CN113410987B - Extreme learning machine-based sliding mode variable structure Buck circuit control method - Google Patents

Extreme learning machine-based sliding mode variable structure Buck circuit control method Download PDF

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CN113410987B
CN113410987B CN202110528445.7A CN202110528445A CN113410987B CN 113410987 B CN113410987 B CN 113410987B CN 202110528445 A CN202110528445 A CN 202110528445A CN 113410987 B CN113410987 B CN 113410987B
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sliding mode
learning machine
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buck circuit
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CN113410987A (en
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陈龙
叶涛
马学条
郑雪峰
李嘉荣
肖淑玲
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Hangzhou Dianzi University
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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
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • 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
    • H02M1/08—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/10—Technologies improving the efficiency by using switched-mode power supplies [SMPS], i.e. efficient power electronics conversion e.g. power factor correction or reduction of losses in power supplies or efficient standby modes

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Abstract

The invention discloses a Buck circuit control method of a sliding mode variable structure based on an extreme learning machine, which comprises the following steps of: s10, analyzing the topological structure of the Buck circuit, and establishing a system mathematical model by adopting a state space average method; s20, defining a sliding mode surface of the controller, designing an extreme learning machine self-adaptation law, and designing the controller according to the system mathematical model in S10; and S30, verifying the existence of the sliding mode and the stability of the system by using the Lyapunov theorem according to the controller in S20. The invention simultaneously monitors the voltage and current signal changes of the circuit and corrects the duty ratio signal output by the system in real time; the system can be ensured to rapidly reach the sliding mode surface from any initial state, the load is switched when the circuit is in a stable state, the system can be stabilized to a stable output voltage value in a short time, the robustness of the controller is ensured, and the buffeting of the system is weakened.

Description

Extreme learning machine-based sliding mode variable structure Buck circuit control method
Technical Field
The invention belongs to the field of electronic power control, and relates to a Buck circuit control method based on a sliding mode variable structure of an extreme learning machine.
Background
Buck circuit belongs to the power electronics technology category, is the power module that many devices and equipment all adopted, compares in ordinary linear power supply, has advantages such as small, light in weight, low power dissipation, efficient, response speed are fast, receives more and more attention in the electronic power technology field. The Buck circuit realizes that input direct-current voltage is converted into lower direct-current voltage for stable output, the performance of the Buck circuit depends on the control strategy of the system to a great extent, the traditional linear PID control has poor anti-interference capability on a nonlinear time-varying system of the Buck circuit, and when the load is disturbed, the problems of unstable performance, low response speed, large overcharge and the like exist.
Therefore, the extreme learning machine and the sliding mode variable structure control are urgently needed to be combined to realize the robust control of the nonlinear system. The extreme learning machine utilizes the super-strong learning ability to fit items with dynamic parameters in a model, the items are regarded as disturbance processing of a system, a self-adaptive law is designed, and the fitted disturbance items are introduced to be used as compensation when a sliding mode variable structure controller is designed, so that the robustness control of the Buck circuit is realized.
Disclosure of Invention
In order to solve the problems, the technical scheme of the invention is a sliding mode variable structure Buck circuit control method based on an extreme learning machine, solves the problems of slow response speed, poor dynamic performance, poor robustness and the like of the Buck circuit in the traditional linear PID control, and realizes the stable tracking control of the output voltage of the Buck circuit.
The Buck circuit control system comprises a Buck circuit, a DSP control circuit, a voltage sensor, a current sensor and a PWM modulator, wherein,
the voltage sensor and the current sensor are both connected with the Buck circuit, the voltage sensor performs attenuation sampling on voltages at two ends of an output load of the Buck circuit, and then a sampled voltage signal is sent to the DSP control circuit;
the current sensor performs attenuation sampling on the current of an inductive element in the Buck circuit, and then sends a sampled current signal to the DSP control circuit;
the DSP control circuit receives the output voltage sampling signal and the inductive current sampling signal, processes the output voltage sampling signal and the inductive current sampling signal to obtain a duty ratio signal, obtains a PWM square wave signal of the duty ratio through a PWM modulator, inputs the obtained PWM square wave signal into a drive switch MOS tube and controls the on-off time of a switch of the Buck circuit;
based on the control system, the control method comprises the following steps:
s10, analyzing the topological structure of the Buck circuit, and establishing a system mathematical model by adopting a state space average method;
s20, defining a sliding mode surface of the controller, designing an extreme learning machine self-adaptation law, and designing the controller according to the system mathematical model in S10;
and S30, verifying the existence of the sliding mode and the stability of the system by using the Lyapunov theorem according to the controller in S20.
Preferably, the Buck circuit topological structure is analyzed, a state space average method is adopted to establish a system mathematical model, the Buck circuit switch is analyzed to work in two states, and an inductor current i is obtained L And an output voltage V o For a system state variable, establishing a system average state space mathematical model according to a circuit kirchhoff law as follows:
Figure BDA0003066186500000021
wherein, V o To output a voltage, i L Is inductor current, R is load resistance, L is filter inductance, C is filter capacitance, V in D is the duty ratio of the controllable switch tube.
Preferably, the sliding mode surface for defining the controller, designing the extreme learning machine adaptive law, and designing the controller according to the system mathematical model in S10 includes the following steps,
s21, the output voltage reference value of Buck circuit is defined as V ref ，V ref Is a constant value; taking the tracking error of the output voltage as the state variable of the controller, i.e. e ═ V o -V ref Then the output voltage tracking error is first derivative
Figure BDA0003066186500000031
S22, according to the establishment of the system mathematical model and the definition of the state variable, obtaining a second-order dynamic mathematical model containing disturbance terms of the system as
Figure BDA0003066186500000032
Wherein u is the output of the sliding mode controller, f is the item to be fitted, and the fitting output of the network is obtained by utilizing the approximation of an extreme learning machine;
s23, defining a system sliding mode surface function as
Figure BDA0003066186500000033
Wherein λ is a controller parameter satisfying λ>0, designing an extreme learning machine self-adaptation law;
s24, designing the output of the sliding mode controller based on the extreme learning machine as follows:
Figure BDA0003066186500000034
wherein,
Figure BDA0003066186500000035
representing the estimated value of the extreme learning machine, H is the hidden layer output of the extreme learning machine,
Figure BDA0003066186500000036
the self-adaptive output weight of the extreme learning machine, sign(s) is a sign function, and epsilon and k are exponential approach rate coefficients.
Preferably, the verifying the existence of the sliding mode and the stability of the system by using the lyapunov theorem according to the controller in S20 includes defining the lyapunov function according to the established mathematical model of the system
Figure BDA0003066186500000037
Wherein gamma is adaptive law coefficient, gamma>0，
Figure BDA0003066186500000038
Is the output weight error of the extreme learning machine,
Figure BDA0003066186500000039
is composed of
Figure BDA00030661865000000310
Transposing; substituting designed controllers into
Figure BDA00030661865000000311
And setting parameters of a controller to ensure that the first derivative of V is negative and half fixed, and verifying the stability of the system.
Preferably, the DSP control circuit includes an error amplifier, a sliding mode controller 42, and a PI controller, wherein the output of the voltage sensor is input to the sliding mode controller 42 through the error amplifier, and then input to the PI controller through a system mathematical model and the error amplifier; the input of the sliding mode controller 42 is connected with the output voltage sampling signal of the voltage sensor and the reference output voltage signal, an error signal obtained by processing of an error amplifier is used as a control quantity to the sliding mode controller 42, a sliding mode surface is defined, and the controller is designed to meet the stability of Lyapunov; substituting the designed controller into a system mathematical model to calculate a reference inductive current signal, carrying out error amplifier processing on the reference inductive current signal and an inductive current sampling signal output by a current sensor, inputting the processed signal into a PI controller, outputting the signal into a PWM modulator by the PI controller, and comparing the obtained duty ratio signal with a fixed frequency sawtooth wave to obtain a PWM wave signal with fixed switching frequency.
Preferably, the voltage sensor comprises an LEMLV25-P chip.
Preferably, the current sensor comprises a LEMHX15-P chip.
Preferably, the DSP control circuit comprises a TMS320F28335 chip.
The invention has at least the following beneficial effects:
1. voltage and current feedback signals are introduced into a double closed-loop control framework in the DSP control circuit at the same time, real-time accurate correction is realized on a required duty ratio signal under the combined action of a voltage loop regulator and a current loop regulator, the tracking precision of the system is improved, and good dynamic and static characteristics of the system are realized;
2. the PWM modulator receives a PWM signal sent by the DSP control circuit, and further drives the switch MOS tube to control the on-off time of the switch of the Buck circuit;
3. the method comprises the steps that a sliding mode variable structure controller based on an extreme learning machine fits an uncertain item of a Buck circuit system according to universal approximation characteristics of the extreme learning machine, then defines a sliding mode surface function, and deduces a control law expression according to an average state space mathematical model of the system;
4. considering the external interference and the internal parameter uncertainty of the system, introducing an interference compensation term into the designed control law expression;
5. substituting the control law of a sliding mode variable structure controller based on an extreme learning machine into a Lyapunov equation to prove that the system is stable under the designed control law;
6. the proportional control item serving as a current inner loop to the PI controller can quickly respond to the error and correct the output, and the integral control item eliminates the steady-state error of the system;
7. the system has the advantages of high response speed, robustness to load disturbance and uncertainty of circuit parameters and high dynamic performance.
Drawings
FIG. 1 is a flow chart of steps of a Buck circuit control method based on a sliding mode variable structure of an extreme learning machine according to an embodiment of the invention;
FIG. 2 is a system block diagram of a Buck circuit control method based on a sliding mode variable structure of an extreme learning machine according to an embodiment of the invention;
FIG. 3 is a system specific composition diagram of a Buck circuit control method based on a sliding mode variable structure of an extreme learning machine according to an embodiment of the invention;
FIG. 4 is a basic circuit topology structure diagram of a Buck circuit of the control method of the sliding mode variable structure Buck circuit based on the extreme learning machine in the embodiment of the invention;
FIG. 5 is a circuit equivalent diagram of two working states of a Buck circuit of the control method of the sliding mode variable structure Buck circuit based on the extreme learning machine in the embodiment of the invention;
fig. 6 is an inductive current and voltage working waveform diagram of the Buck circuit of the sliding mode variable structure Buck circuit control method based on the extreme learning machine according to the embodiment of the invention;
FIG. 7 is a network structure diagram of an extreme learning machine of the Buck circuit control method based on the sliding mode variable structure of the extreme learning machine according to the embodiment of the invention;
FIG. 8 is a diagram showing a comparison result of start response simulation of the Buck circuit control method based on the sliding mode variable structure of the extreme learning machine according to the embodiment of the invention;
FIG. 9 is a graph of a comparison result of load switching response simulation of the sliding mode variable structure Buck circuit control method based on the extreme learning machine according to the embodiment of the invention;
FIG. 10 is a comparative waveform diagram of a starting response experiment of the Buck circuit control method based on the sliding mode variable structure of the extreme learning machine in the embodiment of the invention;
fig. 11 is a comparative waveform diagram of a load switching response experiment of the sliding mode variable structure Buck circuit control method based on the extreme learning machine according to the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
Referring to fig. 1, which is a flow chart illustrating steps of a Buck circuit control method based on a sliding mode variable structure of an extreme learning machine according to an embodiment of the present invention, fig. 2 and fig. 3 are structural diagrams illustrating a control system of the method according to the present invention, wherein the Buck circuit control system includes a Buck circuit 10, a DSP control circuit 40, a voltage sensor 20, a current sensor 30 and a PWM modulator 50, wherein,
the voltage sensor 20 and the current sensor 30 are both connected with the Buck circuit 10, the voltage sensor 20 performs attenuation sampling on voltages at two ends of an output load of the Buck circuit 10, and then a sampled voltage signal is sent to the DSP control circuit 40;
the current sensor 30 performs attenuation sampling on the current of the inductance element in the Buck circuit 10, and then sends a sampled current signal to the DSP control circuit 40;
the DSP control circuit 40 receives the output voltage sampling signal and the inductive current sampling signal, processes the output voltage sampling signal and the inductive current sampling signal to obtain a duty ratio signal, obtains a PWM square wave signal of the duty ratio through a PWM modulator 50, inputs the obtained PWM square wave signal into a drive switch MOS tube, and controls the on-off time of a switch of the Buck circuit 10;
based on the control system, the control method comprises the following steps:
s10, analyzing the topological structure of the Buck circuit, and establishing a system mathematical model by adopting a state space average method;
s20, defining a sliding mode surface of the controller, designing an extreme learning machine self-adaptation law, and designing the controller according to the system mathematical model in S10;
and S30, verifying the existence of the sliding mode and the stability of the system by using the Lyapunov theorem according to the controller in S20.
S10, analyzing the topological structure of the Buck circuit, establishing a system mathematical model by adopting a state space average method, analyzing the Buck circuit switch to work in two states, and obtaining an inductive current i L And an output voltage V o For a system state variable, establishing a system average state space mathematical model according to a circuit kirchhoff law as follows:
Figure BDA0003066186500000061
wherein, V o To output a voltage, i L Is inductor current, R is load resistance, L is filter inductance, C is filter capacitance, V in D is the duty ratio of the controllable switch tube.
S20, defining the sliding mode surface of the controller, designing the self-adaptive law of the extreme learning machine, designing the controller according to the system mathematical model in S10, comprising the following steps,
s21, the output voltage reference value of Buck circuit is defined as V ref ，V ref Is a constant value; taking the tracking error of the output voltage as the state variable of the controller, i.e. e ═ V o -V ref Then the output voltage tracking error is first derivative
Figure BDA0003066186500000071
S22, according to the establishment of the system mathematical model and the definition of the state variable, obtaining a second-order dynamic mathematical model containing disturbance terms of the system as
Figure BDA0003066186500000072
Wherein u is the output of the sliding mode controller, f is the item to be fitted, and the fitting output of the network is obtained by utilizing the approximation of an extreme learning machine;
s23, defining a system sliding mode surface function as
Figure BDA0003066186500000073
Wherein λ is a controller parameter satisfying λ>0, designing an extreme learning machine self-adaptation law;
s24, designing the output of the sliding mode controller based on the extreme learning machine as follows:
Figure BDA0003066186500000074
wherein,
Figure BDA0003066186500000075
representing the estimated value of the extreme learning machine, H is the hidden layer output of the extreme learning machine,
Figure BDA0003066186500000076
the self-adaptive output weight of the extreme learning machine, sign(s) is a sign function, and epsilon and k are exponential approach rate coefficients.
Verifying the existence of sliding mode and the stability of the system by utilizing the Lyapunov theorem according to the controller in S20, wherein the method comprises defining the Lyapunov function according to the established system mathematical model
Figure BDA0003066186500000077
Figure BDA0003066186500000078
Wherein gamma is adaptive law coefficient, gamma>0，
Figure BDA0003066186500000079
Is the output weight error of the extreme learning machine,
Figure BDA00030661865000000710
is composed of
Figure BDA00030661865000000711
Transposing; substituting designed controller into
Figure BDA00030661865000000712
And setting parameters of a controller to ensure that the first derivative of V is negative and half fixed, and verifying the stability of the system.
Referring to fig. 3, the DSP control circuit 40 includes an error amplifier 41, a sliding mode controller 42, and a PI controller 43, wherein the output of the voltage sensor 20 is input to the sliding mode controller 42 through the error amplifier 41, and then input to the PI controller 43 through the system mathematical model and the error amplifier 41; the input of the sliding mode controller 42 is connected with the output voltage sampling signal of the voltage sensor 20 and the reference output voltage signal, an error signal obtained by processing the error amplifier 41 is used as a control quantity to the sliding mode controller 42, a sliding mode surface is defined, and the controller is designed to meet the stability of the Lyapunov; substituting the designed controller into a system mathematical model H(s) to calculate and obtain a reference inductive current signal, carrying out error amplifier 41 processing on the reference inductive current signal and an inductive current sampling signal output by the current sensor 30, inputting the processed signal into a PI controller 43, outputting the signal by the PI controller 43 to enter a PWM (pulse width modulation) 50, and comparing the obtained duty ratio signal with a fixed frequency sawtooth wave to obtain a PWM wave signal with a fixed switching frequency.
In a particular embodiment, the voltage sensor 20 includes an LEMLV25-P chip; the current sensor 30 comprises a LEMHX15-P chip; DSP control circuitry 40 includes a TMS320F28335 chip. The control system can be realized based on an experimental device of a PTS-1000 platform, the experimental device of the PTS-1000 platform comprises a PEK-120 buck converter module for realizing the buck function from direct current input voltage to direct current output voltage, and the module is provided with 5 circuit state observation points, namely input voltage Vin, output voltage Vo, inductive current IL, output current Io and switch control signal PWM square waves for an oscilloscope to observe experimental results;
the programmable direct current electronic load modules PEL-2040 and PEL-2004 are used for providing load resistance of the Buck circuit 10 and are connected with the output port of the PEK-120 converter module;
the programmable direct-current power supply module PSW 250-4.5 is used for providing input voltage of the Buck circuit 10 and is connected with an input port of the PEK-120 converter module;
and the auxiliary power supply module is used for supplying power to the chip of the PEK-120 converter module and is connected with the third port of the PEK-120 converter module.
Referring to fig. 4, it is a basic circuit topology structure diagram of the Buck circuit 10; referring to fig. 5, an equivalent circuit diagram of the Buck circuit 10 when the switch is turned on and off is shown, and it can be seen that the dc voltage reduction function of the Buck circuit is caused by charging and discharging of the inductive element. According to the fact that the circuit works in two states of switch conduction and switch cutoff, when the switch is conducted, the inductor charges, electric energy is converted into magnetic energy, the diode is cut off in the reverse direction, and at the moment, the increase of the current of the inductor is
Figure BDA0003066186500000081
When the switch is turned off, the inductor discharges electricity, the inductor element generates electromotive force due to the stored magnetic energy, the magnetic energy is converted into electric energy, the output resistor supplies power, the diode is conducted in forward direction, and the current of the inductor is reduced to
Figure BDA0003066186500000082
According to the fact that the charging quantity and the discharging quantity of the inductance element are equal under the steady state condition of the circuit, the value is represented by delta i L+ ＝Δi L- The relation between the output voltage and the input voltage of the circuit can be obtained, i.e.
Figure BDA0003066186500000091
Wherein D is the duty ratio of the circuit switch control signal, t on Is the off-on time, t, of one switching cycle off Is the switch off time within one switching cycle; therefore, the Buck circuit 10 is designed with a control algorithm, and actually controls the duty ratio of the input of the switching control signal.
Referring to fig. 6, showing the current and voltage variation waveforms of the inductance element in the Buck circuit 10 during charging and discharging, it can be seen that the average value of the inductance current in one period is the output current. Where Vcon is the control voltage of the PWM wave, V L Is the inductor voltage.
Referring to fig. 7, a network structure diagram of the extreme learning machine is shown, which is a neural network calculation model with a single hidden layer.
Based on the topological structure of the Buck circuit 10 shown in fig. 4, a specific process for establishing a mathematical model of an average state space is as follows:
when the switch is turned on (nT ≦ t ≦ nT + Ton), the system state space may be expressed as:
Figure BDA0003066186500000092
when the switch is turned off (nT + Ton ≦ T ≦ n + 1T), the system state space may be expressed as:
Figure BDA0003066186500000093
combining the equations (2) and (3), the average state space model under the ideal condition of the system in one switching cycle is obtained as follows:
Figure BDA0003066186500000094
in the formula, V o To output a voltage, i L Is inductor current, R is load resistance, L is filter inductance, C is filter capacitance, V in D is the duty ratio of the controllable switching tube;
introducing a switch control function u, wherein the expression is as follows:
Figure BDA0003066186500000101
from equation (5), the system state space expression is:
Figure BDA0003066186500000102
the formula (4) and the formula (6) show that the switching control function u and the duty ratio D are in a corresponding relationship, D is the average value of u in a switching period, and the switching control function u is also the designed sliding mode variable structure control law u.
According to the system mathematical model in the formula (6), selecting an output voltage error signal as a state variable of the sliding mode variable structure controller, namely e is V o -V ref ，V ref The reference value of the output voltage is a constant value; considering load change external disturbance in an actual Buck circuit 10 system, uncertainty of system internal parameters such as capacitance L and inductance C and unknown noise of an external environment, adding an interference term into a system dynamic mathematical model, wherein an expression is as follows:
Figure BDA0003066186500000103
wherein d (t) represents the external noise disturbance of the system, and the condition that | d (t) | is less than or equal to DMAX is met, and DMAX is the upper interference bound.
In the formula (7)
Figure BDA0003066186500000104
Is an uncertainty term that is fit using an extreme learning machine. The output voltage tracking error and the change rate of the tracking error of the Buck circuit 10 system are taken as two network inputs of the extreme learning machine, and the network input and output algorithm is designed as follows:
Figure BDA0003066186500000105
wherein H j And (3) representing the nonlinear mapping output of the jth unit of the network hidden layer, wherein the hidden layer adopts a classical sigmoid nonlinear function. w is a j Representing the network input weights connected to the jth hidden layer unit, b j Representing the jth hidden layer unitBias unit, theta * Representing the ideal output weight, δ represents the estimation error of the ELM extreme learning machine, which is a relatively small positive number in magnitude relative to the fitting quantity f.
Defining Lyapunov (Lyapunov) function
Figure BDA0003066186500000111
The first derivative of V is expressed as follows:
Figure BDA0003066186500000112
the controller expression is designed as follows:
Figure BDA0003066186500000113
substituting the controller designed by the formula (10) into the formula (9), and obtaining the system dynamic mathematical model and the sliding mode surface function
Figure BDA0003066186500000114
The expanded expression of (a) is:
Figure BDA0003066186500000115
since the ELM extreme learning machine approximation error δ is a small positive real number, when the parameter ε > δ + DMAX is chosen, then:
Figure BDA0003066186500000116
Figure BDA0003066186500000117
Figure BDA0003066186500000118
therefore, it is shown by the above evidence that the parameter epsilon is mainly used for counteracting two items of interference caused by the estimation error of the ELM extreme learning machine and disturbance of unknown noise outside the system, and in addition, the parameter design of epsilon, the designed sliding mode controller 42 based on the extreme learning machine meets the stability condition of Lyapunov.
When the uncertainty of system parameters and external interference are large, and the gain of a switching item required by a controller is large, large buffeting is caused at this time, in order to weaken the buffeting, a saturation function sat(s) is adopted in the design of the controller to replace a sign function sign(s), and the specific expression of the saturation function is as follows:
Figure BDA0003066186500000121
wherein rho is a saturation function boundary layer parameter, and rho is greater than 0.
Substituting the designed controller into a system mathematical model, and calculating an expression of the inductance current reference value as follows:
Figure BDA0003066186500000122
the reference current signal obtained by the formula (14) and the current sampling signal are subjected to error processing to obtain an inductive current error signal, i.e. e I ＝I Lref -i L The current-loop PI controller 43 is designed.
The PI controller 43 linearly combines the proportional and integral forms of the error signal to obtain the current loop controller with the expression:
u I ＝K p e I +K i ∫e I dt (16)
wherein, K p 、K i Proportional term parameter and integral term parameter of the PI controller 43.
The circuit parameters adopted by the double closed-loop control system of the Buck circuit 10 in software simulation and hardware experiment are as follows:
Figure BDA0003066186500000123
Figure BDA0003066186500000131
referring to fig. 8, a graph of simulation comparison results of the control system and a conventional PID control system is shown. The method is obtained by building a circuit simulation model of a Buck circuit control system in a PSIM environment. Simulation proves that the control method can reach the reference output voltage value in a short time and is stable, and compared with a PID control method, the overshoot is reduced by 3.86%;
referring to fig. 9, a graph of a comparison of load switching simulation results of the control system is shown. In order to further prove the control effect of the control method, at 0.3s, the simulation circuit simulates load resistance disturbance by connecting a resistor in parallel, and the system can show good transient response when the load suddenly changes.
Referring to fig. 10, a plot of start-up response versus experiment for the control system output voltage is shown. According to experimental results, compared with a PID control method, the sliding mode control method based on the extreme learning machine has a faster response speed, and the overshoot amount during starting is reduced by 2.69%.
Referring to fig. 11, two control method response curves obtained by adjusting the switching of the load resistance value set by the direct current electronic load instrument, switching the load from 20 Ω to 10 Ω respectively and observing on an oscilloscope respectively are shown, when load switching disturbance is applied, the output voltage has a transient fluctuation reduction process, compared with a PID controller, the time for the designed controller to recover to a steady state balance point is reduced by about half, and the disturbance amount is also reduced by 2.21%, which proves that the control method has good robustness to load disturbance in an actual system.
The embodiment verifies through simulation and experiments that the control method has the advantages of high system response speed, robustness to load disturbance and circuit parameter uncertainty and high system dynamic performance.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (6)

1. A Buck circuit control method based on a sliding mode variable structure of an extreme learning machine is characterized in that a Buck circuit control system comprises a Buck circuit, a DSP control circuit, a voltage sensor, a current sensor and a PWM modulator, wherein,
the voltage sensor and the current sensor are both connected with the Buck circuit, the voltage sensor performs attenuation sampling on voltages at two ends of an output load of the Buck circuit, and then a sampled voltage signal is sent to the DSP control circuit;
the current sensor performs attenuation sampling on the current of an inductive element in the Buck circuit, and then sends a sampled current signal to the DSP control circuit;
the DSP control circuit receives the output voltage sampling signal and the inductive current sampling signal, processes the output voltage sampling signal and the inductive current sampling signal to obtain a duty ratio signal, obtains a PWM square wave signal of the duty ratio through a PWM modulator, inputs the obtained PWM square wave signal into a drive switch MOS tube and controls the on-off time of a switch of the Buck circuit;
based on the control system, the control method comprises the following steps:
s10, analyzing the topological structure of the Buck circuit, and establishing a system mathematical model by adopting a state space average method;
s20, defining a sliding mode surface of the controller, designing an extreme learning machine self-adaptation law, and designing the controller according to the system mathematical model in S10;
s30, verifying the existence of the sliding mode and the stability of the system by utilizing the Lyapunov theorem according to the controller in the S20;
the Buck circuit topological structure is analyzed, a system mathematical model is established by adopting a state space averaging method, the Buck circuit switch is analyzed to work in two states, and the inductor current i is obtained L And an output voltage V o To be aAnd (3) establishing a system average state space mathematical model according to a circuit kirchhoff law by using the system state variables as follows:
Figure FDA0003583938520000011
wherein, V o To output a voltage, i L Is inductor current, R is load resistance, L is filter inductance, C is filter capacitance, V in D is the duty ratio of the controllable switching tube;
the sliding mode surface of the controller is defined, the self-adaptation law of the extreme learning machine is designed, the controller is designed according to the system mathematical model in S10, the method comprises the following steps,
s21, the output voltage reference value of Buck circuit is defined as V ref ，V ref Is a constant value; taking the tracking error of the output voltage as the state variable of the controller, i.e. e ═ V o -V ref Then the output voltage tracking error is first derivative
Figure FDA0003583938520000021
S22, according to the establishment of the system mathematical model and the definition of the state variable, obtaining a second-order dynamic mathematical model containing disturbance terms of the system as
Figure FDA0003583938520000022
Wherein u is the output of the sliding mode controller, f is the item to be fitted, and the fitting output of the network is obtained by utilizing the approximation of an extreme learning machine;
s23, defining a system sliding mode surface function as
Figure FDA0003583938520000023
Wherein lambda is a controller parameter, and meets the condition that lambda is more than 0, and an extreme learning machine self-adaptation law is designed;
s24, designing the output of the sliding mode controller based on the extreme learning machine as follows:
Figure FDA0003583938520000024
wherein,
Figure FDA0003583938520000025
representing the estimated value of the extreme learning machine, H is the hidden layer output of the extreme learning machine,
Figure FDA0003583938520000026
the self-adaptive output weight of the extreme learning machine, sign(s) is a sign function, and epsilon and k are exponential approach rate coefficients.
2. The method of claim 1, wherein verifying the presence of slip mode and system stability using the Lyapunov theorem according to the controller in S20 comprises defining a Lyapunov function according to an established mathematical model of the system
Figure FDA0003583938520000027
Wherein gamma is an adaptive law coefficient, gamma is more than 0,
Figure FDA0003583938520000028
is the output weight error of the extreme learning machine,
Figure FDA0003583938520000029
is composed of
Figure FDA00035839385200000210
Transposing; substituting designed controllers into
Figure FDA00035839385200000211
And setting parameters of a controller to ensure that the first derivative of V is negative and half fixed, and verifying the stability of the system.
3. The method of claim 1, wherein the DSP control circuit comprises an error amplifier, a sliding mode controller, a PI controller, wherein the voltage sensor output is input to the sliding mode controller via the error amplifier, and then to the PI controller via a system mathematical model and the error amplifier; the input of the sliding mode controller is connected with an output voltage sampling signal and a reference output voltage signal of the voltage sensor, an error signal obtained by processing of an error amplifier is used as a control quantity to the sliding mode controller, a sliding mode surface is defined, and the controller is designed to meet the stability of the Lyapunov; substituting the designed controller into a system mathematical model to calculate a reference inductive current signal, carrying out error amplifier processing on the reference inductive current signal and an inductive current sampling signal output by a current sensor, inputting the processed signal into a PI controller, outputting the signal into a PWM modulator by the PI controller, and comparing the obtained duty ratio signal with a fixed frequency sawtooth wave to obtain a PWM wave signal with fixed switching frequency.
4. The method of claim 1, wherein the voltage sensor comprises an LEMLV25-P chip.
5. The method of claim 1, wherein the current sensor comprises a LEMHX15-P chip.
6. The method of claim 1, wherein the DSP control circuitry comprises a TMS320F28335 chip.
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