T-s fuzzy boost circuit photovoltaic maximum power point tracking control method

Abstract

This invention discloses a photovoltaic maximum power point tracking (MPPT) control method based on a T-S fuzzy boost circuit. The method models the DC-DC boost converter circuit in the photovoltaic MPT system using a T-S fuzzy model. It determines the inductor current and output capacitor voltage at the system's maximum power output state through a maximum power point scanning method, constructing a photovoltaic MPT reference model. A controller computational model based on an observer observes the T-S fuzzy boost circuit system model and generates state estimates. These state estimates are compared with the state values ​​output by the photovoltaic MPT reference model to generate a PWM control voltage. The system output error is calculated based on the output voltages of the photovoltaic MPT reference model and the T-S fuzzy boost circuit system model, yielding the constraints. This control algorithm enables the photovoltaic MPT system to track efficiently and accurately.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation control technology, and in particular relates to a design method for photovoltaic maximum power point tracking control using a TS fuzzy boost circuit. Background Technology

[0002] Maximum power point tracking (MPPT) ensures that photovoltaic panels always output maximum power under different light and temperature conditions. It is a crucial component of current photovoltaic power generation systems and is widely used in photovoltaic inverters, commercial aerospace, photovoltaic new energy vehicles, and other fields.

[0003] The working principle of photovoltaic maximum power point tracking is to change the equivalent load impedance across the photovoltaic panel by using a power electronic converter (such as a Boost circuit) so that it is always equal to the characteristic impedance of the photovoltaic cell when it outputs maximum power under the current environment.

[0004] Currently, photovoltaic (PV) maximum power point tracking (MPPT) methods mainly fall into two categories: the traditional incremental conductance method and the perturbation-observation method, both of which have been gradually replaced due to their poor accuracy; and the intelligent optimization PV MPPT algorithm, which optimizes the process through data calculation. Both methods have certain drawbacks: different PV panels have different models, limiting the effectiveness of using a single method; the PV system cannot adjust its operating state in time to reach the maximum power point under changing external environmental conditions such as sunlight and temperature; and PV systems contain numerous nonlinear and strongly coupled terms, thus requiring a suitable algorithm to address these issues. Summary of the Invention

[0005] The purpose of this invention is to solve the technical problems of excessive computational data and low tracking efficiency in the optimization algorithm of photovoltaic maximum power point tracking system based on the TS fuzzy boost circuit photovoltaic maximum power point tracking control method.

[0006] To achieve the above objectives, the technical solution adopted by this invention is a photovoltaic maximum power point tracking control method based on TS fuzzy boost circuit, comprising the following steps:

[0007] Step 1) Model the DC-DC Boost converter circuit in the photovoltaic maximum power point tracking system using the TS fuzzy model, and define the TS fuzzy Boost circuit system model in the form of state-space equations.

[0008] Step 2) Determine the inductor current and output capacitor voltage in the photovoltaic maximum power point tracking system at the maximum power output state by means of maximum power point scanning. Based on the inductor current, output capacitor voltage and TS fuzzy Boost circuit system model, construct a photovoltaic maximum power point tracking reference model.

[0009] Step 3) Construct the computational model of the quantizer and the computational model of the observer-based controller, establish system model constraints that meet the requirements of stable system operation and H∞ performance, solve the system model constraints, and obtain the observer gain and controller gain in the computational model of the observer-based controller.

[0010] Step 4) The output voltage of the TS fuzzy Boost circuit system model is quantized using the quantizer's computational model and input into the observer-based controller's computational model. The observer-based controller's computational model observes the quantized output voltage value of the TS fuzzy Boost circuit system model and generates a state estimate. The state estimate is compared with the state value output by the photovoltaic maximum power point tracking reference model to calculate the output PWM control voltage. The PWM control voltage output by the observer-based controller's computational model is quantized using the quantizer's computational model and input into the TS fuzzy Boost circuit system model. The TS fuzzy Boost circuit system model outputs voltage under the control of the quantized PWM control voltage value. The system output error is calculated based on the output voltage of the photovoltaic maximum power point tracking reference model and the output voltage of the TS fuzzy Boost circuit system model to evaluate system stability.

[0011] Furthermore, in step 1), the photovoltaic maximum power point tracking system includes a photovoltaic array and a DC-DC boost converter circuit and a load R connected to the photovoltaic array. O The DC-DC Boost converter circuit includes capacitor C. PV Inductor L, MOSFET, diode D, output capacitor C O The capacitor C PV The voltage output terminal of the photovoltaic array is connected, with one end connected to one end of inductor L and the other end connected to the first terminal of the MOSFET. The other end of inductor L is connected to the second terminal of the MOSFET and the positive terminal of diode D. The output capacitor C... O One end is connected to the cathode of diode D, and the other end is connected to the first terminal of MOSFET. The output capacitor C O The two ends are connected to the load R as the output terminals of the DC-DC Boost converter circuit. O The TS fuzzy boost circuit system model is as follows:

[0012] The tracking system's first The fuzzy rule is described as follows: If yes ,and yes ,So

[0013]

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[0017] in , For the antecedent variable, and For fuzzy sets, For the antecedent variable In fuzzy sets The membership function in For fuzzy basis functions, For system status, The quantized PWM control voltage. For the photovoltaic array output current, For the system output voltage, and Inductors The current and parasitic resistance on it, For capacitor Voltage at both ends, For load The voltage at both ends, for The parasitic resistance of the tube, Diode parasitic resistance, For output capacitor parasitic resistance, For capacitor The capacitance value, For output capacitor The capacitance value, For load Resistance value Inductor inductance value, and The standardized membership function value; and satisfy , , , Given a matrix of constant functions, It is the identity matrix. This represents the time-varying uncertainty term in the model.

[0018] Furthermore, in step 2), the photovoltaic maximum power point tracking reference model is as follows:

[0019]

[0020] , ,

[0021] ;

[0022] in, The state variables output by the reference model. The input to the reference model, The output current of the photovoltaic array at the maximum power point. This represents the current value of inductor L at the maximum power point. Output capacitor at maximum power point The capacitor voltage point value, The output voltage is for the reference model.

[0023] Furthermore, since the measured data is transmitted over a network, the voltage output of the TS fuzzy Boost circuit system model... With PWM control voltage input During transmission, it is quantized by a quantizer. , The calculation model of the quantizer is as follows:

[0024]

[0025]

[0026] in, Represents the quantization function. and This represents the dynamic quantization parameter in the quantization function;

[0027] The quantizer used to quantize the output voltage of the TS fuzzy Boost circuit system model is called quantizer 2, and the quantizer used to quantize the PWM control voltage output by the observer-based controller calculation model is called quantizer 1.

[0028] Furthermore, in step 3), the computational model of the observer-based tracking controller is as follows:

[0029]

[0030] in, and These are the observer's state estimate and output voltage estimate of the tracking system, respectively. , For observer gain, and satisfy , and Given a constant matrix, This represents the time-varying uncertainty term in the computational model;

[0031] The controller's computational model is defined by the following formula:

[0032] ;

[0033] in, , For controller gain, and satisfy , , , is a known constant matrix. This represents the time-varying uncertainty term in the computational model;

[0034] The system state tracking error is defined as: The system output error is defined as: , The constraints that satisfy the system's H∞ performance index are as follows:

[0035]

[0036] in For the final moment, This refers to the system's H∞ performance index.

[0037] Furthermore, the Lyapunov function method and matrix inequality scaling techniques are used to solve for the constraints that satisfy the system's H∞ performance and eliminate uncertainties:

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[0061] in, For Lyapunov matrices, For Lyapunov matrices, It is a Lyapunov matrix; For an auxiliary matrix of appropriate dimension, For an auxiliary matrix of appropriate dimension, For an auxiliary matrix of appropriate dimension, This is the quantization error bound for quantizer 1. This is the quantization error bound for quantizer 2. This is the quantization range of quantizer 1. This is the quantization range of quantizer 2. For H∞ performance indicators, The scalar given to quantizer 1 The scalar given to quantizer 2, The scalar introduced during the calculation process of quantizer 1. The scalar introduced during the calculation process of quantizer 2. To introduce scalars into the calculation process. To introduce scalars into the calculation process. To introduce scalars into the calculation process, the controller and observer gains can be obtained using the following formulas:

[0062] ,

[0063] The solved constraints ensure that the TS fuzzy Boost circuit photovoltaic maximum power point tracking control system is asymptotically stable and meets the H∞ performance requirements.

[0064] Furthermore, the system state variables include inductor current and output capacitor voltage, which are transmitted to the controller via a network. The PWM control voltage that satisfies the constraints is calculated and quantized. The quantized PWM control voltage is then... The drive signal used for the MOSFET in the DC-DC Boost converter circuit.

[0065] Compared with the prior art, the beneficial effects of the present invention are:

[0066] 1. A TS-type fuzzy algorithm is used to establish a TS fuzzy Boost circuit system model for the DC-DC Boost converter circuit of the photovoltaic maximum power point tracking system. The inductor current and output capacitor voltage of the photovoltaic maximum power point tracking system at the maximum power point are substituted into the TS fuzzy Boost circuit system model to construct a photovoltaic maximum power point tracking reference model. The output voltage of the photovoltaic maximum power point tracking reference model is used as the control target. Compared with the traditional optimization method, the method of this invention can accurately control the photovoltaic maximum power point tracking system to the maximum power point while having the advantages of small computational workload and high control efficiency. This reduces the requirements for controller hardware and achieves the purpose of cost reduction and efficiency improvement.

[0067] 2. The TS fuzzy boost circuit system model introduces uncertain parameters. By adjusting the uncertain parameters, the uncertainty caused by the aging of system components can be eliminated. Uncertain parameters are also introduced in the constructed observer calculation model and the observer-based tracking controller calculation model. By adjusting the uncertain parameters, the uncertainty caused by the gain perturbation of the controller and observer can be eliminated. The observer calculation model estimates the state of the system model, which can eliminate the influence of unmeasurable states on system control. Therefore, the method of this invention improves the stability and accuracy of system control.

[0068] 3. By defining the system output error, the constraints of the system H∞ performance index are constructed, and the system output error, as a system stability evaluation index, can efficiently, accurately and simply obtain the stable state of the system.

[0069] 4. Data quantification has a greater impact on data transmission than actual conditions, thus improving control precision.

[0070] 5. By using matrix inequality scaling techniques to solve the constraints, the system model can operate stably and meet the H∞ performance requirements. Attached Figure Description

[0071] Figure 1 This is a schematic diagram of the control system structure of the present invention;

[0072] Figure 2 This is a circuit diagram of the photovoltaic maximum power point tracking system of the present invention;

[0073] Figure 3 The PWM control voltage input quantity of this invention A curve graph;

[0074] Figure 4 The system state tracking error of the present invention A curve graph;

[0075] Figure 5The voltage output of the TS fuzzy boost circuit system model of the present invention Voltage output of the photovoltaic maximum power point tracking reference model The curve graph. Detailed Implementation

[0076] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can fully understand other advantages and technical effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through different specific embodiments, and the details in this specification can also be applied based on different viewpoints, with various modifications or changes made without departing from the overall design concept of the invention. The following exemplary embodiments of the present invention can be implemented in many different forms and should not be construed as being limited to the specific embodiments set forth herein.

[0077] refer to Figure 1 and Figure 2 The photovoltaic maximum power point tracking system in this embodiment includes a photovoltaic array, a DC-DC Boost converter circuit connected to the photovoltaic array, and a load R. O The DC-DC Boost converter circuit includes capacitor C. PV Inductor L, MOSFET, diode D, output capacitor C O The capacitor C PV The voltage output terminal of the photovoltaic array is connected, with one end connected to one end of inductor L and the other end connected to the first terminal of the MOSFET. The other end of inductor L is connected to the second terminal of the MOSFET and the positive terminal of diode D. The output capacitor C... O One end is connected to the cathode of diode D, and the other end is connected to the first terminal of MOSFET. The output capacitor C O The two ends are connected to the load R as the output terminals of the DC-DC Boost converter circuit. O After photoelectric conversion, the photovoltaic array outputs a voltage to the DC-DC Boost converter circuit. The DC-DC Boost converter circuit performs DC-DC conversion on the output voltage of the photovoltaic array to supply power to the load. The purpose of the photovoltaic maximum power point tracking system is to enable the system to achieve maximum power output during the power supply process. Therefore, the controller needs to introduce a control algorithm to regulate the PWM control voltage so that the DC-DC Boost converter circuit can output maximum power.

[0078] This embodiment provides a photovoltaic maximum power point tracking control method based on a TS fuzzy boost circuit, which specifically includes the following steps:

[0079] Step 1) Considering the uncertainties caused by factors such as system aging, this implementation uses the TS fuzzy algorithm to model the DC-DC Boost converter circuit. The TS fuzzy Boost circuit system model is defined in the form of state-space equations. Figure 2 The DC-DC Boost converter circuit can be used to construct a TS fuzzy Boost circuit system model through state equations, as follows:

[0080] The tracking system's first The fuzzy rule is described as follows: If yes ,and yes ,So

[0081]

[0082]

[0083] , , , , ;

[0084] in , For the antecedent variable, and For fuzzy sets, For the antecedent variable In fuzzy sets The membership function in For fuzzy basis functions, For system status, The quantized PWM control voltage. For the photovoltaic array output current, For the system output voltage, and Inductors The current and parasitic resistance on it, For capacitor Voltage at both ends, For load The voltage at both ends, for The parasitic resistance of the tube, Diode parasitic resistance, For output capacitor parasitic resistance, For capacitor The capacitance value, For output capacitor The capacitance value, For load Resistance value Inductor inductance value, and The standardized membership function value; and satisfy , , , Given a constant matrix, It is the identity matrix. The time-varying uncertainty term in the model is represented by the adjustment. The uncertain parameters can eliminate the uncertainties caused by factors such as system aging.

[0085] Step 2) The photovoltaic maximum power point tracking system is brought to a stable state by dynamically adjusting the controller parameters. According to the working principle of maximum power point tracking, a maximum power scan must first be performed on the DC-DC Boost converter circuit before tracking control to obtain the current of inductor L when the DC-DC Boost converter circuit outputs maximum power. and output capacitor voltage The data is then stored and combined with the TS fuzzy Boost circuit system model to construct a photovoltaic maximum power point tracking reference model, as shown in the following formula:

[0086]

[0087] , ,

[0088] ;

[0089] in, The state variables output by the reference model. The input to the reference model, The output current of the photovoltaic array at the maximum power point. This represents the current value of inductor L at the maximum power point. Output capacitor at maximum power point The capacitor voltage point value, The output voltage is for the reference model.

[0090] Step 3) Since the data is transmitted over a network, construct the computational model of the quantizer. Considering the existence of unmeasurable states in the TS fuzzy Boost circuit system model, design a computational model of the observer-based tracking controller. Define the system state tracking error and tracking output error, and establish system model constraints that meet the requirements of stable system operation and H∞ performance. Solve the system model constraints to obtain the observer gain and controller gain in the computational model of the observer-based controller.

[0091] The specific steps are as follows:

[0092] Because the measured data is transmitted over a network, the voltage output of the TS fuzzy Boost circuit system model... With PWM control voltage input During transmission, it is quantized by a quantizer. , The calculation model of the quantizer is as follows:

[0093]

[0094]

[0095] in, Represents the quantization function. and This represents the dynamic quantization parameter in the quantization function.

[0096] The quantizer used to quantize the output voltage of the TS fuzzy boost circuit system model is quantizer 2, with the symbol _____. The quantizer used to quantize the PWM control voltage output by the observer-based controller's computational model is quantizer 1, denoted as . .

[0097] Considering the existence of unmeasurable states in the TS fuzzy Boost circuit system model, an observer is used for system state estimation. The calculation model formula for the observer-based tracking controller is as follows:

[0098]

[0099] in, and These are the observer's state estimate and output voltage estimate of the system, respectively. , For observer gain, and satisfy , and Given a constant matrix, The time-varying uncertainty term in the model is represented by the adjustment. The uncertainty parameter can eliminate the uncertainties caused by observer gain perturbation and unobservable system state.

[0100] The controller is defined by the following formula:

[0101] ;

[0102] in, , For controller gain, and satisfy , , , is a known constant matrix. The time-varying uncertainty term in the model is represented by the adjustment. The uncertainties in the parameters can eliminate the uncertainties caused by controller gain perturbations. The system state tracking error is defined as: The system state tracking error is used to estimate the difference between the observer's estimate of the system state and the system state value output by the reference model, and drives the controller to calculate the PWM control voltage of the model adjustment output, thereby changing the system output voltage. After cyclic adjustment, the system output voltage gets closer and closer to the reference model output voltage, and finally achieves the maximum power output state.

[0103] The system output error is defined as: , The constraints that satisfy the system's H∞ performance index are as follows:

[0104]

[0105] in For the final moment, This refers to the system's H∞ performance index.

[0106] The constraints satisfying the system's H∞ performance requirement and eliminating uncertainties are solved using the Lyapunov function method and matrix inequality scaling techniques:

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[0130] in, For Lyapunov matrices, For Lyapunov matrices, It is a Lyapunov matrix; For an auxiliary variable matrix of appropriate dimension, For an auxiliary variable matrix of appropriate dimension, For an auxiliary variable matrix of appropriate dimension, This is the quantization error bound for quantizer 1. This is the quantization error bound for quantizer 2. This is the quantization range of quantizer 1. This is the quantization range of quantizer 2. For H∞ performance indicators, The scalar given to quantizer 1 The scalar given to quantizer 2, The scalar introduced during the calculation process of quantizer 1. The scalar introduced during the calculation process of quantizer 2. To introduce scalars into the calculation process. To introduce scalars into the calculation process. To introduce scalars into the calculation process, the controller and observer gains can be obtained using the following formulas:

[0131] ,

[0132] The solved constraints ensure that the TS fuzzy Boost circuit photovoltaic maximum power point tracking control system is asymptotically stable and meets the H∞ performance requirements.

[0133] Step 4) The output voltage of the TS fuzzy Boost circuit system model is quantized using the quantizer's computational model and input into the observer-based controller's computational model. The observer-based controller's computational model observes the quantized output voltage value of the TS fuzzy Boost circuit system model and generates a state estimate. The state estimate is compared with the state value output by the photovoltaic maximum power point tracking reference model to calculate the output PWM control voltage. The PWM control voltage output by the observer-based controller's computational model is quantized using the quantizer's computational model and input into the TS fuzzy Boost circuit system model. The TS fuzzy Boost circuit system model outputs voltage under the control of the quantized PWM control voltage value. The system output error is calculated based on the output voltage of the photovoltaic maximum power point tracking reference model and the output voltage of the TS fuzzy Boost circuit system model to evaluate system stability.

[0134] After verifying the stability and reliability of the control algorithm of this invention through Matlab simulation, the control algorithm is written into the controller hardware. System variables are collected and input into the controller hardware, and drive signals are output through the controller hardware. The system state variables include inductor current and output capacitor voltage, which are transmitted to the controller via a network. The PWM control voltage that meets the constraints is calculated and quantized. The quantized PWM control voltage is then... The drive signal used for the MOSFET in the DC-DC Boost converter circuit.

[0135] The stability of the control algorithm in this embodiment was verified through Matlab simulation. The Matlab simulation process included setting the parameter capacitor. ,inductance Diode on-resistance Output capacitor and resistor MOS on-resistance Output resistance Inductance and resistance Output capacitor . Figures 3-5 The simulation data curve is shown below. Figure 3 The PWM control voltage input quantity of this invention The curve, after a certain period of oscillation, shows that the system reaches a convergent state and eventually becomes 0. Figure 4 The system state tracking error of the present invention The curve graph, in which It contains three state components, namely , , . The graph shows that the system model state can track the reference model state with a small error, and eventually the two converge to 0. Figure 5 The voltage output of the TS fuzzy boost circuit system model of the present invention Voltage output of the photovoltaic maximum power point tracking reference model The curves show the voltage output of the TS fuzzy boost circuit system model. The curve is a solid line, representing the voltage output of the photovoltaic maximum power point tracking reference model. The curve is represented by a dashed line. The simulation results show that the system model output can track the reference model's voltage output with a small error, and both eventually converge to zero. This demonstrates that the control algorithm of this invention can maintain good maximum power point tracking performance.

[0136] The technical advantages of this invention are as follows: 1. By using a TS-type fuzzy algorithm to establish a TS fuzzy Boost circuit system model for the DC-DC Boost converter circuit of the photovoltaic maximum power point tracking system, and substituting the inductor current and output capacitor voltage of the photovoltaic maximum power point tracking system at the maximum power point into the TS fuzzy Boost circuit system model, a photovoltaic maximum power point tracking reference model is constructed. The output voltage of the photovoltaic maximum power point tracking reference model is used as the control target. Compared with the traditional optimization method, the method of this invention can accurately control the photovoltaic maximum power point tracking system to the maximum power point while having the advantages of small computational workload and high control efficiency, reducing the requirements for controller hardware, and achieving the purpose of cost reduction and efficiency improvement.

[0137] 2. The TS fuzzy boost circuit system model introduces uncertain parameters. By adjusting the uncertain parameters, the uncertainty caused by the aging of system components can be eliminated. Uncertain parameters are also introduced in the constructed observer calculation model and the observer-based tracking controller calculation model. By adjusting the uncertain parameters, the uncertainty caused by the gain perturbation of the controller and observer can be eliminated. The observer calculation model estimates the state of the system model, which can eliminate the influence of unmeasurable states on system control. Therefore, the method of this invention improves the stability and accuracy of system control.

[0138] 3. By defining the system output error, the constraints of the system H∞ performance index are constructed, and the system output error, as a system stability evaluation index, can efficiently, accurately and simply obtain the stable state of the system.

[0139] 4. Data quantification has a greater impact on data transmission than actual conditions, thus improving control precision.

[0140] 5. By using matrix inequality scaling techniques to solve the constraints, the system model can operate stably and meet the H∞ performance requirements.

[0141] The present invention has been described in detail above through specific embodiments and examples, but these are not intended to limit the invention. Many modifications and improvements can be made by those skilled in the art without departing from the principles of the invention, and these should also be considered within the scope of protection of the present invention.

Claims

1. A photovoltaic maximum power point tracking control method based on TS fuzzy boost circuit, characterized in that, Includes the following steps: Step 1) Model the DC-DC Boost converter circuit in the photovoltaic maximum power point tracking system using the TS fuzzy model, and define the TS fuzzy Boost circuit system model in the form of state-space equations. Step 2) Determine the inductor current and output capacitor voltage in the photovoltaic maximum power point tracking system at the maximum power output state by means of maximum power point scanning. Based on the inductor current, output capacitor voltage and TS fuzzy Boost circuit system model, construct a photovoltaic maximum power point tracking reference model. Step 3) Construct the computational model of the quantizer and the computational model of the observer-based controller, establish system model constraints that meet the requirements of stable system operation and H∞ performance, solve the system model constraints, and obtain the observer gain and controller gain in the computational model of the observer-based controller. Step 4) The output voltage of the TS fuzzy Boost circuit system model is quantized using the quantizer's computational model and input into the observer-based controller's computational model. The observer-based controller's computational model observes the quantized output voltage value of the TS fuzzy Boost circuit system model and generates a state estimate. The state estimate is compared with the state value output by the photovoltaic maximum power point tracking reference model to calculate the output PWM control voltage. The PWM control voltage output by the observer-based controller's computational model is quantized using the quantizer's computational model and input into the TS fuzzy Boost circuit system model. The TS fuzzy Boost circuit system model outputs voltage under the control of the quantized PWM control voltage value. The system output error is calculated based on the output voltage of the photovoltaic maximum power point tracking reference model and the output voltage of the TS fuzzy Boost circuit system model to evaluate system stability.

2. The photovoltaic maximum power point tracking control method based on TS fuzzy Boost circuit as described in claim 1, characterized in that, In step 1), the photovoltaic maximum power point tracking system includes a photovoltaic array, a DC-DC boost converter circuit connected to the photovoltaic array, and a load R. O The DC-DC Boost converter circuit includes capacitor C. PV Inductor L, MOSFET, diode D, output capacitor C O The capacitor C PV The voltage output terminal of the photovoltaic array is connected, with one end connected to one end of inductor L and the other end connected to the first terminal of the MOSFET. The other end of inductor L is connected to the second terminal of the MOSFET and the positive terminal of diode D. The output capacitor C... O One end is connected to the cathode of diode D, and the other end is connected to the first terminal of MOSFET. The output capacitor C O The two ends are connected to the load R as the output terminals of the DC-DC Boost converter circuit. O The TS fuzzy boost circuit system model is as follows: The tracking system's first The fuzzy rule is described as follows: If yes ,and yes ,So ， ， ， ， ； in , For the antecedent variable, and For fuzzy sets, For the antecedent variable In fuzzy sets The membership function in For fuzzy basis functions, For system status, The quantized PWM control voltage. For the photovoltaic array output current, For the system output voltage, and Inductors The current and parasitic resistance on it, For capacitor Voltage at both ends, For load The voltage at both ends, for The parasitic resistance of the tube, Diode parasitic resistance, For output capacitor parasitic resistance, For capacitor The capacitance value, For output capacitor The capacitance value, For load Resistance value Inductor inductance value, and The standardized membership function value; and satisfy , , , Given a constant matrix, It is the identity matrix. This represents the time-varying uncertainty term in the model.

3. The photovoltaic maximum power point tracking control method based on TS fuzzy Boost circuit as described in claim 2, characterized in that, In step 2), the photovoltaic maximum power point tracking reference model is as follows: in ， ， ； in The state variables output by the reference model. The input to the reference model, The output current of the photovoltaic array at the maximum power point. This represents the current value of inductor L at the maximum power point. Output capacitor at maximum power point The capacitor voltage point value, The output voltage is for the reference model.

4. The photovoltaic maximum power point tracking control method based on TS fuzzy Boost circuit as described in claim 3, characterized in that, In step 3), the computational model of the observer-based tracking controller is as follows: in, and These are the observer's state estimate and output voltage estimate of the tracking system, respectively. , For observer gain, and satisfy , and Given a constant matrix, This represents the time-varying uncertainty term in the computational model; The controller's computational model is defined by the following formula: ； in, , For controller gain, and satisfy , , , is a known constant matrix. This represents the time-varying uncertainty term in the computational model; The system state tracking error is defined as: The system output error is defined as: , The constraints that satisfy the system's H∞ performance index are as follows: in For the final moment, This refers to the system's H∞ performance index.

5. The photovoltaic maximum power point tracking control method based on TS fuzzy Boost circuit as described in claim 4, characterized in that, Because the measured data is transmitted over a network, the voltage output of the TS fuzzy Boost circuit system model... With PWM control voltage input During transmission, it is quantized by a quantizer. , The calculation model of the quantizer is as follows: in Represents the quantization function. and This represents the dynamic quantization parameter in the quantization function; The quantizer used to quantize the output voltage of the TS fuzzy Boost circuit system model is called quantizer 2, and the quantizer used to quantize the PWM control voltage output by the observer-based controller calculation model is called quantizer 1.

6. The photovoltaic maximum power point tracking control method based on TS fuzzy Boost circuit as described in claim 5, characterized in that, In step 3), the Lyapunov function method and matrix inequality scaling techniques are used to solve for the constraints that satisfy the system's H∞ performance and eliminate uncertainties: in ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， in, For Lyapunov matrices, For Lyapunov matrices, It is a Lyapunov matrix; For an auxiliary matrix of appropriate dimension, For an auxiliary matrix of appropriate dimension, For an auxiliary matrix of appropriate dimension, This is the quantization error bound for quantizer 1. This is the quantization error bound for quantizer 2. This is the quantization range of quantizer 1. This is the quantization range of quantizer 2. For H∞ performance indicators, The scalar given to quantizer 1 The scalar given to quantizer 2, The scalar introduced during the calculation process of quantizer 1. The scalar introduced during the calculation process of quantizer 2. To introduce scalars into the calculation process. To introduce scalars into the calculation process. To introduce scalars into the calculation process, the controller and observer gains can be obtained using the following formulas: ， The solved constraints ensure that the TS fuzzy Boost circuit photovoltaic maximum power point tracking control system is asymptotically stable and meets the H∞ performance requirements.

7. The photovoltaic maximum power point tracking control method based on TS fuzzy Boost circuit as described in claim 6, characterized in that, The system state variables include inductor current and output capacitor voltage, which are transmitted to the controller via a network. The PWM control voltage that meets the constraints is calculated and quantized. The quantized PWM control voltage is then... The drive signal used for the MOSFET in the DC-DC Boost converter circuit.

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