CN111193386A - Model prediction control method for feedback compensation parameter self-adaption of full-bridge converter - Google Patents
Model prediction control method for feedback compensation parameter self-adaption of full-bridge converter Download PDFInfo
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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
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- H02M1/08—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters
- H02M1/088—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters for the simultaneous control of series or parallel connected semiconductor devices
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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
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Abstract
The invention discloses a model prediction control method of feedback compensation parameter self-adaption of a full-bridge converter, which belongs to the field of control methods of full-bridge converters, wherein in the actual process, the interference of factors such as noise, load mutation and the like can cause the change of parameters, model structures and the like, the feedback error coefficient is updated by using real-time feedback current information and a current predicted value and the error of a current actual value per cycle, the feedback compensation capability of a system can be flexibly adjusted without depending on a system mathematical model, the dynamic response speed of the system is improved, and the robustness is good; in order to improve the control precision, the delay of system sampling calculation is compensated; predicting the reference value of the k +2 period by using the reference values of the first 4 periods, and selecting the duty ratio D corresponding to the predicted value with the k +2 period closest to the reference valuek+1Acting on a k +1 period; the invention effectively improves the control precision and stability, adopts full digital control, has high control precision, is convenient and easy to model and operate, and is not influenced by the actual environment.
Description
Technical Field
The invention belongs to the field of full-bridge converter control methods, and particularly relates to a full-bridge converter feedback compensation parameter adaptive model prediction control method.
Background
With the development of power electronic technology, the characteristics of the full-bridge converter, such as dynamic performance, anti-interference capability and the like, need to reach a higher level, and the purpose of stabilizing the output by fast response under the condition of changes of load or input and the like is needed. Although the traditional PI control has better steady-state performance, the dynamic characteristic is poorer, and the increasingly improved control requirement of the current full-bridge converter cannot be met.
Model Predictive Control (MPC) is convenient to Model, feedback correction closed loop steady state effect is good, and the MPC is widely applied to full-bridge converter Control in recent years. At present, model prediction control based on a full-bridge converter generally adopts a feedback parameter with fixed parameters to realize closed-loop control, the compensation capability of a feedback error cannot be adjusted according to actual conditions, the condition of parameter mutation cannot be adjusted in time, and the dynamic response capability of a system is poor.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a model prediction control method for feedback compensation parameter self-adaption of a full-bridge converter, which has the advantages of high control precision, good dynamic performance and the like.
The technical scheme is as follows: in order to achieve the purpose, the invention provides the following technical scheme:
a feedback compensation parameter adaptive model prediction control method for a full-bridge converter comprises the following steps:
step 1) establishing a state equation of output current of a full-bridge circuit by taking RL as a load, and discretizing the equation by using an Euler method to obtain a system discrete model of a k period; collecting output current i with current sensoroConverting the analog signal into an analog signal with a proper amplitude range through a conditioning circuit, and then converting the analog signal into an output current digital signal i through an ADC (analog-to-digital converter)o(k) Substituting the prediction model to calculate to obtain a current prediction value i of the k +1 periodp(k+1)；
Step 2) predicting the current i of the k +1 periodp(k +1), predicted value of current i for k periodp(k) Solving the compensation parameter h (k) and the error parameter e (k) of the k period by adopting a least square method according to the actual current feedback values acquired for N times in the k period, and comparing the compensation parameter h (k) and the error parameter e (k) with a reference value iref(k) And the actual current feedback value io(k) Comparing the error e (k) to obtain a feedback compensation coefficient h (k) of the k period;
step 3) feeding back the compensated current predicted value i in the k periodpc(k) And the actual feedback value i of the currento(k) The error of the current is compensated to a current predicted value i of a k +1 period through a feedback correction linkp(k + 1); predicting the compensated current ipc(k +1) is substituted into the prediction model as the initial value of the next period prediction to iterate once again to obtain the predicted value I of the output current in the k +2 periodp(k+2)；
Step 4) adopting an extrapolation algorithm to predict the reference current value i of the k +2 period by using the reference values of the previous 4 periodsref(k + 2); selecting and referring current value i by considering delay time of system calculation and samplingref(k +2) predicted value i of current for k +2 period close topSubstituting (k +2) into the prediction model to calculate the duty ratio D acting on the k +1 periodk+1To perform delay compensation; by using Dk+1And generating a modulation signal, comparing the modulation signal with a carrier signal, generating a driving signal to control the on and off of a switching tube, completing the prediction and correction of a k period, and repeating the steps 1) to 4) in the k +1 period.
Further, in step 1), the state equation of the output current of the full bridge circuit is established by taking RL as a load:
the system discrete model of k period obtained by discretizing the Euler method is as follows:
wherein i (k +1) is the output current of k +1 period after dispersion, VoTo output a voltage, DkDuty ratio, V, for k period predicted for last perioddcIs DC bus voltage, L is load equivalent inductance, R is load resistance, TsThe cycle time is controlled for discrete switches.
Further, in step 1), the generation isCalculating to obtain a current predicted value i of a k +1 period by using an input prediction modelp(k +1) is:
wherein: i.e. io(k) The feedback value is actually output for k cycles.
Further, in step 2), the calculating of the compensation parameter h (k) of the k period by using the least square method is:
wherein xm＝ip(k)-io(m)，y(m)＝ip(k+1)-io(m)，ip(k) For the current prediction of k periods, ip(k +1) is the predicted value of the current for the k +1 period, io(m) is an m-time actual output feedback value acquired in k periods, wherein m is 1,2, …; n is the frequency of current output feedback values collected in each period, and the value of N is determined by the performance of the current sensor.
Further, in step 2), the error parameter e (k)' of k period solved by the least square method is:
wherein
Further, in step 2), the optimal feedback compensation parameter h (k) for the k period is:
wherein, omega is an error compensation threshold value and is determined by the specific experimental conditions; 0.005 is the engineering empirical value of the feedback compensation parameter.
Further, in step 3), the compensated power is fed backThe flow prediction value is: i.e. ipc(k+1)＝iP(k+1)+h(k)[ipc(k)-io(k)](ii) a Wherein ipc(k) Feedback compensated current prediction value for k period, io(k) Is the actual current feedback value of k cycles.
Further, in step 4), the reference current value i of the k +2 periodref(k +2) is:
iref(k+2)＝10iref(k)-20iref(k-1)+15iref(k-2)-4iref(k-3)
wherein iref(k)，iref(k-1)，iref(k-2)，irefAnd (k-3) are reference current values of a k period, a k-1 period, a k-2 period and a k-3 period respectively.
Further, in step 4), the predicted value i of the current of the k +2 period isp(k +2) is:
further, in step 4), the duty ratio applied to the k +1 period is:
wherein iref(k +2) is the current reference value of k +2 period, ipc(k +1) is the predicted value of the current after the feedback compensation of the k +1 period, Dk+1Is a duty cycle signal applied to the k +1 period.
Has the advantages that: compared with the prior art, the feedback compensation parameter self-adaptive model prediction control method of the full-bridge converter has the advantages that in the actual process, the interference of factors such as noise, load sudden change and the like can cause the parameters, the model structure and the like to change, the self-adaptive feedback compensation parameter is adopted to realize the closed-loop control of the system, the feedback error coefficient is updated by utilizing the real-time feedback current information and the current predicted value, and the error of the current actual value in each period, so that the feedback compensation capability of the system can be flexibly adjusted without depending on a system mathematical model, and the dynamic response of the system is improvedThe velocity should be used. Meanwhile, the anti-interference capability is better for the condition of sudden change of the parameters of the circuit system, and the robustness is good; the reference current value of the k +2 period is predicted by using the reference values of the first 4 periods, sufficient time can be used for calculating the optimal predicted current tracking given value path of the k +2 period in the current period, and the effect of time delay of a system can be well compensated. And the duty ratio D is solved by calculating the predicted value of the k +2 periodk+1Acting on the k +1 period, the delay of one period caused by system sampling and calculation can be compensated better; the invention effectively improves the control precision and stability, and has the defects of complex circuit design, difficult modification, influence on the control performance of the device along with temperature drift and the like compared with analog control; meanwhile, the invention adopts full digital control, has high control precision, convenient modeling and easy operation, and is not influenced by the environment of actual devices.
Drawings
FIG. 1 is a control block diagram of the present invention;
FIG. 2 is a flow chart of a control program implementation of the present invention;
fig. 3 is a diagram of a current tracking path in the present invention.
Detailed Description
For a better understanding of the contents of the present patent application, the technical solutions of the present invention will be further described below with reference to the accompanying drawings and specific examples.
As shown in fig. 1-3, the main symbol names in the drawings: vdcIs the DC bus voltage, S1,S2,S3,S4Is a power switch device, L is an equivalent inductance, R is an output load, ioIs a current feedback sampling signal irefIs a given value of the current ip(k +1) current value predicted by model, ipc(k +1) is the compensated current value.
The invention mainly discloses a model prediction control method for feedback compensation parameter self-adaption of a full-bridge converter. Taking a full-bridge converter as an example, as shown in fig. 1, the system control block diagram of the present example includes a full-bridge main power circuit, a sampling conditioning circuit and a controller portion. The main circuit comprises S1,S2,S3,S4The four power switch devices form 2 bridge arms, and the load equivalent resistance R and the equivalent inductance L are connected between the midpoints of the 2 bridge arms. The sampling conditioning circuit collects output current once per period, converts the output current into a reasonable amplitude range, performs analog-to-digital conversion on the output current and substitutes the analog-to-digital conversion into a control algorithm for operation. The controller comprises a model prediction control module, a feedback correction module and a PWM generation module. Control flow as shown in fig. 2, a method for model predictive control of full-bridge converter feedback compensation parameter adaptation includes the following steps:
step 1) establishing a state equation of output current of a full-bridge circuit by taking RL as a load, and discretizing the equation by using an Euler method to obtain a system discrete model of a k period; collecting output current i with current sensoroConverting the analog signal into an analog signal with a proper amplitude range through a conditioning circuit, and then converting the analog signal into an output current digital signal i through an ADC (analog-to-digital converter)o(k) Substituting the prediction model to calculate to obtain a current prediction value i of the k +1 periodp(k+1)；
Step 2) predicting the current i of the k +1 periodp(k +1), predicted value of current i for k periodp(k) Solving the compensation parameter h (k) and the error parameter e (k) of the k period by adopting a least square method according to the actual current feedback values acquired for N times in the k period, and comparing the compensation parameter h (k) and the error parameter e (k) with a reference value iref(k) And the actual current feedback value io(k) Comparing the error e (k) to obtain a feedback compensation coefficient h (k) of the k period;
step 3) feeding back the compensated current predicted value i in the k periodpc(k) And the actual feedback value i of the currento(k) The error of the current is compensated to a current predicted value i of a k +1 period through a feedback correction linkp(k + 1); predicting the compensated current ipc(k +1) is substituted into the prediction model as the initial value of the next period prediction to iterate once again to obtain the predicted value I of the output current in the k +2 periodp(k+2)；
Step 4) adopting an extrapolation algorithm to predict the reference current value i of the k +2 period by using the reference values of the previous 4 periodsref(k + 2); selecting and referring current value i by considering delay time of system calculation and samplingref(k +2) predicted value i of current for k +2 period close topSubstitution of (k +2)The duty ratio D acting on the k +1 period is calculated by a prediction modelk+1To perform delay compensation; by using Dk+1And generating a modulation signal, comparing the modulation signal with a carrier signal, generating a driving signal to control the on and off of a switching tube, completing the prediction and correction of a k period, and repeating the steps 1) to 4) in the k +1 period.
In the step 1), an equation of state of the output current of the full-bridge circuit is established by taking RL as a load:
the k period system discrete model obtained by discretizing the k period through the Euler method is as follows:
wherein i (k +1) is the output current of k +1 period after dispersion, VoTo output a voltage, DkDuty ratio, V, for k period predicted for last perioddcIs DC bus voltage, L is load equivalent inductance, R is load resistance, TsThe cycle time is controlled for discrete switches.
In the step 1), substituting the prediction model to calculate to obtain a current prediction value i of a k +1 periodp(k +1) is:
wherein: i.e. io(k) The feedback value is actually output for k cycles.
In the step 2), the compensation parameter h (k) of the k period is solved by adopting a least square method, and the k period is:
wherein xm＝ip(k)-io(m)，y(m)＝ip(k+1)-io(m)，ip(k) For the current prediction of k periods, ipWith (k +1) being k +1 periodPredicted value of current, io(m) is an m-time actual output feedback value acquired in k periods, wherein m is 1,2, …; n is the frequency of current output feedback values collected in each period, and the value of N is determined by the performance of the current sensor.
In the step 2), the error parameter e (k) of the k period is solved by adopting a least square method, and the error parameter e (k) is:
wherein
In step 2), the optimal feedback compensation parameter h (k) of the k period is:
wherein, omega is an error compensation threshold value and is determined by the specific experimental conditions; 0.005 is the engineering empirical value of the feedback compensation parameter.
In step 3), the current prediction value after feedback compensation is as follows: i.e. ipc(k+1)＝iP(k+1)+h(k)[ipc(k)-io(k)](ii) a Wherein ipc(k) Feedback compensated current prediction value for k period, io(k) Is the actual current feedback value of k cycles.
In step 4), reference current value i of k +2 periodref(k +2) is:
iref(k+2)＝10iref(k)-20iref(k-1)+15iref(k-2)-4iref(k-3)
wherein iref(k)，iref(k-1)，iref(k-2)，irefAnd (k-3) are reference current values of a k period, a k-1 period, a k-2 period and a k-3 period respectively.
In step 4), predicting the current i in the k +2 periodp(k +2) is:
in step 4), the duty ratio acting on the k +1 period is:
wherein iref(k +2) is the current reference value of k +2 period, ipc(k +1) is the predicted value of the current after the feedback compensation of the k +1 period, Dk+1Is a duty cycle signal applied to the k +1 period.
Examples
In step 1): the current state equation based on the full-bridge circuit and taking RL as the load is as follows;
wherein: l is load equivalent inductance, R is load resistance, i is output load current, D is duty ratio, VdcIs the dc bus voltage.
Discretizing the current state equation by using an Euler method, and sorting to obtain a discrete model as follows:
wherein i (k +1) is the output current value of k +1 period after dispersion, DkDuty ratio, V, for k period predicted for last perioddcIs DC bus voltage, L is load equivalent inductance, R is load resistance, TsThe cycle time is controlled for discrete switches.
Making the current feedback value of the k period equal to the current predicted value, and operating the predicted model to obtain the current predicted value i of the k +1 periodp(k +1) is:
wherein: t issControlling the period time for discrete switching, DkPredicted contribution to k-period for last periodDuty ratio of io(k) The feedback value is actually output.
In step 2): in order to improve the accuracy of prediction output and ensure the accuracy of a prediction quantity in a k +2 period, the method adds parameter self-adaptive feedback correction on the basis of model prediction control. The error compensation formula of the current prediction value is as follows:
ip(k+1)＝h(k)[ip(k)-io(k)]+e(k)+io(k)；
wherein io(k) To actually output the feedback value, let y (k) ip(k+1)-io(k)，x(k)＝ip(k)-io(k)，e(k)＝iref(k)-io(k) The error between the current reference value and the actual feedback value;
the above equation may be changed as: y (k) ═ h (k) x (k) + e (k) is a linear function, so h (k) and e (k) can be calculated by least squares fitting.
Let xm＝ip(k)-io(m)，y(m)＝ip(k+1)-io(m)，ip(k) For the current prediction of k periods, ip(k +1) is the predicted value of the current for the k +1 period, ioAnd (m) is the m-time actual output feedback values acquired in the k period. N (x) acquired with k periodsm,ym) The slope h (k) and intercept e (k) of the linear function y (k) are calculated.
Wherein
m is 1,2, …; n is the frequency of current output feedback values collected in each period, and the value of N is determined by the performance of the current sensor.
And e (k) 'calculated by using a least square method is compared with an error actual value e (k), if | e (k) < e >, (k) | is less than or equal to omega, the error compensation parameter of the k period is h (k)', otherwise, the calculated error compensation can not meet the actual requirement, and the error compensation parameter of the k period is an engineering empirical value 0.005. Wherein omega is an error compensation threshold value and is determined by the specific conditions of the experiment. The error compensation parameters of k period obtained by sorting are as follows:
in step 3): in order to reduce the interference of factors such as inaccurate model parameters and sampling errors, a feedback correction link is added to compensate the predicted current value so that the predicted value of the k +1 period is closer to a given value, and the compensated current predicted value is as follows:
ipc(k+1)＝iP(k+1)+h(k)[ipc(k)-io(k)]。
wherein Ipc(k) The predicted current value of k period is Io (k), and the actual current feedback value of k period is Io (k).
In the step 4): the reference current value for the k +2 period is:
iref(k+2)＝10iref(k)-20iref(k-1)+15iref(k-2)-4iref(k-3)。
the control strategy adopting two-step prediction is shown in fig. 3, the optimal duty ratio calculated by using a k +2 cycle in a k +1 cycle compensates the control delay, and the predicted value of the current in the k +2 cycle is as follows:
selecting a reference current value i corresponding to the k +2 periodref(k +2) nearest predicted value ip(k +2) making it iref(k+2)≈ip(k +2), substituting the above equation can determine the duty ratio D acting on the k +1 cyclek+1Comprises the following steps:
fig. 1 is a control block diagram of a full-bridge inverter, which comprises a full-bridge main circuit part, a prediction model part, a feedback correction part, a PWM generation part and a digital isolation driving part.
Fig. 2 is a flow portion of a control algorithm, and a sampling output current signal is transmitted into a predictive control model to calculate and generate an optimal duty ratio signal of a main circuit switching device.
Fig. 3 is a path diagram of a predicted current tracking given current, and in consideration of the delay problem of sampling calculation, the optimal duty ratio of k +2 period is calculated at the beginning of k period, and the selected optimal duty ratio is applied to k +1 period through calculation of one period to compensate for delay.
The above embodiments are only specific examples of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the claims.
Claims (10)
1. A full-bridge converter feedback compensation parameter adaptive model prediction control method is characterized by comprising the following steps:
step 1) establishing a state equation of output current of a full-bridge circuit by taking RL as a load, and discretizing the equation by using an Euler method to obtain a system discrete model of a k period; collecting output current i with current sensoroConverting the analog signal into an analog signal with a proper amplitude range through a conditioning circuit, and then converting the analog signal into an output current digital signal i through an ADC (analog-to-digital converter)o(k) Substituting the prediction model to calculate to obtain a current prediction value i of the k +1 periodp(k+1)；
Step 2) predicting the current i of the k +1 periodp(k +1), predicted value of current i for k periodp(k) Solving the compensation parameter h (k) and the error parameter e (k) of the k period by adopting a least square method according to the actual current feedback values acquired for N times in the k period, and comparing the compensation parameter h (k) and the error parameter e (k) with a reference value iref(k) And the actual current feedback value io(k) Comparing the error e (k) to obtain a feedback compensation coefficient h (k) of the k period;
step 3) feeding back the compensated current predicted value i in the k periodpc(k) Andactual feedback value i of currento(k) The error of the current is compensated to a current predicted value i of a k +1 period through a feedback correction linkp(k + 1); predicting the compensated current ipc(k +1) is substituted into the prediction model as the initial value of the next period prediction to iterate once again to obtain the predicted value I of the output current in the k +2 periodp(k+2)；
Step 4) adopting an extrapolation algorithm to predict the reference current value i of the k +2 period by using the reference values of the previous 4 periodsref(k + 2); selecting and referring current value i by considering delay time of system calculation and samplingref(k +2) predicted value i of current for k +2 period close topSubstituting (k +2) into the prediction model to calculate the duty ratio D acting on the k +1 periodk+1To perform delay compensation; by using Dk+1And generating a modulation signal, comparing the modulation signal with a carrier signal, generating a driving signal to control the on and off of a switching tube, completing the prediction and correction of a k period, and repeating the steps 1) to 4) in the k +1 period.
2. The method of claim 1, wherein the method comprises the following steps: in step 1), the RL is used as a load to establish a state equation of the output current of the full bridge circuit:
the system discrete model of k period obtained by discretizing the Euler method is as follows:
wherein i (k +1) is the output current of k +1 period after dispersion, VoTo output a voltage, DkDuty ratio, V, for k period predicted for last perioddcIs DC bus voltage, L is load equivalent inductance, R is load resistance, TsThe cycle time is controlled for discrete switches.
3. The method of claim 2, wherein the method comprises the following steps: in step 1), the current prediction value i of the k +1 period is obtained by calculation through the substitution prediction modelp(k +1) is:
wherein: i.e. io(k) The feedback value is actually output for k cycles.
4. The method of claim 2, wherein the method comprises the following steps: in step 2), the compensation parameter h (k) of k period solved by the least square method is:
wherein xm＝ip(k)-io(m)，y(m)＝ip(k+1)-io(m)，ip(k) For the current prediction of k periods, ip(k +1) is the predicted value of the current for the k +1 period, io(m) is an m-time actual output feedback value acquired in k periods, wherein m is 1,2, …; n is the frequency of current output feedback values collected in each period, and the value of N is determined by the performance of the current sensor.
5. The method of claim 4, wherein the method comprises the following steps: in step 2), the error parameter e (k) of k period solved by the least square method is:
wherein
6. The method of claim 5, wherein the method comprises the following steps: in step 2), the optimal feedback compensation parameter h (k) of the k period is:
wherein, omega is an error compensation threshold value and is determined by the specific experimental conditions; 0.005 is the engineering empirical value of the feedback compensation parameter.
7. The method of claim 1, wherein the method comprises the following steps:
in step 3), the current prediction value after feedback compensation is as follows: i.e. ipc(k+1)＝iP(k+1)+h(k)[ipc(k)-io(k)](ii) a Wherein ipc(k) Feedback compensated current prediction value for k period, io(k) Is the actual current feedback value of k cycles.
8. The method of claim 7, wherein the method comprises the following steps: in step 4), the reference current value i of the k +2 periodref(k +2) is:
iref(k+2)＝10iref(k)-20iref(k-1)+15iref(k-2)-4iref(k-3)
wherein iref(k)，iref(k-1)，iref(k-2)，irefAnd (k-3) are reference current values of a k period, a k-1 period, a k-2 period and a k-3 period respectively.
9. The method of claim 8, wherein the method comprises the following steps: in step 4), the predicted value i of the current of the k +2 periodp(k +2) is:
10. the method of claim 9, wherein the method comprises the following steps: in step 4), the duty ratio acting on the k +1 period is:
wherein iref(k +2) is the current reference value of k +2 period, ipc(k +1) is the predicted value of the current after the feedback compensation of the k +1 period, Dk+1Is a duty cycle signal applied to the k +1 period.
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