A dynamic response regulation system for switching power supply based on digital control

Abstract

The present invention relates to the technical field of digitally controlled switching power supplies, and specifically discloses a dynamic response regulation system for a switching power supply based on digital control. The system includes a digital controller, a multi-channel analog-to-digital conversion module, a parameter identification module, an adaptive control law adjustment module, a pulse width modulation drive module, and a stability monitoring module. By constructing a nonlinear state space model and combining it with an extended Kalman filter algorithm, online identification of key system parameters is achieved, and dynamic compensation is performed for non-minimum phase characteristics such as right half plane zero points. The adaptive control law adjustment module optimizes the control strategy in real time based on the updated model, introduces the Lyapunov stability criterion and predictive control mechanism, and improves the robustness and response speed of the system. The stability monitoring module identifies abnormal conditions through sliding window variance analysis and peak detection, triggering a fault-tolerant mechanism to ensure safe operation of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of digitally controlled switching power supplies, and in particular to a dynamic response regulation system of a switching power supply based on digital control. Background Art

[0002] With the continuous advancement of power electronics technology, switching power supplies (SMPS) are widely used in communications equipment, industrial control, new energy conversion, and aerospace due to their advantages such as high efficiency, small size, and lightweight. These applications place higher demands on the power supply's dynamic response, stability, and reliability. Traditional analog control methods, due to their shortcomings such as fixed parameters and difficulty adapting to complex operating conditions, have been gradually replaced by digital control technologies. Digital control not only offers greater flexibility and reconfigurability but also supports the implementation of advanced control algorithms such as adaptive control, predictive control, and intelligent optimization control, effectively improving the system's dynamic performance and robustness. In practical applications, many switching power supply topologies (such as boost, flyback, and Cuk) exhibit non-minimum phase characteristics, i.e., right-half-plane zeros (RHP zeros). This results in reverse response to sudden load changes, compromising stability. Therefore, achieving high-precision modeling and adaptive regulation of such systems within digital control architectures has become a key research focus.

[0003] The existing technology has the following deficiencies:

[0004] In some complex power systems, particularly high-order resonant converters or digitally controlled switching power supplies with non-minimum phase characteristics, right-half-plane zeros (RHP zeros) may exist in the system transfer function, resulting in frequency responses that differ significantly from those of traditional minimum-phase systems. These non-minimum-phase systems exhibit unintuitive dynamic behaviors such as "reverse response," making traditional stability criteria based on gain margin and phase margin ineffective. Summary of the Invention

[0005] The object of the present invention is to provide a dynamic response regulation system of a switching power supply based on digital control to solve the above-mentioned problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A switching power supply dynamic response regulation system based on digital control, comprising:

[0008] a digital controller for executing an adaptive control algorithm and generating a control signal;

[0009] A multi-channel analog-to-digital conversion module, connected to the digital controller, for real-time acquisition of input voltage, output voltage and inductor current signals of the switching power supply system;

[0010] A parameter identification module, integrated into the digital controller, for performing online identification of key system parameters based on an extended Kalman filter algorithm to update the system model;

[0011] an adaptive control law adjustment module, coupled to the parameter identification module, for dynamically adjusting control gains and control strategies according to the updated system model to compensate for the impact of non-minimum phase characteristics on system stability;

[0012] A pulse width modulation drive module is connected to the digital controller and is used to generate corresponding PWM control pulses according to the adjusted control signal to drive the power switch device;

[0013] The stability monitoring module is used to monitor the system operation status and trigger the fault tolerance mechanism in abnormal situations to maintain the basic functions of the system.

[0014] As a further solution of the present invention: the parameter identification module performs the following steps:

[0015] Construct a nonlinear state-space model of the switching power supply system, including the state equations and observation equations for the inductor current and output voltage;

[0016] Based on the extended Kalman filter algorithm, the state equation is predicted and updated in real time to estimate the internal state variables and unknown parameters of the system;

[0017] The input voltage, output voltage and inductor current data collected by the multi-channel analog-to-digital conversion module are used as observation inputs to drive the parameter identification process;

[0018] The updated system parameters are periodically output to the adaptive control law adjustment module to achieve dynamic compensation.

[0019] As a further solution of the present invention: the noise covariance adaptive adjustment mechanism is introduced into the extended Kalman filter algorithm, specifically including:

[0020] Monitor system operation status in real time and evaluate observation residuals;

[0021] Dynamically adjust the process noise covariance matrix Q and the observation noise covariance matrix R according to the residual change trend;

[0022] Improve parameter identification accuracy and enhance system robustness under sudden load changes or input disturbances.

[0023] As a further solution of the present invention: the parameter identification module further includes the steps of identifying and compensating the zero point position of the right half plane:

[0024] After the system model is updated, the transfer function information is extracted to determine whether there is a right half plane zero point;

[0025] If it exists, its position information is fed back to the adaptive control law adjustment module;

[0026] The controller adjusts the control strategy based on this information, including gain limiting, phase compensation, or switching control modes to suppress dynamic instability caused by non-minimum phase behavior;

[0027] The parameter identification module achieves rapid convergence by:

[0028] Inject a small excitation signal of limited duration during the system startup phase;

[0029] The extended Kalman filter algorithm is used to process the stimulus response data to accelerate the initial parameter estimation process;

[0030] Shorten the identification time during the system cold start phase and improve power-on response speed and stability.

[0031] As a further solution of the present invention: the adaptive control law adjustment module performs the following steps:

[0032] Receive updated system model information from the parameter identification module, including inductor current dynamics, output voltage response characteristics, and right half plane zero position;

[0033] Determine whether the current operating state deviates from the stable area based on the change trend of the system model;

[0034] Dynamically adjust the proportional, integral, and differential gain coefficients of the PID controller;

[0035] The updated control law is output to the pulse width modulation drive module to optimize the transient response performance under sudden load changes.

[0036] As a further solution of the present invention: the adaptive control law adjustment module further includes a stability criterion evaluation step based on the Lyapunov function:

[0037] Construct a candidate Lyapunov function suitable for the current system model;

[0038] Calculate its derivatives in real time to determine the energy convergence of the system;

[0039] If an unstable trend is detected, the control gain reduction mechanism is triggered or the system switches to a preset safety control mode;

[0040] Maintain system closed-loop stability without sacrificing response speed.

[0041] As a further solution of the present invention: the adaptive control law adjustment module automatically switches the control strategy according to whether there is a right half plane zero point, specifically including:

[0042] When it is identified that the system has non-minimum phase characteristics, the lead-lag compensation control structure is enabled;

[0043] Limit the controller bandwidth to avoid phase reversal and oscillation at high frequencies;

[0044] A feed-forward term is introduced to offset the reverse response behavior caused by the right half plane zero;

[0045] Dynamically reconfigure the control law to achieve robust control of non-minimum phase systems;

[0046] The adaptive control law adjustment module also implements the following enhanced control strategy:

[0047] Predict possible future operating states based on the system model change rate;

[0048] Adjust control gains in advance to cope with impending load jumps or input disturbances;

[0049] Combine historical data with current identification results to perform multi-step rolling optimization;

[0050] Improve the smoothness of the system's dynamic response and anti-interference ability through predictive control strategies.

[0051] As a further solution of the present invention: the pulse width modulation drive module performs the following steps:

[0052] Receive dynamic adjustment control signals from digital controllers and convert them into high-resolution PWM waveforms;

[0053] Generate complementary and symmetrical PWM drive pulses according to the current switching frequency setting value and dead time configuration;

[0054] In the generation process, the optimal dead time is dynamically calculated and inserted by combining the turn-on and turn-off delay characteristics of the power switching devices;

[0055] The output is sent to the power switching device to achieve efficient and low-distortion energy conversion while preventing the risk of shoot-through in the upper and lower bridge arms.

[0056] As a further solution of the present invention: the pulse width modulation drive module further implements the following enhanced control and protection strategy:

[0057] Implement soft start control during system power-up, gradually increasing the PWM duty cycle to suppress startup inrush current.

[0058] Real-time monitoring of overcurrent, overvoltage and temperature abnormality signals, and immediate limitation or shutdown of PWM output when a fault is detected;

[0059] Automatically restart PWM output and synchronize controller status after fault recovery to ensure safe and reliable system operation;

[0060] At the same time, the switching frequency is dynamically adjusted according to the controller instructions to optimize the efficiency and stability of the system under different load conditions.

[0061] As a further solution of the present invention: the stability monitoring module performs the following steps:

[0062] Real-time collection and analysis of time series data of output voltage, inductor current and controller output control quantity;

[0063] Identify whether the system is in an oscillatory or unstable state based on sliding window variance analysis and peak detection algorithm;

[0064] When it is detected that the overshoot exceeds the set threshold, the recovery time is abnormally prolonged, or the phase margin drops to a critical value, it is determined to be a stability abnormality;

[0065] Triggering fault tolerance mechanisms, including switching to a preset stable control parameter set, limiting controller bandwidth, or initiating a soft restart process, to restore system stability and maintain basic power supply functions.

[0066] Beneficial effects of the present invention:

[0067] (1) The present invention achieves high-precision modeling and adaptive control of the operating state of a switching power supply system by constructing an online parameter identification mechanism based on the extended Kalman filter (EKF), combined with real-time identification and dynamic compensation strategies for the key feature right half plane zero (RHP Zero) in non-minimum phase systems. The parameter identification module can continuously update key parameters including inductance, capacitance, load impedance, and system pole / zero positions based on multi-channel collected input voltage, output voltage, and inductor current signals, using a nonlinear state space model and a recursive estimation algorithm, thereby ensuring that the controller always makes decisions based on the current actual system characteristics.

[0068] Especially in complex topologies with right-half-plane zeros, the system may exhibit non-intuitive behaviors such as "reverse response" during dynamic response. Traditional control methods based on fixed gain or linear compensation are difficult to effectively address, which can easily lead to stability degradation or even system instability. However, the present invention significantly improves the robustness and controllability of the system under the influence of non-minimum phase characteristics by feeding back RHP zero information to the adaptive control law adjustment module, triggering intelligent switching of control strategies. For example, this method introduces a lead-lag compensation structure, limits the controller bandwidth, or activates a feedforward adjustment mechanism.

[0069] Compared with the static control parameter configuration method commonly used in the existing technology, the present invention breaks through the limitations of traditional model matching-based control, effectively solves the control failure problem caused by system parameter drift, operating condition changes or modeling errors, and significantly enhances the dynamic response capability and closed-loop stability of the switching power supply under complex operating conditions such as load mutation, input disturbance and environmental changes, providing a practical and feasible technical path for the intelligent and adaptive development of high-performance digital control power supply systems.

[0070] (2) This invention overcomes the technical bottlenecks of traditional switching power supplies, such as fixed control strategies, delayed responses, and limited fault tolerance, by constructing a collaborative control system that integrates an adaptive control law adjustment module with an intelligent stability monitoring mechanism. Based on the real-time updated model information provided by the parameter identification module, the system dynamically optimizes the core parameters of the controller (such as proportional, integral, and differential gains) and automatically switches the control structure based on the changing trend of the system's operating status, achieving a flexible transition from PID control to lead-lag compensation, feedforward regulation, and even predictive multi-step rolling optimization control.

[0071] At the control strategy level, this invention introduces the Lyapunov function as a tool for evaluating the system's energy convergence. By calculating its time derivative in real time, it determines whether the system is in a stable operating region. Once an energy divergence trend is detected, a control gain reduction or mode switching mechanism is triggered to maintain the stability of the closed-loop system without sacrificing response speed. Furthermore, the system possesses forward-looking control capabilities, predicting possible future load jumps or input disturbances based on historical data and the current model, and adjusting the control strategy in advance, thereby significantly improving the system's anti-interference performance and dynamic response smoothness.

[0072] To enhance the system's ability to identify and respond to abnormal operating conditions, this invention further integrates multi-level stability monitoring and fault-tolerance mechanisms. Using sliding window variance analysis and peak detection algorithms, the system accurately identifies unstable characteristics such as output voltage overshoot, prolonged recovery time, and control signal oscillation. Based on these characteristics, it initiates appropriate fault-tolerance measures, including switching to a preset stable control parameter set, limiting controller bandwidth, or executing a soft restart process, ensuring that the system maintains basic power supply functionality after a fault occurs.

[0073] The pulse-width modulation drive module utilizes high-resolution PWM waveform generation technology, combined with online identification of the turn-on and turn-off delay characteristics of power switching devices, to dynamically optimize dead-time configuration, effectively preventing the risk of shoot-through between the upper and lower bridge arms, improving power conversion efficiency and operational safety. The module also supports adaptive frequency regulation, soft-start control, and multiple protection mechanisms, ensuring efficient and stable system operation under varying load conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The present invention will be further described below with reference to the accompanying drawings.

[0075] Figure 1 This is a flow chart of a dynamic response regulation system of a switching power supply based on digital control according to the present invention. DETAILED DESCRIPTION

[0076] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0077] See also Figure 1 As shown, the present invention is a switching power supply dynamic response regulation system based on digital control, comprising:

[0078] a digital controller for executing an adaptive control algorithm and generating a control signal;

[0079] A multi-channel analog-to-digital conversion module, connected to the digital controller, for real-time acquisition of input voltage, output voltage and inductor current signals of the switching power supply system;

[0080] A parameter identification module, integrated into the digital controller, for performing online identification of key system parameters based on an extended Kalman filter algorithm to update the system model;

[0081] an adaptive control law adjustment module, coupled to the parameter identification module, for dynamically adjusting control gains and control strategies according to the updated system model to compensate for the impact of non-minimum phase characteristics on system stability;

[0082] A pulse width modulation drive module is connected to the digital controller and is used to generate corresponding PWM control pulses according to the adjusted control signal to drive the power switch device;

[0083] Stability monitoring module, used to monitor the system operation status and trigger the fault tolerance mechanism in abnormal situations to maintain the basic functions of the system;

[0084] The key parameters include: the inductance value, capacitance value, load impedance, controller gain, system poles, system zero position and right half plane zero of the switching power supply.

[0085] The digital controller utilizes an embedded digital signal processor (DSP) or field-programmable gate array (FPGA) with floating-point computing capabilities, integrated with an adaptive control algorithm module for real-time execution of advanced control strategies, including but not limited to model reference adaptive control (MRAC) and parameter estimation adaptive control. The controller periodically receives data input from a multi-channel analog-to-digital conversion module, constructs a complete information map of the current system operating status, and dynamically adjusts control law parameters accordingly, thereby improving the system's dynamic response performance and stability under sudden load changes, input disturbances, and non-minimum phase characteristics.

[0086] The multi-channel analog-to-digital conversion module consists of multiple high-precision, synchronously sampled 16-bit ADC channels, connected to the switching power supply's input voltage and output voltage terminals, as well as the power inductor's current sensing node. This module collects input voltage (Vin), output voltage (Vout), and inductor current (IL) signals in real time, converts the analog values ​​into digital signals, and transmits them to the digital controller. To ensure accurate signal acquisition and timing consistency, the multi-channel ADC module supports hardware-triggered synchronous sampling and features a built-in anti-aliasing filter to suppress high-frequency noise interference.

[0087] Furthermore, during the system initialization phase, the digital controller generates an initial PWM control signal based on preset startup control parameters and drives the power switching devices. Simultaneously, the multi-channel analog-to-digital conversion module continuously collects system operating data and feeds it back to the parameter identification module for online modeling. Based on the updated system model, the controller automatically adjusts control gains and control strategies, achieving a smooth transition from cold start to steady-state operation while maintaining good response characteristics and stability during dynamic operation.

[0088] The specific process of the parameter identification module is as follows:

[0089] The parameter identification module is integrated into the digital controller and constructs a state-space model based on the nonlinear dynamic behavior of the switching power supply system. This model is used to describe the relationship between key variables in the system, including the changes in inductor current and output voltage.

[0090] The state-space model consists of two core parts:

[0091] State equation: This describes how the system's internal state changes over time. The evolution of the inductor current and output voltage is affected by the current input control variable (such as duty cycle) and the system's inherent parameters (such as inductance, capacitance, and load impedance).

[0092] Observation equation: describes the mapping relationship between the system's measurable variables (such as input voltage, output voltage, and inductor current) and internal states. It is used to infer state variables that cannot be directly measured through observable variables.

[0093] After completing the state modeling, the parameter identification module uses the extended Kalman filter algorithm to estimate the system state and unknown parameters in real time. Specifically, the algorithm includes the following steps:

[0094] Prediction phase: Based on the currently known system state and control input, predict the system state and its uncertainty at the next moment;

[0095] Update phase: The prediction results are corrected using the actual input voltage, output voltage, and inductor current data collected by the multi-channel analog-to-digital conversion module.

[0096] Recursive calculation: Continuously repeat the above prediction and update process to gradually approach the actual system state and parameter values.

[0097] In order to improve the accuracy and robustness of parameter identification, the module also introduces a noise covariance matrix adaptive adjustment mechanism. Specifically:

[0098] The system continuously monitors the difference between the actual observed values ​​and the predicted values, which are called observation residuals;

[0099] According to the changing trend of the residual, two important parameters used to describe the uncertainty of the system are automatically adjusted: the process noise covariance matrix and the observation noise covariance matrix;

[0100] This mechanism enables the system to maintain high identification accuracy and stability when facing sudden load changes, input disturbances or environmental changes.

[0101] In addition, to cope with systems with non-minimum phase characteristics, especially when there are right half plane zeros, the parameter identification module also includes a set of identification and compensation mechanisms. The specific operations are as follows:

[0102] After each system model update is completed, the transfer function information of the current system is extracted;

[0103] Determine whether there is a zero point located to the right of the complex plane;

[0104] If such a right half plane zero is detected, its position information is fed back to the adaptive control law adjustment module;

[0105] The controller takes corresponding measures accordingly, such as limiting the controller gain, introducing a phase compensation link, or switching to a more stable control mode to suppress transient instability caused by the reverse response.

[0106] In order to speed up the parameter convergence speed during the system cold start phase, the parameter identification module is also configured with an excitation signal injection strategy. The specific implementation method is as follows:

[0107] At the initial stage of system power-up, the controller actively sends a small high-frequency excitation signal of limited duration, such as a small periodic disturbance of the duty cycle;

[0108] The multi-channel analog-to-digital conversion module synchronously collects the resulting changes in input voltage, output voltage, and inductor current;

[0109] The parameter identification module uses these response data to drive the extended Kalman filter algorithm to accelerate the initial parameter estimation process;

[0110] This method significantly shortens the identification time during the cold start phase and improves the response speed and operational stability of the system after power-on.

[0111] In summary, the present invention achieves high-precision online identification of key parameters of the switching power supply system by establishing a nonlinear state-space model, introducing the extended Kalman filter algorithm, combining the noise covariance adaptive adjustment mechanism, the right half-plane zero point identification and compensation strategy, and the excitation signal injection method in the cold start phase, thereby significantly improving the dynamic response performance and stability of the system under complex operating conditions.

[0112] The specific implementation process of the adaptive control law adjustment module is as follows:

[0113] The adaptive control law adjustment module is integrated into the digital controller. Its core function is to dynamically adjust the control strategy and controller parameters based on the system model update information output by the parameter identification module to achieve high-performance closed-loop control of the switching power supply system.

[0114] The adaptive control law adjustment module performs the following steps:

[0115] First, it receives real-time updates from the parameter identification module, including key characteristics such as the inductor current trend, output voltage response speed, system time constant, and the presence of right-half-plane zeros. This information reflects the current system operating status and stability boundaries.

[0116] Next, based on the updated system model, the module analyzes the system's dynamic trends to determine whether the current operating state has deviated from the stable region. For example, if it detects a prolonged output voltage recovery time, increased control gain fluctuations, or a delayed system response, it indicates that the system may be unstable or critically stable.

[0117] The module then automatically adjusts the controller's core parameters, including proportional gain, integral time constant, and derivative gain. This adjustment process is not a fixed value setting, but rather a continuous optimization based on the current system characteristics, allowing the controller to maintain good steady-state accuracy and transient response performance under different load conditions.

[0118] After the adjustment is complete, the new control law is output to the pulse width modulation drive module, which generates a PWM control signal adapted to the current system state, thereby driving the power switching devices. This process effectively improves the system's response speed and anti-interference ability in the event of sudden load changes or input disturbances.

[0119] In order to further enhance the stability of the system, the adaptive control law adjustment module also introduces a stability evaluation mechanism based on the Lyapunov function. Specifically, it includes:

[0120] Construct a reasonable energy function based on the current system model as a candidate Lyapunov function;

[0121] Calculate the rate of change of the function over time in real time to evaluate whether the overall energy of the system tends to converge;

[0122] If the energy change rate is found to be abnormally increased, indicating that the system is at risk of instability, the control strategy switching mechanism will be triggered immediately;

[0123] Once triggered, the controller automatically reduces gain, limits bandwidth, or switches to a preset safety control mode to ensure that the system continues to operate without losing control.

[0124] In addition, for systems with non-minimum phase characteristics, especially when the right half plane zero is identified, the module automatically enables an improved control structure with lead-lag compensation control strategy. The specific operations are as follows:

[0125] Enable the advance link to increase the system phase margin and improve the response speed;

[0126] At the same time, a hysteresis link is added to suppress the phase reversal phenomenon in the high frequency band to prevent oscillation;

[0127] Limit the overall bandwidth of the controller to avoid system instability caused by reverse response caused by right half plane zeros;

[0128] A feedforward control term is introduced to compensate in advance for the reverse trend of the output voltage caused by the right half plane zero point;

[0129] The entire control law structure is dynamically reconstructed so that the system can maintain good control performance under non-minimum phase conditions.

[0130] To further enhance the system's ability to cope with future disturbances, the adaptive control law adjustment module also has predictive control capabilities, including:

[0131] Predict the possible operating status in the future based on the rate of change of the system model and historical operating data;

[0132] Adjust controller parameters in advance before the load is about to jump or the input voltage fluctuates;

[0133] Utilizes a multi-step rolling optimization algorithm to dynamically optimize control gain configuration by combining current identification results with historical response curves;

[0134] Through this forward-looking control strategy, the smoothness of the system's dynamic response is significantly improved, overshoot and recovery time are reduced, and the system's robustness to external disturbances is enhanced.

[0135] In summary, the present invention achieves high-precision, high-stability and high-response-speed dynamic control of complex switching power supply systems by introducing a variety of advanced control strategies and technical means, including adaptive adjustment of control parameters based on system model update, Lyapunov stability criterion, right half-plane zero compensation mechanism and predictive control strategy. It is particularly suitable for high-performance digital power supply applications with non-minimum phase characteristics.

[0136] The pulse width modulation drive module is specifically implemented as follows:

[0137] The pulse width modulation drive module is integrated into the periphery of the digital controller or configured independently. Its core function is to convert the dynamic control signal output by the controller into a drive signal suitable for the power switching device, and provide multi-level safety protection mechanisms and operation optimization strategies while ensuring efficient energy conversion.

[0138] The pulse width modulation drive module performs the following steps:

[0139] First, it receives a real-time control signal from a digital controller. This signal, dynamically adjusted based on the current system state and the adaptive control algorithm, contains information such as the target duty cycle and switching frequency setpoint. The module then converts the received control signal into a high-resolution pulse-width modulated waveform to achieve precise control of the power switching devices.

[0140] The module then generates a pair of complementary, symmetrical PWM drive pulses based on the currently set switching frequency and dead-time parameters. These symmetrical pulses are used to drive the upper and lower bridge power devices, such as MOSFETs or IGBTs, in a half-bridge or full-bridge topology, ensuring the main circuit operates in an ideal state, reducing voltage distortion and improving efficiency.

[0141] During PWM signal generation, the module dynamically calculates and inserts the optimal dead time based on the actual turn-on and turn-off delay characteristics of the power switching devices. This dead time setting is not fixed but is adjusted online based on device type, temperature feedback, and system operating status. This effectively prevents the risk of shoot-through short circuits caused by simultaneous conduction of the upper and lower bridge arms, thereby improving system safety and reliability.

[0142] To ensure stable operation during system startup, the PWM driver module also integrates a soft-start control function. During the initial power-up phase, the module gradually increases the duty cycle of the PWM signal rather than immediately ramping it to full power. This process significantly reduces the potential current surge during startup, preventing damage to the input power supply, filter components, and load.

[0143] In addition, the module also has a variety of enhanced control and protection mechanisms, including:

[0144] Real-time monitoring of key electrical parameters during system operation, such as output current, output voltage, and operating temperature of power devices;

[0145] When an overcurrent, overvoltage or temperature anomaly signal is detected that exceeds the preset threshold, the PWM output duty cycle is immediately limited or the PWM drive signal is completely turned off to prevent the fault from expanding;

[0146] After the fault is cleared, the module automatically restarts the PWM output and synchronously updates the controller status information, allowing the system to resume operation smoothly without manual intervention;

[0147] At the same time, the module can dynamically adjust the switching frequency according to the instructions of the digital controller, reducing the frequency under light load conditions to reduce switching losses, and increasing the frequency under heavy load conditions to improve response speed and control accuracy, thereby maintaining high overall efficiency and stability under different load conditions.

[0148] In summary, the present invention achieves efficient and safe driving of power switching devices by constructing a high-precision, dynamically adjustable PWM generation mechanism, combined with innovative means such as intelligent dead-zone control, soft-start strategy, multiple fault protection, and frequency adaptive adjustment. It is particularly suitable for high-performance, high-reliability digital control switching power supply systems.

[0149] The stability monitoring module is specifically implemented as follows:

[0150] The stability monitoring module is integrated into the digital controller and is used to monitor the operating status of the switching power supply system in real time. When it detects that the system enters an unstable or critically stable state, it actively triggers the corresponding fault tolerance mechanism to maintain the basic power supply function of the system and prevent the fault from expanding.

[0151] The stability monitoring module performs the following steps:

[0152] First, the system continuously collects key operating parameters of the switching power supply system, including time series data such as output voltage, inductor current, and the control signal output by the controller. This data is acquired in real time by the multi-channel analog-to-digital conversion module and analyzed online by the stability monitoring module.

[0153] The module then processes the collected data using a sliding window variance analysis algorithm. Specifically, it selects historical data within a specific time period as the analysis window and calculates the variance trend to determine whether the system has abnormal fluctuations. It also incorporates a peak detection algorithm to identify unstable characteristics such as overshoot, oscillation, or slow recovery in the output voltage or inductor current.

[0154] When the analysis results show any of the following situations, the system is considered to have stability anomalies:

[0155] The overshoot of the output voltage exceeds the preset safety threshold;

[0156] After a sudden load change or input disturbance occurs, the voltage recovery time is significantly prolonged, exceeding the normal response time range;

[0157] The control signal output by the controller exhibits high-frequency oscillation or periodic fluctuation, indicating that the phase margin has dropped to a critical level;

[0158] The system exhibits unexpected dynamic behavior, such as increased response delay or abnormally high control gains.

[0159] Once a stability abnormality is detected, the module immediately initiates the corresponding fault tolerance mechanism, which includes one or more of the following measures:

[0160] Switch to a set of pre-set stable control parameters, which are optimized to significantly improve system stability at the expense of a small amount of response speed;

[0161] Actively limit the controller's bandwidth, reduce the gain in the high-frequency band, and prevent the spread of oscillations caused by phase inversion or right-half-plane zeros;

[0162] Start the soft restart process, gradually turn off the current PWM drive signal, reinitialize the controller state, and re-establish stable output according to the soft start strategy to avoid the interruption risk caused by direct reset;

[0163] At the same time, the abnormal information is fed back to the system main control unit to record the fault event and provide a basis for subsequent diagnosis.

[0164] Furthermore, to adapt to stability requirements under varying operating conditions, the stability monitoring module supports adaptive operating mode switching. For example, under light loads, where system stability issues are more likely, the module automatically enhances the sensitivity of the stability criteria and proactively adjusts control parameters. In heavy loads or scenarios with frequent dynamic load changes, the stability trigger threshold is appropriately relaxed to balance response speed and system robustness.

[0165] In summary, the present invention realizes intelligent monitoring and dynamic intervention of the operating status of the switching power supply system by constructing a stability criterion based on sliding window variance analysis and peak detection algorithm, combined with a multi-level fault-tolerant mechanism and control parameter switching strategy, significantly improving the system's anti-interference ability and long-term operation reliability under complex working conditions. It is particularly suitable for high-performance digital power supply systems with high dynamic response requirements and non-minimum phase characteristics.

[0166] Working Principle of the Invention: The present invention aims to address the problems of poor stability and slow response speed encountered by traditional control methods in non-minimum phase systems. The system comprises a digital controller, a multi-channel analog-to-digital conversion module, a parameter identification module, an adaptive control law adjustment module, a pulse width modulation drive module, and a stability monitoring module. The digital controller utilizes a high-performance DSP or FPGA to execute the adaptive control algorithm. The multi-channel ADC module acquires input voltage, output voltage, and inductor current signals in real time. The parameter identification module uses the extended Kalman filter algorithm to online identify key system parameters and update the system model, specifically identifying and compensating for right-half-plane zeros. The adaptive control law adjustment module dynamically adjusts the control strategy based on the updated model, introduces a Lyapunov function to assess stability, and combines predictive control to enhance robustness. The pulse width modulation drive module generates high-resolution PWM signals with dynamic dead-time adjustment, soft-start, and frequency adaptation capabilities. The stability monitoring module uses sliding window variance analysis and peak detection to determine system status. In the event of an anomaly, a fault-tolerant mechanism is triggered to ensure stable system operation. The present invention realizes high-precision modeling, intelligent control and dynamic optimization of complex switching power supply systems, significantly improving the response performance and stability of the system under load mutations, input disturbances and non-minimum phase characteristics, and is suitable for high-reliability digital power supply application scenarios.

[0167] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0168] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0169] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0170] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0171] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A switching power supply dynamic response regulation system based on digital control, characterized in that: include: a digital controller for executing an adaptive control algorithm and generating a control signal; A multi-channel analog-to-digital conversion module, connected to the digital controller, for real-time acquisition of input voltage, output voltage and inductor current signals of the switching power supply system; A parameter identification module, integrated into the digital controller, for performing online identification of key system parameters based on an extended Kalman filter algorithm to update the system model; an adaptive control law adjustment module, coupled to the parameter identification module, for dynamically adjusting control gains and control strategies according to the updated system model to compensate for the impact of non-minimum phase characteristics on system stability; A pulse width modulation drive module is connected to the digital controller and is used to generate corresponding PWM control pulses according to the adjusted control signal to drive the power switch device; Stability monitoring module, used to monitor the system operation status and trigger the fault tolerance mechanism in abnormal situations to maintain the basic functions of the system; The adaptive control law adjustment module performs the following steps: Receive updated system model information from the parameter identification module, including inductor current dynamics, output voltage response characteristics, and right half plane zero position; Determine whether the current operating state deviates from the stable area based on the change trend of the system model; Dynamically adjust the proportional, integral, and differential gain coefficients of the PID controller; Output the updated control law to the pulse width modulation drive module to optimize the transient response performance under sudden load changes; The adaptive control law adjustment module also includes a stability criterion evaluation step based on the Lyapunov function: Construct a candidate Lyapunov function suitable for the current system model; Calculate its derivatives in real time to determine the energy convergence of the system; If an unstable trend is detected, the control gain reduction mechanism is triggered or the system switches to a preset safety control mode; Maintain system closed-loop stability without sacrificing response speed; The adaptive control law adjustment module automatically switches the control strategy according to whether there is a right half plane zero point, specifically including: When it is identified that the system has non-minimum phase characteristics, the lead-lag compensation control structure is enabled; Limit the controller bandwidth to avoid phase reversal and oscillation at high frequencies; A feed-forward term is introduced to offset the reverse response behavior caused by the right half plane zero; Dynamically reconfigure the control law to achieve robust control of non-minimum phase systems; The adaptive control law adjustment module also implements the following enhanced control strategy: Predict possible future operating states based on the system model change rate; Adjust control gains in advance to cope with impending load jumps or input disturbances; Combine historical data with current identification results to perform multi-step rolling optimization; Improve the smoothness of the system's dynamic response and anti-interference ability through predictive control strategies.

2. The digitally controlled switching power supply dynamic response regulation system according to claim 1, characterized in that: The parameter identification module performs the following steps: Construct a nonlinear state-space model of the switching power supply system, including the state equations and observation equations for the inductor current and output voltage; Based on the extended Kalman filter algorithm, the state equation is predicted and updated in real time to estimate the internal state variables and unknown parameters of the system; The input voltage, output voltage and inductor current data collected by the multi-channel analog-to-digital conversion module are used as observation inputs to drive the parameter identification process; The updated system parameters are periodically output to the adaptive control law adjustment module to achieve dynamic compensation.

3. The digitally controlled switching power supply dynamic response regulation system according to claim 2, characterized in that: The extended Kalman filter algorithm introduces a noise covariance adaptive adjustment mechanism, which specifically includes: Monitor system operation status in real time and evaluate observation residuals; Dynamically adjust the process noise covariance matrix Q and the observation noise covariance matrix R according to the residual change trend; Improve parameter identification accuracy and enhance system robustness under sudden load changes or input disturbances.

4. The digitally controlled switching power supply dynamic response regulation system according to claim 2, characterized in that: The parameter identification module also includes the steps of identifying and compensating the right half plane zero point position: After the system model is updated, the transfer function information is extracted to determine whether there is a right half plane zero point; If it exists, its position information is fed back to the adaptive control law adjustment module; The controller adjusts the control strategy based on this information, including gain limiting, phase compensation, or switching control modes to suppress dynamic instability caused by non-minimum phase behavior; The parameter identification module achieves rapid convergence by: Inject a small excitation signal of limited duration during the system startup phase; The extended Kalman filter algorithm is used to process the stimulus response data to accelerate the initial parameter estimation process; Shorten the identification time during the system cold start phase and improve power-on response speed and stability.

5. The digitally controlled switching power supply dynamic response regulation system according to claim 1, characterized in that: The pulse width modulation drive module performs the following steps: Receive dynamic adjustment control signals from digital controllers and convert them into high-resolution PWM waveforms; Generate complementary and symmetrical PWM drive pulses according to the current switching frequency setting value and dead time configuration; In the generation process, the optimal dead time is dynamically calculated and inserted by combining the turn-on and turn-off delay characteristics of the power switching devices; The output is sent to the power switching device to achieve efficient and low-distortion energy conversion while preventing the risk of shoot-through in the upper and lower bridge arms.

6. The digitally controlled switching power supply dynamic response regulation system according to claim 5, characterized in that: The PWM drive module also implements the following enhanced control and protection strategies: Implement soft start control during system power-up, gradually increasing the PWM duty cycle to suppress startup inrush current. Real-time monitoring of overcurrent, overvoltage and temperature abnormality signals, and immediate limitation or shutdown of PWM output when a fault is detected; Automatically restart PWM output and synchronize controller status after fault recovery to ensure safe and reliable system operation; At the same time, the switching frequency is dynamically adjusted according to the controller instructions to optimize the efficiency and stability of the system under different load conditions.

7. The digitally controlled switching power supply dynamic response regulation system according to claim 1, characterized in that: The stability monitoring module performs the following steps: Real-time collection and analysis of time series data of output voltage, inductor current and controller output control quantity; Identify whether the system is in an oscillatory or unstable state based on sliding window variance analysis and peak detection algorithm; When it is detected that the overshoot exceeds the set threshold, the recovery time is abnormally prolonged, or the phase margin drops to a critical value, it is determined to be a stability abnormality; Triggering fault tolerance mechanisms, including switching to a preset stable control parameter set, limiting controller bandwidth, or initiating a soft restart process, to restore system stability and maintain basic power supply functions.

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