CN108566088A - Two close cycles RBF neural sliding moding structure self-adaptation control method - Google Patents
Two close cycles RBF neural sliding moding structure self-adaptation control method Download PDFInfo
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- CN108566088A CN108566088A CN201810330075.4A CN201810330075A CN108566088A CN 108566088 A CN108566088 A CN 108566088A CN 201810330075 A CN201810330075 A CN 201810330075A CN 108566088 A CN108566088 A CN 108566088A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M3/00—Conversion of dc power input into dc power output
- H02M3/02—Conversion of dc power input into dc power output without intermediate conversion into ac
- H02M3/04—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters
- H02M3/10—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
- H02M3/145—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
- H02M3/155—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only
- H02M3/156—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of output voltage or current, e.g. switching regulators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M1/00—Details of apparatus for conversion
- H02M1/0003—Details of control, feedback or regulation circuits
- H02M1/0006—Arrangements for supplying an adequate voltage to the control circuit of converters
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- Power Engineering (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses two close cycles RBF neural sliding moding structure self-adaptation control method, controller makes its output voltage stabilization to preset reference output voltage according to the desired output voltage of setting and acquired Real-time Feedback output voltage, Real-time Feedback output current output drive signal control Buck converters.Compared with prior art, the buffeting that Sliding mode variable structure control generates in the process in control is reduced using boundary layer sliding formwork control technology.Meanwhile more accurate description is carried out to the state equation of Buck converters during the work time due to the uncertain problem that system parameter variations and external unknown disturbances cause for Buck converters；And, for the uncertainty of system, the present invention increases self adaptive control on the basis of the Sliding Mode Controller of design, can be carried out to external environment adaptive while can utmostly reduce the various influences interfered to Buck converters in external environment and not lose robustness.
Description
Technical field
The invention belongs to DC-DC converter automation field more particularly to two close cycles RBF neural sliding moding structures
Self-adaptation control method.
Background technology
With the development of human society, people are constantly increasing the demand of electric energy, while to the quality requirements of electric energy
Also it constantly improves.So the processing and conversion to electric energy have become indispensable one in the human lives of social development
Part.Electric energy Power Processing and transformation using electric energy aspect to playing increasingly important role, to its processing and transformation
Method has become the hot spot of area research.
Switch converters can be divided into following several citation forms by electric energy processing unit according to power conversion type：AC/DC
(rectifying conversion), AC/AC (AC-AC converter), DC/AC (inverse transformation), DC-DC (DC converting).Wherein DC-DC converter
Research belong to the scope of electronic power, theoretical method is related to the multi-door subject such as electron electric power, control theory and engineering.
Important branch of the DC-DC converter as electron electric power technology, in eighties of last century the seventies just in Europe, the U.S., day
The ground such as this started research upsurge, and extensively using communicated with computer, office automatic, data and industrial instrument and
The fields such as aerospace military.So far from last century the seventies, the research of theory analysis and control method has been achieved for big
Progress, DC-DC converter is just at an unprecedented rate towards the side such as efficient, high frequency, light-duty, green, integrated at present
To development.
DC-DC converter realizes output as a kind of electric power converter by changing the ratio of switching tube turn-on time
The adjusting of voltage, and its power bracket can from very low (baby battery) to very high (high voltage power transmission).DC-DC converter
Mainly there are pulse frequency modulated (PFM) and pulse width modulation (PWM) two ways, is mainly converted herein with PWM types DC-DC
Device is that object is studied.
Currently, DC-DC converter largely uses LINEARIZED CONTROL and Sliding mode variable structure control (SMC) technology.It is common
Proportional-integral-differential (PID) controller be that linearisation small-signal model based on controlled cell carries out performance design, no
It is suitble to will produce the nonlinear system of larger signal disturbance.Also, when system is there are when uncertain factor, to ensure system
There are good output performance, PID controller parameter to need passively to be adjusted repeatedly.In addition, when load wide variation, it is special
When not being band nonlinear load, switch converters have the shortcomings that dynamic responding speed is slow, output waveform has distortion.Sliding moding structure
Control is a kind of Non-Linear Control Theory, has natural applicability to the non-linear speciality of Power Electronic Switching Converters, uses
The converter of Sliding mode variable structure control have many advantages, such as stability range is wide, dynamic response is fast, strong robustness, control realize it is simple.
However, Sliding mode variable structure control process is similar to a high frequency, uncertain switch control signal, so being passed through during control
Chattering phenomenon can often occur near sliding-mode surface.So it is Sliding mode variable structure control process that buffeting, which how is effectively reduced or eliminated,
In a problem frequently encountering.
Therefore in view of the drawbacks of the prior art, it is really necessary to propose a kind of technical solution to solve skill of the existing technology
Art problem.
Invention content
In view of this, the present invention proposes two close cycles RBF neural sliding moding structure self-adaptation control method, to solve
The ineffective problem of certainly existing Buck convertor controls realizes the good output performance of Buck converters.
In order to overcome technological deficiency of the existing technology, technical scheme is as follows：
Two close cycles RBF neural sliding moding structure self-adaptation control method, controller are electric according to the desired output of setting
Pressure and acquired Real-time Feedback output voltage, Real-time Feedback output current output drive signal control Buck converters keep its defeated
Go out voltage stabilization to preset reference output voltage, wherein following steps are executed in controller：
Step S1：It designs RBF neural sliding moding structure adaptive controller and realizes that Voltage loop control, the sliding formwork become knot
Structure adaptive controller is according to input parameter (reference output voltage urWith Real-time Feedback output voltage uo) obtain sliding moding structure certainly
The switching variable u of adaptive controller；Inductance (is referred to as one of electric current loop input parameter using the output valve of Voltage loop
Electric current ir), the calculation formula with reference to inductive current is as follows：
Wherein, uiFor real-time input voltage, L is inductor current value；
Step S2：Realize that current loop control, PID controller are obtained according to Voltage loop with reference to inductance electricity using PID controller
Flow irAnd the Real-time Feedback electric current i that sampling obtainsLOutput drive signal U controls Buck converters, the meter of drive signal
It is as follows to calculate formula：
Wherein, ei=ir-iLei=ir-iL, iLFor Real-time Feedback inductive current；kp, kp, kpRespectively ratio integrates and micro-
Divide control coefrficient.
As optimal technical scheme, the design of controller further comprises following steps：
Step 1：Establish Buck changer system state equations；
The state equation of Buck circuits is as follows：
In formula, R is load resistance；C is filtering (output) capacitance in parallel with load resistance；
Above formula is expressed as with matrix equation：
In formula, u is switch function, is defined as follows：
In formula, T is switch periods, and D is duty ratio, and in ccm mode,
Step 2：Sliding variable designs；
The approximant sliding moding structure adaptive controller of neural network is designed, first Sliding Mode Controller is set
Meter：
The error of system defines shown in an accepted way of doing sth (5)：
E=ur-uo (5)
In formula：urRepresent reference output voltage, uoRepresent Real-time Feedback output voltage definition status variables：
x1=uo (6)
To x1State variable x is obtained into derivation2：
System state equation can be obtained according to formula (6) and formula (7) arrangement：
In formula, f (t) is system interference, | f (t) |≤F, F ＞ 0；In order to simplify derivation, enable Then formula (8) is reduced to：
Sliding variable s is defined as：
In formula：λ ＞ 0；To being after s derivations：
U can be designed to following expression by observation type (11)：
In formula, η >=F；Formula (12) is brought into formula (11) and is arranged：
Expression formula then can be obtained：
Formula (4-37) meets the stable condition of Lyapunov, shows the reasonability of u designs；
Step 3：Neural network approaches item design；
Adaptable System chooses the mode approached of RBF neural, it passes through the reference value, output valve, controller of system
Last time control output quantity and interference calculation go out controller it is current approach itemFor reducing error and interference to system
The influence brought；
In RBF networks, input vector X=[x1,x2,...,xm]TIt indicates, and the mark that the output of network is input
Quasi-functionExpression formula is as follows：
In formula, n is the quantity of hidden layer neuron；Ci=[ci1,ci2,...,cim]TFor hidden neuron i center to
Amount；wiIt is the weight of the neuron i in linear convergent rate neuron.Radial basis function usually selects Gaussian function：
hi(||X-Ci| |)=exp (- βi||X-Ci||2) i=1,2 ..., n (16)
In formula：βiFor the width of hidden layer neuron i, and βi＞ 0；Gaussian bases are local actions for center vector
, i.e.,
Influence of the change of one neuron parameter for the network layer input value far from the neuron is very little；It is given
The certain condition of activation primitive, RBF networks can be used as RnThe general of compact subset approaches device；This means that one has enough
Any continuous function that the RBF networks of the hidden neuron of quantity can be closed with the bounded closed set that arbitrary accuracy approaches；
After being completed to the Sliding Mode Controller design on basis, the design that neural network approaches item is proceeded by, is used
In approaching due to systematic parameter (such as a1,a2,a3) the caused uncertain and system interference of variation；
If the input of network is：
It sets the output of network to：
Nm(X)=WTH(X)+ε (19)
In formula, H (X) is the radial base vector of network hidden layer；W is the connection weight of network；ε approaches mistake for network
Difference, and | ε | ＜ εN, εNFor the positive real number of very little；
H (X)=[h1 h2 h3 … hn]T (20)
Wherein, hi(i=1,2 ... expression n) is listed in formula (16)：
W=[w1 w2 w3 … wn]T (21)
It is if what network exported approaches item in real time：
In formula,Term coefficient is approached for W：
Observation type (12) enables
Nm(X)=a1x2+a2x1 (24)
Then according to formula (12) and formula (24), u can be write as：
Formula (25) indicates that the Sliding mode variable structure control design that RBF neural approaches item is added；
Step 4：Adaptive law designs；
Formula (25) is substituted into formula (11) to obtain：
Definition
In formula,ThenIt is represented by：
The design acquires the expression formula of sliding moding structure adaptive controller using Lyapunov direct method, original
The adaptive item of neural network is added in liapunov function：
In formula：α1＞ 0, α2＞ 0；
Derivation is carried out to V to obtain：
According to formula (30), choosing adaptive law is：
Step 5：System stability analysis；
For access control device and the reasonability of adaptive design, formula (31) is brought into formula (30)：
Due to α1＞ 0, so when choosing η >=εNWhen+F,Perseverance is set up, and meets the stable condition of Lyapunov at this time,
To enable systematic error to converge to zero；The main function of η sign (s) items in formula (32) can be understood as subtracting
Nervelet network approximate error and unknown disturbances are to influence caused by system；
Step 6：Voltage loop output design；
The expression of sliding moding structure adaptive controller can be obtained according to the calculation formula of the reference inductive current in step S1
Formula：
Sliding formwork control signal is discrete, sign function sign (s) is contained in formula (33), when sliding variable reaches sliding formwork
Shake is will produce when plane, in order to reduce discrete shake, with the sign function in saturation function sat (s) substituteds (33)
sign(s)：
In formula, the expression formula of saturation function sat (s) is：
In formula, constant δ ＞ 0, δ be diverter surface boundary layer the upper limit, using saturation function for boundary layer outside motor point
It is acted on to talk about switching, and the motor point of inside boundary is then linear change；By choosing suitable δ values, enable error
Zero is converged to, is buffeted to reduce；
Step 7：Design of current ring；
Electric current loop uses PID controller, the error of electric current loop to define shown in an accepted way of doing sth (36)：
ei=ir-iL (29)
In formula, iLFor actual inductive current.PID control formula is：
In formula, kp, kp, kpRespectively ratio, integral and derivative control coefficient；U is the output controlled quentity controlled variable of final system.
As optimal technical scheme, controller uses chip microcontroller.
Compared with prior art, the present invention has the following technical effect that：
(1) present invention is generated to reduce Sliding mode variable structure control in control in the process using boundary layer sliding formwork control technology
It buffets.
(2) be directed to Buck converters during the work time due to system parameter variations and external unknown disturbances cause not really
Qualitative question carries out more accurate description to the system mode of Buck converters, unknown bounded is added on the basis of original
Distracter, and adaptive controller is designed on this basis.
(3) it is directed to the uncertainty of system, the present invention increases RBF god on the basis of the Sliding Mode Controller of design
It is controlled through network self-adapting, using systematic error and its derivative as the input of RBF networks, the output of network is then approached as system
, and network weight is updated according to adaptive law, design a kind of novel sliding formwork change knot based on RBF neural
Structure adaptive control algorithm.Difference lies in can carry out external environment adaptive same designed algorithm with pid algorithm maximum
When can utmostly reduce various influences of the interference to Buck converters in external environment and not lose robustness.
(4) it is to solve the problems such as single closed loop controlling structure stability is not strong, voltage responsive overshoot is also bigger, the present invention
It uses using capacitance (output) voltage and inductive current and forms respective closed loop configuration as feedback quantity, to form double-closed-loop control
System.Wherein, outer shroud is electric to output using the designed sliding moding structure adaptive controller approached based on RBF neural
Pressure is adjusted, and inner ring is then adjusted inductive current using traditional PID controller.With the addition of current feedback amount,
It enables the system to carry out high-precision tracking, realizes the good dynamic and static characteristic of converter.
Description of the drawings
Fig. 1 is the whole functional block diagram of Buck control systems of the present invention.
Fig. 2 is the control structure block diagram of controller in the present invention.
Fig. 3 is the functional block diagram of sliding moding structure adaptive controller of the present invention.
Fig. 4 is the program flow diagram of controller in the present invention.
Fig. 5 is Buck reduction voltage circuit topological structures.
Equivalent circuit when Fig. 6 is switch conduction.
Equivalent circuit when Fig. 7 is switch OFF.
Fig. 8 is RBF neural network structure.
Fig. 9 (a)-(c) is respectively the voltage responsive of system under three kinds of control strategies, load disturbance and electric source disturbance emulation
Curve.
Figure 10 (a)-(c) is respectively that the voltage responsive of system under three kinds of control strategies, load disturbance and electric source disturbance are real
Survey curve.
Following specific embodiment will be further illustrated the present invention in conjunction with above-mentioned attached drawing.
Specific implementation mode
Technical solution provided by the invention is described further below with reference to attached drawing.
Nowadays, both direction is broadly divided into the research of DC-DC converter：First, a kind of new converter topology knot of research
Structure improves energy conversion efficiency；Second is that it is good that a kind of new control performance is optimized or designed to the control algolithm first having
Good, strong robustness control strategy realizes the superior output performance of system and improves system effectiveness and stability.Buck
The various conventional Control Methods of type DC-DC converter are analyzed and are compared, and find out suitable Buck converters system on this basis
The control strategy of system, and being combined with advanced control algolithm proposes a kind of superior performance, the control program of strong robustness, from
And improve the output performance of converter, also there is certain facilitation to the research of supply convertor nonlinear control algorithm.
The present invention designs a kind of sliding moding structure adaptive controller of combination neural network, uses boundary layer sliding formwork control
Technology reduces shake, weakens the influence of systematic uncertainty using RBF neural Adaptable System, by the control method of design
It is applied in Buck converters, obtains good control effect.
The present invention for Sliding mode variable structure control buffeting characteristic and converter during the work time due to systematic parameter
The uncertain problem that variation and external unknown disturbances cause carries out more accurate description, on original basis to state equation
On add the distracter of unknown bounded, and using Sliding mode variable structure control in such a way that neural network algorithm is combined to this not
Certainty item is adaptively approached.In Buck system models, using systematic error and its derivative as the input of RBF networks,
The output of network then approaches item as system, and is updated to network weight according to adaptive law, devises a kind of based on RBF
The sliding moding structure adaptive controller (RBF-SMAC) that neural network is approached.God is added on the basis of Sliding mode variable structure control
It is controlled through network self-adapting, realizes faster response, effectively reduce the steady-state error of system, and being capable of self-adapting load
Variation and reduce system interference influence.
Referring to Fig. 1, show the whole functional block diagram of Buck control systems of the present invention, the system include Buck converters,
Controller, power management module, drive module, AD sampling modules, keyboard input module and display module, wherein power supply pipe
It manages module and provides burning voltage for device system in order to control and Buck converters, drive module is used for the output electricity of controller
Pressure carries out driving enhancing to drive Buck converters；AD sampling modules are used to carry out the output voltage and electric current of Buck converters
It samples and Real-time Feedback output voltage, Real-time Feedback output current will be obtained and be sent to controller, to grasp converter in real time
Output state；This system uses double-closed-loop control structure, controller to handle the voltage and current sampled, obtains system
Voltage and current error respectively as system outer shroud and inner ring feedback quantity.Key-press module is in addition to can open system
The operations such as beginning, pause, reset, can be also used for the switching of control strategy.OLED display module is for showing the defeated of current system
Enter the information such as voltage, input voltage, output current, control algolithm type, duty ratio, facilitates observation and debugging.System protection mould
Block has the function of overcurrent protection, overheating protection, reverse connecting protection etc., implements safeguard measure immediately when system jam, prevents
System damage damage causes danger, the stability of maintenance system.
For DC-DC converter, a control structure that is rational, meeting the converter is selected to tend to improve converter
Stability, accuracy and conversion performance.The System control structures that prior art major part DC-DC converter uses are singly to close
Loop voltag controls, and this control structure design comparison is simply and readily realized, but the stability of system is not strong, voltage responsive overshoot
Amount is also bigger.Deficiency to solve single closed loop controlling structure show the control structure of controller in the present invention referring to Fig. 2
Block diagram, including sliding moding structure adaptive controller and PID controller, use using capacitance (output) voltage and inductive current as
Feedback quantity forms respective closed loop configuration to form double closed-loop control system.Wherein, outer shroud is voltage regulator, and inner ring is electricity
Throttle regulator, both adjusters can select identical or different controller according to actual needs.With current feedback amount
It is added so that system can carry out high-precision tracking, realize the good dynamic and static characteristic of converter.
The outer shroud voltage regulator to play a leading role selects the sliding moding structure adaptive controller based on RBF neural
RBF-SMAC, it can quickly track given reference voltage, have good control performance.The current regulator of inner ring selects
With traditional PID controller, the addition of electric current loop can be able to be not only that system realizes high-precision tracking, can also limit and be
The maximum current and output power of system, automatic protection converter and driving circuit, ensure that system is safely and steadily run.
Since this system uses double-closed-loop control structure, sliding moding structure adaptive controller (Voltage loop) output is as electricity
The reference inductive current of ring is flowed, output equation is：
In formula, irFor with reference to inductive current (i.e. the output controlled quentity controlled variable of sliding moding structure adaptive controller RBF-SMAC), ui
For real-time input voltage, u is the sliding moding structure adaptive controller according to input parameter (reference output voltage urWith it is anti-in real time
Feedthrough voltage uo) obtain the switching variable of sliding moding structure adaptive controller；L is inductor current value.
PID controller PID control formula is：
In formula, kp, kp, kpRespectively ratio, integral and derivative control coefficient；ei=ir-iL, iLFor inductance Real-time Feedback electricity
Stream；U is the output controlled quentity controlled variable of final system.
Referring to Fig. 3, it show the functional block diagram of sliding moding structure adaptive controller of the present invention, including sliding moding structure control
Device, controlled cell, Adaptable System and backfeed loop processed.Backfeed loop according to reference value and output valve computing system error,
Input of the obtained systematic error as Sliding Mode Controller.Adaptable System chooses the mode that RBF neural is approached,
It controls output quantity and interference calculation by the reference value of system, output valve, controller last time and goes out that controller is current to force
Nearly itemThe influence brought to system for reducing error and interference.Sliding Mode Controller is to systematic error and adaptively
It approaches item to be handled, obtained result is sent to controlled cell as controlled quentity controlled variable.System, can also be by other than receiving controlled quentity controlled variable
To the influence of system interference, system interference includes mainly uncertain and external environment caused by changing due to Internal system parameters
Change caused unknown disturbances.A part for system interference is sent to Adaptable System, and Adaptable System can be effectively
Reduce influence of the interference to system, system is made to remain at optimal or suboptimum state at runtime.
In a preferred embodiment, controller uses microcontroller.
Referring to Fig. 4, it show the program flow diagram of controller in the present invention, after system power supply, modules are carried out just
Beginningization is prepared for converter startup；When converter starts detection circuit whether overcurrent, overheat, then system if a failure occurs
It is out of service；After starting successfully, it is arranged by key-press module and it is expected output voltage, then real-time output voltage is sampled, is obtained
Input quantity as RBF-SMAC sliding moding structure adaptive controllers of result and setting value, adjust output voltage；Then again
Take PID control strategy that inductive current is adjusted.
Controller exports electricity according to the desired output voltage of setting and acquired Real-time Feedback output voltage, Real-time Feedback
Stream output drive signal control Buck converters make its output voltage stabilization to preset reference output voltage, wherein controller
Middle execution following steps：
Step S1：It designs the approximant sliding moding structure adaptive controller of RBF neural and realizes Voltage loop control, the cunning
Moding structure adaptive controller is according to input parameter (reference output voltage urWith Real-time Feedback voltage uo) obtain sliding moding structure
The switching variable u of adaptive controller；Using the output valve of Voltage loop as one of electric current loop input parameter (with reference to electricity
Inducing current ir), the calculation formula with reference to inductive current is as follows：
Wherein, uiFor real-time input voltage, L is inductor current value；
Step S2：Realize that current loop control, PID controller are obtained according to Voltage loop with reference to inductance electricity using PID controller
Flow irAnd the Real-time Feedback electric current output drive signal U that sampling obtains controls Buck converters, the meter of drive signal
It is as follows to calculate formula：
Wherein, e=ir-iL；iLFor Real-time Feedback inductive current；kp, kp, kpRespectively ratio, integral and differential control system
Number.
Further, the design of controller further comprises following steps：
Step 1：Establish Buck changer system models；
It is Buck reduction voltage circuits topological structure and its isoboles, wherein u such as Fig. 5iFor input voltage；uoFor output (electricity
Hold) voltage；L is energy storage inductor；iLFor inductive current；VT is power switch tube；DT is fly-wheel diode；C is filter capacitor；R
For load resistance.
Buck converters under electric current continuous operation mode (CCM) are studied, Buck circuits can be obtained according to analysis
State equation：
It is expressed as with matrix equation：
In formula, u is switch function, is defined as follows：
In formula, T is switch periods, and D is duty ratio, and in ccm mode
Step 2：Sliding variable designs；
The approximant sliding moding structure adaptive controller of neural network is designed, first Sliding Mode Controller is carried out
Design.The error of system defines shown in an accepted way of doing sth (5)：
E=ur-uo (5)
In formula：urRepresent desired output voltage, uoRepresent reality output (capacitance) voltage.Definition status variable：
x1=uo (6)
To x1State variable x is obtained into derivation2：
System state equation can be obtained according to formula (6) and formula (7) arrangement：
In formula, f (t) is system interference, | f (t) |≤F, F ＞ 0.In order to simplify derivation, enable Then formula (8) is reduced to：
Sliding variable s is defined as：
In formula：λ ＞ 0.To being after s derivations：
U can be designed to following expression by observation type (11)：
In formula, η >=F,
Formula (12) is brought into formula (11) and is arranged：
Expression formula then can be obtained：
Formula (14) meets the stable condition of Lyapunov, shows the reasonability of u designs.
Step 3：Neural network approaches item design；
Fig. 3 is the functional block diagram of sliding moding structure adaptive control algorithm, it includes Sliding Mode Controller, controlled list
Member, Adaptable System and backfeed loop.Backfeed loop according to reference value and output valve computing system error, miss by obtained system
Input of the difference as Sliding Mode Controller.Adaptable System chooses the mode that RBF neural is approached, it passes through system
Reference value, output valve, controller last time control output quantity and interference calculation go out controller it is current approach itemFor
It reduces error and interferes the influence brought to system.Sliding Mode Controller to systematic error and adaptive fidelity term at
Reason, obtained result are sent to controlled system as controlled quentity controlled variable.System also suffers from system interference other than receiving controlled quentity controlled variable
It influences, system interference, which includes mainly uncertain changes with external environment caused by changing due to Internal system parameters, to be caused
Unknown disturbances.A part for system interference is sent to Adaptable System, and Adaptable System can effectively reduce interference to being
The influence of system makes system remain at optimal or suboptimum state at runtime.
In RBF networks, input vector X=[x1,x2,...,xm]TIt indicates, and the mark that the output of network is input
Quasi-functionExpression formula is as follows：
In formula, n is the quantity of hidden layer neuron；Ci=[ci1,ci2,...,cim]TFor hidden neuron i center to
Amount；wiIt is the weight of the neuron i in linear convergent rate neuron.Radial basis function usually selects Gaussian function
hi(||X-Ci| |)=exp (- βi||X-Ci||2) i=1,2 ..., n (16)
In formula：βiFor the width of hidden layer neuron i, and βi＞ 0.Gaussian bases are local actions for center vector
, i.e.,
Influence of the change of one neuron parameter for the network layer input value far from the neuron is very little.It is given
The certain condition of activation primitive, RBF networks can be used as RnThe general of compact subset approaches device.This means that one has enough
Any continuous function that the RBF networks of the hidden neuron of quantity can be closed with the bounded closed set that arbitrary accuracy approaches.
After being completed to the Sliding Mode Controller design on basis, the design that neural network approaches item is proceeded by, is used
In approaching due to systematic parameter (such as a1,a2,a3) the caused uncertain and system interference of variation.
If the input of network is：
It sets the output of network to：
Nm(X)=WTH(X)+ε (19)
In formula, H (X) is the radial base vector of network hidden layer；W is the connection weight of network；ε approaches mistake for network
Difference, and | ε | ＜ εN, εNFor the positive real number of very little.
H (X)=[h1 h2 h3 … hn]T (20)
Wherein, hi(i=1,2 ... expression n) is listed in formula (16).
W=[w1 w2 w3 … wn]T (21)
It is if what network exported approaches item in real time：
In formula,Term coefficient is approached for W：
Observation type (12) enables
Nm(X)=a1x2+a2x1 (24)
Then according to formula (12) and formula (24), u can be write as：
Formula (25) indicates that the Sliding mode variable structure control design that RBF neural approaches item is added.
Step 4：Adaptive law designs；
Formula (25) is substituted into formula (11) to obtain：
Definition
In formula,ThenIt is represented by：
The design acquires the expression formula of sliding moding structure adaptive controller using Lyapunov direct method, original
The adaptive item of neural network is added in liapunov function：
In formula：α1＞ 0, α2＞ 0.
Derivation is carried out to V to obtain：
According to formula (30), choosing adaptive law is：
Step 5：System stability analysis；
For access control device and the reasonability of adaptive design, formula (31) is brought into formula (30)：
Due to α1＞ 0, so when choosing η >=εNWhen+F,Perseverance is set up, and meets the stable condition of Lyapunov at this time,
To enable systematic error to converge to zero.Theoretical foundation is provided for subsequent system emulation.η sign in formula (32)
(s) main function of item can be understood as reducing neural network approximate error and unknown disturbances to influence caused by system.
Step 6：Voltage ring design；
For DC-DC converter, a control structure that is rational, meeting the converter is selected to tend to improve converter
Stability, accuracy and conversion performance.The System control structures that most of DC-DC converter uses are single closed loop voltage control
System, this control structure design comparison is simply and readily realized, but the stability of system is not strong, and voltage responsive overshoot also compares
Greatly.To solve the deficiency of single closed loop controlling structure, the present invention is used using capacitance (output) voltage and inductive current as feedback quantity
Respective closed loop configuration is formed to form double closed-loop control system.Wherein, outer shroud is voltage regulator, and inner ring is current regulation
Device, both adjusters can select identical or different controller according to actual needs.With the addition of current feedback amount, make
The system of obtaining can carry out high-precision tracking, realize the good dynamic and static characteristic of converter.
The control block diagram of system is based on RBF neural as shown in Fig. 2, the outer shroud voltage regulator to play a leading role is selected
The sliding moding structure adaptive controller RBF-SMAC approached, it can quickly track given reference voltage, have good
Control performance.The current regulator of inner ring selects traditional PID controller, and the addition of electric current loop can be able to be not only system
It realizes high-precision tracking, the maximum current and output power of system, automatic protection converter and driving electricity can also be limited
Road ensures that system is safely and steadily run.
Since this system uses double-closed-loop control structure, the reference inductive current of Voltage loop exported as electric current loop, root
It can be obtained according to formula (1)：
In formula, irFor with reference to inductive current (i.e. the output controlled quentity controlled variable of sliding moding structure adaptive controller RBF-SMAC), ui
For real-time input voltage.Formula (33) both sides are integrated and abbreviation can obtain the expression formula of sliding moding structure adaptive controller：
In formula
Sliding formwork control signal is discrete, sign function sign (s) is contained in formula (35), when sliding variable reaches sliding formwork
It will produce shake when plane, in order to reduce discrete shake, we use the symbol in saturation function sat (s) substituteds (34)
Function sign (s)：
In formula, the expression formula of saturation function sat (s) is：
In formula, constant δ ＞ 0, δ be diverter surface boundary layer the upper limit, using saturation function for boundary layer outside motor point
It is acted on to talk about switching, and the motor point of inside boundary is then linear change.By choosing suitable δ values, enable error
Zero is converged to, is buffeted to reduce.Formula (35) be the sliding moding structure adaptive controller that is optimized using RBF neural most
Whole expression formula.
Step 7：Design of current ring；
Next it is then the design to electric current loop after the sliding moding structure adaptive controller design of Voltage loop is completed,
Current regulator selects PID controller.It is assumed that the error of electric current loop defines shown in an accepted way of doing sth (37)：
ei=ir-iL (37)
In formula, iLFor inductance Real-time Feedback electric current.PID control formula is：
In formula, kp, kp, kpRespectively ratio, integral and derivative control coefficient；U is the output controlled quentity controlled variable of final system.
Emulation experiment：
In order to verify design sliding moding structure adaptive controller effect, in MATLAB establish model and imitated
Very, certainly to the sliding moding structure of PID controller, traditional Sliding Mode Controller (CSMC) and RBF neural optimization
Adaptive controller (RBF-SMAC) compares and analyzes.
First to the present invention relates to some Buck transducer parameters be briefly described, shown in table 2 specific as follows.
2 Buck transducer parameters of table
Referring to Fig. 9, it show PID, CSMC, RBF-SMAC simulation curve, wherein (a), (b), (c) difference corresponding voltage
The case where response, load disturbance, electric source disturbance, specific test data is shown in the following table 3.
3 PID, CSMC, RBF-SMAC simulation performance of table compares
Actual measurement experiment：
The program write is downloaded in system controller with emulator；By slide rheostat change its resistance value into
Row system load disturbance experiments；The data that system generates can be sent to host computer using serial ports convenient for observation in real time.By
Tracking curve of output under host computer is once only able to display a kind of control strategy, then by three kinds of control strategies in host computer
Experimental data under (PID, CSMC, RBF-SMAC) is acquired, and is handled these three experimental datas using Matlab, so
After be shown on same figure, make in this way measured result convenient for observation and compare.
The relevant parameter for adjusting PID, CSMC, RBF-SMAC, makes the control performance of three kinds of controllers be optimal, and carries out real
Survey contrast experiment.Figure 10 (a)-(c) is respectively voltage responsive, load disturbance and the electric source disturbance of system under three kinds of control strategies
Measured curve.Specific measured data is as shown in table 4 below.
4 PID, CSMC, RBF-SMAC measured performance of table compares
Measured data can slightly increase than emulation data, but survey substantially uniform with the comparison result of emulation.Starting
The response time ratio CSMC of response phase, RBF-SMAC lacks about 3ms, about 24ms fewer than PID, and voltage overshoot only has 2.88%；
When the load disturbance stage, the regulating time ratio CSMC of RBF-SMAC has lacked about 7ms, fewer than PID 19ms, and RBF-SMAC is controlled
Voltage disturbance amount under system only has 1.05%, is better than CSMC and PID, these, which all embody FASMAC, has very strong anti-interference energy
Power；In the electric source disturbance stage, voltage disturbance momentum caused by RBF-SMAC is minimum, and regulating time ratio CSMC lacks about 13ms, than
PID lacks about 22ms, and the strong robustness that RBF-SMAC is showed when further illustrating in the presence of interference is in CSMC and PID；When being
System reaches stable state, RBF-SMAC can adaptive approximation system indeterminate variation to reduce error.Pass through above actual measurement
Experimental verification RBF-SMAC control performances designed under identical conditions are better than PID and CSMC.
It is noted that the control performance in emulation is more preferable than the result in actual measurement.The reason is as follows that：1) in emulation experiment, institute
Element be all ideal, such as inductance and capacitance, i.e., will not change in system operation；However, element in practice
Actual value be devious with ideal value, this is an important factor for influencing experimental precision；2) in hardware design, reality output
Voltage obtains in such a way that electric resistance partial pressure is sampled by Chip Microcomputer A/D again, and there are errors in the process for this, this also affects control
Precision；3) it is worth noting that, the period of host computer acquisition experimental data is 5ms, this is that the control performance of system is caused to reduce
Other factors.Therefore, adjustment appropriate is carried out using the element of high-quality and to the Hardware Design, this is to further changing
Kind tracking performance plays very important effect.In addition, being needed in systems using the advanced microcontroller with high-speed sampling rate
Device and host computer, to obtain more accurately data.
The explanation of above example is only intended to facilitate the understanding of the method and its core concept of the invention.It should be pointed out that pair
For those skilled in the art, without departing from the principle of the present invention, the present invention can also be carried out
Some improvements and modifications, these improvement and modification are also fallen within the protection scope of the claims of the present invention.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest range caused.
Claims (3)
1. two close cycles RBF neural sliding moding structure self-adaptation control method, which is characterized in that controller is according to phase of setting
Hope output voltage and acquired Real-time Feedback output voltage, the control Buck transformation of Real-time Feedback output current output drive signal
Device makes its output voltage stabilization to preset reference output voltage, wherein following steps are executed in controller：
Step S1：It designs RBF neural sliding moding structure adaptive controller and realizes Voltage loop control, the sliding moding structure is certainly
Adaptive controller is according to input parameter (reference output voltage urWith Real-time Feedback output voltage uo) to obtain sliding moding structure adaptive
The switching variable u of controller；Inductive current (is referred to as one of electric current loop input parameter using the output valve of Voltage loop
ir), the calculation formula with reference to inductive current is as follows：
Wherein, uiFor real-time input voltage, L is inductor current value；
Step S2：Realize that current loop control, PID controller obtain referring to inductive current i according to Voltage loop using PID controllerrWith
And the Real-time Feedback electric current i that sampling obtainsLOutput drive signal U controls Buck converters, and the calculating of drive signal is public
Formula is as follows：
Wherein, ei=ir-iLei=ir-iL, iLFor Real-time Feedback inductive current；kp, kp, kpRespectively ratio, integral and differential control
Coefficient processed.
2. two close cycles RBF neural sliding moding structure self-adaptation control method according to claim 1, feature exist
In,
The design of controller further comprises following steps：
Step 1：Establish Buck changer system state equations；
The state equation of Buck circuits is as follows：
In formula, R is load resistance；C is filtering (output) capacitance in parallel with load resistance；
Above formula is expressed as with matrix equation：
In formula, u is switch function, is defined as follows：
In formula, T is switch periods, and D is duty ratio, and in ccm mode,
Step 2：Sliding variable designs；
The approximant sliding moding structure adaptive controller of neural network is designed, first Sliding Mode Controller is designed：
The error of system defines shown in an accepted way of doing sth (5)：
E=ur-uo (5)
In formula：urRepresent reference output voltage, uoRepresent Real-time Feedback output voltage definition status variables：
x1=uo (6)
To x1State variable x is obtained into derivation2：
System state equation can be obtained according to formula (6) and formula (7) arrangement：
In formula, f (t) is system interference, | f (t) |≤F, F ＞ 0；In order to simplify derivation, enable Then formula (8) is reduced to：
Sliding variable s is defined as：
In formula：λ ＞ 0；To being after s derivations：
U can be designed to following expression by observation type (11)：
In formula, η >=F；Formula (12) is brought into formula (11) and is arranged：
Expression formula then can be obtained：
Formula (4-37) meets the stable condition of Lyapunov, shows the reasonability of u designs；
Step 3：Neural network approaches item design；
Adaptable System chooses the mode that RBF neural is approached, it passes through in the reference value, output valve, controller of system one
Secondary control output quantity and interference calculation go out controller it is current approach itemIt is brought to system for reducing error and interference
Influence；
In RBF networks, input vector X=[x1,x2,...,xm]TIt indicates, and the canonical function that the output of network is inputExpression formula is as follows：
In formula, n is the quantity of hidden layer neuron；Ci=[ci1,ci2,...,cim]TFor the center vector of hidden neuron i；wi
It is the weight of the neuron i in linear convergent rate neuron.Radial basis function usually selects Gaussian function：
hi(||X-Ci| |)=exp (- βi||X-Ci||2) i=1,2 ..., n (16)
In formula：βiFor the width of hidden layer neuron i, and βi＞ 0；Gaussian bases are local actions for center vector, i.e.,
Influence of the change of one neuron parameter for the network layer input value far from the neuron is very little；Given activation
The certain condition of function, RBF networks can be used as RnThe general of compact subset approaches device；This means that one has sufficient amount
Hidden neuron any continuous function that can be closed with the bounded closed set that arbitrary accuracy approaches of RBF networks；
After being completed to the Sliding Mode Controller design on basis, the design that neural network approaches item is proceeded by, for forcing
Closely due to systematic parameter (such as a1,a2,a3) the caused uncertain and system interference of variation；
If the input of network is：
It sets the output of network to：
Nm(X)=WTH(X)+ε (19)
In formula, H (X) is the radial base vector of network hidden layer；W is the connection weight of network；ε is the approximate error of network, and |
ε | ＜ εN, εNFor the positive real number of very little；
H (X)=[h1 h2 h3 … hn]T (20)
Wherein, hi(i=1,2 ... expression n) is listed in formula (16)：
W=[w1 w2 w3 … wn]T (21)
It is if what network exported approaches item in real time：
In formula,Term coefficient is approached for W：
Observation type (12) enables
Nm(X)=a1x2+a2x1 (24)
Then according to formula (12) and formula (24), u can be write as：
Formula (25) indicates that the Sliding mode variable structure control design that RBF neural approaches item is added；
Step 4：Adaptive law designs；
Formula (25) is substituted into formula (11) to obtain：
Definition
In formula,ThenIt is represented by：
The design acquires the expression formula of sliding moding structure adaptive controller using Lyapunov direct method, in original Li Ya
The adaptive item of neural network is added in Pu Nuofu functions：
In formula：α1＞ 0, α2＞ 0；
Derivation is carried out to V to obtain：
According to formula (30), choosing adaptive law is：
Step 5：System stability analysis；
For access control device and the reasonability of adaptive design, formula (31) is brought into formula (30)：
Due to α1＞ 0, so when choosing η >=εNWhen+F,Perseverance is set up, and meets the stable condition of Lyapunov at this time, to make
Systematic error can converge to zero；The main function of η sign (s) items in formula (32) can be understood as reducing nerve
Network approximate error and unknown disturbances are to influence caused by system；
Step 6：Voltage loop output design；
The expression formula of sliding moding structure adaptive controller can be obtained according to the calculation formula of the reference inductive current in step S1：
Sliding formwork control signal is discrete, sign function sign (s) is contained in formula (33), when sliding variable reaches slipform design
When will produce shake, in order to reduce discrete shake, with the sign function sign in saturation function sat (s) substituteds (33)
(s)：
In formula, the expression formula of saturation function sat (s) is：
In formula, constant δ ＞ 0, δ are the upper limit in diverter surface boundary layer, using saturation function for the motor point outside boundary layer
Play switching, and the motor point of inside boundary is then linear change；By choosing suitable δ values, error is enable to restrain
To zero, buffeted to reduce；
Step 7：Design of current ring；
Electric current loop uses PID controller, the error of electric current loop to define shown in an accepted way of doing sth (36)：
ei=ir-iL (29)
In formula, iLFor actual inductive current.PID control formula is：
In formula, kp, kp, kpRespectively ratio, integral and derivative control coefficient；U is the output controlled quentity controlled variable of final system.
3. two close cycles RBF neural sliding moding structure self-adaptation control method according to claim 1 or 2, feature
It is, controller uses chip microcontroller.
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Application publication date: 20180921 Assignee: HANGZHOU KONXIN SOC Co.,Ltd. Assignor: HANGZHOU DIANZI University Contract record no.: X2021330000825 Denomination of invention: Sliding mode variable structure adaptive control method based on double closed-loop RBF neural network Granted publication date: 20190927 License type: Common License Record date: 20211220 |