A method and system for adaptive control of a sweeper's operating speed

Abstract

The present application relates to the technical field of sweeper control, and discloses a kind of sweeper operation speed self-adaptive control method and system, including dust detection module, vehicle speed detection module, control module, drive motor control module and storage module.The present application is distributed by the sensor array of dust detection module different weight calculation road dust amount characteristic value filter sudden noise signal;By comparing road dust amount characteristic value with threshold value, different branch logic is executed to output specific speed or by nonlinear attenuation mathematical model to solve target operation speed, and when lacking hardware floating point operation unit, discrete look-up table dimensionality reduction matching branch addressing is enabled to obtain target operation speed, to avoid program blocking risk;By target operation speed and actual driving speed calculation speed deviation value, speed adjustment control signal is generated by executing proportional integral algorithm with anti-integration saturation mechanism and dead zone determination mechanism, to prevent excessive overshoot and mechanical oscillation.

Description

Technical Field

[0001] This application relates to the field of sweeper control technology, and in particular to a sweeper operating speed adaptive control method and system. Background Technology

[0002] Current sweepers rely on fixed speeds to advance the cleaning process. This fixed operating mode lacks a feedback mechanism to dynamically adjust the speed based on road surface pollution levels. Because of the constant speed, dust is missed when the sweeper passes through areas with accumulated pollution due to the short brush action time, while the lower speed increases overall operation time and consumes more chassis power when passing through unpolluted areas. To obtain road pollution data, some sweeping equipment uses dust sensors. However, existing sensor structures use single-point measurement, and the optical detection window is easily obstructed by road obstacles, causing abrupt changes in the measured values. Sensors mounted on the sides of the sweeper chassis are susceptible to data spikes due to interference from dust from adjacent lanes, making it impossible for the onboard control unit to objectively calculate the true pollution level across the entire sweeping width.

[0003] The vehicle control system generates a large amount of floating-point computational load when processing nonlinear speed regulation logic, exceeding the computing power limit of the microprocessor's underlying hardware and causing the control program to block. In the motor speed regulation closed-loop control network, the lack of error accumulation limiting action in the face of sudden load disturbances leads to dynamic overshoot in the drive current output. When the actual driving speed is close to the expected value, small speed data deviations continuously trigger the actuator to produce high-frequency adjustment actions, forcing the sweeper chassis transmission system to produce mechanical vibrations.

[0004] Therefore, this invention proposes an adaptive control method and system for the operating speed of a sweeper to address the shortcomings of existing technologies. Summary of the Invention

[0005] The technical problem solved by this invention is that the sweeper lacks a linkage feedback mechanism to dynamically adjust the driving speed according to the road pollution status during operation. When passing through polluted road sections, dust is left behind. When passing through unpolluted road sections, the operation time and chassis energy consumption are increased. The motor speed regulation closed loop is subject to mechanical vibration and system overshoot due to load disturbances.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The first aspect of the present invention provides an adaptive control system for the operating speed of a sweeper, including a dust detection module, a vehicle speed detection module, a control module, a drive motor control module, and a storage module.

[0008] The dust detection module is fixedly installed at the front end of the sweeper chassis, the vehicle speed detection module is mounted on the end of the sweeper's drive shaft, the signal input port of the control module is connected to the signal output terminals of the dust detection module and the vehicle speed detection module respectively, the communication port of the control module is connected to the data read / write port of the storage module, the control command output terminal of the control module is connected to the signal receiving terminal of the drive motor control module, and the drive current output terminal of the drive motor control module is connected to the power input terminal of the sweeper's drive motor.

[0009] A second aspect of the present invention provides a method for adaptive control of the operating speed of a sweeper, comprising the following steps:

[0010] The dust detection module detects the dust condition on the road surface in front of the sweeper and outputs an analog signal to the control module. The control module analyzes the analog signal to generate a value representing the amount of dust on the road surface.

[0011] The vehicle speed detection module collects the actual driving speed data of the sweeper and sends the actual driving speed data to the control module;

[0012] The control module reads the road dust volume representation value and queries the dust volume and vehicle speed mapping database in the storage module to determine the target operating speed;

[0013] The calculation process for the difference between the actual driving speed data and the target operating speed is executed within the control module to obtain the speed deviation value;

[0014] The control module generates a speed adjustment control signal based on the speed deviation value and outputs it to the drive motor control module to change the current parameters input to the sweeper's drive motor, so that the actual driving speed data approaches the target operating speed.

[0015] The system repeatedly executes steps S10 to S50 according to a preset time step, forming a closed-loop feedback regulation and control loop.

[0016] The specific steps and operational logic of the adaptive control method for sweeper operating speed are as follows:

[0017] In step S10, the dust detection module consists of a sensor array composed of multiple independent laser dust sensors arranged at equal intervals along the width of the sweeper vehicle. The control module periodically scans the analog signal output by the dust detection module and converts it into dust concentration parameters. A pre-calibrated array of weighting parameters is stored internally in the storage module. Different weighting coefficients are mapped one-to-one with laser dust sensors at different installation positions. The control module assigns higher weights to laser dust sensors located in the center of the sensor array and lower weights to those located on the edges of the array. The control module extracts the dust concentration parameters from each data channel and performs multiplication, addition, and division calculations with the corresponding weighting coefficients to obtain the road dust level characterization value.

[0018] In step S20, the timer channel of the microprocessor unit inside the vehicle speed detection module continuously monitors the electrical pulse signal. Within the time sampling window, the timer channel accumulates the number of rising edges of the electrical pulse signal to obtain the total number of pulses reflecting the mechanical rotation angle of the sweeper's half-shaft. The microprocessor unit extracts the total number of pulses, multiplies it by the tire's outer diameter circumference, and divides it by the length of the time sampling window to obtain the actual driving speed data. In response to the timer interrupt signal, the microprocessor unit loads the actual driving speed data from the buffer into the data field of the CAN message and sends it to the control module.

[0019] In step S30, the internal logic comparator of the control module compares the value representing the amount of road dust with the preset upper and lower threshold values. When the value representing the amount of road dust exceeds the preset upper threshold value, the control module forcibly assigns the target operating speed variable to the minimum operating speed of a fixed value.

[0020] When the value of the road dust level is less than the preset lower limit threshold, the control module will forcibly set the target operating speed to the maximum economic speed of a fixed value.

[0021] When the value of the road surface dust is greater than the preset lower threshold and less than the preset upper threshold, the control module calls the nonlinear decay mathematical model inside the storage module to solve for the target operation speed.

[0022] When the main processor of the control module lacks a hardware floating-point arithmetic unit, it automatically switches and enables the discrete lookup table dimensionality reduction matching branch. The control program defines multiple sets of dust concentration boundary thresholds in the system memory in a stepped manner. The control module converts the continuously input road pollution data into integer index coordinates according to the fixed division step size parameter. The arithmetic logic unit calls the integer index coordinates to directly extract the discretized target operation speed value bound inside the one-dimensional data array.

[0023] In step S40, the internal data scheduling channel of the control module synchronously extracts the target operation speed variable and the actual driving speed data. The control program calls a unit unification algorithm to forcibly convert the data units of the target operation speed variable and the actual driving speed data to the standard unit of meters per second. The internal arithmetic logic unit of the control module calculates the difference between the target operation speed and the actual driving speed data to obtain the speed deviation value.

[0024] In step S50, the core of the control module synchronously accesses the storage module to read the pre-calibrated proportional control coefficient and integral control coefficient. The arithmetic logic unit executes the proportional operation branch logic, multiplying the speed deviation value of the current operation cycle with the proportional control coefficient to obtain the proportional adjustment term. The control module executes the integral operation branch logic, and the accumulator collects the speed deviation values ​​from the historical operation cycles to form the error integral term. The error integral term is multiplied with the integral control coefficient to obtain the integral adjustment term. When the value of the integral adjustment term touches the preset control quantity boundary limit, the anti-integral saturation mechanism forcibly stops the error accumulation operation.

[0025] The control module adds the proportional and integral adjustment terms to synthesize the final speed regulation control signal. The main control chip inside the drive motor control module analyzes the speed regulation control signal and converts it into the target stator current parameter of the drive motor. The current closed-loop logic node inside the main control chip performs a difference comparison calculation between the target stator current parameter and the actual operating current to obtain the underlying current deviation data used to drive the power devices. Based on the underlying current deviation data, the main control chip dynamically changes the pulse width of the gate drive signal of the insulated-gate bipolar transistor in the three-phase inverter bridge circuit, changing the amplitude of the actual quadrature-axis current injected into the stator winding of the drive motor, and forcibly changing the electromagnetic torque output by the rotor shaft of the drive motor.

[0026] In step S60, the control module introduces a dead-zone determination mechanism during the recalculation of the speed deviation value. This mechanism determines the convergence state of the entire vehicle system by checking whether the absolute value of the speed deviation falls within the allowable hardware tolerance range. When the absolute value comparison condition is met, the sweeper's operating state is determined to have converged to the desired operational requirements. The control module forcibly freezes the error accumulation action within the proportional-integral algorithm at the code level, and the microcontroller's pulse-width modulation peripheral channel lock register maintains the duty cycle value output from the previous cycle. When the absolute value comparison condition is not met, the main control chip skips the dead-zone logic branch and continues to execute the conventional proportional-integral operation to generate a new speed adjustment control signal.

[0027] In summary, the present invention has at least one of the following beneficial technical effects:

[0028] 1. This invention constructs a sensor array by arranging multiple independent laser dust sensors at equal intervals along the width of the sweeper vehicle. The laser dust sensors located in the central area of ​​the sensor array are assigned larger weights, while those located on both sides of the array edge are assigned smaller weights to calculate the road dust level. This method prevents data abrupt interference caused by local obstacles in single-point measurements, filters out sudden noise signals caused by local interference sources at the chassis edge, and objectively reflects the pollution status within the sweeping width of the entire vehicle.

[0029] 2. This invention compares the value of road dust volume with preset upper and lower threshold values. Based on the comparison results, it executes different branch logic to force the output of the minimum operating speed, the maximum economic speed, or calls a nonlinear decay mathematical model to solve for the target operating speed. When the microcontroller lacks a hardware floating-point unit, it uses discrete lookup table reduction and matching branch addressing to obtain the target operating speed. It matches the cleaning brush operation time to complete deep cleaning, reduces chassis energy consumption in unpolluted road sections, and avoids the risk of program blocking caused by insufficient computing power of microprocessor chips.

[0030] 3. This invention obtains the speed deviation value by subtracting the target operating speed from the actual driving speed data, executes a proportional-integral algorithm with an anti-integral saturation mechanism to generate a speed adjustment control signal, and introduces a dead zone judgment mechanism during the recalculation of the speed deviation value. When the integral adjustment term touches the extreme value boundary, the error accumulation action is forcibly stopped to avoid excessive overshoot. When the absolute value of the speed deviation value falls within the hardware tolerance range, the duty cycle value is kept constant to prevent mechanical vibration of the chassis transmission system due to high-frequency adjustment. Attached Figure Description

[0031] Figure 1 This is a system framework diagram of the present invention;

[0032] Figure 2 This is a flowchart of the method of the present invention;

[0033] Figure 3 This is a flowchart illustrating step S10 of the present invention;

[0034] Figure 4 This is a flowchart illustrating step S20 of the present invention;

[0035] Figure 5 This is a flowchart illustrating step S30 of the present invention;

[0036] Figure 6 This is a flowchart illustrating step S40 of the present invention;

[0037] Figure 7 This is a flowchart illustrating step S50 of the present invention;

[0038] Figure 8 This is a flowchart illustrating step S60 of the present invention;

[0039] Figure 9 This is a comparison diagram showing the mapping relationship between the target operating speed and the amount of dust on the road surface in this invention. Detailed Implementation

[0040] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] See attached document Figure 1 The present invention provides an adaptive control system for the operating speed of a sweeper, comprising: a dust detection module, a vehicle speed detection module, a control module, a drive motor control module, and a storage module.

[0042] The dust detection module is fixedly installed at the front end of the sweeper chassis, and the vehicle speed detection module is mounted on the end of the sweeper's drive shaft. The signal input port of the control module is connected to the signal output terminals of the dust detection module and the vehicle speed detection module, respectively. The communication port of the control module is connected to the data read / write port of the storage module through an SPI bus, an I2C bus, or an internal parallel data bus. The control command output terminal of the control module is connected to the signal receiving terminal of the drive motor control module, and the drive current output terminal of the drive motor control module is connected to the power input terminal of the sweeper's drive motor.

[0043] After the sweeper's adaptive operating speed control system is powered on, it starts its operating program. During operation, the dust detection module monitors the dust condition on the road surface in front of the sweeper in real time and outputs a value representing the amount of dust on the road surface to the control module; at the same time, the vehicle speed detection module collects the actual driving speed data under the current condition and sends it to the control module.

[0044] After receiving the above data, the control module reads the road dust level and queries the dust level-vehicle speed mapping database in the storage module to determine the target operating speed for the current road conditions. By calculating the difference between the actual driving speed and the target operating speed, the system's core calculation unit obtains the speed deviation value and generates a speed adjustment control signal, which is then transmitted to the drive motor control module. Upon receiving the adjustment signal, the drive motor control module dynamically changes the current amplitude parameter input to the stator winding of the sweeper's drive motor.

[0045] The final drive motor accelerates or decelerates based on changes in current amplitude parameters, causing the actual driving speed to approach the target operating speed. The entire control system cyclically executes the complete workflow from environmental data acquisition to issuing drive commands according to a preset time step, thus forming a closed-loop control system for the sweeper's driving speed.

[0046] See attached document Figure 2 This invention provides an adaptive control method for the operating speed of a sweeper, comprising the following steps:

[0047] S10, the dust detection module detects the dust condition on the road in front of the sweeper and outputs an analog signal to the control module. The control module generates a value representing the amount of dust on the road by parsing the analog signal.

[0048] S20, the vehicle speed detection module collects the actual driving speed data of the sweeper and sends the actual driving speed data to the control module;

[0049] S30, the control module reads the road dust volume characterization value and queries the dust volume and vehicle speed mapping relationship database in the storage module to determine the target operation speed;

[0050] S40, the calculation process of the difference between the actual driving speed data and the target working speed is executed inside the control module to obtain the speed deviation value;

[0051] S50, the control module generates a speed adjustment control signal based on the speed deviation value and outputs it to the drive motor control module to change the current parameters input to the sweeper's drive motor so that the vehicle speed approaches the target operating speed;

[0052] S60, the system repeatedly executes the workflow from S1 to S5 according to the preset time step to form a closed-loop feedback regulation and control loop.

[0053] The following section provides a detailed explanation of the operational logic of the adaptive control method for the sweeper's operating speed.

[0054] See attached document Figure 3 The S10 step includes the following sub-steps:

[0055] S101, a horizontal mounting bracket is installed at the front of the sweeper chassis. The horizontal mounting bracket extends parallel to the center line of the sweeper's front wheel axle. The dust detection module is fixedly mounted on the mounting surface of the horizontal mounting bracket facing the ground. Fixing the module at the front of the sweeper chassis ensures that the dust detection module obtains the actual pollution data of the original road surface before the sweeping brush contacts the road surface.

[0056] The S102 dust detection module is composed of multiple independent laser dust sensors. These sensors are arranged in a straight line at equal intervals along the width of the sweeper vehicle, forming a sensor array. Each laser dust sensor is connected to a horizontal mounting bracket via nuts and fasteners. The center-to-center distance between two adjacent laser dust sensors is determined by the sweeper vehicle's width and the total number of sensors. The formula for calculating the center-to-center distance is as follows:

[0057] ;

[0058] In the formula, This represents the installation center distance between two adjacent laser dust sensors; This represents the width parameter of the sweeper vehicle. This represents the reserved installation distance between the center of the outermost laser dust sensor and the edge of the vehicle body; This represents the total number of laser dust sensors.

[0059] The array-based arrangement provides lateral multi-point sampling channels covering the entire sweeping width of the vehicle, preventing data abrupt changes caused by local road obstacles from interfering with single-point measurements.

[0060] In S103, the optical detection window of each laser dust sensor faces the road surface directly in front of the sweeper and maintains a fixed downward angle. The downward angle parameter is set within the range of 10 to 30 degrees. The downward-sloping arrangement prevents direct sunlight from entering the photoelectric receiver and causing interference, while simultaneously collecting the concentration parameters of suspended particulate matter within a 20cm height of the road surface.

[0061] For the photoelectric conversion working principle of the laser dust sensor that emits a laser beam and receives scattered light and converts it into a concentration electrical signal, those skilled in the art can consult the standard optical sensor technical manual. The relevant photoelectric conversion working principle is a well-known technology in this field and will not be elaborated here.

[0062] S104, the analog-to-digital conversion channel inside the control module periodically scans the analog signal output by the dust detection module. Multiple laser dust sensors synchronously send real-time detection values ​​to the control module. The system reads the voltage value of each sensor channel according to the preset time step and converts it into a digital dust concentration parameter. Laser dust sensors in different installation positions correspond to independent data channel identifiers for the control module to distinguish logically.

[0063] For the working principle of analog-to-digital conversion for microcontrollers to read multiple analog signals, those skilled in the art can consult the microprocessor hardware manual. The relevant analog-to-digital conversion working principle is a well-known technology in this field and will not be elaborated here.

[0064] S105, the storage module has a set of pre-calibrated weight parameter arrays embedded inside. Different weight coefficients are mapped and associated with laser dust sensors at different installation positions. The control module assigns larger weights to laser dust sensors located in the central area of ​​the sensor array, while assigning smaller weights to laser dust sensors located on both sides of the array edge. Sensors located in the edge area of ​​the sweeper chassis are prone to abnormal data spikes due to dust interference from adjacent lanes. The preset unequal weight allocation logic can filter out sudden noise signals caused by local interference sources at the edge.

[0065] S106, the control module extracts the dust concentration parameters and corresponding weighting coefficients from each data channel and performs multiplication, addition, and division calculations. After multi-channel data processing, its internal calculation core outputs a global-level road dust quantity representation value. Taking the weighted average of the sensor detection values ​​as the road dust quantity representation value objectively reflects the pollution status within the entire sweeping width range and smooths out local abrupt changes in data. The calculation formula for the road dust quantity representation value is as follows:

[0066] ;

[0067] In the formula, This represents the amount of dust on the road surface. This represents the total number of laser dust sensors. The discrete variable number representing the laser dust sensor; Representing the Weighting coefficients for each laser dust sensor; Representing the The detection values ​​from the laser dust sensor are used to calculate the amount of dust on the road surface. The calculated value is stored in the internal buffer of the control module for direct use by the lower-level speed control logic.

[0068] See attached document Figure 4 The S20 step includes the following sub-steps:

[0069] The S201 speed detection module is mounted on the end of the half-shaft of the sweeper's drive axle. The module integrates a Hall effect sensor and a signal conditioning circuit. A metal gear ring, rotating synchronously with the half-shaft, is fitted onto the outer side of the half-shaft. The detection end of the Hall effect sensor faces the tip of the outer teeth of the metal gear ring. During operation, the metal gear ring rotates with the half-shaft, continuously cutting the magnetic field generated by the Hall effect sensor. The signal conditioning circuit converts the magnetic field changes into continuous electrical pulse signals.

[0070] The S202 vehicle speed detection module contains a microprocessor unit. The microprocessor unit's timer channel continuously monitors the electrical pulse signal output by the signal conditioning circuit. The running program defines a fixed time sampling window. Within this window, the timer channel accumulates the number of rising edges of the electrical pulse signal. The accumulated total number of pulses reflects the mechanical rotation angle of the sweeper's half-shaft within the time sampling window.

[0071] S203, the microprocessor unit extracts the total number of pulses and performs mathematical calculations based on the outer diameter parameters of the sweeper's tires. The calculation logic multiplies the total number of pulses by the tire's outer diameter circumference and divides it by the length of the time sampling window to obtain the actual driving speed data. The microprocessor unit packages the actual driving speed data into a standard format data frame and stores it in the transmission buffer for later use.

[0072] For the working principle of the Hall sensor speed measurement circuit, those skilled in the art can consult the automotive electronic manual. The relevant Hall speed measurement principle is a well-known technology in this field and will not be elaborated here.

[0073] The S204 vehicle speed detection module integrates a CAN transceiver chip, and the control module is also equipped with a corresponding bus communication interface. Twisted pair cables connect the ports of the two to form a standard communication network. During the system initialization phase, a specific message identifier is assigned to the actual driving speed data. The control module reads the message identifier to identify the true source of the data frame.

[0074] The S205 vehicle speed detection module's running program includes a timed interrupt task with a timing parameter set to 20ms. The microprocessor unit responds to the timed interrupt signal and loads the actual driving speed data in the buffer into the data field of the CAN message. The timed transmission mechanism enables the control module to obtain the latest vehicle operating status at fixed time intervals, preventing continuous data transmission from causing the bus load rate to exceed the limit.

[0075] The S206 CAN transceiver chip converts message data into differential level signals and outputs them to the twisted-pair cable. These differential level signals are received via the control module's bus communication interface and restored to digital logic levels. Subsequently, the internal processor extracts the actual driving speed data from the data field and overwrites the old cached variables.

[0076] For the conflict arbitration mechanism in the CAN network communication process, those skilled in the art can consult the fieldbus communication standard literature. The relevant underlying bus communication principles are well-known technologies in this field and will not be elaborated here.

[0077] See attached document Figure 5 The S30 step includes the following sub-steps:

[0078] S301, the non-volatile storage unit inside the storage module has a preset upper limit threshold with a fixed value pre-programmed. The preset upper limit threshold corresponds to a heavily polluted working condition where a large amount of garbage is piled up on the road. At the beginning of each operation cycle, the control module reads the preset upper limit threshold from the storage module and stores it in the memory variable area for later use.

[0079] S302, the internal logic comparator of the control module compares the received road dust quantity value with the preset upper limit threshold. When the road dust quantity value is greater than the preset upper limit threshold, the control module determines that the road section in front of the sweeper is a heavily polluted area and triggers the over-limit interruption program.

[0080] S303, the over-limit interruption program takes over the system control and outputs a level trigger signal to the alarm unit in the sweeper's cab. After receiving the level trigger signal, the alarm unit activates the audible and visual alarm. The alarm unit consists of a flashing LED indicator on the instrument panel and a piezoelectric buzzer to remind the operator to pay attention to complex road conditions ahead.

[0081] For the working principles of LED circuits and buzzer driver circuits, those skilled in the art can consult basic electronic circuit design guidelines. The working principles of the relevant driver circuits are well-known technologies in this field and will not be elaborated here.

[0082] S304, the control module forcibly skips the normal calculation steps in the over-limit interrupt program, and directly assigns the target operation speed variable to the minimum operation speed with a fixed value. The minimum operation speed parameter setting mechanism controls the sweeper to slowly pass over the polluted area, and matches the operation time of the sweeping brush and the vacuum fan to complete the deep cleaning operation of the road surface.

[0083] S305, the read-only storage area inside the storage module contains a preset lower limit threshold. The preset lower limit threshold corresponds to a clean operating condition where the road surface is basically free of dust pollution. After the control module reads the road dust level indicator value, it simultaneously retrieves the preset lower limit threshold from the storage module as a reference benchmark.

[0084] S306, the logic comparator compares the road dust level indicator with a preset lower threshold. When the road dust level indicator is less than the preset lower threshold, the core operation determines that the area in front of the sweeper is a clean section. The control program then triggers the corresponding acceleration branch logic and skips complex intermediate state calculations.

[0085] S307, the variable assignment channel of the control module forces the target operating speed to a fixed value of the maximum economic speed. The maximum economic speed parameter matches the optimal energy consumption range of the sweeper's power system. The maximum economic speed value is greater than the average travel speed during regular sweeping operations.

[0086] S308, the acceleration action enables the sweeper to quickly pass through pollution-free areas. Acceleration is controlled to reduce overall vehicle operating time and chassis energy consumption. For the calibration principles of the motor's economic operating range, those skilled in the art can consult motor control engineering manuals; the relevant motor energy consumption characteristic calibration principles are well-known in the field and will not be elaborated upon here.

[0087] S309, the logic comparator compares the road dust level with preset lower and upper threshold values. When the road dust level is greater than the preset lower threshold but less than the preset upper threshold, the control module determines that the sweeper has entered the normal operating zone. The system program automatically triggers the nonlinear mapping operation branch and connects the data channel of the floating-point unit.

[0088] The S310 storage module has a pre-installed nonlinear decay mathematical model. The thickness of road dust accumulation and the operating resistance of the sweeping brush exhibit a nonlinear increasing relationship. A linear speed reduction strategy can easily lead to dust leakage in moderately polluted road sections. Logically, the nonlinear decay mathematical model is expressed as a nonlinear function relationship calibrated through experiments. The road dust volume representation value and the target operating speed satisfy the following correlation:

[0089] ;

[0090] In the formula, Represents the target operating speed; This represents the amount of dust on the road surface. It is a monotonically decreasing function, reflecting the adjustment mechanism that the greater the amount of dust, the lower the target operation speed.

[0091] To facilitate the execution of specific arithmetic logic by the microcontroller's underlying hardware, the nonlinear decay mathematical model exhibits an exponential decay trend based on the increase of the road dust volume representation value. The floating-point unit calls this monotonically decreasing nonlinear decay mathematical model to accurately solve for the target operating speed. Its underlying mathematical formulas are as follows:

[0092] ;

[0093] In the formula, Represents the target operating speed; Represents the minimum operating speed; Represents the highest economical speed; Represents the nonlinear attenuation coefficient; This represents the amount of dust on the road surface. This represents the preset lower threshold.

[0094] S311, the nonlinear attenuation coefficient is stored in the read-only parameter area inside the storage module for the control module to read. The nonlinear attenuation coefficient is pre-calibrated by engineers based on the load characteristics of the sweeper's vacuum fan. The floating-point unit executes an exponential function to calculate and output the floating-point value of the target operating speed. The control program stores the target operating speed value in a designated data register for later use by the next-level algorithm.

[0095] For the working mechanism of the floating-point arithmetic unit inside the microcontroller in processing complex mathematical functions, those skilled in the art can consult the microprocessor architecture technical manual. The relevant floating-point data processing logic is a well-known technology in this field and will not be described in detail here.

[0096] When the S312 control module's main processor lacks a hardware floating-point unit, it automatically switches to and enables a discrete lookup table for dimensionality reduction and matching. The control program defines multiple sets of progressively increasing dust concentration boundary thresholds in system memory. These multiple dust concentration boundary thresholds proportionally divide the continuous dust variation range between the preset lower and upper thresholds into multiple discrete dust quantity determination sub-intervals. Different discrete dust quantity determination sub-intervals correspond to different levels of pollution severity on the road surface ahead of the sweeper's operation.

[0097] S313, the storage module's internal non-volatile storage area stores a database mapping dust volume to vehicle speed. This database is logically represented as a one-dimensional data array structure. The array sequentially stores discretized target operating speed values ​​pre-calculated offline by engineers. The control module converts continuously input road pollution data into integer index coordinates based on a fixed step size parameter. The arithmetic logic unit directly retrieves the discretized target operating speed values ​​bound within the one-dimensional data array using these integer index coordinates. The mathematical conversion logic for the integer index coordinates is as follows:

[0098] ;

[0099] In the formula, Represents integer index coordinates; This represents the floor function operator; This represents the amount of dust on the road surface. This represents the preset lower threshold. This represents a fixed division step size.

[0100] S314: The control module extracts the discretized target operation speed value stored at the corresponding coordinate position and assigns it to the target operation speed variable in the next processing stage. The lookup table addressing alternative reduces the dimensionality of the high-consumption exponential and floating-point multiplication calculations to a combination of basic integer subtraction and division operations. This calculation method conversion strategy avoids the risk of program blocking caused by insufficient computing power of low-end microprocessor chips, while fully meeting the millisecond-level response time requirements of the control system's real-time output speed adjustment signal.

[0101] For the code implementation logic of the microcontroller data lookup table addressing algorithm, those skilled in the art can refer to the embedded software development guide. The relevant lookup table addressing code implementation logic is a well-known technology in this field and will not be described in detail here.

[0102] See attached document Figure 6 The S40 step includes the following sub-steps:

[0103] S401, the control module's internal data scheduling channel synchronously extracts the target operating speed variable and the actual driving speed data. Since the actual driving speed data and the target operating speed originate from different computational branches and hardware acquisition terminals, the control program calls a unit unification algorithm before performing the difference calculation. This algorithm forcibly converts the units of both data to the standard unit of meters per second. This data type alignment operation prevents unit inconsistencies from causing magnitude errors in subsequent calculation results.

[0104] S402, the arithmetic logic unit inside the control module executes a basic subtraction instruction on the variables after unifying their dimensions. This calculation unit subtracts the target operating speed from the actual driving speed data, and directly obtains the speed deviation value reflecting the degree to which the sweeper's current operating state deviates from the desired state through the subtraction operation. The deviation calculation formula is as follows:

[0105] ;

[0106] In the formula, This represents the speed deviation value; Represents the target operating speed; This represents the actual driving speed data.

[0107] In S403, the control module analyzes the calculated speed deviation value using positive and negative flags and its absolute value. When the speed deviation value is greater than zero, the control program determines that the current vehicle speed is lower than the expected operating conditions, and the sweeper is in a state of waiting to accelerate. When the speed deviation value is less than zero, the control program determines that the current vehicle speed exceeds the expected operating conditions, and the sweeper is in a state of waiting to decelerate. The calculated speed deviation value is written to the specified address space of the static random access memory (SRAM) for later use by the next-level motor adjustment control program.

[0108] For the hardware operation mechanism of the arithmetic logic unit inside the microprocessor for processing basic subtraction instructions, those skilled in the art can consult the microprocessor instruction set manual. The relevant arithmetic logic instruction operation mechanism is a well-known technology in this field and will not be described in detail here.

[0109] See attached document Figure 7 The S50 step includes the following sub-steps:

[0110] S501: The internal processing core of the control module retrieves the speed deviation value calculated in the previous stage from the static random access memory. Simultaneously, the processing core accesses the storage module to read the pre-calibrated proportional control coefficients and integral control coefficients. These proportional and integral control coefficients constitute the basic parameters of the proportional-integral control algorithm. The parameter calibration process matches the system characteristics of the sweeper's curb weight and road surface friction resistance.

[0111] S502, the arithmetic logic unit, executes the proportional operation branch logic. The calculation process multiplies the speed deviation value of the current operation cycle by the proportional control coefficient to obtain the proportional adjustment term. The proportional adjustment term provides immediate adjustment based on the instantaneous magnitude of the current vehicle speed deviating from the expected value. When a sudden change in road pollution levels causes a significant change in the target operating speed, the proportional adjustment term drives the motor output torque to change rapidly, reducing the dynamic adjustment time during acceleration or deceleration of the sweeper.

[0112] In S503, the control module executes integral operation branch logic and introduces an anti-integral saturation mechanism. The accumulator collects speed deviation values ​​from historical operation cycles since the system began running, forming an error integral term. The error integral term is multiplied by the integral control coefficient to obtain the integral adjustment term. The integral adjustment term is used to eliminate system steady-state errors and ensure that the actual driving speed perfectly matches the target operating speed. When the value of the integral adjustment term touches the preset control variable extreme value boundary, the anti-integral saturation mechanism forcibly stops the error accumulation operation. The boundary limit strategy prevents the drive motor from entering an overload state and avoids excessive overshoot in the system response.

[0113] The S504 control module adds the proportional and integral control terms to synthesize the final speed regulation control signal. This speed regulation control signal is represented internally by the microcontroller as a duty cycle value. The corresponding mathematical formula for discrete position proportional-integral control is as follows:

[0114] ;

[0115] In the formula, This represents the speed adjustment control signal output during the current computation cycle. Represents the proportional control coefficient; The value representing the speed deviation of the current computation cycle; Represents the integral control coefficient; The sequence number representing the current computation cycle; Discrete independent variables representing the historical operation period; Representing the The speed deviation value for each operation cycle.

[0116] The S505's pulse width modulation peripheral channel within the control module receives the speed adjustment control signal. This peripheral channel generates corresponding high and low level pulse waveforms based on the duty cycle value and sends them to the next-level hardware node. For the code compilation process of the discrete proportional-integral control algorithm implemented by the digital microcontroller, those skilled in the art can consult textbooks on automatic control principles. The compilation process of the underlying code for the closed-loop control algorithm is well-known in the field and will not be elaborated upon here.

[0117] The S506 control module transmits the speed regulation control signal, including the duty cycle value, to the drive motor control module via a hardware signal line. The drive motor control module has an optocoupler at its internal input. The optocoupler provides electrical isolation for the input pulse signal. This isolation disconnects the direct ground connection between the high-voltage drive circuit and the low-voltage control network, blocking the reverse propagation of electromagnetic interference generated by the high-frequency switching of high-power devices back to the control module.

[0118] S507, the main control chip inside the drive motor control module captures and parses the speed regulation control signal, and at the same time, according to the pre-programmed mapping relationship model, converts the parsed duty cycle value into the target stator current parameter of the drive motor.

[0119] The S508 drive motor control module integrates a three-phase inverter bridge circuit and a phase current sensor. The phase current sensor continuously collects the actual operating current in the drive motor stator windings. The current closed-loop logic node inside the main control chip performs a difference comparison calculation between the target stator current parameter and the actual operating current to obtain the underlying current deviation data used to drive the underlying power devices.

[0120] The S509 main control chip dynamically changes the pulse width of the gate drive signal of the insulated-gate bipolar transistor (IGBT) in the three-phase inverter bridge circuit based on the underlying current deviation data. The gate drive signal directly controls the on-time and off-time ratio of the IGBT, thereby changing the actual quadrature-axis current amplitude injected into the stator winding of the drive motor. This change in the actual quadrature-axis current amplitude forces a change in the electromagnetic torque output from the drive motor rotor shaft. This changing electromagnetic torque overcomes the mechanical frictional resistance between the sweeper chassis transmission system and the road surface, forcing the vehicle to accelerate or decelerate to approach the desired driving state. The mathematical formula for the electromagnetic torque of the drive motor is as follows:

[0121] ;

[0122] In the formula, Represents the electromagnetic torque output by the drive motor; Represents the number of pole pairs of the drive motor; The permanent magnet flux linkage parameter representing the rotor of the drive motor; This represents the actual quadrature-axis current amplitude injected into the drive motor.

[0123] For the underlying hardware operation process of the three-phase inverter bridge circuit driving the AC motor to rotate, those skilled in the art can consult power electronics technology literature. Its inverter power switch control mechanism is a well-known technology in this field and will not be described in detail here.

[0124] See attached document Figure 8 The S60 step includes the following sub-steps:

[0125] The S601 control module's internal advanced timer channel generates a fixed, periodic hardware interrupt signal. Upon capturing the interrupt signal, the microprocessor automatically resets the internal software execution flow, triggering a new control operation cycle. At the start of the cycle, the system data bus reactivates the data acquisition channels of the vehicle speed detection module and the dust detection module, synchronously acquiring the latest actual driving speed data and road pollution parameters.

[0126] In S602, the computational core sends the updated actual driving speed data and the target operating speed to the arithmetic logic unit. A dead-zone determination mechanism is introduced during the recalculation of the speed deviation. This mechanism aims to suppress frequent actuator movements caused by minor data disturbances when the system approaches steady state. The dead-zone determination mechanism determines the convergence state of the entire vehicle system by checking whether the absolute value of the vehicle speed deviation falls within the allowable hardware tolerance range.

[0127] In S603, the underlying control program uses absolute value instructions and floating-point comparison instructions to perform logical operations for convergence. The mathematical formula for determining state convergence is as follows:

[0128] ;

[0129] In the formula, Represents the absolute value operator; Represents actual driving speed data; Represents the target operating speed; This represents the preset steady-state dead zone tolerance threshold.

[0130] S604, when the above formula conditions are met, it is determined that the sweeper's operating state has converged to the desired operational requirements. The control module will forcibly freeze the error accumulation action within the proportional-integral algorithm at the code level. The microcontroller's pulse-width modulation peripheral channel will then lock the register, maintaining the duty cycle value output from the previous cycle. A constant speed regulation control signal forces the drive motor to maintain a stable electromagnetic torque, preventing mechanical vibrations in the chassis transmission system due to high-frequency regulation.

[0131] S605. If the absolute value comparison condition is not met, it indicates that the sweeper control system still has dynamic errors exceeding the tolerance limit. The main control chip automatically skips the dead-zone logic branch and continues to perform regular proportional-integral operations to generate a new speed regulation control signal.

[0132] For the logic code writing method of PID algorithm dead zone setting and anti-oscillation in the closed-loop control link of the control system, those skilled in the art can refer to the motion control system design specifications. The dead zone anti-oscillation algorithm logic is a well-known technology in this field and will not be elaborated here.

[0133] Specific application examples:

[0134] To verify the effectiveness of the adaptive control system and method for the sweeper's operating speed of the present invention in a real engineering environment, parameter calibration and experimental verification were carried out using a certain model of pure electric road sweeper as a carrier.

[0135] Hardware deployment and parameter initialization:

[0136] For step S102, the width parameter of the sweeper vehicle is known. Set the installation margin for the outermost sensor. ,use An array of laser dust sensors is constructed. Substituting the values ​​into the installation center distance calculation formula:

[0137] ;

[0138] The installation center distance between two adjacent laser dust sensors was calculated. .

[0139] For step S105, the spatial position weighting coefficients of the five sensors along the width direction of the vehicle body are set as follows: , , , , This weighting logic suppresses dust interference at the vehicle's edges caused by vehicles passing in adjacent lanes.

[0140] For step S30, the calibration parameters are burned into the storage module: maximum economic speed. Minimum operating speed Preset lower threshold Preset upper limit threshold Nonlinear attenuation coefficient Set the steady-state dead zone tolerance threshold. .

[0141] Data processing and target speed calculation:

[0142] The sweeper truck entered a moderately polluted section of road. In step S104, the control module simultaneously collected the detection values ​​from five sensors. The concentrations were 40 mg / m3, 55 mg / m3, 60 mg / m3, 52 mg / m3, and 45 mg / m3, respectively.

[0143] Substitute the values ​​of road surface dust from step S106 into the calculation formula:

[0144] ;

[0145] ;

[0146] at this time This triggers the nonlinear mapping operation branch in step S310. The floating-point unit substitutes the underlying calculation formula of the nonlinear decay mathematical model:

[0147] ;

[0148] ;

[0149] The calculation shows that the target operating speed under the current road conditions is 2.12 m / s.

[0150] Closed-loop execution and steady-state determination:

[0151] In step S402, if the actual driving speed data currently collected by the vehicle speed detection module... The arithmetic logic unit performs basic subtraction:

[0152] ;

[0153] If the speed deviation is less than zero, the system determines that it is in a state of waiting to decelerate. It generates a speed regulation control signal through the proportional-integral control algorithm of S504, changes the actual quadrature axle current amplitude, and causes the vehicle to decelerate.

[0154] When the vehicle decelerates to its actual driving speed data Then, substitute the dead zone determination formula of S603:

[0155] ;

[0156] If the conditions are met, the microcontroller freezes the accumulated error, maintains the current duty cycle, and prevents mechanical oscillation.

[0157] Experimental verification and effect comparison section:

[0158] To verify the technical advantages of the nonlinear attenuation mathematical model in this invention compared with the traditional linear deceleration strategy, the engineering team laid out a test section with a continuously gradually changing dust concentration in a closed test field for comparative experiments.

[0159] The conclusions are as follows:

[0160] Reference Appendix Figure 9 The figure visually demonstrates the dynamic trend of the system's output target operating speed under different road dust levels. A comparison between the solid line representing the nonlinear attenuation mathematical model of this invention and the dashed line representing the existing linear speed reduction strategy shows that when the road dust level just exceeds the preset lower threshold, i.e., enters the light pollution range, the traditional linear speed reduction strategy reduces the vehicle speed extremely slowly, and the target operating speed remains as high as 4.1 m / s. This directly leads to insufficient cleaning brush operation time, making it easy for dust to be missed in moderately polluted road sections.

[0161] The system of this invention automatically triggers the nonlinear attenuation mathematical model, and the target operating speed is rapidly and sensitively pulled down to about 2.6m / s, which reserves sufficient operating time for the bottom vacuum fan and cleaning brush, and solves the pain point of cleaning dead corners under medium and light pollution road conditions.

[0162] As the amount of dust approaches the heavily polluted area, the solid line smoothly converges to the minimum operating speed. The entire speed adjustment process takes into account both the quality of operation and the energy consumption of the motor, which proves the reliability and superiority of the control strategy and related calculation formulas of this invention in practical engineering applications.

Claims

1. A method for adaptive control of the operating speed of a sweeper, characterized in that, Includes the following steps: The dust detection module detects the dust condition on the road ahead of the sweeper and outputs an analog signal to the control module. The control module then parses the analog signal to generate a value representing the amount of dust on the road. The actual driving speed data of the sweeper collected by the vehicle speed detection module is sent to the control module; The control module reads the road dust volume characterization value and queries the dust volume and vehicle speed mapping relationship database in the storage module to determine the target operation speed. The control module performs a speed deviation calculation process by calculating the difference between the actual driving speed data and the target operating speed. Based on the speed deviation value, the control module generates a speed adjustment control signal and outputs it to the drive motor control module. This changes the current parameters input to the sweeper's drive motor, causing the actual driving speed data to approach the target operating speed. The aforementioned steps are repeated cyclically according to a preset time step to form a closed-loop feedback adjustment control circuit.

2. The adaptive control method for the operating speed of a sweeper according to claim 1, characterized in that, The step of detecting the dust condition on the road surface in front of the sweeper based on the dust detection module and outputting an analog signal to the control module, and the control module parsing the analog signal to generate a value representing the amount of road dust, includes: The control module periodically scans the analog signals output by multiple laser dust sensors in the dust detection module and converts them into digital dust concentration parameters. The control module assigns corresponding weight coefficients to the laser dust sensors at different installation locations. The control module extracts the dust concentration parameters corresponding to each of the laser dust sensors and performs a weighted average mathematical operation with the corresponding weight coefficients, and takes the weighted average value of the dust concentration parameters corresponding to each of the laser dust sensors as the characterization value of the road surface dust amount.

3. The adaptive control method for the operating speed of a sweeper according to claim 1, characterized in that, The step of sending the actual driving speed data of the sweeper collected by the vehicle speed detection module to the control module includes: The timer channel inside the vehicle speed detection module accumulates the number of rising edge triggers of the electrical pulse signal output by the signal conditioning circuit inside the vehicle speed detection module within a set time sampling window to obtain the total number of pulses. The microprocessor unit inside the vehicle speed detection module extracts the total number of pulses and performs mathematical calculations in conjunction with the outer diameter parameters of the sweeper tires to obtain the actual driving speed data; The microprocessor unit packages the actual driving speed data into standard format data frames and sends them to the control module at regular intervals.

4. The adaptive control method for the operating speed of a sweeper according to claim 1, characterized in that, The step of determining the target operating speed by reading the road dust volume characterization value from the control module and querying the dust volume-vehicle speed mapping database in the storage module includes: The control module reads a preset upper limit threshold from the storage module and performs a size comparison operation between the received road dust amount characterization value and the preset upper limit threshold; When the value of the road dust level exceeds the preset upper limit threshold, the control module determines that the road section in front of the sweeper is a heavily polluted area and triggers the over-limit interruption procedure. The control module forces the target operating speed to a fixed minimum operating speed within the over-limit interruption program.

5. The adaptive control method for the operating speed of a sweeper according to claim 4, characterized in that, The step of determining the target operating speed by reading the road dust volume characterization value from the control module and querying the dust volume-vehicle speed mapping database in the storage module further includes: The control module retrieves a preset lower threshold from the storage module and performs a numerical comparison operation between the road dust quantity characterization value and the preset lower threshold. When the value of the road surface dust is less than the preset lower threshold, the control module triggers the acceleration branch logic; The variable assignment channel of the control module forces the target operating speed to be set to a fixed value of the maximum economic speed.

6. The adaptive control method for the operating speed of a sweeper according to claim 5, characterized in that, The step of determining the target operating speed by reading the road dust volume characterization value from the control module and querying the dust volume-vehicle speed mapping database in the storage module further includes: When the value representing the amount of dust on the road surface is greater than the preset lower threshold and less than the preset upper threshold, the control module enables the discrete lookup table dimensionality reduction matching branch. The control module converts the continuously input road dust quantity characterization values ​​into integer index coordinates based on a fixed division step size parameter. The control module calls the integer index coordinates to extract the discrete target operating speed value bound inside the dust amount and vehicle speed mapping relationship database, and assigns it to the target operating speed.

7. The adaptive control method for the operating speed of a sweeper according to claim 1, characterized in that, The steps by which the control module calculates the difference between the actual driving speed data and the target operating speed to obtain the speed deviation value include: The internal data scheduling channel of the control module synchronously extracts the target operating speed and the actual driving speed data, and calls the unit unification algorithm to forcibly convert the units of the target operating speed and the actual driving speed data into standard units; The arithmetic logic unit inside the control module executes a basic subtraction instruction on the target operating speed and the actual driving speed data after unification of dimensions, and calculates the difference between the target operating speed and the actual driving speed data to obtain the speed deviation value that reflects the degree of deviation from the expected state.

8. The adaptive control method for the operating speed of a sweeper according to claim 1, characterized in that, The control module generates a speed adjustment control signal based on the speed deviation value and outputs it to the drive motor control module. The steps of changing the current parameters input to the sweeper's drive motor include: The control module reads the pre-calibrated proportional control coefficient and integral control coefficient; The control module multiplies the speed deviation value of the current calculation cycle by the proportional control coefficient to obtain the proportional adjustment term; The control module collects the speed deviation values ​​within the historical calculation cycle to form an error integral term, and multiplies the error integral term by the integral control coefficient to obtain the integral adjustment term. The control module adds the proportional adjustment term and the integral adjustment term to synthesize the speed regulation control signal, which is ultimately expressed as a duty cycle value, and transmits it to the drive motor control module.

9. The adaptive control method for the operating speed of a sweeper according to claim 8, characterized in that, The process of repeatedly executing the aforementioned steps according to a preset time step to form a closed-loop feedback regulation and control loop includes: The control module introduces a dead zone determination mechanism during the recalculation of the speed deviation value. The control module checks whether the absolute value of the speed deviation falls within the preset steady-state dead zone tolerance threshold. When the absolute value of the speed deviation falls within the steady-state dead zone tolerance threshold, the control module forcibly freezes the accumulation action of the error integral term, maintaining the duty cycle value output of the previous cycle to form the closed-loop feedback regulation control loop.

10. A sweeper vehicle operating speed adaptive control system, characterized in that, The method for adaptive control of sweeper operating speed as described in any one of claims 1-9 includes: The dust detection module is used to detect the dust condition on the road in front of the sweeper and output an analog signal; The vehicle speed detection module is used to synchronously collect actual driving speed data under the current state; The storage module is used for an internal database mapping the amount of solidified dust to vehicle speed. The control module is used to receive and parse the analog signal to generate a road dust amount characterization value, receive the actual driving speed data, read the road dust amount characterization value and query the dust amount and vehicle speed mapping relationship database in the storage module to obtain the target working speed corresponding to the current road conditions, calculate the speed deviation value by performing difference calculation between the actual driving speed data and the target working speed, and generate a speed adjustment control signal accordingly. The drive motor control module is used to receive the speed adjustment control signal and dynamically change the current parameters input to the sweeper drive motor to make the actual driving speed data approach the target operating speed.

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