Piezo Speaker Driving Circuit with Reduced-Rate PWM Table Learning
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Solution Overview
Problem
Conventional driving circuits, such as class-AB, -D, and -G amplifiers, are not suitable for driving piezoelectric-actuated speakers due to their highly capacitive nature, leading to inaccuracies in pulse width modulation (PWM) signal generation and degraded sound quality caused by manufacturing mismatches and voltage fluctuations.
Innovation Solution
A table learning method is implemented in the driving circuit to adaptively adjust pulse width control codes (PWCC) for generating PWM signals, using a controller that performs table learning operations to update PWCC based on feedback signals, thereby improving the accuracy of PWM signals and reducing distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional driving circuits (class-AB, -D, -G amplifiers) are used to drive piezoelectric-actuated speakers, then the circuit design is simple and follows conventional amplifier topology, but the PWM signal generation becomes inaccurate and sound quality degrades due to highly capacitive loading
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the PWM pulse width based on feedback signals and stored control codes. The system modifies operational parameters (pulse width, duty cycle) in response to capacitive loading conditions, enabling accurate driving of piezoelectric speakers without requiring a complete redesign of the amplifier topology. This allows conventional amplifier circuits to adapt to highly capacitive loads through parameter modulation.
Solution Approach 2:
The patent implements feedback mechanisms where feedback signals from the piezoelectric speaker are continuously monitored and used to update PWM control codes stored in memory. The controller adjusts subsequent PWM signals based on this feedback, ensuring accurate pulse width modulation despite the highly capacitive nature of the load. This closed-loop feedback system resolves the accuracy issue without fundamentally changing the amplifier architecture.
2Measurement precision
If table learning operation is performed at high rate continuously, then PWM signal accuracy improves through frequent updates of pulse width control codes, but power consumption increases
Solution Approach 1:
The patent applies periodic action by performing table learning operations at different rates based on operational phases. During an initial learning period, updates occur at a first (higher) rate to quickly adapt to the specific piezoelectric speaker. After this initial period, the update rate is reduced to a second (lower) rate, maintaining accuracy while significantly reducing power consumption. This phased periodic update strategy balances precision requirements with energy efficiency.
Solution Approach 2:
The patent implements dynamics by making the table learning update rate variable rather than constant. The system dynamically adjusts the learning rate based on the operational phase (initial learning vs. steady-state operation), allowing high precision during critical adaptation periods while conserving energy during stable operation. This dynamic rate adjustment resolves the contradiction between continuous high-rate updates and power consumption.
3Adaptability or versatility
If pulse width control codes are frequently updated to accommodate device variations and voltage fluctuations, then sound quality improves, but processing time and computational load increase
Solution Approach 1:
The patent applies preliminary action by pre-storing multiple pulse width control codes in a table/memory structure before actual operation. These pre-computed control codes are prepared in advance to cover various operating conditions and device variations. During operation, the system simply retrieves and applies the appropriate pre-computed code based on feedback signals, avoiding real-time complex calculations. This preliminary preparation enables rapid adaptation to device variations without incurring high processing time penalties during actual playback.
Data Source
AI summary
A method is applied in a controller within a driving circuit comprising a driving sub-circuit configured to drive a load. The method comprises steps of: performing a table learning operation on a table at least at a first rate during a learning period; and performing the table learning operation on the table at a second rate lower than the first rate after the learning period; wherein the table is stored in a memory within the controller. The table learning operation comprises steps of: receiving a first feedback signal from the load corresponding to a first cycle; obtaining a control code from the table according to the first feedback signal; generating a control signal according to the control code; receiving a second feedback signal from the load corresponding to the second cycle; and updating the control code and saving the updated control code back to the table in the memory.