CN106300977A - A kind of single input fuzzy PID control method of Buck type DC DC changer - Google Patents

Abstract

The single input fuzzy PID control method of a kind of Buck type DC DC changer, initially sets up system transter before compensation；Then use frequency domain method that PID controller carries out parameter tuning, and design the membership function of fuzzy logic input/output variable；Secondly, by analytic transformation device system response curve and the mapping in fuzzy reasoning table thereof, set up and there is quickly response and the two-dimentional Toeplitz Fuzzy rule list of low overshoot characteristic, obtain dual input fuzzy controller；Finally, symbolization distance method carries out dimensionality reduction to dual input fuzzy controller, obtain single input fuzzy controller to reduce the hardware resource occupation rate of algorithm, use genetic algorithm to carry out parameter optimization, it is ensured that the single input fuzzy controller that dimensionality reduction obtains has the control performance of equal value with dual input fuzzy controller simultaneously.

Description

A kind of single input fuzzy PID control method of Buck type DC-DC converter

Technical field

The present invention relates to Buck type DC-DC converter, the single input of a kind of Buck type DC-DC converter obscures PID control method, belongs to switch power technology field.

Background technology

DC-DC switch converters has higher conversion efficiency relative to linear stabilized power supply, thus at portable electronic Equipment is widely used.Along with semiconductor technology and the lifting to product performance demands, large scale integrated circuit bag Containing several functions and mode of operation, for ensureing that it runs safely and reliably, the dynamic property of changer is particularly important.Use The dynamic property of effective control strategy Lifting Transform device becomes the study hotspot of field of power electronics, wherein digital control approach Being easier to realize advanced, complicated and intelligent control strategy makes changer obtain excellent dynamic property, and allows designer Algorithm is modified and without changing peripheral circuit, there is the strongest motility online.

Conventional fuzzy logic controller needs to detect DC-DC converter output voltage relative to the deviation of expected value and partially The rate of change of difference, will be both as input variable, and the fuzzy inference rule according to designing in advance calculates at current variator The knots modification of pid parameter under working condition, thus PID controller is carried out Automatic adjusument, make the dynamic property phase of changer For using conventional linear control strategy to be significantly improved.In the engineer applied of fuzzy controller, generally each is inputted Variable is respectively divided into 7 fuzzy subsets, therefore for conventional dual input fuzzy controller, it is desirable to have 49 fuzzy rules come Process all of input combination.The inductive decision of huge fuzzy rule base and complexity is conciliate fuzzy operation and is needed to take in a large number Hardware resource, and DC-DC converter require controller complete in a switch periods complexity calculating process, this is at high frequency Field of switch power proposes challenge to the operational capability of processor.

Conventional dual input fuzzy logic controller needs two input signals, therefore fuzzy rule number and input variable Fuzzy subset's number become quadratic relationship, although the quantity of fuzzy rule can be reduced by reducing the number of fuzzy subset, but Control performance can be reduced simultaneously.This just requires that the fuzzy rule to algorithm and reasoning process simplify, and is not reducing dual input Its hardware resource occupation rate and computational load is effectively reduced on the premise of fuzzy PID algorithm control performance.

Summary of the invention

It is an object of the invention to provide the single input fuzzy PID control method of a kind of Buck type DC-DC converter, with less Hardware resource occupation rate make changer obtain excellent dynamic property.

The technical scheme that the present invention takes is as follows: the single input fuzzy-adaptation PID control side of a kind of Buck type DC-DC converter Method, based on including the analog-digital converter ADC being sequentially connected with, digitial controller, digital pulse-width modulator DPWM and driving module The main topology closed loop in series of the control system that Driver is constituted, this control system and Buck type DC/DC changer, its feature It is: digitial controller uses single input fuzzy controller, uses one-dimensional single input Fuzzy PID to substitute two dimension Dual input Fuzzy PID, controls including obfuscation module, dimensionality reduction module, single input fuzzy logic controller module and PID Molding block；Warp after subtracting each other with reference voltage Vref after the main topology output voltage in analog-digital converter ADC sampling different cycles moment Digitized deviation signal e [n] and digitized change of error amount signal ec [n] is produced, then at obfuscation mould after delay, adder Digitized deviation signal e [n] and digitized change of error amount signal ec [n] are carried out Fuzzy processing, Fuzzy processing by block After result carry out dimension-reduction treatment by dimensionality reduction module, by two dimension dual input input variable be converted into one-dimensional single input variable d_s, afterwards, after one-dimensional single input variable being carried out fuzzy reasoning and ambiguity solution by single input fuzzy logic controller module Obtain K_p、K_iAnd K_dThree parameter values export to pid control module, and pid control module is according to K_p、K_iAnd K_dThree parameters calculate Deliver to digital pulsewidth modulation module to corresponding duty cycle command signal, then output duty cycle is variable after driving module Square-wave signal, is used for controlling the turn-on and turn-off time of two complementary MOS power tube M1, M2 in the main topology of changer, thus The output voltage values of regulation DC/DC changer；The most again the output voltage of main topology is carried out ADC sample conversion, repeat on The process of stating is circulated and controls until system stability, is allowed to level off to expectation reference voltage level.

Above-mentioned single input fuzzy PID control method comprises the following steps:

(1) output voltage uo (t) of changer obtains analog output voltage Vo [t] and by its turn through analog-digital converter ADC It is changed to digital voltage signal Vo [n], after being subtracted each other with reference voltage Vref by digital voltage signal Vo [n] after delay, adder Obtain digitized deviation signal i.e. voltage error value e [n] of current sample period and give record, simultaneously by error current record Subtract each other with last error log and obtain digitized change of error amount signal i.e. error deviation value ec [n], by e [n] and ec [n] two Individual signal exports the obfuscation module to digitial controller, in obfuscation module, e [n] and ec [n] is carried out obfuscation, mould The digitized deviation signal of gelatinizing and digitized change of error amount signal export to dimensionality reduction module；

(2) two input variables (e, ec) obtained in (1) are carried out at dimensionality reduction by dimensionality reduction module symbolization distance method Reason, with single input variable d_sReplace, wherein single input variable d_sRepresent any diagonal in two dimension fuzzy rule list right to master Linea angulata L_ZODistance, calculate the optimal solution of parameter lambda by genetic algorithm, thus calculate to obtain d_sValue；Comprise the following steps:

1) two dimension fuzzy leading diagonal L is set_ZOLinear equation be ec+ λ e=0, wherein λ represents the slope of leading diagonal, then Any bar diagonal to the distance of leading diagonal is:

2) MATLAB is used to realize the parameter lambda optimizing of genetic algorithm

A) solution space and the code encoding/decoding mode of parameter lambda are determined

Changer response curve based on dual input fuzzy and single input Fuzzy PID deviates from each other more Seriously, the valued space of parameter lambda is defined as [0,16], uses the binary coding string of a length of 10 to represent parameter lambda, is designated as λ_Bi, wherein Gao Siwei represents integer part, and low six represent fractional part, then between 0000000000 to 1111111111 Discrete binary coded strings is corresponding in turn to the decimal value between 0 to 15.984375, constitutes chromosome coding mode, Need the binary coding string λ of 10 bit lengths during decoding_BiBe converted to the decimal number λ in the solution space of correspondence_i, compile according to individuality The definition of code mode and solution space obtains decoding formula:

${\mathrm{\λ}}_i=\underset{j=1}{\overset{10}{\Σ}}{\mathrm{\λ}}_{Bi}\left(j\right)\×2^{4-j}---\left(2\right)$

B) individual evaluation fitness function is determined

The selection principle of parameter lambda is to make DC-DC based on single input fuzzy and dual input Fuzzy PID become Parallel operation has the performance of equivalence, and i.e. two system response curves should overlap as far as possible, by under synchronization two kinds of algorithm controls Changer output voltage compares and obtains deviation signal v_d, choose deviation signal v_dThe maximum M of absolute value_dAnd time deviation Integrated value ITAE of absolute value is constituted as index evaluates individual good and bad fitness function F (λ), and in formula (5), parameter is chosen for ω₁ =1, ω₂=1000, the optimizing of parameter lambda is the minima seeking object function J (λ)；

M_d=max | v_d(t)| (3)

ITAE=∫ t | v_d(t)|dt (4)

$F\left(\mathrm{\λ}\right)=\frac{1}{{\mathrm{\ω}}_1{M}_d+{\mathrm{\ω}}_{2}ITAE}---\left(5\right)$

$J\left(\mathrm{\λ}\right)=\frac{1}{F\left(\mathrm{\λ}\right)}---\left(6\right)$

C) genetic operator is set

Use single-point crossover operator as crossing operation mode, the chromosome of parents is blocked exchanging point and exchanges tail Portion, crossover probability P_cDetermining new individual generation speed in population of future generation, be taken as 0.4～0.99, mutation operator determines population Multiformity during evolution, thus affect the quality of solution, in order to obtain higher excellent of quality in the biggest space Dissolve, and don't make algorithm the convergence speed the slowest, mutation probability P_mIt is taken as 0.0001～0.1；

In order to ensure that the individuality of initial population is uniformly distributed in solution space, useRandom fashionGenerate each of chromosome Position gene, if initial population is excessive, can affect convergence of algorithm speed, otherwise, multiformity and the optimizing knot of individuality can be reduced The quality of fruit, selects initial population size M=20, terminates evolutionary generation G and determines the iteration terminal of optimized algorithm, when entering of population Change algebraically when reaching G, the optimum individual in current population is exported as optimizing solution, in order to take into account convergence of algorithm speed and excellent The quality dissolved, terminates algebraically and is taken as 100～500；

(3) by single input variable d_sIt is divided into 7 fuzzy subset: L_NB, L_NM, L_NS, L_ZO, L_PS, L_PM, L_PB, its membership function Flex point parameter keeps consistent with e, es.Thus two dimension Toeplitz Fuzzy rule list is reduced to one-dimensional vector, for entering one Step reduces the computational complexity of the single input fuzzy controller obtained, and can make single input fuzzy PID algorithm by choosing Chain of command is piecewise linear input variable e [n], the membership function of ec [n] and output variable Δ Kp, Δ Ki, being subordinate to of Δ Kd Function so that single input fuzzy PID algorithm has one-dimensional segmenting chain of command, this makes it possible to directly pass through MATLAB with three The piecewise linear function that middle fuzzy function generates replaces whole single input fuzzy controller；

(4) then pass through single input fuzzy logic controller module and carry out fuzzy reasoning, i.e. judge according to fuzzy reasoning table Fuzzy subset belonging to output variable Δ Kp, Δ Ki, Δ Kd, then carries out ambiguity solution, i.e. from Δ Kp, Δ by centre of area method Ki, Δ Kd domain in find out one can represent fuzzy decoupling effect may the exact value of distribution, respectively with the PID ginseng set Number initial value Kp0, Ki0, Kd0 are added and obtain real-time Kp, Ki, Kd, and output is to PID controller；In order to make single input fuzzy calculate The chain of command of method is piecewise linearity, fuzzy logic input variable e [n], the membership function of ec [n] and output variable Δ Kp, Δ Ki, The membership function of Δ Kd conciliates fuzzy operation must meet following condition:

1) input variable e [n], the membership function of ec [n] are necessary for triangular form, and the membership function of adjacent fuzzy subset must Must there is the overlap of at least 50%；

2) output variable Δ Kp, Δ Ki, the membership function of Δ Kd are necessary for stand alone；

(5) PID controller carries out PID arithmetic according to Kp, Ki, Kd, and output discrete duty cycles signal d [n] is to digital pulse width Manipulator DPWM, digital pulse-width modulator DPWM output continuous print duty cycle signals d [t], to driving module Driver, drive single Unit's Driver output duty cycle is the switch of pwm signal control MOS power tube M1, M2 of d, thus regulated output voltage；

(6) by the output voltage of (5) through analog-digital converter ADC sample conversion again, sequentially pass through e [n] and ec [n] and produce Circuit, digitial controller, digital pulse width modulation cells D PWM and driving module Driver, form new pwm signal and control power The switch of pipe M1, M2, loop control is until output voltage Vo [t] is consistent with digital reference voltage Vref.

Advantages of the present invention and beneficial effect: by fuzzy-adaptation PID control, reduce switch converters unintentional nonlinearity special The property impact on mapping, optimizes the mapping of digitalizer；Control accuracy is high, and robustness is good, inputs or loads Under disturbance, overshoot is little, fast response time；By single input fuzzy-adaptation PID control, reduce the hardware resource occupation rate of algorithm, same to luck Parameter optimization is carried out, it is ensured that the single input fuzzy controller that dimensionality reduction obtains has and dual input fuzzy control by genetic algorithm The control performance that device processed is of equal value.

Accompanying drawing explanation

Fig. 1 is Buck type DC-DC converter system architecture diagram based on single input Fuzzy PID；

Fig. 2 is single input fuzzy logic controller structured flowchart；

Fig. 3 is symbolic distance variable d_sDerivation schematic diagram；

Fig. 4 is genetic algorithm optimizing flow process；

Fig. 5 is object function convergence curve；

Fig. 6 is one-dimensional fuzzy rule vector table；

Fig. 7 is single input fuzzy PID algorithm piecewise linearity chain of command.

Detailed description of the invention

The present invention compensates front system transter by setting up；Then use frequency domain method that PID controller is carried out parameter Adjust, and design the membership function of fuzzy logic input/output variable；Secondly, by analytic transformation device system response curve and Mapping in fuzzy reasoning table, sets up and has quickly response and the two-dimentional Toeplitz Fuzzy rule list of low overshoot characteristic, Obtain dual input fuzzy controller；Finally, to reduce the hardware resource occupation rate of algorithm, use genetic algorithm to carry out simultaneously Parameter optimization, it is ensured that the single input fuzzy controller that dimensionality reduction obtains has the control of equal value with dual input fuzzy controller Performance.

Such as Fig. 1, the Buck type DC-DC converter of single input Fuzzy PID of the present invention, become including Buck DC-DC The main topological structure of parallel operation power stage, analog-digital converter ADC, digitial controller, digital pulse-width modulator (DPWM), driver element (Driver), wherein the main topological structure of inverter power level by NMOS tube M1, PMOS M2, inductance L, inductance equivalent resistance RL, Electric capacity C, capacitor equivalent series resistance R_ESRForm with output load resistance Ro, the drain electrode of NMOS tube M1 and input voltage u_inJust End connects, and source electrode and the substrate of NMOS tube M1 link together with the source electrode of PMOS M2 and one end of inductance L, and inductance L's is another One end links together with one end of inductance equivalent resistance RL, the other end of inductance equivalent resistance RL and capacitor equivalent series resistance R_ESROne end of one end, the input of ADC and output load resistance Ro link together, capacitor equivalent series resistance R_ESR's The other end links together with one end of electric capacity C, the other end of output load resistance Ro, the other end of electric capacity C and PMOS The drain electrode of M2 and input voltage u_inNegative terminal link together and ground connection；After the signal that ADC produces and reference voltage Vref are subtracted each other Produce signal e [n] after delayed, adder and ec [n], signal e [n] and ec [n] input connects to digitial controller, digital control Device processed is connected with digital pulse width modulation unit (DPWM), and DPWM is connected (Driver) with driver element；

Described DC-DC converter works in accordance with the following steps:

ADC unit sampling output voltage Vo [t], is converted to digital signal Vo [n] by analog voltage signal, by Vo [n] with Digital reference voltage signal Vref compares, and obtains voltage error value e [n] of current sample period and gives record, ought simultaneously Front error log is subtracted each other with last error log and is obtained error deviation value ec [n], and two signals of e [n] and ec [n] of generation are extremely In digitial controller obfuscation, then in obfuscation module, e [n] and ec [n] is carried out obfuscation, the number of obfuscation Word deviation signal and digitized change of error amount signal are input in dimensionality reduction module, as shown in Figure 2.

1) in dimensionality reduction module, symbolization distance method is by 1) in obtained by two of dual input fuzzy controller Input variable (e, ec) carries out dimension-reduction treatment, with single input variable d_sReplace.Wherein single input variable d_sRepresent two dimension fuzzy rule In table, any diagonal is to leading diagonal L_ZODistance.As it is shown on figure 3, set two dimension fuzzy leading diagonal L_ZOStraight line side Journey is ec+ λ e=0, and wherein λ represents the slope of leading diagonal, then any bar diagonal to the distance of leading diagonal is:

For traditional dual input analog logic controller, if the obfuscation grade of two input variables is P, then The quantity of fuzzy rule is P², and a single input fuzzy logic control of equal value has only to P rule, therefore single input obscures The topmost feature of logic controller is exactly that rule quantity reduces, thus reduces the hardware resource occupation rate of algorithm.

What genetic algorithm was the genetic mechanism in simulation nature and biological evolution theory and was formed a kind of searches parallel at random The method of rope optimal solution.The theory of biologic evolution that it will " be selected the superior and eliminated the inferior, survival of the fittest " introduces the coding group that parameter optimization is formed In body, by replicating, intersecting and make a variation and by selected fitness function, individuality screened, make the individuality quilt that adaptation value is high Remaining the colony that composition is new, new colony had both inherited the information of previous generation, had been better than again previous generation.So go round and begin again, Ideal adaptation degree in colony improves constantly, until meet certain evolution conditions, its algorithm is simple, can parallel processing, can obtain To globally optimal solution.The basic operation process of heredity optimizing algorithm is as follows:

A) replicate

Duplication is the individual process producing new population selecting vitality strong from Geju City population.According to individual adaptation Value carries out replicating operation, makes the individuality with high adaptation value more likely produce one or more descendants in the next generation, replicates Operation can be realized by random method computer program.

B) intersect

Replicate operation and can select outstanding person from old population, but new individuality can not be created.Intersect and simulate biological evolution During reproductive patterns, by two chromosomes exchange combination produce new improved seeds.Crossover process is: in coupling Optional two chromosomes in pond, randomly choose exchange point position, and exchange parental chromosomes exchanges the part on the right of point, the most available Two new Chromosome number word strings.

C) variation

If only replicating and intersecting, cannot scan in the space outside initial gene combines, make evolutionary process early Phase is just absorbed in local solution and terminates, thus affects the quality of solution.Mutation operator be used for simulating biological in natural genotypic environment by The gene mutation caused in various accidentalia, it changes genetic value at random with the least probability.Compile at binary system In code system, some gene of chromosome is become 0 from 1 by mutation operation randomly, or is become 1 from 0.

Calculate the optimal solution of parameter lambda by genetic algorithm, thus calculate to obtain the value of ds.The optimal value of λ is found by genetic algorithm, The single input fuzzy controller after simplification and dual input fuzzy controller is made to have the control performance of equivalence, its algorithm stream As shown in Figure 4, wherein h represents evolutionary generation to journey.

λ searching process based on genetic algorithm is realized by following steps design:

A) solution space and the code encoding/decoding mode of parameter lambda are determined

Through iterating, emulation finds, when λ value is more than 16, based on dual input fuzzy and single input fuzzy The changer response curve of control algolithm deviates from each other more serious, therefore the valued space of parameter lambda be defined as [0,16).Use The binary coding string of a length of 10 represents parameter lambda, is designated as λ_Bi, wherein Gao Siwei represents integer part, and low six expressions are little Fractional part.Then the discrete binary coded strings between 0000000000 to 1111111111 is corresponding in turn in 0 to 15.984375 Between decimal value, constitute chromosome coding mode.Need the binary coding string λ of 10 bit lengths during decoding_BiConversion For the decimal number λ in corresponding solution space_i, obtaining decoding formula according to the definition of individual UVR exposure mode and solution space is:

${\mathrm{\λ}}_i=\underset{j=1}{\overset{10}{\Σ}}{\mathrm{\λ}}_{Bi}\left(j\right)\×2^{4-j}---\left(2\right)$

B) individual evaluation fitness function is determined

The selection principle of parameter lambda is to make DC-DC based on single input fuzzy and dual input Fuzzy PID become Parallel operation has the performance of equivalence, and i.e. two system response curves should overlap as far as possible.By under synchronization two kinds of algorithm controls Changer output voltage compares and obtains deviation signal v_d, choose deviation signal v_dThe maximum M of absolute value_dAnd time deviation Integrated value ITAE of absolute value is constituted as index evaluates individual good and bad fitness function F (λ), and in formula (5), parameter is chosen for ω₁ =1, ω₂=1000, the optimizing of parameter lambda is the minima seeking object function J (λ).

M_d=max | v_d(t)| (3)

ITAE=∫ t | v_d(t)|dt (4)

$F\left(\mathrm{\λ}\right)=\frac{1}{{\mathrm{\ω}}_1{M}_d+{\mathrm{\ω}}_{2}ITAE}---\left(5\right)$

$J\left(\mathrm{\λ}\right)=\frac{1}{F\left(\mathrm{\λ}\right)}---\left(6\right)$

C) genetic operator is set

Use single-point crossover operator as crossing operation mode herein, at exchange point, the chromosome of parents is blocked and exchanges Afterbody.Crossover probability P_cDetermine new individual generation speed in population of future generation, be typically taken as 0.4～0.99, choose P herein_c= 0.90.Mutation operator determines population multiformity during evolution, thus affects the quality of solution, in order at the biggest sky Obtain the optimization solution that quality is higher between, and don't make algorithm the convergence speed the slowest, mutation probability P_mTypically it is taken as 0.0001 ～0.1, elect P herein as_m=0.001.

In order to ensure that the individuality of initial population is uniformly distributed in solution space, useRandom fashionGenerate each of chromosome Position gene.If initial population is excessive, convergence of algorithm speed can be affected, otherwise, multiformity and the optimizing knot of individuality can be reduced The quality of fruit, selects initial population size M=20 herein.Terminate evolutionary generation G and determine the iteration terminal of optimized algorithm, work as population Evolutionary generation when reaching G, the optimum individual in current population is exported as optimizing solution.In order to take into account convergence of algorithm speed The quality solved with optimization, terminates algebraically and is typically taken as 100～500, elect G=100 herein as.MATLAB is used to realize heredity optimizing Algorithm, its optimization process as it is shown in figure 5, optimum results shows that object function minima is 0.4217, now λ=8.9688.

By single input variable d_sIt is divided into 7 fuzzy subset: L_NB, L_NM, L_NS, L_ZO, L_PS, L_PM, L_PB, its membership function flex point Parameter keeps consistent with e, es.Thus two dimension Toeplitz Fuzzy rule list is reduced to one-dimensional vector, as shown in Figure 6. For reducing the computational complexity of the single input fuzzy controller obtained further, single input fuzzy can be made by choosing The chain of command of algorithm is piecewise linear input variable e [n], the membership function of ec [n] and output variable Δ Kp, Δ Ki, Δ Kd Membership function so that single input fuzzy PID algorithm has one-dimensional segmenting chain of command, thus directly can pass through with three The piecewise linear function that in MATLAB, fuzzy function generates replaces whole single input fuzzy controller；In order to make single input The chain of command of fuzzy PID algorithm is piecewise linearity, fuzzy logic input variable e [n], the membership function of ec [n] and output variable Δ Kp, Δ Ki, the membership function of Δ Kd are conciliate fuzzy operation and must be met following condition:

A) input variable e [n], the membership function of ec [n] are necessary for triangular form, and the membership function of adjacent fuzzy subset must Must there is the overlap of at least 50%；

B) output variable Δ Kp, Δ Ki, the membership function of Δ Kd are necessary for stand alone；

C) ambiguity solution operation uses centre of area method.

The membership function of the input/output variable fuzzy subset of present invention design meets conditions above, according to the centre of area Method carries out ambiguity solution operation, then design the single input fuzzy PID algorithm obtained and have piecewise linearity chain of command as shown in Figure 7, Now can replace control algolithm with three piecewise linear functions, reduce the design complexities of controller further.

3) then carry out fuzzy reasoning, i.e. judge the mould belonging to output variable Δ Kp, Δ Ki, Δ Kd according to fuzzy reasoning table Stick with paste subset；Then carry out ambiguity solution by centre of area method, i.e. from Δ Kp, Δ Ki, Δ Kd domain find out one and can represent Fuzzy decoupling effect may distribution exact value, respectively with set pid parameter initial value Kp0, Ki0, Kd0 be added obtain real-time Kp, Ki, Kd, output is to PID controller；

4) PID controller carries out PID arithmetic according to Kp, Ki, Kd, and output discrete duty cycles signal d [n] to digital pulse width is adjusted The input of unit processed, digital pulse width modulation unit output continuous print duty cycle signals d [t], to the input of driver element, drives Moving cell output duty cycle is that the pwm signal of d controls the switch of power tube M1, M2 thus regulated output voltage.

By 4) output voltage through ADC unit sample conversion again, sequentially pass through e [n] and ec [n] and produce circuit, numeral Controller, digital pulse width modulation cells D PWM and driving module Driver, form new pwm signal and control power tube switch, follow Ring controls until switch converters output voltage values is consistent with reference voltage level.

Claims (2)

1. a single input fuzzy PID control method for Buck type DC-DC converter, based on including the analog digital conversion that is sequentially connected with The control system that device ADC, digitial controller, digital pulse-width modulator DPWM and driving module Driver are constituted, this control system Main topology closed loop in series with Buck type DC/DC changer, it is characterised in that: digitial controller uses single input to obscure PID controller, uses one-dimensional single input Fuzzy PID to substitute two dimension dual input Fuzzy PID, including obscuring Change module, dimensionality reduction module, single input fuzzy logic controller module and pid control module；Analog-digital converter ADC sampling different week After delay, adder, digitized deviation signal is produced after subtracting each other with reference voltage Vref after the main topology output voltage in moment phase E [n] and digitized change of error amount signal ec [n], then by digitized deviation signal e [n] and numeral in obfuscation module Changing change of error amount signal ec [n] and carry out Fuzzy processing, the result after Fuzzy processing is carried out at dimensionality reduction by dimensionality reduction module Reason, is converted into one-dimensional single input variable d by the dual input input variable of two dimension_s, afterwards, by single input fuzzy logic control Device module obtains K after one-dimensional single input variable is carried out fuzzy reasoning and ambiguity solution_p、K_iAnd K_dThree parameter values export to PID Control module, pid control module is according to K_p、K_iAnd K_dThree parameters are calculated corresponding duty cycle command signal and deliver to numeral Pulse width modulation module, then the square-wave signal that output duty cycle is variable after driving module, be used for controlling the main topology of changer In turn-on and turn-off time of two complementary MOS power tube M1, M2, thus regulate the output voltage values of DC/DC changer；So After again output voltage to main topology carry out ADC sample conversion, repeat said process and be circulated control until system stability, It is allowed to level off to expectation reference voltage level.

The single input fuzzy PID control method of Buck type DC-DC converter the most according to claim 1, including following step Rapid:

(1) output voltage uo (t) of changer obtains analog output voltage Vo [t] through analog-digital converter ADC and is converted into Digital voltage signal Vo [n], obtains after being subtracted each other with reference voltage Vref by digital voltage signal Vo [n] after delay, adder Digitized deviation signal i.e. voltage error value e [n] of current sample period also gives record, simultaneously by error current record with upper First-order error record subtracts each other and obtains digitized change of error amount signal i.e. error deviation value ec [n], by e [n] and ec [n] two letter E [n] and ec [n], to the obfuscation module in digitial controller, is carried out obfuscation, obfuscation in obfuscation module by number output Digitized deviation signal and digitized change of error amount signal export to dimensionality reduction module；

(2) dimensionality reduction module symbolization distance method carries out dimension-reduction treatment to two input variables (e, ec) obtained in (1), uses Single input variable d_sReplace, wherein single input variable d_sRepresent in two dimension fuzzy rule list any diagonal to leading diagonal L_ZODistance, calculate the optimal solution of parameter lambda by genetic algorithm, thus calculate to obtain d_sValue；Comprise the following steps:

1) two dimension fuzzy leading diagonal L is set_ZOLinear equation be ec+ λ e=0, wherein λ represents the slope of leading diagonal, arbitrary Bar diagonal to the distance of leading diagonal is:

2) MATLAB is used to realize the parameter lambda optimizing of genetic algorithm

A) solution space and the code encoding/decoding mode of parameter lambda are determined

Changer response curve based on dual input fuzzy and single input Fuzzy PID deviates from each other more serious, The valued space of parameter lambda is defined as [0,16], uses the binary coding string of a length of 10 to represent parameter lambda, is designated as λ_Bi, wherein Gao Siwei represents integer part, and low six represent fractional part, then discrete two between 0000000000 to 1111111111 Scale coding string is corresponding in turn to the decimal value between 0 to 15.984375, constitutes chromosome coding mode, during decoding Need the binary coding string λ of 10 bit lengths_BiBe converted to the decimal number λ in the solution space of correspondence_i, according to individual UVR exposure mode Definition with solution space obtains decoding formula:

${\mathrm{\λ}}_i=\underset{j=1}{\overset{10}{\Σ}}{\mathrm{\λ}}_{Bi}\left(j\right)\×2^{4-j}---\left(2\right)$

B) individual evaluation fitness function is determined

The selection principle of parameter lambda is to make based on single input fuzzy and the DC-DC converter of dual input Fuzzy PID Having the performance of equivalence, i.e. two system response curves should overlap as far as possible, by the conversion of under synchronization two kinds of algorithm controls Device output voltage compares and obtains deviation signal v_d, choose deviation signal v_dThe maximum M of absolute value_dAbsolute with time deviation Integrated value ITAE of value is constituted as index evaluates individual good and bad fitness function F (λ), and in formula (5), parameter is chosen for ω₁=1, ω₂=1000, the optimizing of parameter lambda is the minima seeking object function J (λ)；

M_d=max | v_d(t)| (3)

ITAE=∫ tv_d(t)|dt (4)

$F\left(\mathrm{\λ}\right)=\frac{1}{{\mathrm{\ω}}_1{M}_d+{\mathrm{\ω}}_{2}ITAE}---\left(5\right)$

$J\left(\mathrm{\λ}\right)=\frac{1}{F\left(\mathrm{\λ}\right)}---\left(6\right)$

C) genetic operator is set

Use single-point crossover operator as crossing operation mode, at exchange point, the chromosome of parents is blocked and exchange afterbody, hand over Fork probability P_cDetermining new individual generation speed in population of future generation, be taken as 0.4～0.99, mutation operator determines that population is being evolved During multiformity, thus affect the quality of solution, in order to obtain the optimization solution that quality is higher in the biggest space, and The most do not make algorithm the convergence speed the slowest, mutation probability P_mIt is taken as 0.0001～0.1；

In order to ensure that the individuality of initial population is uniformly distributed in solution space, useRandom fashionGenerate each base of chromosome Cause, if initial population is excessive, can affect convergence of algorithm speed, otherwise, multiformity and the optimizing result of individuality can be reduced Quality, selects initial population size M=20, terminates evolutionary generation G and determines the iteration terminal of optimized algorithm, when the evolution generation of population When number reaches G, the optimum individual in current population is exported as optimizing solution, solve with optimizing to take into account convergence of algorithm speed Quality, terminate algebraically be taken as 100～500；

(3) by single input variable d_sIt is divided into 7 fuzzy subset: L_NB, L_NM, L_NS, L_ZO, L_PS, L_PM, L_PB, its membership function flex point Parameter keeps consistent with e, es.Thus two dimension Toeplitz Fuzzy rule list is reduced to one-dimensional vector, for fall further The computational complexity of the low single input fuzzy controller obtained, by choosing the control that can make single input fuzzy PID algorithm Face is piecewise linear input variable e [n], the membership function of ec [n] and output variable Δ Kp, Δ Ki, the membership function of Δ Kd, Make single input fuzzy PID algorithm have one-dimensional segmenting chain of command, this makes it possible to three directly by MATLAB The piecewise linear function that fuzzy function generates replaces whole single input fuzzy controller；

(4) then pass through single input fuzzy logic controller module and carry out fuzzy reasoning, i.e. judge output according to fuzzy reasoning table Fuzzy subset belonging to variable Δ Kp, Δ Ki, Δ Kd, then carries out ambiguity solution, i.e. from Δ Kp, Δ Ki, Δ by centre of area method The domain of Kd is found out one can represent fuzzy decoupling effect may the exact value of distribution, respectively with the pid parameter set at the beginning of Value Kp0, Ki0, Kd0 are added and obtain real-time Kp, Ki, Kd, and output is to PID controller；In order to make single input fuzzy PID algorithm Chain of command is piecewise linearity, fuzzy logic input variable e [n], the membership function of ec [n] and output variable Δ Kp, Δ Ki, Δ Kd Membership function conciliate fuzzy operation and must meet following condition:

1) input variable e [n], the membership function of ec [n] are necessary for triangular form, and the membership function of adjacent fuzzy subset must have The overlap of at least 50%；

2) output variable Δ Kp, Δ Ki, the membership function of Δ Kd are necessary for stand alone；

(5) PID controller carries out PID arithmetic according to Kp, Ki, Kd, and output discrete duty cycles signal d [n] is to digital pulse width modulation Device DPWM, digital pulse-width modulator DPWM output continuous print duty cycle signals d [t] are to driving module Driver, driver element Driver output duty cycle is the switch of pwm signal control MOS power tube M1, M2 of d, thus regulated output voltage；

(6) by the output voltage of (5) through analog-digital converter ADC sample conversion again, sequentially pass through e [n] and ec [n] and produce electricity Road, digitial controller, digital pulse width modulation cells D PWM and driving module Driver, form new pwm signal and control power tube The switch of M1, M2, loop control is until output voltage Vo [t] is consistent with digital reference voltage Vref.