System and method for improving feedback and stability in digital amplifiers

Abstract

The invention relates to the field of digital amplifiers, in particular to a system and method for improving feedback and stability in digital amplifiers. 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 body module, the signal acquisition module accurately acquires an output signal; the feedback signal processing module filters and amplifies output information and compensates delay, amplitude attenuation and environmental factors; the adaptive adjustment module adjusts equipment feedback parameters 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 body outputs the amplified signal. The invention not only can greatly improve the precision of feedback signals and enhance the stability of the amplifier under various working conditions, but also can effectively improve the quality of transmission signals, reduce distortion and noise, improve the reliability of the system and prolong the service life of the system, and has a good application prospect.

Description

Technical Field

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

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

[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, which causes the amplifier to not respond to changes in the output signal in a timely manner, thus affecting the audio quality or the accuracy of the communication signal. At the same time, due to factors such as quantization errors and noise interference in the digital signal processing process, the feedback signal is easily distorted, further reducing the stability of the amplifier.

[0004] In addition, under different load conditions, the output characteristics of the digital amplifier will change. It is difficult for the traditional feedback system to adaptively adjust the feedback parameters, which may cause the amplifier to experience 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: A signal acquisition module, used for real-time acquisition of the output signal of the digital amplifier; The 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 operation 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.

[0006] Preferably, the signal acquisition module adopts a high-precision analog-to-digital converter.

[0007] Preferably, the filtering in the feedback signal processing module adopts a finite length unit impulse response filter.

[0008] Preferably, the amplification in the feedback signal processing module adopts a programmable gain amplifier.

[0009] Preferably, the comparison and control module adopts a proportional-integral-derivative controller.

[0010] In another embodiment, a method for improving feedback and stability in a digital amplifier is included: 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.

[0011] Feedback signal processing: filtering, amplifying and compensating the collected digital signals.

[0012] Preferably, the compensation process includes delay compensation, amplitude attenuation compensation and environmental factor compensation.

[0013] Preferably, 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. The model takes into account the delay factors in the signal analog-to-digital conversion, filtering, and amplification process, and is expressed as follows: ,in is the delay time in 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 when the signal is read from the buffer.

[0014] Preferably, the amplitude attenuation compensation specifically includes: Amplitude monitoring: monitor the amplitude change of the feedback signal in real time and compare it with the preset reference amplitude; Attenuation analysis: Based on the monitored amplitude changes, the signal is segmented according to the frequency components, and the attenuation of different frequency signals during transmission and processing is analyzed; 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: ; In the formula, is the preset reference amplitude, is the actual amplitude of the signal with frequency f.

[0015] Preferably, the environmental factor compensation specifically includes: Environmental parameter collection: collect environmental parameters in real time 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 characteristic parameters of signals as input and takes gain coefficients to be compensated as output; Compensation adjustment: The obtained gain compensation coefficient is multiplied by the gain in the amplitude attenuation compensation to obtain the final gain adjustment value, and then the signal is gain adjusted through the programmable gain amplifier.

[0016] Adaptive adjustment: The adaptive adjustment module monitors load conditions and system operating status in real time, and automatically adjusts feedback parameters according to preset algorithms and models.

[0017] Preferably, the adaptive adjustment module monitors the load conditions and system operation status in real time, and automatically adjusts the feedback parameters according to the preset algorithm and model, specifically including: 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 through 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 load changes are detected, the fuzzy control algorithm is used to quickly adjust the approximate range of feedback parameters; Neural network model optimization and adjustment: Based on the preliminary adjustment of the fuzzy control algorithm, the neural network model is used to further optimize the feedback parameters; Real-time adjustment and feedback: During the operation of the system, the load is continuously monitored, and the feedback parameters are adjusted in real time according to 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 according to 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 according to the actual operation results to improve the accuracy and efficiency of adaptive adjustment.

[0018] Preferably, the in-depth analysis of the dynamic change 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 occurs. If the rate of change of the load fluctuates periodically, it indicates that load fluctuations occur; When load mutation and load fluctuation occur, feedback parameters are adjusted; The calculation formula of the load change rate is: ; In the formula, is the load resistance value at the current time t, It was the last moment The load resistance value, is the time interval.

[0019] Preferably, the approximate range of the feedback parameter to be quickly adjusted using the fuzzy control algorithm specifically includes: Constructing a fuzzy control rule set according to 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 obtain the fuzzy output according to the fuzzy control rule set, 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: ; In the formula, 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.

[0020] Preferably, 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 operation status are used as input vectors of the neural network model. After calculation, the model outputs the optimized feedback parameter adjustment value. Adding the feedback parameter adjustment value output by the model to the preliminary feedback parameter adjustment value obtained above to obtain the final feedback parameter; The output of the neural network is expressed as: ; In the formula, is the weight matrix of the neural network, is the input vector, b is the bias vector, and f is the activation function.

[0021] Compare and control: Compare the processed feedback signal with the reference signal and generate a control signal based on the comparison result.

[0022] Amplification output: The digital amplifier body amplifies the input signal according to the control signal and outputs the amplified signal.

[0023] Compared with the prior art, the advantages of the present invention are: 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, allowing the digital amplifier to respond to changes in the output signal more quickly and accurately; 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; Improved sound and signal quality: Due to improved feedback and stability, digital amplifiers can amplify input signals more accurately, reduce distortion and noise, thereby improving the sound quality of audio and the quality of communication signals. DETAILED DESCRIPTION

[0024] In one embodiment, a system for improving feedback and stability in a digital amplifier includes: A signal acquisition module, used for real-time acquisition of the output signal of the digital amplifier; The 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 operation 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.

[0025] In this embodiment, an ADC with a 24-bit resolution and a sampling frequency of 192kHz is selected to collect the output audio signal of the digital amplifier. At the same time, an anti-aliasing filter is configured at the front end of the ADC, and its cutoff frequency is set to 96kHz to prevent high-frequency signals higher than half of the sampling frequency from being aliased into the useful signal.

[0026] 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 in the audio signal.

[0027] A programmable gain amplifier (PGA) is used to amplify the digital signal, and its gain adjustment range is 1 to 1000. The gain of the PGA is controlled in real time by a microcontroller, and the amplification factor is dynamically adjusted according to the strength of the feedback signal.

[0028] A high-precision comparator is used to compare the processed feedback signal with the reference signal and output an error signal. A PID controller is used to generate a control signal based on the error signal to adjust the input signal of the digital amplifier. The proportional coefficient of the PID controller is set to 0.5, the integral coefficient is set to 0.1, and the differential coefficient is set to 0.05.

[0029] A high-performance digital amplifier chip is selected, which adopts a switch mode design and has the advantages of high efficiency and low distortion. The power switch tube, drive circuit and protection circuit are integrated internally, which can improve the reliability and safety of the system while ensuring the amplification performance.

[0030] In another embodiment, a method for improving feedback and stability in a digital amplifier includes: 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.

[0031] 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.

[0032] Feedback signal processing: filtering, amplifying and compensating the collected digital signals.

[0033] The collected digital signal is input into the FIR filter to remove high-frequency noise and interference and improve the purity of the signal.

[0034] The microcontroller monitors the strength of the feedback signal in real time and dynamically adjusts the gain of the PGA based on the comparison result with the preset threshold. For example, when the feedback signal strength is weak, the gain of the PGA is increased; when the feedback signal strength is strong, the gain of the PGA is reduced.

[0035] Delay compensation: The delay time of the signal is measured through a high-precision clock signal and a signal processing algorithm. Assume that after measurement and analysis, the delay time of the signal in the entire processing process is about 100μs. Use a FIFO buffer for delay compensation, store the signal in the buffer, and then read it out after 100μs.

[0036] Amplitude attenuation compensation: monitor the amplitude change of the feedback signal in real time and compare it with the reference amplitude. Assuming the reference amplitude is 1V, when the amplitude of a signal of a certain frequency is detected to be attenuated to 0.8V, the gain to be compensated is calculated to be 1 / 0.8=1.25. The gain of the signal of this frequency is adjusted through the PGA to restore its amplitude to 1V.

[0037] Environmental compensation: Use temperature sensors and humidity sensors to collect ambient temperature and humidity in real time. Use machine learning algorithms (such as neural networks) to build an environmental compensation model and predict the gain factor that needs to be compensated based on environmental parameters. For example, in a high temperature environment, the model predicts that a 5% gain increase is needed to compensate for signal attenuation.

[0038] Adaptive adjustment: The adaptive adjustment module monitors load conditions and system operating status in real time, and automatically adjusts feedback parameters according to preset algorithms and models.

[0039] The adaptive adjustment module monitors the voltage and current of the load in real time, calculates the resistance value of the load, and analyzes the dynamic changes of the load. When the load resistance changes, the fuzzy control algorithm is first used to quickly adjust the approximate range of the feedback parameters, and then the neural network model is used to further optimize the feedback parameters to ensure that the digital amplifier can maintain a stable working state under various load conditions.

[0040] Compare and control: Compare the processed feedback signal with the reference signal and generate a control signal based on the comparison result.

[0041] 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.

[0042] Amplification output: The digital amplifier body amplifies the input signal according to the control signal and outputs the amplified signal.

[0043] The digital amplifier body amplifies the input signal according to the control signal, outputs the amplified audio signal, and drives the speaker to make sound. During the amplification process, it continuously receives feedback signals and adjusts the amplification parameters in real time according to the changes in the feedback signals to ensure the stability and accuracy of the output signal. At the same time, the protection circuit monitors the working status of the amplifier in real time. When abnormal conditions such as overcurrent, overvoltage, and overheating occur, protective measures are taken in time to prevent the amplifier from being damaged.

[0044] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0045] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. 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: A signal acquisition module, used for real-time acquisition of the output signal of the digital amplifier; The 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 operation 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.

2. The system for improving feedback and stability in a digital amplifier according to claim 1, characterized in that 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 according to 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 process includes delay compensation, amplitude attenuation compensation and environmental factor compensation.

4. The method for improving feedback and stability in a digital amplifier according to claim 3, characterized in that: 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. The model takes into account the delay factors in the signal analog-to-digital conversion, filtering, and amplification process, and is expressed as follows: ,in is the delay time in 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 when the signal is read from the buffer.

5. The method for improving feedback and stability in a digital amplifier according to claim 3, characterized in that: The amplitude attenuation compensation specifically includes: Amplitude monitoring: monitor the amplitude change of the feedback signal in real time and compare it with the preset reference amplitude; Attenuation analysis: Based on the monitored amplitude changes, the signal is segmented according to the frequency components, and the attenuation of different frequency signals during transmission and processing is analyzed; 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: ; In the formula, is the preset 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, characterized in that: The environmental factor compensation specifically includes: Environmental parameter collection: collect environmental parameters in real time 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 characteristic parameters of signals as input and takes gain coefficients to be compensated as output; Compensation adjustment: The obtained gain compensation coefficient is multiplied by the gain in the amplitude attenuation compensation to obtain the final gain adjustment value, and 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, characterized in that: The adaptive adjustment module monitors the load conditions and system operation status in real time and automatically adjusts the feedback parameters according to the preset algorithms and models, specifically including: 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 through 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 load changes are detected, the fuzzy control algorithm is used to quickly adjust the approximate range of feedback parameters; Neural network model optimization and adjustment: Based on the preliminary 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, retrain the neural network model, and at the same time, adjust and optimize the rules of the fuzzy control algorithm according to the actual operation results to improve the accuracy and efficiency of adaptive adjustment.

8. The method for improving feedback and stability in a digital amplifier according to claim 7, characterized in that: The in-depth analysis of the dynamic change 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 occurs. If the rate of change of the load fluctuates periodically, it indicates that load fluctuations occur; When load mutation and load fluctuation occur, feedback parameters are adjusted; The calculation formula of the load change rate is: ; In the formula, is the load resistance value at the current time t, It was the last moment The load resistance value, is the time interval.

9. The method for improving feedback and stability in a digital amplifier according to claim 7, characterized in that: The approximate range of using the fuzzy control algorithm to quickly adjust the feedback parameters specifically includes: Constructing a fuzzy control rule set according to 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 obtain the fuzzy output according to the fuzzy control rule set, 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: ; In the formula, 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.

10. The method for improving feedback and stability in a digital amplifier according to claim 7, characterized in that: The further optimization of the feedback parameters by using the neural network model specifically includes: The current load parameters including voltage, current, resistance, load change rate and system operation status are used as input vectors of the neural network model. After calculation, the model outputs the optimized feedback parameter adjustment value. Adding the feedback parameter adjustment value output by the model to the preliminary feedback parameter adjustment value obtained above to obtain the final feedback parameter; The output of the neural network is expressed as: ; In the formula, 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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