CN103997274A - Model reference self-adaptive system parameter self-tuning method based on one-dimensional fuzzy control - Google Patents

Abstract

The invention relates to a model reference self-adaptive system parameter self-tuning method based on one-dimensional fuzzy control. The method comprises the following steps that (1) a model reference self-adaptive system based on the one-dimensional fuzzy control is structured according to an actual physical model of a permanent magnet synchronous motor; (2) direct-axis currents and quadrature-axis currents are calculated respectively according to the actual model and a reference model, and a current error variable e(t) of the direct-axis currents and the quadrature-axis currents is obtained according to an error calculation formula; (3) the changing range of e(t) is defined as current error variable pulsation band e(t)band, and the e(t)band is sent into a one-dimensional fuzzy controller after action of the input scale factor Ke of the one-dimensional fuzzy controller; (4) fuzzy controlled quantity output u(t) is obtained according to one-dimensional fuzzy rules; (5) u(t) is acted by the output scale factor Ku of the one-dimensional fuzzy controller and amplified to the actual controlled quantity on PI parameters; (6) u(t) is converted into a precise output value u0(t) and is overlapped on the PI parameters. The model reference self-adaptive system parameter self-tuning method based on the one-dimensional fuzzy control can be widely applied to the field of control over motors without position sensors.

Description

Model reference adaptive system parameters automatic setting method based on one dimension fuzzy control

Technical field

The present invention relates to a kind of motor control method, particularly about a kind of model reference adaptive system parameters automatic setting method based on one dimension fuzzy control.

Background technology

Model reference adaptive system, due to simple in structure, be easy to realize, thereby be widely adopted in Permanent Magnet Synchronous Motor Speed Sensorless control.At present, in traditional model reference adaptive system, conventionally performance can be well controlled when PI (proportional integral) controller is applied to constant load operating mode, but in the time being applied to the compressor load operating mode of pulsation, constant single PI controller but can not meet the requirement of control performance well.Reason is in the permagnetic synchronous motor drive compression machine system of employing model reference adaptive system, the rotor speed pulsation situation of estimation is subject to the impact of PI parameter in model reference adaptive system to a great extent, and then cause between the rotor-position of estimation and actual rotor-position and have error, cause in the time of Front feedback control, can not get accurate rotor position information, and then affected the performance of Front feedback control.Therefore in actual control, in the time adopting permagnetic synchronous motor to drive pulsating load, need to correspondingly regulate the PI parameter in model reference adaptive system according to actual running speed and load state, often cause inconvenience to a certain extent.

In traditional model reference adaptive system, the PI parameter problem of current deviation and sensitivity to parameter problem tend to affect the performance of whole control system, and model reference adaptive system based on conventional is only applicable to constant load operating mode, and rotor-position angular accuracy needs further to be improved.Meanwhile, along with the development of fuzzy control technology, the research that fuzzy control method is applied to PMSM Drive System has become a study hotspot in recent years.

Summary of the invention

For the problems referred to above, the object of this invention is to provide a kind of model reference adaptive system parameters automatic setting method based on one dimension fuzzy control.

For achieving the above object, the present invention takes following technical scheme: the model reference adaptive system parameters automatic setting method based on one dimension fuzzy control, it comprises the following steps: 1) according to the actual physics model of permagnetic synchronous motor, with reference to traditional model reference adaptive system, the model reference adaptive system of structure based on one dimension fuzzy control; 2) according to the realistic model of permagnetic synchronous motor and described step 1) structure reference model, calculate respectively d-axis and quadrature axis electric current under corresponding input, and calculate the current error variable e (t) between realistic model and reference model according to following error calculation formula:

$e\left(t\right)=i_d{\hat{i}}_q-i_q{\hat{i}}_d-\frac{{\mathrm{\ψ}}_r}{L_d}(i_q-{\hat{i}}_q)$

Wherein, i _d, i _qfor d-axis and the quadrature axis electric current of real electrical machinery output, unit is A; for d-axis and the quadrature axis electric current of the output of model reference adaptive system, unit is A; ψ _rfor motor magnetic linkage, unit is Vs; L _dfor motor d-axis inductance, unit is H; 3) excursion of current error variable e (t) is defined as to current error variable pulsation band e (t) _band, and by the input scale factor K of one dimension fuzzy controller _eafter effect, by e (t) _bandsend in one dimension fuzzy controller; 4) calculate fuzzy controller output u (t) according to one dimension fuzzy rule, described one dimension fuzzy rule is as following table:

Wherein, Linguistic Value variable " NB " expression " negative large ", Linguistic Value variable " NM " expression " in negative ", Linguistic Value variable " NS " expression " negative little ", Linguistic Value variable " ZE " expression " zero ", Linguistic Value variable " PS " expression " just little ", Linguistic Value variable " PM " expression " center ", Linguistic Value variable " PB " expression " honest "; e ₀(t) be the initial value of e (t); Δ K _pfor K in PI parameter _pthe variable quantity of parameter; 5) the output u (t) of the one dimension fuzzy controller obtaining is passed through to the output-scale-factor K of one dimension fuzzy controller _uact on and be amplified to the working control amount in PI parameter; 6) adopt the central value method of average one dimension fuzzy controller output u (t) to be converted to the accurate output valve u of fuzzy control ₀, and be added in PI parameter (t).

Described step 3) in, by one dimension fuzzy controller input scale factor K _eeffect universe of fuzzy sets is limited between [6 ,+6].

Described step 3) in, the input scale factor K of one dimension fuzzy controller _eeffect refer to:

As e (t) _bandwithout input scale factor K _eeffect or work as K _e=1 o'clock, e (t) _bandeffective range identical with universe of fuzzy sets, be [6 ,+6], and the part exceeding can be imposed restrictions between [6 ,+6], now one dimension fuzzy controller to input variable the value sensitivity between [6 ,+6], to exceed part value seem insensitive;

Work as K _ewhen <1, e (t) _bandeffective range become [6/K _e,+6/K _e], and its scope is along with K _ereduce and constantly increase, one dimension fuzzy controller is to e (t) _bandsphere of action increase, also can be described as to e (t) _bandcontrol action along with K _ereduce and constantly weaken;

Work as K _ewhen >1, e (t) _bandeffective range be still [6/K _e,+6/K _e], but its scope is along with K _eincrease and constantly reduce, one dimension fuzzy controller is to e (t) _bandcontrol action along with K _eincrease constantly strengthen.

Described step 5) in, the output-scale-factor K of one dimension fuzzy controller _ueffect refer to:

When the output u of one dimension fuzzy controller (t) is without output-scale-factor K _ueffect or work as K _u=1 o'clock, the Δ K in one dimension fuzzy controller output u (t) and the actual PI parameter being added in model reference adaptive system _por Δ K _ibe worth identical;

Work as K _uwhen <1, in the PI parameter being added in model reference adaptive system after the effect of one dimension fuzzy controller output u (t) is weakened, and K _uless, stack amount is less, and whole system need could arrive stable state through longer dynamic process;

Work as K _uwhen >1, in the PI parameter being added in model reference adaptive system after the effect of one dimension fuzzy controller output u (t) is reinforced, and K _ularger, stack amount is larger, and along with K _uincrease, the improvement effect of the dynamic property to whole control system is larger, but works as K _uwhen excessive, easily there is PI parametric oscillation, then cause the unstable of whole control system.

The present invention is owing to taking above technical scheme, it has the following advantages: 1, the present invention is due in permagnetic synchronous motor compressor assembly, adopt the model reference adaptive system based on one dimension fuzzy control, make PI control parameter in model reference adaptive system to carry out automatic adjusting along with running speed and load state, easy to use.2, the present invention has been owing to having introduced fuzzy control, and after making to control parameter and automatically adjusting, rotor-position angle error has obtained effective reduction.3, the present invention, owing to having introduced fuzzy control, for traditional model reference adaptive system, has good adaptability to different load states, has good dynamic and static state performance.4, the present invention is due to fuzzy control is combined with model reference adaptive system, the amount of calculation increasing in traditional model reference adaptive system is less, systematic cost is less, thereby the present invention can be widely used in position-sensor-free Motor Control Field.

Brief description of the drawings

Fig. 1 is traditional model reference adaptive system rotor position and turn count method control block diagram

Fig. 2 is the rotor position estimation method control block diagram that the present invention is based on fuzzy model reference adaptive system

Fig. 3 is the current error variable pulsation band e (t) that the present invention defines _band

Fig. 4 is the input scale factor K of one dimension fuzzy controller of the present invention _eimpact

Fig. 5 is the output-scale-factor K of one dimension fuzzy controller of the present invention _uimpact

Fig. 6 is Fuzzy Calculation control block diagram example of the present invention

Fig. 7 is the standard one dimension fuzzy controller in prior art

Fig. 8 is the equally distributed input and output membership function that the present invention adopts

Fig. 9 is the input membership function of standard one dimension fuzzy controller of the present invention

Figure 10 is the output membership function that the present invention calculates according to one dimension fuzzy rule

Figure 11 is the simulation result of the present invention while adopting constant output torque control

Figure 12 is the simulation result of the present invention while adopting load torque approximate match constant output torque control

Figure 13 is the experimental result of the present invention while adopting constant output torque control

Figure 14 is the experimental result of the present invention while adopting load torque approximate match constant output torque control

Embodiment

Below the specific embodiment of the present invention is further described.

In traditional model reference adaptive system, current error variable e (t) obtains Speed Identification through adaptive rate.The present invention adopts a kind of nonlinear adaptive rule based on fuzzy logic to substitute the PI controller in conventional model reference adaptive system speed observer, and e (t) directly obtains Speed Identification by one dimension fuzzy controller.

The model reference adaptive system parameters automatic setting method that the present invention is based on one dimension fuzzy control comprises the following steps:

1) according to the actual physics model of permagnetic synchronous motor, with reference to traditional model reference adaptive system (as shown in Figure 1), the model reference adaptive system (as shown in Figure 2) of structure based on one dimension fuzzy control.

2) according to the realistic model of permagnetic synchronous motor and described step 1) structure reference model, calculate respectively d-axis and quadrature axis electric current under corresponding input, and calculate the current error variable e (t) between realistic model and reference model according to following error formula:

$e\left(t\right)=i_d{\hat{i}}_q-i_q{\hat{i}}_d-\frac{{\mathrm{\&psi;}}_r}{L_d}(i_q-{\hat{i}}_q)---\left(1\right)$

Wherein, i _d, i _qfor d-axis and the quadrature axis electric current of real electrical machinery output, unit is A (ampere); for d-axis and the quadrature axis electric current of the output of model reference adaptive system, unit is A (ampere); ψ _rfor motor magnetic linkage, unit is Vs (weber); L _dfor motor d-axis inductance, unit is H (Henry).

3) as shown in Figure 3, the excursion of current error variable e (t) is defined as to current error variable pulsation band e (t) _band, and by the input scale factor K of one dimension fuzzy controller _eafter effect, by e (t) _bandsend in one dimension fuzzy controller.

As shown in Figure 4, the input scale factor K of one dimension fuzzy controller _ethe control performance of whole control system is had to larger impact.In order to study conveniently, the present invention is limited to universe of fuzzy sets between [6 ,+6], and the ordinate in figure represents universe of fuzzy sets, and abscissa represents above defined e (t) _bandbasic domain.If e (t) _bandwithout input scale factor K _eeffect or work as K _e=1 o'clock, e (t) _bandeffective range identical with universe of fuzzy sets, be [6 ,+6], and the part exceeding can be imposed restrictions between [6 ,+6], now one dimension fuzzy controller to e (t) _bandvalue sensitivity between [6 ,+6], seems insensitive to the value that exceeds part; Work as K _ewhen <1, e (t) _bandeffective range become [6/K _e,+6/K _e], and its scope is along with K _ereduce and constantly increase, one dimension fuzzy controller is to e (t) _bandsphere of action increase, also can be described as to e (t) _bandcontrol action along with K _ereduce and constantly weaken; Same, work as K _ewhen >1, e (t) _bandeffective range be still [6/K _e,+6/K _e], but its scope is along with K _eincrease and constantly reduce, one dimension fuzzy controller is to e (t) _bandcontrol action along with K _eincrease constantly strengthen.Therefore,, in one dimension fuzzy controller, need to determine input scale factor K how to choose one dimension fuzzy controller _e.

4) calculate fuzzy controller output u (t) according to one dimension fuzzy rule.In emulation, find along with the K in model reference adaptive system _pthe increase of parameter, the error between estimation rotor speed pulsation band and actual rotor speed ripple band diminishes, e (t) simultaneously _bandalso diminish thereupon.Due to the K in model reference adaptive system _iparameter is less on the impact of total system, and therefore the present invention can not consider its impact herein, only analyzes and provide K _pthe fuzzy rule design (as shown in table 1) of parameter.

The fuzzy rule of table 1 one dimension fuzzy model reference adaptive system

Herein, Linguistic Value variable " NB " expression " negative large ", Linguistic Value variable " NM " expression " in negative ", Linguistic Value variable " NS " expression " negative little ", Linguistic Value variable " ZE " expression " zero ", Linguistic Value variable " PS " expression " just little ", Linguistic Value variable " PM " expression " center ", Linguistic Value variable " PB " expression " honest "; e ₀(t) be the initial value of e (t); Δ K _pfor K in PI parameter _pthe variable quantity of parameter.

5) the output u (t) of the one dimension fuzzy controller obtaining is passed through to the output-scale-factor K of one dimension fuzzy controller _uact on and be amplified to the working control amount in PI parameter.

As shown in Figure 5, the output-scale-factor K of one dimension fuzzy controller _uselection the control performance of whole control system is also had to larger impact.Utilize and describing method like above-mentioned input ratio factor type, analyze the output-scale-factor K of one dimension fuzzy controller _uimpact.When the output u of one dimension fuzzy controller (t) is without output-scale-factor K _ueffect or work as K _u=1 o'clock, the Δ K in the output u (t) of one dimension fuzzy controller and the actual PI parameter being added in model reference adaptive system _por Δ K _ibe worth identical; And work as K _uwhen <1, in the PI parameter being added in model reference adaptive system after the effect of the output u (t) of one dimension fuzzy controller is weakened, and K _uless, stack amount is less, and whole system need could arrive stable state through longer dynamic process; Work as K _uwhen >1, in the PI parameter being added in model reference adaptive system after the effect of the output u (t) of one dimension fuzzy controller is reinforced, and K _ularger, stack amount is larger, and along with K _uincrease, the improvement effect of the dynamic property to whole control system is larger, but works as K _uwhen excessive, easily there is PI parametric oscillation, then cause the unstable of whole control system.Therefore, need to determine the suitable output-scale-factor K of How to choose _u.

6) adopt the central value method of average output u (t) of one dimension fuzzy controller to be converted to the accurate output valve u of fuzzy control ₀, and be added in PI parameter (t).

As shown in Figure 6, the present invention is the PI parameter K of determining model reference adaptive system according to the size of current error variable e (t) _pand K _i, being then applied to actual model reference adaptive system, concrete computational process is as follows:

1) as shown in Figure 7, the error originated from input variable Δ e (t) of standard one dimension fuzzy controller is:

Δe(t)＝e(t)-e ₀(t) (2)

2) error originated from input variable Δ e (t) is converted to the input membership function of one dimension fuzzy controller.The input and output membership function of standard one dimension fuzzy controller has multiple distribution, the present invention adopts has equally distributed input membership function (as shown in Fig. 8 (a)) and output membership function (as shown in Fig. 8 (b)), universe of fuzzy sets is [6 ,+6].Δ e (t) is by the input scale factor K of one dimension fuzzy controller _eafter effect, universe of fuzzy sets is limited to [6 ,+6].

As shown in Figure 9, when the effective range of the error originated from input variable Δ e (t) of the standard one dimension fuzzy controller shown in Fig. 7 is between 0 to 2, when 0≤Δ e (t) <2, can obtain formula:

$u_{\mathrm{ZE}}=1-\frac{e\left(t\right)}{2},u_{\mathrm{PS}}=\frac{e\left(t\right)}{2}---\left(3\right)$

Formula (3) is described as to the form that fuzzy language value is expressed, obtains formula:

$\mathrm{\&Delta;e}\left(t\right)=e\left(t\right)\&DoubleRightArrow;\left\{\begin{array}{c}\mathrm{ZE}(1-\frac{e\left(t\right)}{2})\\ \mathrm{PS}\left(\frac{e\left(t\right)}{2}\right)\end{array}\right.---\left(4\right)$

Wherein, the value in bracket is its degree of membership.

3), according to the input and output membership function of the standard one dimension fuzzy controller shown in the one dimension fuzzy rule shown in table 1 and Fig. 8, just can calculate the output valve of one dimension fuzzy controller.In standard one dimension fuzzy controller, do not need to consider regular former piece and regular implication to get little operation, directly can obtain the degree of membership of consequent according to one dimension fuzzy rule, the value of this degree of membership equates with the degree of membership value of fuzzy input.Therefore,, according to the one dimension fuzzy rule of formula (4) and table 1, just can obtain the membership function u (t) of fuzzy output:

$u\left(t\right)=\left\{\begin{array}{c}\mathrm{ZE}(1-\frac{e\left(t\right)}{2})\\ \mathrm{PS}\left(\frac{e\left(t\right)}{2}\right)\end{array}\right.---\left(5\right)$

4) the output valve u (t) of the one dimension fuzzy controller obtaining is passed through to the output-scale-factor K of one dimension fuzzy controller _ueffect is amplified to the working control amount in PI parameter.

5) adopt the central value method of average one dimension fuzzy controller output valve u (t) to be converted to the accurate output valve u of fuzzy control ₀, and be added in PI parameter (t).

As shown in figure 10, in the present invention, because the output membership function adopting is symmetrical triangle, therefore can adopt the central value method of average to carry out reverse gelatinization computing, and then obtain the accurate output valve u of fuzzy control ₀(t):

$u_0\left(t\right)=\frac{(1-\frac{e\left(t\right)}{2})\&times;0+\frac{e\left(t\right)}{2}\&times;2}{1-\frac{e\left(t\right)}{2}+\frac{e\left(t\right)}{2}}=e\left(t\right)(0\&le;\mathrm{\&Delta;e}\left(t\right)<2)---\left(6\right)$

According to inference step above, the accurate output valve u of the input Δ e (t) that calculates one dimension fuzzy controller between remaining area time ₀(t), can obtain:

u ₀(t)＝e(t)(-6≤Δe(t)≤6) (7)

For the model reference adaptive system based on one dimension fuzzy control of verifying that the present invention proposes, can on Matlab/Simulink platform, carry out simulating, verifying to the present invention.The impact of the input and output scale factor of the one dimension fuzzy controller described in consideration Fig. 4 and Fig. 5, in emulation, the value of the input and output scale factor of one dimension fuzzy controller adopts representative value:

K _e＝0.12、K _u＝0.16 (8)

As shown in Figure 11, Figure 12, given emulation rotating speed is 1200r/min, adopts respectively constant torque output control and two kinds of modes of load torque approximate match output torque control to carry out emulation.The parameter of electric machine following (as shown in table 2) that emulation is used:

Table 2 simulation parameter

Experiment parameter	Value
		Stator resistance R	1.093Ω
D axle inductance L d	5.674mH
		Q axle inductance L q	16.138mH
Back emf coefficient K E	0.2979V/(rad/s)
		Number of pole-pairs p	2
DC bus-bar voltage V dc	300V
		Control frequency f	5kHz
Load torque T L	(T Lmax:3.6N·m,θ max＝200°)

Wherein, Figure 11 (a), 12 (a) are motor speed waveform, as can be seen from the figure in the time that simulation time is 0.6s, have cut the model reference adaptive system based on one dimension fuzzy controller proposed by the invention; Figure 11 (b), Figure 12 (b) are K in model reference adaptive system _pthe automatic adjustment waveform of parameter; Figure 11 (c), Figure 12 (c) are K _pactual rotor position angle before parameter is adjusted automatically and estimation rotor position angle waveform, now position angle error is respectively 7.0 ° and 5.5 °, and Figure 11 (d), Figure 12 (d) are K _pactual rotor position angle after parameter is adjusted automatically and estimation rotor position angle waveform, position angle error is now about respectively 2.8 ° and 2.2 °.Can find out from simulation result, in the time adopting constant torque output to control and two kinds of modes of torque control are exported in load torque approximate match, the present invention can make rotor-position angle error reduce respectively 4.2 ° and 3.3 °.

From above-mentioned simulation result, under two kinds of output torque control modes, adopt the model reference adaptive system based on one dimension fuzzy control proposed by the invention can realize control parameter and automatically adjust, and effectively reduce rotor-position angle error after adjusting.

The present invention can also carry out experimental verification to the model reference adaptive system based on one dimension fuzzy control, and the parameter of electric machine using when experimental verification is identical during with simulating, verifying.Load torque is by providing with the coaxial servomotor being connected of magneto, and breakdown torque is 3.6Nm, and mechanical location angle corresponding to breakdown torque is 200 °, and system control frequency is 5kHz.

The impact of the input and output scale factor of the one dimension fuzzy controller described in consideration Fig. 4 and Fig. 5, in experiment, adopts the representative value of one dimension fuzzy controller input and output scale factor:

K _e＝0.015、K _u＝0.0002 (9)

As shown in Figure 13, Figure 14, adopt respectively constant motor output torque control and two kinds of modes of load torque approximate match motor output torque control to test.Wherein, Figure 13 (a), 14 (a) are speed waveform; Figure 13 (b), 14 (b) are K _iparameter self-tuning waveform; Figure 13 (c), 14 (c) are K _iactual rotor position angle before parameter adjustment and estimation rotor position angle waveform; Figure 13 (d), 14 (d) are K _iactual rotor position angle after parameter adjustment and estimation rotor position angle waveform; Figure 13 (e), 14 (e) are K _irotor position angle error waveform before parameter adjustment, position angle error is now about respectively 15.8 ° and 4.0 °; Figure 13 (f), 14 (f) are K _irotor position angle error waveform after parameter adjustment, position angle error is now about respectively 10.5 ° and 3.2 °.Can find out from experimental result, in the time adopting constant motor output torque control and two kinds of modes of load torque approximate match motor output torque control, the present invention can make rotor-position angle error reduce respectively 5.3 ° and 0.8 °.

Above-mentioned experimental result shows: in permagnetic synchronous motor compressor assembly, adopt the model reference adaptive system based on one dimension fuzzy controller in implementation model reference adaptive system, to control parameter along with loading condition is carried out automatic adjusting, there is good dynamic and static state performance, and rotor-position angle error after parameter adjustment can effectively be reduced.

The various embodiments described above are only for the present invention is described, every equivalents of carrying out on the basis of technical solution of the present invention and improvement, all should not get rid of outside protection scope of the present invention.

Claims (5)

1. the model reference adaptive system parameters automatic setting method based on one dimension fuzzy control, it comprises the following steps:

1) according to the actual physics model of permagnetic synchronous motor, with reference to traditional model reference adaptive system, the model reference adaptive system of structure based on one dimension fuzzy control;

2) according to the realistic model of permagnetic synchronous motor and described step 1) structure reference model, calculate respectively d-axis and quadrature axis electric current under corresponding input, and calculate the current error variable e (t) between realistic model and reference model according to following error calculation formula:

$e\left(t\right)=i_d{\hat{i}}_q-i_q{\hat{i}}_d-\frac{{\mathrm{\&psi;}}_r}{L_d}(i_q-{\hat{i}}_q)$

Wherein, i _d, i _qfor d-axis and the quadrature axis electric current of real electrical machinery output, unit is A; for d-axis and the quadrature axis electric current of the output of model reference adaptive system, unit is A; ψ _rfor motor magnetic linkage, unit is Vs; L _dfor motor d-axis inductance, unit is H;

3) excursion of current error variable e (t) is defined as to current error variable pulsation band e (t) _band, and by the input scale factor K of one dimension fuzzy controller _eafter effect, by e (t) _bandsend in one dimension fuzzy controller;

4) calculate fuzzy controller output u (t) according to one dimension fuzzy rule, described one dimension fuzzy rule is as following table:

Wherein, Linguistic Value variable " NB " expression " negative large ", Linguistic Value variable " NM " expression " in negative ", Linguistic Value variable " NS " expression " negative little ", Linguistic Value variable " ZE " expression " zero ", Linguistic Value variable " PS " expression " just little ", Linguistic Value variable " PM " expression " center ", Linguistic Value variable " PB " expression " honest "; e ₀(t) be the initial value of e (t); Δ K _pfor K in PI parameter _pthe variable quantity of parameter;

5) the output u (t) of the one dimension fuzzy controller obtaining is passed through to the output-scale-factor K of one dimension fuzzy controller _uact on and be amplified to the working control amount in PI parameter;

6) adopt the central value method of average one dimension fuzzy controller output u (t) to be converted to the accurate output u of fuzzy control ₀, and be added in PI parameter (t).

2. the model reference adaptive system parameters automatic setting method based on one dimension fuzzy control as claimed in claim 1, is characterized in that: described step 3) in, by one dimension fuzzy controller input scale factor K _eeffect universe of fuzzy sets is limited between [6 ,+6].

3. the model reference adaptive system parameters automatic setting method based on one dimension fuzzy control as claimed in claim 1 or 2, is characterized in that: described step 3) in, the input scale factor K of one dimension fuzzy controller _eeffect refer to:

As e (t) _bandwithout input scale factor K _eeffect or work as K _e=1 o'clock, e (t) _bandeffective range identical with universe of fuzzy sets, be [6 ,+6], and the part exceeding can be imposed restrictions between [6 ,+6], now one dimension fuzzy controller to input variable the value sensitivity between [6 ,+6], to exceed part value seem insensitive;

Work as K _ewhen <1, e (t) _bandeffective range become [6/K _e,+6/K _e], and its scope is along with K _ereduce and constantly increase, one dimension fuzzy controller is to e (t) _bandsphere of action increase, also can be described as to e (t) _bandcontrol action along with K _ereduce and constantly weaken;

Work as K _ewhen >1, e (t) _bandeffective range be still [6/K _e,+6/K _e], but its scope is along with K _eincrease and constantly reduce, one dimension fuzzy controller is to e (t) _bandcontrol action along with K _eincrease constantly strengthen.

4. the model reference adaptive system parameters automatic setting method based on one dimension fuzzy control as claimed in claim 1 or 2, is characterized in that: described step 5) in, the output-scale-factor K of one dimension fuzzy controller _ueffect refer to:

When the output u of one dimension fuzzy controller (t) is without output-scale-factor K _ueffect or work as K _u=1 o'clock, the Δ K in one dimension fuzzy controller output u (t) and the actual PI parameter being added in model reference adaptive system _por Δ K _ibe worth identical;

Work as K _uwhen <1, in the PI parameter being added in model reference adaptive system after the effect of one dimension fuzzy controller output u (t) is weakened, and K _uless, stack amount is less, and whole system need could arrive stable state through longer dynamic process;

Work as K _uwhen >1, in the PI parameter being added in model reference adaptive system after the effect of one dimension fuzzy controller output u (t) is reinforced, and K _ularger, stack amount is larger, and along with K _uincrease, the improvement effect of the dynamic property to whole control system is larger, but works as K _uwhen excessive, easily there is PI parametric oscillation, then cause the unstable of whole control system.

5. the model reference adaptive system parameters automatic setting method based on one dimension fuzzy control as claimed in claim 3, is characterized in that: described step 5) in, one dimension fuzzy controller output-scale-factor K _ueffect refer to:

When the output u of one dimension fuzzy controller (t) is without output-scale-factor K _ueffect or work as K _u=1 o'clock, the Δ K in one dimension fuzzy controller output u (t) and the actual PI parameter being added in model reference adaptive system _por Δ K _ibe worth identical;

Work as K _uwhen <1, in the PI parameter being added in model reference adaptive system after the effect of one dimension fuzzy controller output u (t) is weakened, and K _uless, stack amount is less, and whole system need could arrive stable state through longer dynamic process;

Work as K _uwhen >1, in the PI parameter being added in model reference adaptive system after the effect of one dimension fuzzy controller output u (t) is reinforced, and K _ularger, stack amount is larger, and along with K _uincrease, the improvement effect of the dynamic property to whole control system is larger, but works as K _uwhen excessive, easily there is PI parametric oscillation, then cause the unstable of whole control system.