System and method for improving feedback and stability in digital amplifiers

Abstract

The present application relates to the field of digital amplifier, specifically to a system and method for improving feedback and stability in digital amplifier. The system comprises a signal acquisition module, a feedback signal processing module, an adaptive adjustment module, a comparison and control module and a digital amplifier main module; the signal acquisition module accurately acquires output signals; the feedback signal processing module filters, amplifies and compensates for delay, amplitude attenuation and environmental factors on the output information; the adaptive adjustment module adjusts the feedback parameters of the device in real time through a fuzzy control algorithm and a neural network model; the comparison and control module adjusts the input signal according to the error signal, and the digital amplifier main module outputs the amplified signal. The present application not only greatly improves the accuracy of the feedback signal, enhances the stability of the amplifier under various working conditions, but also effectively improves the quality of the transmission signal, reduces distortion and noise, improves the reliability of the system and prolongs its service life, and has good application prospect.

Description

Technical Field

[0001] The present invention relates to the field of digital amplifiers, and in particular to a system and method for improving feedback and stability in a digital amplifier. Background Art

[0002] Digital amplifiers are widely used in audio, communications, and other fields due to their high efficiency and compact size. However, feedback and stability issues have always been key factors restricting the performance of digital amplifiers.

[0003] The feedback mechanism of traditional digital amplifiers has certain limitations. For example, the sampling accuracy and real-time performance of the feedback signal are insufficient, resulting in the amplifier's slow response to changes in the output signal, which in turn affects audio quality and the accuracy of communication signals. Furthermore, factors such as quantization errors and noise interference during digital signal processing can easily distort the feedback signal, further reducing the amplifier's stability.

[0004] In addition, under different load conditions, the output characteristics of the digital amplifier will change. Traditional feedback systems find it difficult to adaptively adjust the feedback parameters, which can cause the amplifier to exhibit unstable phenomena such as oscillation and distortion under certain load conditions, affecting the reliability and performance of the system. Summary of the Invention

[0005] In one embodiment, a system for improving feedback and stability in a digital amplifier is included:

[0006] Signal acquisition module, used for real-time acquisition of the output signal of the digital amplifier;

[0007] Feedback signal processing module processes the collected output signal, including filtering, amplification and compensation operations;

[0008] Adaptive adjustment module, automatically adjusts feedback parameters according to different load conditions and system operating status;

[0009] A comparison and control module compares the processed feedback signal with the reference signal and generates a control signal according to the comparison result, which is used to adjust the input signal of the digital amplifier;

[0010] The digital amplifier body amplifies the input signal according to the control signal and outputs the amplified signal.

[0011] Preferably, the signal acquisition module adopts a high-precision analog-to-digital converter.

[0012] Preferably, the filtering in the feedback signal processing module adopts a finite length unit impulse response filter.

[0013] Preferably, the amplification in the feedback signal processing module employs a programmable gain amplifier.

[0014] Preferably, the comparison and control module employs a proportional-integral-derivative controller.

[0015] In another embodiment, a method for improving feedback and stability in a digital amplifier comprises:

[0016] Signal acquisition: using a signal acquisition module to acquire the output signal of the digital amplifier in real time and convert it into a digital signal.

[0017] Feedback signal processing: filtering, amplifying and compensating the acquired digital signal.

[0018] Preferably, the compensation process includes delay compensation, amplitude attenuation compensation and environmental factor compensation.

[0019] Preferably, the delay compensation specifically includes:

[0020] Delay measurement: marking the characteristic points of the signal at the signal acquisition end and the processed output end respectively, and obtaining the delay time of the signal by comparing the time difference between the two characteristic points;

[0021] Delay model establishment: establishing a delay model according to the transmission path and processing link of the signal, which considers the delay factors in the signal analog-to-digital conversion, filtering and amplification process, and is expressed in the formula as: , where is the delay time in the analog-to-digital conversion process, is the delay time in the filtering link, is the delay time in the amplification link;

[0022] Delay compensation: based on the measured delay time and the established delay model, using a FIFO buffer to compensate for the delay of the signal, storing the acquired signal in the FIFO buffer in chronological order, and adjusting the timing of reading the signal from the buffer according to the delay time .

[0023] Preferably, the amplitude attenuation compensation specifically includes:

[0024] Amplitude monitoring: real-time monitoring of the amplitude change of the feedback signal and comparison with the pre-set reference amplitude;

[0025] Attenuation analysis: according to the monitored amplitude change, the signal is segmented and processed according to the frequency component, and the attenuation of different frequency signals in the transmission and processing process is analyzed;

[0026] Gain compensation: for signals of different frequency components, corresponding gain compensation algorithms are used for amplitude adjustment; specifically, according to the frequency f and the degree of attenuation of the signal, the gain that needs to be compensated is calculated;

[0027] The formula for calculating the required compensation gain is:

[0028] ;

[0029] In the formula, is a pre-set reference amplitude, is the actual amplitude of the signal with frequency f.

[0030] Preferably, the environmental factor compensation specifically includes:

[0031] Environmental parameter acquisition: real-time acquisition of environmental parameters through sensors, including temperature and humidity parameters;

[0032] Establishment of environmental compensation model: an environmental compensation model is established using a machine learning algorithm, which takes environmental parameters and signal characteristic parameters as input and the gain coefficient that needs to be compensated as output;

[0033] Compensation adjustment: multiply the obtained gain compensation coefficient with the gain in amplitude attenuation compensation to obtain the final gain adjustment value, and then use a programmable gain amplifier to adjust the gain of the signal.

[0034] Adaptive adjustment: the adaptive adjustment module monitors the load condition and system running state in real time, and automatically adjusts the feedback parameters according to the preset algorithm and model.

[0035] Preferably, the adaptive adjustment module monitors the load condition and system running state in real time, and automatically adjusts the feedback parameters according to the preset algorithm and model, specifically including:

[0036] Parameter measurement: use high-precision current and voltage sensors to accurately detect the voltage and current of the load in real time, and use the acquired voltage and current data to calculate the resistance value of the load using Ohm's law;

[0037] Dynamic analysis: based on the obtained parameters of the load, the dynamic changes of the load are analyzed in depth;

[0038] Data recording and storage: record the various parameters and changes of the load in detail and store them in the database of the system;

[0039] Preliminary adjustment of fuzzy control algorithm: when the change of the load is monitored, the fuzzy control algorithm is used to preliminarily adjust the feedback parameters to generate a preliminary adjustment value of the feedback parameters;

[0040] Neural network model optimization and adjustment: Based on the initial adjustment of the fuzzy control algorithm, the neural network model is used to further optimize the feedback parameters;

[0041] Real-time adjustment and feedback: During system operation, the load is continuously monitored and feedback parameters are adjusted in real time based on the monitoring results. After each adjustment, the adjusted feedback parameters are applied to the system and the changes in the system's output signal are observed. If the output signal still deviates, the feedback parameters are further adjusted based on the deviation.

[0042] Model update and optimization: Regularly collect new load data and system operation data, add them to the training data set, and retrain the neural network model; at the same time, adjust and optimize the rules of the fuzzy control algorithm based on the actual operation results to improve the accuracy and efficiency of adaptive adjustment.

[0043] Preferably, the in-depth analysis of the dynamic changes of the load specifically includes:

[0044] Calculate the load change rate based on the acquired load parameters;

[0045] Set a mutation threshold. If the load change rate exceeds the mutation threshold, it indicates that a load mutation has occurred.

[0046] If the rate of change of the load fluctuates periodically, it indicates that load fluctuation occurs;

[0047] When load mutation and load fluctuation occur, feedback parameters are adjusted;

[0048] The calculation formula for the load change rate is:

[0049] ;

[0050] Where, is the load resistance value at the current time t, It's the last moment The load resistance value, is the time interval.

[0051] Preferably, the approximate range of the feedback parameter to be quickly adjusted using the fuzzy control algorithm specifically includes:

[0052] Constructing a fuzzy control rule set based on known expert rules, wherein the fuzzy control rule set is a series of conditional statements, wherein the load resistance change rate is an input and the feedback parameter is an output;

[0053] Input the current load resistance change rate, and according to the fuzzy control rule set, obtain the fuzzy output, and then obtain the preliminary adjustment value of the feedback parameter through defuzzification processing;

[0054] The defuzzification method adopts the centroid method, and its calculation formula is:

[0055] ;

[0056] Where, is the initial adjustment value of the feedback parameter, is the membership degree of the i-th fuzzy rule, is the feedback parameter value corresponding to the i-th fuzzy rule, and n is the number of fuzzy rules.

[0057] Preferably, the further optimization of the feedback parameters using the neural network model specifically includes:

[0058] The current load parameters including voltage, current, resistance, load change rate and system operating status are used as input vectors of the neural network model. After calculation, the model outputs the optimized feedback parameter adjustment value.

[0059] Add the feedback parameter adjustment value output by the model and the preliminary feedback parameter adjustment value obtained above to obtain the final feedback parameter;

[0060] The output of the neural network is expressed as:

[0061] ;

[0062] Where, is the weight matrix of the neural network, is the input vector, b is the bias vector, and f is the activation function.

[0063] Compare and control: Compare the processed feedback signal with the reference signal and generate a control signal based on the comparison result.

[0064] Amplification output: The digital amplifier body amplifies the input signal according to the control signal and outputs the amplified signal.

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

[0066] Improve feedback accuracy: By adopting high-precision signal acquisition modules and advanced feedback signal processing technology, the feedback signal can be accurately collected and processed, which improves the accuracy and real-time performance of the feedback signal, enabling the digital amplifier to respond to changes in the output signal more quickly and accurately;

[0067] Enhanced stability: The adaptive adjustment module can automatically adjust the feedback parameters according to different load conditions and system operating status, effectively overcoming the problem of poor stability of traditional feedback systems under different loads, so that the digital amplifier can maintain a stable working state under various load conditions;

[0068] Improved sound and signal quality: Due to improved feedback and stability, digital amplifiers can amplify input signals more accurately, reducing distortion and noise, thereby improving the sound quality of audio and the quality of communication signals. DETAILED DESCRIPTION

[0069] In one embodiment, a system for improving feedback and stability in a digital amplifier includes:

[0070] Signal acquisition module, used for real-time acquisition of the output signal of the digital amplifier;

[0071] Feedback signal processing module processes the collected output signal, including filtering, amplification and compensation operations;

[0072] Adaptive adjustment module, automatically adjusts feedback parameters according to different load conditions and system operating status;

[0073] A comparison and control module compares the processed feedback signal with the reference signal and generates a control signal according to the comparison result, which is used to adjust the input signal of the digital amplifier;

[0074] The digital amplifier body amplifies the input signal according to the control signal and outputs the amplified signal.

[0075] In this embodiment, a 24-bit ADC with a sampling frequency of 192kHz is used to acquire the output audio signal of the digital amplifier. An anti-aliasing filter with a cutoff frequency of 96kHz is placed in front of the ADC to prevent high-frequency signals above half the sampling frequency from aliasing into the desired signal.

[0076] The collected digital signal is filtered using an FIR filter. According to the characteristics of the audio signal, a 64-order FIR filter is designed with a cutoff frequency of 20kHz to remove high-frequency noise and interference from the audio signal.

[0077] A programmable gain amplifier (PGA) is used to amplify the digital signal, with a gain adjustment range of 1 to 1000. The PGA gain is controlled in real time by a microcontroller, dynamically adjusting the amplification factor based on the strength of the feedback signal.

[0078] A high-precision comparator compares the processed feedback signal with the reference signal and outputs an error signal. A PID controller generates a control signal based on the error signal to adjust the input signal of the digital amplifier. The PID controller's proportional coefficient is set to 0.5, the integral coefficient to 0.1, and the differential coefficient to 0.05.

[0079] A high-performance digital amplifier chip is selected. It adopts a switch-mode design with advantages such as high efficiency and low distortion. It integrates power switching tubes, drive circuits, and protection circuits to ensure amplification performance while improving system reliability and safety.

[0080] In another embodiment, a method for improving feedback and stability in a digital amplifier includes:

[0081] Signal acquisition: Use the signal acquisition module to collect the output signal of the digital amplifier in real time and convert it into a digital signal.

[0082] The audio signal output by the digital amplifier is first pre-processed by an anti-aliasing filter, and then enters the ADC for analog-to-digital conversion, converting the analog signal into a digital signal, providing an accurate data basis for subsequent feedback processing.

[0083] Feedback signal processing: filtering, amplifying and compensating the collected digital signals.

[0084] The collected digital signal is input into the FIR filter to remove high-frequency noise and interference and improve the purity of the signal.

[0085] The microcontroller monitors the strength of the feedback signal in real time and dynamically adjusts the PGA gain based on the comparison with a preset threshold. For example, when the feedback signal is weak, the PGA gain is increased; when the feedback signal is strong, the PGA gain is decreased.

[0086] Delay compensation: Measure signal delay using a high-precision clock signal and signal processing algorithms. Assume that, after measurement and analysis, the signal delay during the entire processing process is approximately 100 μs. Delay compensation is performed using a FIFO buffer, storing the signal in the buffer and then reading it out after 100 μs.

[0087] Amplitude attenuation compensation: The feedback signal amplitude is monitored in real time and compared with a reference amplitude. Assuming the reference amplitude is 1V, when the amplitude of a signal at a certain frequency is detected to have attenuated to 0.8V, the required compensation gain is calculated to be 1 / 0.8 = 1.25. The PGA adjusts the gain of the signal at that frequency to restore its amplitude to 1V.

[0088] Environmental compensation: Temperature and humidity sensors are used to collect real-time ambient temperature and humidity data. Machine learning algorithms (such as neural networks) are used to build an environmental compensation model, which predicts the required gain coefficient based on environmental parameters. For example, in a high-temperature environment, the model predicts a 5% increase in gain to compensate for signal attenuation.

[0089] Adaptive adjustment: The adaptive adjustment module monitors load conditions and system operating status in real time and automatically adjusts feedback parameters based on preset algorithms and models.

[0090] The adaptive adjustment module monitors the load voltage and current in real time, calculates the load resistance, and analyzes dynamic load changes. When the load resistance changes, a fuzzy control algorithm is first used to quickly adjust the approximate range of the feedback parameters. A neural network model is then used to further optimize the feedback parameters to ensure the digital amplifier maintains stable operation under various load conditions.

[0091] Compare and control: Compare the processed feedback signal with the reference signal and generate a control signal based on the comparison result.

[0092] The comparator compares the processed feedback signal with the reference signal and outputs an error signal. The PID controller generates a control signal based on the error signal and adjusts the input signal of the digital amplifier to make the output signal as close to the reference signal as possible.

[0093] Amplification output: The digital amplifier body amplifies the input signal according to the control signal and outputs the amplified signal.

[0094] The digital amplifier amplifies the input signal based on the control signal and outputs the amplified audio signal, which drives the speaker to produce sound. During the amplification process, it continuously receives feedback signals and adjusts the amplification parameters in real time based on changes in the feedback signal to ensure the stability and accuracy of the output signal. Furthermore, the protection circuit monitors the amplifier's operating status in real time and takes timely protective measures to prevent damage to the amplifier if abnormal conditions such as overcurrent, overvoltage, and overheating occur.

[0095] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0096] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A system for improving feedback and stability in a digital amplifier, characterized in that include: Signal acquisition module, used for real-time acquisition of the output signal of the digital amplifier; Feedback signal processing module processes the collected output signal, including filtering, amplification and compensation operations; Adaptive adjustment module, automatically adjusts feedback parameters according to different load conditions and system operating status; A comparison and control module compares the processed feedback signal with the reference signal and generates a control signal according to the comparison result, which is used to adjust the input signal of the digital amplifier; The digital amplifier body amplifies the input signal according to the control signal and outputs the amplified signal; The automatic adjustment of feedback parameters according to different load conditions and system operating states specifically includes: Parameter measurement: Use high-precision current sensors and voltage sensors to accurately detect the voltage and current of the load in real time. Use Ohm's law to calculate the resistance value of the load based on the collected voltage and current data. Dynamic analysis: Based on the acquired load parameters, conduct an in-depth analysis of the dynamic changes of the load; Data recording and storage: Record various parameters and changes of the load in detail and store them in the system database; Initial adjustment of fuzzy control algorithm: When a load change is detected, the fuzzy control algorithm is used to make initial adjustments to the feedback parameters to generate initial adjustment values ​​of the feedback parameters. Neural network model optimization and adjustment: Based on the initial adjustment of the fuzzy control algorithm, the neural network model is used to further optimize the feedback parameters; Real-time adjustment and feedback: During system operation, the load is continuously monitored and feedback parameters are adjusted in real time based on the monitoring results. After each adjustment, the adjusted feedback parameters are applied to the system and the changes in the system's output signal are observed. If there is still a deviation in the output signal, the feedback parameters are further adjusted based on the deviation. Model update and optimization: Regularly collect new load data and system operation data, add them to the training data set, and retrain the neural network model. At the same time, adjust and optimize the rules of the fuzzy control algorithm based on the actual operation results to improve the accuracy and efficiency of adaptive adjustment.

2. The system for improving feedback and stability in a digital amplifier according to claim 1, wherein: The comparison and control module adopts a proportional-integral-differential controller.

3. A method for improving feedback and stability in a digital amplifier, characterized in that The following steps are involved: Signal acquisition: Use the signal acquisition module to collect the output signal of the digital amplifier in real time and convert it into a digital signal; Feedback signal processing: filtering, amplifying and compensating the collected digital signals; Adaptive adjustment: The adaptive adjustment module monitors load conditions and system operating status in real time and automatically adjusts feedback parameters based on preset algorithms and models; Compare and control: Compare the processed feedback signal with the reference signal and generate a control signal based on the comparison result; Amplification output: The digital amplifier body amplifies the input signal according to the control signal and outputs the amplified signal; The compensation processing includes delay compensation, amplitude attenuation compensation and environmental factor compensation; The real-time monitoring of load conditions and system operating status and automatic adjustment of feedback parameters according to preset algorithms and models specifically include: Parameter measurement: Use high-precision current sensors and voltage sensors to accurately detect the voltage and current of the load in real time. Use Ohm's law to calculate the resistance value of the load based on the collected voltage and current data. Dynamic analysis: Based on the acquired load parameters, conduct an in-depth analysis of the dynamic changes of the load; Data recording and storage: Record various parameters and changes of the load in detail and store them in the system database; Initial adjustment of fuzzy control algorithm: When a load change is detected, the fuzzy control algorithm is used to make initial adjustments to the feedback parameters to generate initial adjustment values ​​of the feedback parameters. Neural network model optimization and adjustment: Based on the initial adjustment of the fuzzy control algorithm, the neural network model is used to further optimize the feedback parameters; Real-time adjustment and feedback: During system operation, the load is continuously monitored and feedback parameters are adjusted in real time based on the monitoring results. After each adjustment, the adjusted feedback parameters are applied to the system and the changes in the system's output signal are observed. If there is still a deviation in the output signal, the feedback parameters are further adjusted based on the deviation. Model update and optimization: Regularly collect new load data and system operation data, add them to the training data set, and retrain the neural network model. At the same time, adjust and optimize the rules of the fuzzy control algorithm based on the actual operation results to improve the accuracy and efficiency of adaptive adjustment.

4. The method for improving feedback and stability in a digital amplifier according to claim 3, wherein: The delay compensation specifically includes: Delay measurement: Mark the characteristic points of the signal at the signal acquisition end and the processed output end respectively, and obtain the signal delay time by comparing the time difference between the two characteristic points; Delay model establishment: A delay model is established based on the signal transmission path and processing links. This model takes into account the delay factors in the signal analog-to-digital conversion, filtering, and amplification processes, and is expressed as follows: ,in is the delay time during the analog-to-digital conversion process, is the delay time of the filtering link, is the delay time of the amplification link; Delay compensation: Based on the measured delay time and the established delay model, the FIFO buffer is used to compensate the signal delay. The collected signals are stored in the FIFO buffer in chronological order. Adjusts the timing of signal reading from the buffer.

5. The method for improving feedback and stability in a digital amplifier according to claim 3, wherein: The amplitude attenuation compensation specifically includes: Amplitude monitoring: monitor the amplitude change of the feedback signal in real time and compare it with the pre-set reference amplitude; Attenuation analysis: Based on the monitored amplitude changes, the signal is segmented according to the frequency components to analyze the attenuation of different frequency signals during transmission and processing; Gain compensation: For signals with different frequency components, the corresponding gain compensation algorithm is used to adjust the amplitude; specifically, the gain to be compensated is calculated based on the frequency f and attenuation degree of the signal; The formula for calculating the required compensation gain is: ; Where, is the pre-set reference amplitude, is the actual amplitude of the signal with frequency f.

6. The method for improving feedback and stability in a digital amplifier according to claim 3, wherein: The environmental factor compensation specifically includes: Environmental parameter collection: Real-time collection of environmental parameters through sensors, including temperature and humidity parameters; Establishment of environmental compensation model: Use machine learning algorithm to establish environmental compensation model, which takes environmental parameters and signal characteristic parameters as input and outputs the gain coefficient to be compensated; Compensation adjustment: The obtained gain compensation coefficient is multiplied by the gain in the amplitude attenuation compensation to obtain the final gain adjustment value. Then, the signal is gain adjusted through the programmable gain amplifier.

7. The method for improving feedback and stability in a digital amplifier according to claim 3, wherein: The in-depth analysis of the dynamic changes of the load specifically includes: Calculate the load change rate based on the acquired load parameters; Set a mutation threshold. If the load change rate exceeds the mutation threshold, it indicates that a load mutation has occurred. If the rate of change of the load fluctuates periodically, it indicates that load fluctuation occurs; When load mutation and load fluctuation occur, feedback parameters are adjusted; The calculation formula for the load change rate is: ; Where, is the load resistance value at the current time t, It's the last moment The load resistance value, is the time interval.

8. The method for improving feedback and stability in a digital amplifier according to claim 3, wherein: The preliminary adjustment of the feedback parameter using the fuzzy control algorithm to generate a preliminary adjustment value of the feedback parameter specifically includes: Constructing a fuzzy control rule set based on known expert rules, wherein the fuzzy control rule set is a series of conditional statements, wherein the load resistance change rate is an input and the feedback parameter is an output; Input the current load resistance change rate, and according to the fuzzy control rule set, obtain the fuzzy output, and then obtain the preliminary adjustment value of the feedback parameter through defuzzification processing; The defuzzification method adopts the centroid method, and its calculation formula is: ; Where, is the initial adjustment value of the feedback parameter, is the membership degree of the i-th fuzzy rule, is the feedback parameter value corresponding to the i-th fuzzy rule, and n is the number of fuzzy rules.

9. The method for improving feedback and stability in a digital amplifier according to claim 3, wherein: The further optimization of the feedback parameters using the neural network model specifically includes: The current load parameters including voltage, current, resistance, load change rate and system operating status are used as input vectors of the neural network model. After calculation, the model outputs the optimized feedback parameter adjustment value. Add the feedback parameter adjustment value output by the model and the preliminary feedback parameter adjustment value obtained above to obtain the final feedback parameter; The output of the neural network is expressed as: ; Where, is the weight matrix of the neural network, is the input vector, b is the bias vector, and f is the activation function.

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