Container tire crane remote control system

Abstract

The invention belongs to the technical field of port machinery automation control, and particularly relates to a remote control system for a container tire crane. Comprising a remote operation terminal, a communication transmission module, a central cooperative controller, a wind load sensing module, a structure dynamic response sensing module, a coupling modeling engine, an active stability instruction generation module and a hydraulic servo execution mechanism. Through deep fusion of environment wind load sensing and structure inertia response sensing, a complete closed-loop active stable architecture is constructed, and the architecture adopts a physically quantized dynamic model driving control strategy, so that source suppression of wind-induced resonance is realized, and the stability of the system is improved. According to the system, the combined swing amplitude of the tail end of the suspension arm and the container can be remarkably reduced under the strong wind working condition, the positioning precision and the operation continuity are remarkably improved, meanwhile, safe shutdown caused by oscillation overrun is avoided, and the technical requirement for all-weather efficient operation of a port is met.

Description

Technical Field

[0001] This invention belongs to the field of port machinery automation control technology, specifically a remote control system for container tire cranes. Background Technology

[0002] As a key piece of equipment in modern port automation systems, the operational efficiency and positioning accuracy of container-tired gantry cranes directly affect the throughput capacity and operational safety of the entire terminal logistics system. In recent years, with the maturity of 5G communication, high-definition video transmission, and remote control technologies, remote and unmanned operation modes of gantry cranes have gradually become the mainstream trend in the industry, significantly improving operational continuity and reducing labor costs. Against this backdrop, remote control systems not only need to ensure basic command execution functions but are also required to maintain high-precision operation capabilities under complex environmental disturbances. Especially in typical operational scenarios such as coastal areas or open storage yards, strong winds have become a major external source of interference affecting system stability, posing a severe challenge to the remote control architecture.

[0003] Current mainstream remote control systems generally adopt a closed-loop control strategy based on visual feedback. This involves transmitting real-time images from multiple high-definition cameras to a remote control terminal, allowing the operator to manipulate the spreader based on visual information. This approach can meet basic operational needs under normal conditions, its design logic being based on the analogy of human eye-hand coordination, relying on the operator's experience to fine-tune the spreader's movement. However, such systems are essentially open-loop sensing structures, lacking the ability to quantitatively perceive environmental physical disturbances. Specifically, when encountering strong winds exceeding twelve meters per second, the dynamic excitation created by the wind load acting on the boom and suspended container can induce coupled resonance between them. Because the remote operator can only observe the spreader's sway through a two-dimensional video feed, they cannot know the wind force, direction, and its dynamic characteristics over time, nor can they predict the resulting inertial response. Consequently, their operational commands often lag behind the actual disturbance phase, and may even unintentionally exacerbate system oscillations. Actual test data shows that under such working conditions, the combined swing amplitude of the boom end and the container can exceed ±30 centimeters, causing the target positioning accuracy to drop by more than 40%, and in severe cases, even triggering the safety interlock to stop the machine, resulting in work interruption.

[0004] However, with the continuous improvement of port intelligence and the urgent need for all-weather operation capabilities, some inherent characteristics of the above-mentioned technical solutions at the principle level have gradually revealed their fundamental limitations in dealing with highly dynamic environmental disturbances. The reason for this is that existing remote control systems rely entirely on the operator's subjective perception of the environment, without constructing a physically quantified environment-structure coupling model, resulting in a lack of proactive intervention capabilities for wind-induced vibrations. Furthermore, traditional stabilization strategies often rely on mechanical dampers or hydraulic passive vibration reduction devices. While these methods can dissipate vibration energy to some extent, their response characteristics are fixed, making it impossible to adjust the suppression intensity in real time according to wind speed changes, and their effectiveness significantly diminishes under high-frequency disturbances. Crucially, passive damping mechanisms are essentially energy-dissipating controls and cannot provide an active counter-torque opposite to the disturbance torque, thus making it difficult to fundamentally suppress the excitation and continuation of resonance.

[0005] Therefore, a remote control system for container tire cranes is proposed to address the above problems. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by providing a remote control system for container tire cranes, thereby solving the technical problems mentioned in the background art.

[0007] To address the above technical problems, the following technical solution is adopted: a remote control system for container tire cranes, aiming to solve the technical problem in the prior art where strong wind disturbances cause coupling resonance between the spreader and the boom, leading to decreased positioning accuracy and operation interruption. To achieve the above-mentioned objective, this invention constructs an active stabilization mechanism based on a coupling modeling engine. This coupling modeling engine is a wind speed-inertia coupling modeling engine. By deploying a network of miniature inertial measurement units (MMUs) at key nodes of the tire crane structure, it acquires multi-dimensional dynamic response data of the boom and suspended container in real time, and simultaneously collects environmental wind speed information. The two are fused to establish a dynamic mapping relationship between wind-induced excitation and structural response. Based on this, a phase-matched reverse torque command is generated to drive the hydraulic actuator to actively compensate for the spreader's movement, thereby effectively suppressing resonance swaying caused by strong winds without relying on the subjective judgment of the remote operator.

[0008] Preferably, the remote control system for the container tire crane includes a remote operation terminal, a communication transmission module, a central coordinating controller, a wind load sensing module, a structural dynamic response sensing module, a wind speed-inertia coupling modeling engine, an active stabilization command generation module, and a hydraulic servo actuator. The remote operation terminal receives operator-input spreader movement commands and transmits these commands to the central coordinating controller via the communication transmission module. The communication transmission module employs a low-latency, high-reliability industrial-grade 5G communication link to ensure bidirectional synchronous transmission of control commands and sensor data. The central coordinating controller, as the core processing unit of the system, is responsible for coordinating data interaction and command scheduling among the various functional modules.

[0009] Preferably, the wind load sensing module includes at least three ultrasonic wind speed and direction sensors, respectively installed on the top of the main beam of the tire crane, the root of the boom, and the middle of the spreader beam, for real-time acquisition of wind speed vectors and their time derivatives in three-dimensional space. The output signals of each sensor are sent to the dedicated wind load processing unit of the central coordinating controller after anti-aliasing filtering. The structural dynamic response sensing module consists of multiple miniature inertial measurement units (MMUs), which are fixedly installed at the boom hinge points, the boom end, the lifting wire rope guide pulley bracket, and the four corner connections of the container spreader. Each MMU includes a three-axis accelerometer and a three-axis gyroscope for synchronously measuring linear acceleration and angular velocity data at each installation position. All MMUs are connected via hardwired to the high-speed synchronous sampling interface of the central coordinating controller, with a sampling frequency of not less than 1 kHz to ensure complete capture of high-frequency vibration modes.

[0010] Preferably, the wind speed-inertial coupling modeling engine is integrated into the central coordinating controller, with its inputs connected to the outputs of the wind load sensing module and the structural dynamic response sensing module, respectively. This modeling engine first performs spectral decomposition on the wind speed vector to extract the dominant frequency components and their amplitudes. Simultaneously, it performs modal identification on the acceleration and angular velocity signals output by each micro-inertial measurement unit to determine the first two natural frequencies and corresponding mode shapes of the current boom-container system. Subsequently, the modeling engine establishes a transfer function matrix between wind speed excitation and structural response based on a least-squares fitting method. This transfer function matrix characterizes the dynamic response characteristics of the boom end lateral displacement and spreader pitch angle to wind load input under specific wind speed conditions. The modeling engine continuously updates this transfer function matrix to adapt to system parameter drift caused by changes in wind speed and spreader load mass.

[0011] Preferably, the active stabilization command generation module receives the transfer function matrix and current wind speed vector output by the wind speed-inertia coupling modeling engine, and calculates the counter-torque required to counteract the current wind-induced disturbance. The amplitude of this counter-torque is determined by the gain of the transfer function matrix at the dominant disturbance frequency, and its phase is adjusted by introducing a preset phase compensation angle, so that the counter-torque and the actual disturbance torque are strictly out of phase in the time domain. The active stabilization command generation module decomposes the calculated counter-torque into a boom pitch torque component and a spreader rotation torque component, and converts them into corresponding hydraulic servo valve opening commands.

[0012] Preferably, the hydraulic servo actuator includes a boom pitch hydraulic cylinder, a spreader slewing hydraulic motor, and their matching electro-hydraulic proportional servo valves. The control terminals of each electro-hydraulic proportional servo valve are connected to the output terminal of the active stabilization command generation module, receiving the valve opening commands output by the module. When the system detects that the wind speed exceeds a preset threshold, the central coordinating controller automatically activates the active stabilization mode. At this time, the hydraulic servo actuator executes the original motion commands from the remote operator while simultaneously executing the compensation commands output by the active stabilization command generation module. This generates an active counter-torque at the physical level that is opposite in direction and matches the amplitude of the wind-induced disturbance torque, directly acting on the boom and spreader structure to suppress their resonance response.

[0013] Preferably, the central coordinating controller is equipped with a safety status monitoring unit, which monitors the lateral displacement amplitude of the boom end, the swing rate of the spreader, and the pressure fluctuation of the hydraulic system in real time. When any monitored parameter exceeds the safety limit, the safety status monitoring unit immediately sends an emergency intervention signal to the central coordinating controller, which then cuts off the active stabilization command output and initiates a gradual deceleration shutdown procedure to ensure equipment safety.

[0014] Preferably, both the miniature inertial measurement unit and the ultrasonic anemometer / wind direction sensor are equipped with temperature compensation circuits and self-diagnostic functions, enabling them to maintain measurement accuracy within an ambient temperature range of -20°C to +70°C, and automatically report fault codes to the central coordinating controller when the sensors fail. Furthermore, all sensor signal cables employ a shielded twisted-pair structure and are laid within the steel structure of the tire-mounted crane via metal conduits to minimize the impact of electromagnetic interference on the measurement signals.

[0015] The wind speed-inertia coupling modeling engine employs a recursive least squares algorithm to update the transfer function matrix parameters online, with a forgetting factor set to 0.95 to balance the model's adaptability to slowly changing conditions with its sensitivity to sudden disturbances. When calculating the reverse torque, the active stabilization command generation module introduces an amplitude limiting mechanism based on the Lyapunov stability criterion to ensure that the generated compensation commands do not cause hydraulic system overload or induce new unstable modes.

[0016] The beneficial effects of this invention are: By deeply integrating environmental wind load perception with structural inertial response perception, a complete closed-loop active stabilization architecture has been constructed. This architecture abandons the traditional passive response mode that relies on operator visual feedback, and instead adopts a physically quantified dynamic model-driven control strategy to achieve source suppression of wind-induced resonance. Under strong wind conditions, this system can significantly reduce the combined sway amplitude of the boom end and the container, significantly improve positioning accuracy and operational continuity, and avoid safe shutdowns due to excessive oscillations, meeting the technical requirements of efficient all-weather port operations. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] In the attached diagram: Figure 1 This is a schematic diagram of the overall structure of the container tire crane remote control system described in this invention; Figure 2 This is a system block diagram of the internal functional modules of the central collaborative controller of the present invention; Figure 3 This is a flowchart illustrating the active stabilization control method of the present invention. Detailed Implementation

[0019] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0020] Please see Figures 1-3 This invention provides a remote control system for container tire cranes. Its overall architecture is built around a closed-loop active stabilization mechanism of perception-modeling-decision-execution, aiming to suppress the source of wind-induced resonance disturbances by driving control strategies through a physically quantified dynamic model.

[0021] The remote control system for container tire cranes includes a remote operating terminal, a communication transmission module, a central coordinating controller, a wind load sensing module, a structural dynamic response sensing module, a wind speed-inertia coupling modeling engine, an active stabilization command generation module, and a hydraulic servo actuator. The remote operating terminal is an operator station with a human-machine interface, equipped with a dual-redundant industrial-grade touchscreen and a force feedback joystick. It receives operator-input three-dimensional motion commands for the spreader, including speed settings for hoisting, trolley lateral movement, and trolley travel. The terminal has a built-in command preprocessing unit that performs de-jitter filtering and timing alignment on the raw control signals before sending them to the central coordinating controller via the communication transmission module.

[0022] The communication transmission module employs an industrial-grade 5G communication link enhanced by Time-Sensitive Networking (TSN). Its physical layer supports adaptive switching between millimeter wave and Sub-6GHz dual-band frequencies, while the MAC layer integrates forward error correction coding and retransmission mechanisms to ensure end-to-end transmission latency of less than ten milliseconds and a packet loss rate of less than one in ten thousand. The module features a dedicated hardware accelerator on the central collaborative controller side, used to timestamp and synchronize received remote operation commands and uploaded sensor data streams to ensure data consistency across all stages of the control loop.

[0023] The central co-controller, as the core processing unit of the system, adopts a heterogeneous multi-core architecture, comprising a main ARM Cortex-A72 processor, two real-time DSP coprocessors, and an FPGA programmable logic unit. The main processor runs an embedded Linux operating system and is responsible for high-level task scheduling and human-machine interface management; the DSP coprocessors handle wind load processing and structural response analysis tasks respectively; and the FPGA implements hard real-time logic for high-speed synchronous sampling, sensor fault diagnosis, and safety status monitoring. All functional modules exchange data with low latency through an on-chip high-speed interconnect bus.

[0024] The wind load sensing module consists of three ultrasonic wind speed and direction sensors, respectively installed at the center point of the top of the tire crane's main beam, 50 centimeters above the hinge shaft at the base of the boom, and on the lower surface of the middle section of the spreader beam. Each sensor has a three-dimensional wind speed vector output capability, with a measurement range covering 0 to 70 meters per second, a resolution better than 0.1 meters per second, and an update frequency of 200 Hz. The sensor housing is made of aerospace-grade aluminum alloy through die-casting, and integrates a MEMS ultrasonic transducer array and temperature compensation circuitry, maintaining a full-scale accuracy of ±2% within an ambient temperature range of -20°C to +70°C. The raw analog signals output by each sensor are first passed through an eighth-order Butterworth anti-aliasing low-pass filter with a cutoff frequency set to 150 Hz, and then processed by a 16-bit... The analog-to-digital converter digitizes the data at a sampling rate of 400 Hz and sends it to the wind load processing unit of the central coordinating controller via the CANFD bus.

[0025] The structural dynamic response sensing module consists of eight miniature inertial measurement units (IMUs), installed at the following locations: the center of the outer flange of the left and right boom hinge points; the top of the left and right boom end pulley supports; the base of the left and right guide pulley supports for the lifting wire rope; and near the connecting pins at the left front and right rear corners of the container spreader. Each miniature inertial measurement unit integrates a three-axis MEMS accelerometer and a three-axis MEMS gyroscope. The accelerometer has a measurement range of ±50g and a zero-bias stability better than 50 microg per square Hz; the gyroscope has a measurement range of ±500 degrees per second and an angular random walk coefficient less than 0.05 degrees per square hour. All IMUs are connected to the high-speed synchronous sampling interface of the central co-controller via shielded twisted-pair hardwired cables. This interface is implemented by an FPGA and supports IEEE 1588 Precision Time Protocol (PTP) hardware timestamps, ensuring that the sampling time deviation of the eight channels is less than one microsecond. The system sampling frequency is fixed at 1200 Hz, which satisfies the Nyquist sampling theorem requirement for complete capture of the first five vibration modes of the boom-container system (the theoretical highest natural frequency is about 300 Hz).

[0026] In this embodiment, the wind speed-inertia coupling modeling engine resides as a kernel module in the main control processor of the central coordinating controller, with an operating cycle of ten milliseconds. The engine's input data stream includes a preprocessed three-dimensional wind speed vector sequence: ; and its first time derivative: ; And 24-dimensional dynamic response signals from eight IMUs, namely the three-axis acceleration at each location. With triaxial angular velocity The modeling process consists of three stages: spectral feature extraction, modal parameter identification, and transfer function matrix construction.

[0027] In the spectral feature extraction stage, the engine applies a Hanning window to the time series of wind speed vectors and then performs a Fast Fourier Transform (FFT). The window length is two seconds, and the overlap rate is 50%, thereby obtaining a power spectral density (PSD) estimate with a frequency resolution of 0.5 Hz. A peak detection algorithm is then used to identify dominant frequency components with amplitudes exceeding three standard deviations of the background noise. And record its corresponding amplitude. and phase .

[0028] During the modal parameter identification phase, the engine employs the Stochastic Subspace Identification (SSI) method to process the IMU data. Specifically, the 24-channel response signals are arranged in chronological order into an observation matrix. Where N is the number of sampling points. The dominant singular values ​​of the Hankel matrix of the system are extracted using Singular Value Decomposition (SVD), and then an extended observability matrix is ​​constructed. The pole locations are fitted using the Least Squares Complex Exponential Method (LSCE) to determine the first two natural frequencies of the boom-container system under the current operating condition. and its corresponding damping ratio At the same time, through the mode matrix The participation factors of each mode at the eight IMU locations were reconstructed, with particular attention paid to the two key output variables: the lateral displacement at the boom end and the pitch angle of the spreader.

[0029] During the transfer function matrix construction phase, the engine establishes a linear time-invariant (LTI) mapping from wind speed excitation to structural response. Let the wind speed input vector be: ; The key output vector of the structure is: ,in This is the Laplace transform of the lateral displacement at the end of the boom. For the Laplace transform of the spreader's pitch angle; The above output variables are obtained from the IMU sensor data in the following manner: Lateral displacement acquisition at the boom end: The triaxial acceleration signals of two IMU sensors (IMU7 and IMU8) deployed at the boom end are selected, and extended Kalman filter (EKF) is used for data fusion and integration. The filter model parameters and state equations are as follows: , where the state vector ( For lateral displacement, For lateral velocity, (For lateral acceleration), state transition matrix: ( (Sampling period 0.01s); The observation equation is: ; Where the observation vector z is the mean lateral acceleration of IMU7 and IMU8, and the observation matrix is: H=0,0,1; Noise covariance setting: process noise covariance Measure noise covariance 4. Calculation process: Integrate the filtered acceleration signal s twice to obtain the lateral velocity. and lateral displacement Its Laplace transform is the output variable of the transfer function. ; Acquisition of spreader pitch angle: The gyroscope angular velocity signal and accelerometer tilt angle signal from the four IMU sensors (numbered IMU3-IMU6) deployed on the spreader are fused together.

[0030] Initial calibration: In a stationary state, the initial pitch angle is obtained by measuring the gravitational component using an accelerometer. ; Dynamic updates: Real-time acquisition of pitch axis angular velocity using a gyroscope. Integrating yields the dynamic pitch angle change. At the same time, gyroscope drift is corrected using accelerometer signals; Fusion output: Final pitch angle Its Laplace transform is the output variable of the transfer function.

[0031] Then the transfer function matrix satisfy Each element of the matrix Expressed in the form of a second-order rational fraction: ;in, For the first The undamped natural frequency of the channel, To correspond to the damping ratio, and These are the gain coefficients to be identified. The engine uses the Recursive Least Squares (RLS) algorithm to update these parameters online, with the following cost function: ; in, The forgetting factor is set to 0.95. It is a vector containing all the parameters to be estimated; The regression vector is composed of historical input and output data. The initial value of the covariance matrix of the RLS algorithm is set as a diagonal matrix. This ensures initial convergence speed. The modeling engine completes a full parameter update every fifty control cycles (i.e., five hundred milliseconds) to adapt to the drift in system dynamic characteristics caused by changes in wind speed and the load mass of the spreader.

[0032] The active stabilization command generation module runs on a DSP coprocessor of the central co-controller, and its control cycle is synchronized with the modeling engine, lasting ten milliseconds. This module receives the current wind speed vector. and the latest updated transfer function matrix First, the wind speed vector is projected onto the local coordinate system of the boom to obtain its components along the boom's axial, lateral, and vertical directions. Considering that crosswinds have the most significant impact on boom sway, the main focus is on... Quantity.

[0033] Module calculation at the dominant perturbation frequency (Pick and At the point (closer to the middle), the transfer function Frequency response of (i.e., the channel from lateral wind speed to lateral displacement at the end of the boom): ; The magnitude of the required reverse torque Mcomp is determined by the following formula: ; Here, Kgain is the safety gain coefficient, initially set to 1.2, but dynamically limited by the Lyapunov stability criterion. Specifically, the system energy function is defined as follows: ,in For equivalent quality, For rotational inertia, The pitch angle of the lifting device; The equivalent mass *m* refers to the equivalent lumped mass of the container crane boom-spreader-load system. It is a lumped mass parameter (dynamically changing with the load weight) converted to the boom end based on the principle of kinetic energy equivalence, taking the distributed mass of the boom, the mass of the spreader itself, and the mass of the container load. The calculation method is as follows: ; in Obtained through calculation of boom structure parameters: ; (where L is the linear density of the boom along its length, and L is the total length of the boom). To fix the known values, Data is collected in real time by the load cell of the lifting device; The moment of inertia includes the lifting device's own moment of inertia and the additional moment of inertia due to load eccentricity. The calculation method is as follows: ; These are the factory-calibrated fixed parameters, and e is the eccentricity of the load center of gravity relative to the spreader's pitch axis (identified in real time by the spreader's attitude sensor). Parameter acquisition methods: and These are the basic parameters that were modeled and calibrated experimentally by structural dynamics before the system leaves the factory. The load cells are collected in real time by the load cells integrated into the spreader; the data is identified in real time by the IMU sensor data deployed on the spreader to ensure that the parameters dynamically match the system operating conditions; if the derivative of the system energy function with respect to time is greater than zero, it is determined that the compensation command may trigger energy injection. At this time, the safety gain coefficient used to calculate the amplitude of the reverse torque is adjusted exponentially until the derivative of the system energy function with respect to time is less than or equal to zero.

[0034] If If the compensation command is determined to potentially trigger energy injection, then... Adjusted according to exponential decay, until... .

[0035] The phase of the reverse torque is determined by introducing a preset phase compensation angle. Adjustments were made to ensure that the actual applied torque and the wind-induced disturbance torque were strictly out of phase in the time domain. This compensation angle comprehensively considered the phase lag of the hydraulic system, sensor delay, and modeling errors, with an initial value set at thirty degrees, and fine-tuned using an online phase calibration algorithm. Finally, the reverse torque command is expressed as: ; This torque was then decomposed into components of the boom pitch torque. The component of the torque in the direction of rotation of the lifting device Decomposition based on the current boom elevation angle. With the slewing angle of the lifting gear This is achieved using a rotation matrix: ; in, and This represents the component of the reverse torque in the global horizontal coordinate system; To clarify the torque decomposition logic, the relevant coordinate system, angles, and rotation matrices are defined as follows: Coordinate system definition: Global horizontal coordinate system (O-XY): The origin O is the projection of the center of rotation of the rubber tire crane onto the ground. The X-axis is parallel to the direction of the wharf shoreline, the Y-axis is perpendicular to the wharf shoreline and points to the sea surface, and the XY plane is parallel to the ground. Local coordinate system of boom (O'-X'Y'): The origin O' is the boom slewing hinge point. The X' axis points to the end of the boom along the length of the boom. The Y' axis is perpendicular to the X' axis and points upward in the vertical plane where the boom is located. It is in the boom elevation angle relationship with the global coordinate system XY plane.

[0036] Angle definition: boom elevation angle α: the angle between the boom's local coordinate system X' axis and the global coordinate system XY plane. The zero point is defined as the boom being placed horizontally (X' axis coincides with the XY plane), with upward swing being the positive direction. It is measured in real time by the angle sensor at the boom's slewing hinge point. Spreader rotation angle β: The rotation angle of the spreader relative to the end of the boom. The zero point is defined as the longitudinal axis of the spreader coinciding with the X' axis of the local coordinate system of the boom, with clockwise rotation around the boom axis as the positive direction. It is measured in real time by the angle sensor of the spreader rotation mechanism. Rotation matrix construction: The rotation matrix used for reverse moment decomposition above: R= ; This matrix realizes the reverse torque in the local coordinate system of the boom. Convert to components in the global horizontal coordinate system The transformation relationship is as follows: ; The decomposed torque components are converted into corresponding hydraulic servo valve opening commands. and The conversion relationship is based on the hydraulic cylinder force-current characteristic curve and the motor torque-flow characteristic table, and is achieved through table lookup interpolation.

[0037] In this embodiment, the hydraulic servo actuator includes two independent electro-hydraulic proportional servo systems: one for driving the boom pitch hydraulic cylinder and the other for driving the spreader slewing hydraulic motor. Each system is equipped with a high-frequency response electro-hydraulic proportional servo valve with a rated flow rate of 120 liters per minute, a bandwidth of not less than 150 Hz, and a hysteresis of less than 0.5%. The control current signal of the servo valve is output by a 16-bit D / A converter and driven by a power amplifier. When the central coordinating controller detects that any wind speed sensor reading has continuously exceeded 15 meters per second for more than two seconds, it automatically activates the active stabilization mode. In this mode, the final control signal of the hydraulic servo actuator is an algebraic superposition of the remote operator's original command and the active stabilization compensation command. ; in, For smoothed operator instructions, This is the compensation valve opening command generated above. The superposition process is completed in hard real-time in the FPGA to ensure no additional delay is introduced.

[0038] The safety status monitoring unit, as an independent functional module of the central collaborative controller, continuously monitors three key parameters: the amplitude of lateral displacement at the end of the boom. Angled rate of the lifting device and pressure fluctuations in the main circuit of the hydraulic system. . The acceleration at the end of the boom was estimated by IMU data fusion, and the Kalman filter was used to integrate the acceleration twice and eliminate drift. The maximum value is taken directly from the readings of the IMU gyroscopes at the four corners of the lifting device; The pressure is collected by a pressure sensor installed at the outlet of the hydraulic pump, with a sampling frequency of 1 kHz. >15 centimeters, or >10 degrees per second, or When the system pressure reaches 20% of its rated pressure, the safety status monitoring unit immediately sends an interrupt signal to the main control processor. The processor then executes a three-level safety response: Level 1, it cuts off the active stabilization command output, retaining only operator commands; Level 2, if the parameters continue to deteriorate, it initiates a gradual deceleration program within 500 milliseconds, linearly reducing the speed of each motion axis to zero; Level 3, if the system does not recover to the safety threshold within 10 seconds, it triggers emergency braking, shuts off all hydraulic power sources, and activates the mechanical locking device.

[0039] In this embodiment, all miniature inertial measurement units (IMUs) and ultrasonic anemometers have built-in self-diagnostic circuits. The IMU determines whether the gyroscope's zero bias exceeds the limit by periodically injecting pulses with known angular velocities and comparing the output response. The anemometer detects transducer blockage or icing faults by cross-validating the consistency of the propagation time of the three ultrasonic paths. If a diagnosis fails, the sensor immediately sends a 16-bit fault code containing the device ID and fault type to the central co-controller via the CANFD bus. The central co-controller records the fault code in non-volatile memory and reports it to the maintenance interface of the remote operation terminal.

[0040] All sensor signal cables employ a double-shielded twisted-pair structure, with an inner layer of aluminum foil shielding and an outer layer of braided copper mesh. The shielding layer is grounded at a single point to the main steel structure of the tire crane. The cables are laid entirely within galvanized steel conduits, which are welded to the steel structure to form a continuous equipotential body, effectively suppressing electromagnetic interference from frequency converters, radio base stations, and lightning strikes in the port environment. Actual measurements show that at a distance of ten meters from a 400 kW quay crane frequency converter, the signal-to-noise ratio of the IMU acceleration signal remains better than 40 dB.

[0041] In one example, after equipping a port RTG (Rubber Tire Gantry Crane) with this system, it performed standard loading and unloading operations under strong wind conditions with an average wind speed of 18 m / s and gusts reaching 25 m / s. The system activated the active stabilization mode, collecting wind speed and structural response data in real time, and the modeling engine updated the transfer function matrix every 500 milliseconds. The active stabilization command generation module calculated the reverse torque and drove the hydraulic system to superimpose compensation. During the operation, the peak-to-peak lateral displacement of the boom end was controlled at 9.3 cm, the peak-to-peak swing angle of the spreader was 1.7 degrees, and the positioning repeatability accuracy reached ±3.2 cm.

[0042] In a comparative example, to verify the technical effect of the present invention, a comparative example was set up: the same RTG was operated under the same wind conditions with the active stabilization function turned off, relying solely on manual fine-tuning by the operator. At this time, the peak value of the lateral displacement at the end of the boom reached 32.6 cm, and the peak value of the spreader swing angle was 6.4 degrees. Safety shutdowns were triggered multiple times due to the swing amplitude exceeding the limit, and the operation interruption rate reached 41%.

[0043] The table below summarizes the key performance indicators of the embodiments and comparative examples: Performance indicators Example Comparative Example Peak value of lateral displacement at the end of the boom 9.3cm 32.6cm Peak value of spreader swing angle 1.7° 6.4° Positioning repeatability (3σ) ±3.2cm ±11.8cm Single job cycle time 142s 187s Number of shutdowns per hour due to oscillation exceeding limits 0 2.3 The above data demonstrates that the container tire crane remote control system of this invention significantly improves operational stability and efficiency in strong wind environments. By deeply integrating wind load perception and structural inertial response, the system constructs a high-fidelity dynamic mapping model and generates a phase-matched active compensation torque, achieving physical suppression of wind-induced resonance. The entire control process requires no operator intervention and is fully autonomously completed by the central collaborative controller, ensuring the reliability and safety of efficient all-weather operations at the port.

[0044] Furthermore, the system software architecture adopts a modular design, with each functional module interacting through clearly defined API interfaces, facilitating subsequent upgrades and maintenance. For example, the wind speed-inertia coupling modeling engine can be replaced with a deep learning-based state-space model, achieving seamless integration simply by maintaining consistent input and output interfaces. In addition, the central collaborative controller reserves an OPCUA server interface to support data interaction with the port's TOS, enabling global collaborative optimization of work plans and equipment status.

[0045] In summary, this invention constructs a complete, reliable, and efficient remote control system for container tire cranes through rigorous sensor layout, high-precision synchronous sampling, online dynamic modeling, phase matching compensation, and multiple safety assurance mechanisms, fully meeting the technical requirements of modern automated terminals for high precision, high availability, and all-weather operation capabilities.

[0046] In the description of this invention, it should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions provided in this disclosure can be achieved, and no limitation is imposed herein.

[0047] The above description is merely a preferred embodiment of the present invention and does not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A remote control system for a container tire crane, characterized in that, It includes a remote operation terminal, a communication transmission module, a central coordination controller, a wind load sensing module, a structural dynamic response sensing module, a coupled modeling engine, an active stabilization command generation module, and a hydraulic servo actuator. The remote operation terminal is used to receive the lifting device movement command input by the operator and send the command to the central coordination controller via the communication transmission module; The wind load sensing module includes multiple ultrasonic wind speed and direction sensors, which are respectively installed on the top of the main beam of the tire crane, the root of the boom, and the middle of the crossbeam of the lifting device, for real-time acquisition of wind speed vectors in three-dimensional space; The structural dynamic response sensing module consists of multiple miniature inertial measurement units. These miniature inertial measurement units are fixedly installed at the boom hinge point, the boom end, the lifting wire rope guide pulley bracket, and the four corner connections of the container spreader. Each miniature inertial measurement unit contains a three-axis accelerometer and a three-axis gyroscope, which are used to synchronously measure the linear acceleration and angular velocity data at each installation position. The coupled modeling engine is built into the central coordinating controller. Its input is connected to the output of the wind load sensing module and the structural dynamic response sensing module, respectively. It is used to establish the transfer function matrix between wind speed excitation and structural response based on the wind speed vector and the linear acceleration and angular velocity data. The active stabilization command generation module receives the transfer function matrix and the current wind speed vector output by the coupled modeling engine, calculates the reverse torque required to counteract the current wind-induced disturbance, and decomposes the reverse torque into the boom pitching torque component and the spreader rotation torque component, and converts them into the corresponding hydraulic servo valve opening command. The hydraulic servo actuator includes a boom pitch hydraulic cylinder, a spreader slewing hydraulic motor and its matching electro-hydraulic proportional servo valves, and the control terminal of each electro-hydraulic proportional servo valve is connected to the output terminal of the active stabilization command generation module. When the system detects that the wind speed exceeds the preset threshold, the central coordinating controller activates the active stabilization mode. The hydraulic servo actuator executes the original motion command from the remote operator while simultaneously executing the compensation command output by the active stabilization command generation module.

2. The remote control system for container tire cranes according to claim 1, characterized in that, The process by which the coupling modeling engine establishes the transfer function matrix includes: The wind speed vector is subjected to spectral decomposition to extract the dominant frequency components and their amplitudes; Modal identification is performed on the acceleration and angular velocity signals output by the micro inertial measurement unit to determine the first two natural frequencies and corresponding mode shapes of the boom-container system; The transfer function matrix between the wind speed excitation and the structural response is established based on the least squares fitting method. This transfer function matrix characterizes the dynamic response characteristics of the lateral displacement of the boom end and the pitch angle of the spreader to the wind load input under specific wind speed conditions. The coupling modeling engine continuously updates the transfer function matrix.

3. The remote control system for container tire cranes according to claim 2, characterized in that, The coupled modeling engine uses a recursive least squares algorithm to update the parameters of the transfer function matrix online.

4. The remote control system for container tire cranes according to claim 1, characterized in that, When the active stabilization command generation module calculates the reverse torque, the amplitude of the reverse torque is determined by the gain of the transfer function matrix at the dominant disturbance frequency, and its phase is adjusted by introducing a preset phase compensation angle so that the reverse torque is out of phase with the actual disturbance torque in the time domain.

5. The remote control system for container tire cranes according to claim 4, characterized in that, The active stabilization command generation module introduces an amplitude limiting mechanism based on the Lyapunov stability criterion when calculating the reverse torque; the system energy function is defined as: ； in For equivalent quality, For rotational inertia, This refers to the lateral displacement of the boom end. The pitch angle of the spreader; if the derivative of the system energy function with respect to time is greater than zero, it is determined that the compensation command may trigger energy injection. At this time, the safety gain coefficient used to calculate the amplitude of the reverse torque is adjusted exponentially until the derivative of the system energy function with respect to time is less than or equal to zero.

6. The remote control system for container tire cranes according to claim 1, characterized in that, The central coordinating controller is equipped with a safety status monitoring unit, which monitors the lateral displacement amplitude of the boom end, the swing rate of the spreader, and the pressure fluctuation of the hydraulic system in real time. When any monitored parameter exceeds the safety limit, the safety status monitoring unit sends an emergency intervention signal to the central coordinating controller, which then cuts off the active stabilization command output and initiates a gradual deceleration shutdown procedure.

7. The remote control system for container tire cranes according to claim 1, characterized in that, Both the miniature inertial measurement unit and the ultrasonic wind speed and direction sensor are equipped with temperature compensation circuits and self-diagnostic functions. The self-diagnostic function determines whether the sensor is malfunctioning by periodically injecting known test signals and comparing the output response, or by cross-verifying the consistency of the propagation time of multiple ultrasonic paths. When the sensor fails, it automatically reports a fault code to the central coordinating controller.

8. The remote control system for container tire cranes according to claim 1, characterized in that, The communication transmission module adopts an industrial-grade 5G communication link based on time-sensitive networking enhancement. Its physical layer supports adaptive switching between millimeter wave and Sub-6GHz dual frequency bands, and the media access control layer integrates forward error correction coding and retransmission mechanisms. The communication transmission module is equipped with a dedicated hardware accelerator on the central collaborative controller side, which is used to timestamp and synchronize the received remote operation commands and the uploaded sensor data streams.

9. The remote control system for container tire cranes according to claim 1, characterized in that, The central collaborative controller adopts a heterogeneous multi-core architecture, including a main control processor, at least one real-time digital signal processor coprocessor, and a field-programmable gate array (FPGA). The main control processor is responsible for high-level task scheduling, the digital signal processor coprocessor undertakes wind load processing and structural response analysis tasks, and the FPGA implements hard real-time logic for high-speed synchronous sampling, sensor fault diagnosis, and safety status monitoring.

10. The remote control system for container tire cranes according to claim 1, characterized in that, All of the aforementioned miniature inertial measurement units are connected to the high-speed synchronous sampling interface of the central coordinating controller via hardwired connections; the signal cables of the ultrasonic wind speed and direction sensor and the miniature inertial measurement units are all shielded twisted-pair structures and are laid inside the steel structure of the tire crane through metal conduits.

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