linear closed-loop controller 4 by two displacement controllers 41,42.In the present invention, two displacement controllers all select PID controller, and its parameter adjusts according to working control object, as shown in Figure 4.
7, fuzzy neural network inverse controller is formed.Before linear closed-loop controller 4 is serially connected in fuzzy neural network inverse 22, jointly fuzzy neural network inverse controller 5 is formed, as shown in Figure 5 by linear closed-loop controller 4, fuzzy neural network inverse 22, Park inverse transformation 11, Clark inverse transformation 12, current track inverter 13.
According to the above, just the present invention can be realized.
Claims (1)
1. a building method for Suspension Subsystem of PM Type Bearingless Motors controller, is characterized in that adopting following steps successively:
1) composite controlled object (15) is composed in series successively by Park inverse transformation (11), Clark inverse transformation (12), current track inverter (13) and controlled Suspension Subsystem of PM Type Bearingless Motors (14);
2) Mathematical Modeling of composite controlled object (15) is set up, with having 6 input nodes, the fuzzy neural network (21) of 2 output nodes adds 4 integrators and forms the fuzzy neural network inverse (22) with 2 input nodes, 2 output nodes, wherein, first of fuzzy neural network inverse (22) inputs first input as fuzzy neural network (21), and first input of fuzzy neural network inverse (22) is through an integrator s
-1output be second input of fuzzy neural network (21), second input of fuzzy neural network (21) is again through an integrator s
-1after output be the 3rd input of fuzzy neural network (21); Second of fuzzy neural network inverse (22) inputs the 4th input as fuzzy neural network (21), and second input of fuzzy neural network inverse (22) is through an integrator s
-1output be the 5th input of fuzzy neural network (21), the 5th input of fuzzy neural network (21) is again through an integrator s
-1after output be the 6th input of fuzzy neural network (21), the output of fuzzy neural network (21) is the output of fuzzy neural network inverse (22);
3) adjust and determine parameter and the weight coefficient of fuzzy neural network, fuzzy neural network inverse (22) is made to realize the inverse system function of composite controlled object (15), form pseudo-linear system (3) before fuzzy neural network inverse (22) being series at composite controlled object (15), pseudo-linear system (3) linearized decoupling zero is two single-input single-output displacement Second Order Integral subsystems;
The parameter of fuzzy neural network (21) and the defining method of weight coefficient are: by step excitation signal
,
be added to the input of composite controlled object (15), step excitation signal
,
the output signal of i.e. fuzzy neural network (21), gather radial displacement x, y of Suspension Subsystem of PM Type Bearingless Motors, and radial displacement x, y off-line is asked its single order, second dervative respectively, and standardization processing is done to signal, the training sample set of composition fuzzy neural network (21)
,
,
,
,
,
,
,
, training is carried out to fuzzy neural network (21) and determines parameter and weight coefficient;
4) corresponding to two displacement Second Order Integral subsystem design respectively two displacement controllers (41,42) form linear closed-loop controller (4);
5), before linear closed-loop controller (4) is serially connected in fuzzy neural network inverse (22), jointly fuzzy neural network inverse controller (5) is formed by linear closed-loop controller (4), fuzzy neural network inverse (22), Park inverse transformation (11), Clark inverse transformation (12) and current track inverter (13).
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103647487B (en) * | 2013-08-13 | 2016-01-20 | 江苏大学 | Based on the permanent-magnetic electric machine with bearing suspension system control method of two inversion model |
| CN103425052B (en) * | 2013-08-21 | 2016-05-25 | 江苏大学 | A kind of building method of radially active magnetic bearings control device |
| CN104362925B (en) * | 2013-10-21 | 2017-05-03 | 江苏大学 | Method for structuring simplified active disturbance rejection controllers with bearingless asynchronous motor radial position system |
| CN103647481B (en) * | 2013-12-13 | 2016-03-02 | 江苏大学 | Bearing-free permanent magnet synchronous motor radial position neural Network Adaptive Inversion Control device building method |
| CN106849793B (en) * | 2017-03-01 | 2019-03-01 | 西安交通大学 | A kind of Over Electric Motor with PMSM fuzzy Neural Network Control System |
| CN109600083B (en) * | 2018-11-19 | 2021-06-22 | 江苏大学 | Two-degree-of-freedom bearingless permanent magnet synchronous motor suspension force subsystem decoupling controller |
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| US5656911A (en) * | 1994-12-27 | 1997-08-12 | Fuji Electric Company | Circuit for driving permanent-magnet synchronous motor using proportional controller |
| CN202043069U (en) * | 2011-04-12 | 2011-11-16 | 江苏大学 | Decoupling controller of five-degree-freedom bearingless synchronous reluctance motor |
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| US5656911A (en) * | 1994-12-27 | 1997-08-12 | Fuji Electric Company | Circuit for driving permanent-magnet synchronous motor using proportional controller |
| CN202043069U (en) * | 2011-04-12 | 2011-11-16 | 江苏大学 | Decoupling controller of five-degree-freedom bearingless synchronous reluctance motor |
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