Control system for magneto-rheological semi-active suspension

Abstract

The invention relates to the technical field of control systems, and discloses a control system for a magneto-rheological semi-active suspension, comprising an acceleration sensor configured to collect vibration acceleration signals of sprung mass and unsprung mass of a vehicle; the displacement sensor is configured to monitor the dynamic deflection of the suspension; the gyroscope is configured to obtain attitude information of a pitch angle and a roll angle of the vehicle body; the vehicle speed sensor is configured to detect the vehicle running speed in real time; the binocular camera is configured to collect pavement environment image information; the magneto-rheological damper is configured to adjust the damping force according to the current of the electromagnetic coil; the MCU controller comprises a signal acquisition module, a control processing module, a current driving module and a current sampling module; the signal acquisition module is configured to convert a sensor signal into a physical quantity; the control processing module is configured to fuse multi-source information to calculate expected damping force; the current driving module is configured to output PWM driving current to the magnetorheological damper; and the current sampling module is configured to feed back an actual current value to realize closed-loop control.

Description

Technical Field

[0001] The present invention relates to the technical field of control systems, and in particular to a control system for a magnetorheological semi-active suspension. Background Art

[0002] The suspension system is a crucial component of a vehicle, directly impacting its driving stability, comfort, and safety. Traditional suspension systems typically utilize mechanical springs and hydraulic shock absorbers, but their performance adjustment capabilities are limited for varying road and driving conditions, making them unable to optimize based on real-time changes in road conditions and vehicle dynamics. This has led to the emergence of electronically controlled suspension systems, such as semi-active and active suspension systems, which utilize electronic control devices to adjust suspension characteristics, thereby improving suspension system performance. Traditional semi-active and active suspension systems rely on mechanical or hydraulic control systems, resulting in slow response, low control accuracy, and system complexity.

[0003] As a new type of controllable vibration damping device, magnetorheological dampers (MRDs) offer advantages such as fast response, high control accuracy, and a simple structure. Consequently, they are widely used in semi-active suspension systems. Their core principle is to adjust the damping force of the shock absorber by utilizing the viscosity of the MR fluid under magnetic field conditions. Compared to traditional hydraulic shock absorbers, MRDs can adjust the damping force in a much shorter time, adapting to complex and changing driving conditions.

[0004] Electronically controlled suspension systems based on magnetorheological dampers (MRDs) still face numerous challenges in practical applications, particularly in terms of control accuracy, system response speed, and system complexity. Existing control systems often rely on complex hardware structures and algorithms, and have limitations in collecting and processing real-time vehicle status information. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention proposes a control system for magnetorheological semi-active suspension, aiming to improve the response speed, control accuracy, reliability and adaptability to complex vehicle conditions of the magnetorheological semi-active suspension system through control system and software and hardware design.

[0006] A control system for a magnetorheological semi-active suspension, comprising: an acceleration sensor configured to collect vibration acceleration signals of a sprung mass and an unsprung mass of a vehicle; a displacement sensor configured to monitor suspension dynamic deflection; A gyroscope configured to obtain vehicle body pitch and roll attitude information; A vehicle speed sensor configured to detect a vehicle speed in real time; A binocular camera configured to collect image information of a road environment; a magnetorheological damper configured to adjust a damping force based on an electromagnetic coil current; MCU controller, including signal acquisition module, control processing module, current drive module and current sampling module; The signal acquisition module is configured to convert the sensor signal into a physical quantity; The control processing module is configured to fuse multi-source information to calculate the desired damping force; The current driving module is configured to output a PWM driving current to the magnetorheological damper; The current sampling module is configured to feed back the actual current value to implement closed-loop control.

[0007] Furthermore, the control processing module integrates vehicle body pitch and roll angle posture information, road environment image information and vehicle speed information, and uses a neural network to identify the road surface grade of the sprung mass acceleration, the root mean square value of the suspension dynamic deflection and the vehicle speed, and dynamically switches the preset control parameters based on the identification results.

[0008] Furthermore, the neural network takes sprung mass acceleration, suspension dynamic deflection root mean square value and vehicle speed as input features and outputs a road surface grade classification result.

[0009] Furthermore, the dynamically switched preset control parameters include PID gain, damping force threshold and control frequency.

[0010] Furthermore, the current sampling module collects actual current through the resistance on the electromagnetic coil, and adjusts the PWM duty cycle in combination with the closed-loop control algorithm so that the actual current tracks the target current value.

[0011] Furthermore, the signal acquisition module includes: a level conversion circuit configured to adjust the sensor signal amplitude to a safe voltage range of the processor ADC interface; a low-pass filter configured to filter out high-frequency noise; A voltage limiting circuit is configured to prevent signal overvoltage from damaging the processor.

[0012] Furthermore, the current driving module is configured to convert the PWM signal into an adjustable driving current.

[0013] Furthermore, the communication module is connected to the host computer through a first interface, and the communication module is connected to the vehicle domain controller through a second interface.

[0014] Furthermore, the MCU controller is configured as multiple independent PWM output channels, and each output channel is connected to a magnetorheological damper.

[0015] Furthermore, the environmental image data collected by the binocular camera is input into the control processing module after edge feature extraction for identifying potholes and speed bumps on the road.

[0016] The invention adopting the above technical solution has the following advantages: The signal acquisition module in the MCU controller of the present invention converts sensor signals into physical signals, and the control processing module calculates the expected force required by the suspension system based on these signals. The MCU controller generates precise electromagnetic coil drive current through the current drive module, while the current sampling module provides real-time feedback of the actual current and adjusts the current through closed-loop control to ensure that the actual operating current is close to the target value, thereby achieving precise control of the damping force. In addition, the MCU controller's communication module supports real-time data exchange and information interaction with the host computer and the vehicle domain controller, and other subsystems implement information interaction, realizing multi-system collaborative control, so that the suspension system can adapt to different driving conditions. This system and the MCU controller have fast response, high output accuracy and good adaptability, and are particularly suitable for semi-active suspension control under complex vehicle working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention, the following briefly introduces the drawings required for use in the specific embodiments. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0018] Figure 1 A system diagram of a MUC controller in a control system for a magnetorheological semi-active suspension according to the present invention; Figure 2 This is a block diagram of working condition identification and dynamic switching control in a control system for magnetorheological semi-active suspension of the present invention; Figure 3 This is a schematic diagram of the current tracking principle in a control system for a magnetorheological semi-active suspension according to the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0021] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0022] like Figures 1 to 3 As shown, a control system for a magnetorheological semi-active suspension of the present invention includes: an acceleration sensor configured to collect vibration acceleration signals of a sprung mass and an unsprung mass of a vehicle; a displacement sensor configured to monitor suspension dynamic deflection; A gyroscope configured to obtain vehicle body pitch and roll attitude information; A vehicle speed sensor configured to detect a vehicle speed in real time; A binocular camera configured to collect image information of a road environment; a magnetorheological damper configured to adjust a damping force based on an electromagnetic coil current; MCU controller, including signal acquisition module, control processing module, current drive module and current sampling module; The signal acquisition module is configured to convert the sensor signal into a physical quantity; The control processing module is configured to fuse multi-source information to calculate a desired damping force; The current driving module is configured to output a PWM driving current to the magnetorheological damper; The current sampling module is configured to feed back the actual current value to achieve closed-loop control.

[0023] Specifically, the signal acquisition module in the MCU controller of the present invention converts sensor signals into physical signals, and the control processing module calculates the desired force required by the suspension system based on these signals. The MCU controller generates precise electromagnetic coil drive current through the current drive module, while the current sampling module provides real-time feedback of the actual current and adjusts the current through closed-loop control to ensure that the actual operating current is close to the target value, thereby achieving precise control of the damping force. In addition, the MCU controller's communication module supports real-time data exchange and information interaction with the host computer and the vehicle domain controller, and other subsystems realize information interaction, realizing multi-system coordinated control, so that the suspension system can adapt to different driving conditions. This system and the MCU controller have fast response, high output accuracy, and good adaptability, and are particularly suitable for semi-active suspension control under complex vehicle operating conditions.

[0024] The present invention comprises an acceleration sensor, a displacement sensor, a gyroscope, a vehicle speed sensor, a binocular camera, an MCU controller, and a magnetorheological damper. The MCU controller is divided into software and hardware. The hardware includes a signal acquisition module, a current drive module, a power supply module, a communication module, a control processing module, and a current sampling module. The software includes basic software and application software.

[0025] In this embodiment, the communication module connects the digital SPI gyroscope signal to the control processing module through the SPI interface, implements the SPI interface driver and reads data information; and connects the binocular camera through the IIC interface to obtain environmental signals.

[0026] In this embodiment, the control processing module integrates vehicle body pitch and roll angle posture information, road environment image information, and vehicle speed information, and uses a neural network to identify the road surface grade based on the sprung mass acceleration, the root mean square value of the suspension dynamic deflection, and the vehicle speed, and dynamically switches the preset control parameters based on the identification results.

[0027] In this embodiment, the neural network uses sprung mass acceleration, suspension dynamic deflection root mean square value and vehicle speed as input features and outputs a road surface grade classification result.

[0028] In this embodiment, the dynamically switched preset control parameters include PID gain (proportional, integral, and differential gain coefficients of the PID controller), damping force threshold (maximum allowable value of the desired damping force), and control frequency (execution frequency of the control algorithm).

[0029] In this embodiment, edge feature extraction is performed on the environmental image data captured by the binocular camera and then fed into the control processing module for identifying potholes and speed bumps. (The binocular camera imagery is processed using a Canny operator to extract edge features, which are then fed into the control processing module for pothole and speed bump identification. The identified structure is then fused with gyroscope attitude data to correct the road surface grade output by the neural network.)

[0030] Specifically, this invention not only relies on acceleration and displacement sensors to obtain vehicle response information, but also adds a gyroscope to monitor vehicle posture, a speed sensor to obtain vehicle speed, and a binocular camera to capture surrounding environmental information. By integrating vehicle posture information and environmental recognition (such as road surface information) into the control algorithm, the MCU controller can determine the need for suspension adjustment, improving the system's adaptability in complex road conditions and highly dynamic environments.

[0031] like Figure 2 As shown in the figure, the control processing module first integrates vehicle pitch and roll angle information, vehicle speed, and road edge feature information captured by a binocular camera. Using a pretrained neural network model (using sprung mass acceleration, suspension RMS dynamic deflection, and vehicle speed as input features), it identifies the current road grade and vehicle state. Based on the identification results, it then dynamically switches to a preset control parameter set (including PID gains, damping force thresholds, and control frequency). Finally, it calculates the desired damping force for the magnetorheological semi-active suspension based on the selected parameter set and real-time sensor signals (acceleration, displacement, attitude, etc.).

[0032] In this embodiment, the current sampling module collects the actual current through the resistor on the electromagnetic coil, and adjusts the PWM duty cycle in combination with the closed-loop control algorithm so that the actual current tracks the target current value.

[0033] Specifically, the current sampling module provides real-time feedback of the current, combined with a closed-loop control algorithm, to minimize the error between the actual current and the target current. This design ensures precise control of the electromagnetic coil drive current of the magnetorheological shock absorber, thereby improving the damping force adjustment accuracy and stability of the suspension system. The closed-loop control algorithm uses the PID algorithm, such as Figure 3 shown.

[0034] Among them, x s -x u represents the displacement difference between the sprung mass and the unsprung mass. The desired force F is converted into the desired current I through the MRD inverse model. The error e is the difference between the desired current I and the actual current I. r The difference between the two values ​​is calculated by the PID controller to obtain ΔI, which is used to adjust the actual output PWM duty cycle to reduce the error. The expression of the control quantity ΔI is: in, is the proportionality coefficient, Integration time constant, is the differential time constant.

[0035] In this embodiment, the signal acquisition module includes: a level conversion circuit configured to adjust the sensor signal amplitude to a safe voltage range of the processor ADC interface; a low-pass filter configured to filter out high-frequency noise; A voltage limiting circuit is configured to prevent signal overvoltage from damaging the processor.

[0036] Specifically, the signal acquisition module performs level conversion, filtering, and voltage limiting on the sensor signal before inputting it into the processor. The MCU controller of the present invention designs corresponding acquisition and processing circuits for analog acceleration sensors and displacement sensors. For analog sensors, after passing through the level conversion circuit, the signal is converted into a safe signal range and input into the ADC interface of the processor through low-pass filtering and voltage limiting.

[0037] In this embodiment, the current driving module is configured to convert the PWM signal into an adjustable driving current.

[0038] In this embodiment, the communication module is connected to the host computer through the first interface, and the communication module is connected to the vehicle domain controller through the second interface.

[0039] Specifically, this system features two-way communication with a host computer and a vehicle domain controller, enabling real-time information observation and interaction. The communication module connects to the host computer via a first interface (UART), providing real-time observation of sensor parameters and vehicle operating status, enabling real-time algorithm programming and calibration. The communication module connects to the vehicle domain controller via a second interface (CAN), acquiring additional vehicle information and enabling information exchange with other subsystems, enabling multi-system coordinated control.

[0040] In this embodiment, the MCU controller is configured as multiple independent PWM output channels, and each output channel is connected to a magnetorheological damper.

[0041] In this embodiment, the MCU controller further includes a power supply module.

[0042] Specifically, the control processing module is mainly composed of hardware and software. The hardware is the minimum system to achieve automotive-grade single-chip processing, mainly providing external clock signals, processor reset signals, and power supply to ensure the normal operation of the processor and correct input and output I / O port configuration; the software is responsible for the processing and calculation of input signals, implementation of control algorithms, control compensation calculation and PWM duty cycle output, sensor signal observation, and CAN communication.

[0043] The software system is divided into basic software and task application software, which consists of hardware interface layer, hardware driver layer and task application layer. Among them, the basic software includes the interface and driver layer parts, which mainly completes the initialization of the ECU hardware platform and provides boot, drive, clock, interrupt and scheduling and other operating environment preparations for the application software; the task application software deploys relevant control algorithms, calls the interface to obtain input signals, calculates the output PWM duty cycle through the algorithm, and calls the driver layer to realize the control signal output.

[0044] The present invention has four output controls to respectively control the four magnetorheological dampers at the front and rear of the semi-active suspension of the vehicle. The drive control process of the present invention uses a low-resistance, high-precision, high-power precision resistor connected in series with the actuator electromagnetic coil to achieve closed-loop sampling control of the actual current value.

[0045] The power supply module outputs the voltage required by each module from a 12V power supply, and supplies power to the signal acquisition module, control processing module, current drive module and communication module.

[0046] The MCU controller embodiment preferably uses the NXP S32K312 chip. This single-chip microcontroller processor has a maximum operating frequency of 120MHz, 2MB Flash, 192KB RAM, and supports interfaces such as I2C, SPI, and UART. Debugging, simulation, and programming of MCU software based on the S32K312 processor are performed in the S32DS / Simulink environment via the J-Link interface. The S32K312 processor implements the MCU control system's input signal acquisition and processing, control algorithm calculations, actuator PWM control output, CAN bus communication, and host computer monitoring functions.

[0047] Acceleration sensors and displacement sensors are installed at the top and both ends of the shock absorber to collect vibration acceleration signals and displacement signals. The signal acquisition module converts the acceleration analog signal, vehicle speed analog signal, displacement analog signal, etc. into 0-5V, and connects it to the ADC interface of the S32K312 processor through low-pass filtering and voltage limiting; the 0-5V voltage signal is connected to the ADC interface of the S32K312 processor through low-pass filtering and voltage limiting; the MPU6050 gyroscope is installed at the center of mass of the vehicle and connected to the S32K312 processor through the SPI digital interface of the communication module to obtain real-time vehicle posture information; the camera is connected through the IIC interface to obtain environmental information; the S32K312 processor obtains real physical signals through calculation and processing, and connects to the host computer through the UART interface, and uses the Freemaster software to observe the signals in real time.

[0048] The current drive module uses a 12V voltage and adopts a Darlington transistor to drive the MOS tube method, and controls the drive current by outputting a PWM pulse width modulation signal.

[0049] The power module converts 12V into ±12V and 5V voltages to power each module.

[0050] The system workflow is: Sensors (acceleration, displacement, vehicle speed, gyroscope, and camera) collect signals in real time. The signal acquisition module processes the signals. The control processing module performs multi-source information fusion, neural network operating condition identification, dynamic parameter switching, and desired damping force calculation. The current drive module outputs PWM drive current. The current sampling module provides feedback on the actual current. PID closed-loop control adjusts the PWM duty cycle. This precisely controls the damping force of the magnetorheological damper. Simultaneously, the communication module enables observation and calibration with the host computer and information exchange and collaboration with the domain controller.

[0051] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0052] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0053] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0054] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0055] In addition, the functional units in the various embodiments of the application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software program modules.

[0056] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk, or optical disk, etc., various media that can store program code.

[0057] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0058] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A control system for a magnetorheological semi-active suspension, characterized in that: include: an acceleration sensor configured to collect vibration acceleration signals of a sprung mass and an unsprung mass of a vehicle; a displacement sensor configured to monitor suspension dynamic deflection; A gyroscope configured to obtain vehicle body pitch and roll attitude information; A vehicle speed sensor configured to detect a vehicle speed in real time; A binocular camera configured to collect image information of a road environment; a magnetorheological damper configured to adjust a damping force based on an electromagnetic coil current; MCU controller, including signal acquisition module, control processing module, current drive module and current sampling module; The signal acquisition module is configured to convert the sensor signal into a physical quantity; The control processing module is configured to fuse multi-source information to calculate the desired damping force; The current driving module is configured to output a PWM driving current to the magnetorheological damper; The current sampling module is configured to feed back the actual current value to implement closed-loop control.

2. The control system for a magnetorheological semi-active suspension according to claim 1, characterized in that: The control processing module integrates vehicle body pitch and roll angle posture information, road environment image information and vehicle speed information, and uses a neural network to identify the road surface grade of the sprung mass acceleration, the root mean square value of the suspension dynamic deflection and the vehicle speed, and dynamically switches the preset control parameters based on the identification results.

3. The control system for magnetorheological semi-active suspension according to claim 2, characterized in that: The neural network takes sprung mass acceleration, suspension dynamic deflection root mean square value and vehicle speed as input features and outputs road surface grade classification results.

4. The control system for a magnetorheological semi-active suspension according to claim 2, characterized in that: The dynamically switched preset control parameters include PID gain, damping force threshold and control frequency.

5. The control system for magnetorheological semi-active suspension according to claim 1, characterized in that: The current sampling module collects the actual current through the resistance on the electromagnetic coil, and adjusts the PWM duty cycle in combination with the closed-loop control algorithm to make the actual current track the target current value.

6. The control system for magnetorheological semi-active suspension according to claim 1, characterized in that: The signal acquisition module includes: a level conversion circuit configured to adjust the sensor signal amplitude to a safe voltage range of the processor ADC interface; a low-pass filter configured to filter out high-frequency noise; A voltage limiting circuit is configured to prevent signal overvoltage from damaging the processor.

7. The control system for a magnetorheological semi-active suspension according to claim 1, characterized in that: The current driving module is configured to convert the PWM signal into an adjustable driving current.

8. The control system for magnetorheological semi-active suspension according to claim 1, characterized in that: The communication module is connected to the host computer via a first interface, and is connected to the vehicle domain controller via a second interface.

9. The control system for magnetorheological semi-active suspension according to claim 1, characterized in that: The MCU controller is configured as multiple independent PWM output channels, and each output channel is connected to a magnetorheological damper.

10. The control system for magnetorheological semi-active suspension according to claim 2, characterized in that: The environmental image data collected by the binocular camera is input into the control processing module after edge feature extraction for identifying road potholes and speed bump features.

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