Ship low-frequency active control system and method based on intelligent material and adaptive algorithm

Abstract

The invention discloses a ship low-frequency active control system and method based on an intelligent material and an adaptive algorithm, and relates to the technical field of ship vibration control. The remote monitoring management platform is in communication connection with an intelligent material layer module, a sensor network module, a self-adaptive controller module, a self-diagnosis module and a power supply module; the intelligent material layer module is used for sensing ship vibration by adopting an intelligent material and generating corresponding control force; the sensor network module is used for collecting ship vibration data in real time through sensors arranged at key parts of a ship. By integrating intelligent materials and a self-adaptive control algorithm, ship vibration can be sensed in real time, corresponding control force can be quickly generated, vibration can be effectively counteracted, and compared with a traditional passive control technology, the response speed is remarkably increased, the ship vibration can be responded in a shorter time, and therefore the safety and comfort of ship navigation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ship vibration control, and in particular to a ship low-frequency active control system and method based on intelligent materials and adaptive algorithms. Background Art

[0002] During navigation, ships are inevitably affected by wave impacts, mechanical operation and other external factors, generating low-frequency vibrations. This vibration not only affects the comfort of the crew, but also has a negative impact on the structural safety of the ship, the operating efficiency of the equipment and its service life. With the development of marine transportation, the number of large ships is increasing, and their low-frequency vibration problem is becoming more and more prominent.

[0003] Traditional low-frequency vibration control of ships mainly relies on passive control technologies, such as shock absorbers and dampers, which have problems such as slow response, low precision, and high energy consumption. They cannot respond to the complex vibration conditions of ships in a timely manner, and the vibration control effect in the low-frequency band is not good. In addition, passive control technology usually consumes a lot of energy, resulting in high energy consumption and increased operating costs of ships. Therefore, how to achieve real-time monitoring and active control of low-frequency vibrations of ships by integrating intelligent materials, sensor networks and adaptive control algorithms, improve response speed and control accuracy, and reduce energy consumption is the problem to be solved by the present invention. To this end, a ship low-frequency active control system and method based on intelligent materials and adaptive algorithms are proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a ship low-frequency active control system and method based on intelligent materials and adaptive algorithms to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: In the first aspect, a low-frequency active control system for ships based on smart materials and adaptive algorithms includes a remote monitoring and management platform, which is communicatively connected to a smart material layer module, a sensor network module, an adaptive controller module, a self-diagnosis module, and a power supply module; The remote monitoring and management platform transmits the ship's vibration data and control status to the shore-based monitoring center via a wireless network, enabling remote monitoring and management, supporting remote monitoring and management, improving management efficiency, facilitating timely detection and resolution of problems, and reducing operating costs; The smart material layer module is used to sense ship vibrations and generate corresponding control forces using smart materials; The sensor network module is used to collect ship vibration data in real time through sensors arranged at key parts of the ship, including acceleration sensors, displacement sensors and strain sensors; The adaptive controller module is used to receive ship vibration data collected by the sensor network, establish a ship vibration model using an adaptive algorithm, determine a reference model, calculate the error between the actual vibration state and the reference model, and adjust the control parameters of the smart material layer based on the error to make the actual vibration state approach the reference model; The self-diagnosis module is used to monitor the system operating status in real time and automatically alarm when a fault occurs, thereby improving the reliability and safety of the system, facilitating timely detection and handling of faults, and reducing maintenance costs; The power supply module is used to provide stable power support for the system and is powered by solar panels or the ship's own power system.

[0006] A further improvement of the technical solution of the present invention is that the smart material layer module specifically includes: Smart materials such as piezoelectric materials, shape memory alloys or magnetostrictive materials are installed on the ship structure by bonding or embedding, forming an integrated structure with the ship structure, including the bottom, deck or key bulkheads, to ensure a tight fit; When a ship experiences low-frequency vibration, the smart material layer generates electrical signals or deformations due to mechanical stress or temperature changes, directly sensing the ship's vibrations. The smart material's physical properties convert the vibration signals into processable electrical or deformation signals. Based on the sensed vibration signals, the smart material layer module rapidly responds through the internal mechanisms of the smart material, adjusting its internal structure or redistributing its charge to generate a control force that matches the direction and magnitude of the ship's vibrations, thereby offsetting the ship's low-frequency vibrations. The generated control forces are applied directly to the ship's structure, interacting with the ship's low-frequency vibrations and thus canceling them out.

[0007] A further improvement of the technical solution of the present invention is that the internal mechanism of the smart material is: The internal mechanism of piezoelectric materials is the piezoelectric effect, which converts electrical signals into mechanical deformation through the inverse piezoelectric effect, generating a force opposite to the vibration direction; The internal mechanism of shape memory alloys is the shape memory effect, through which the material quickly recovers the predetermined shape under current or temperature stimulation, thereby generating control force; The internal mechanism of magnetostrictive materials is the magnetostrictive effect, which produces dimensional changes under the action of a magnetic field, thereby generating a control force.

[0008] A further improvement of the technical solution of the present invention is that the sensor network module specifically includes: Acceleration sensors, displacement sensors, and strain sensors were placed at key locations on the ship, including the bottom, deck, and critical bulkheads. The sensors were connected to power and communication lines, initialized, and set to a sampling frequency (100 Hz) and range, and placed in a standby state. When a ship generates low-frequency vibration, the sensor senses and collects the corresponding physical quantities in real time to obtain ship vibration data. The collected ship vibration data is then pre-processed by filtering and amplifying to remove noise interference and improve signal quality. The pre-processed vibration data is transmitted wirelessly to the remote monitoring and management platform in real time, and then sent to the adaptive controller module for analysis and processing.

[0009] A further improvement of the technical solution of the present invention is that the adaptive controller module specifically includes: Receive ship vibration data collected from the sensor network module, including acceleration, displacement, and strain data, and extract key features including vibration amplitude, frequency, and phase. Then, use an adaptive algorithm to combine the ship's structural characteristics and historical ship vibration data to establish a ship vibration model and determine the ideal ship vibration reference model. The vibration amplitude is determined by calculating the peak value of the acceleration data. Frequency analysis uses the fast Fourier transform method to extract frequency components from the time domain signal. Phase information is obtained by analyzing the waveform characteristics of the signal. After establishing the reference model, the error between the actual vibration state and the reference model is calculated, including vibration amplitude error, frequency deviation, and phase difference. An adaptive algorithm is used to adjust the control parameters of the smart material layer in real time. By optimizing the control parameters, the actual vibration state of the ship gradually approaches the reference model, effectively suppressing the ship's low-frequency vibration. By receiving new ship vibration data transmitted by the sensor network module in real time, the ship vibration model is continuously updated, and the error between the actual vibration state and the reference model is recalculated, and the control parameters are dynamically adjusted to adapt to the vibration changes of the ship under different working conditions.

[0010] A further improvement of the technical solution of the present invention is that the process of determining the ship vibration model and the ship vibration reference model is as follows: Based on the extracted key features including vibration amplitude, frequency, and phase, combined with the ship's structural characteristics and historical ship vibration data, a system identification method is used to establish a ship vibration model. The ship's structural characteristics include the ship's size, shape, material properties, and structural layout. The ship's structural characteristics and historical ship vibration data are combined with the currently collected key feature data, and a physical model fitting method is used to construct a ship vibration model that describes the ship's vibration behavior. On the basis of the established ship vibration model, the ideal ship vibration reference model is determined according to the design requirements and operating conditions of the ship. The ideal reference model represents the ideal vibration state of the ship when there is no vibration or extremely low vibration, and is the target of vibration control.

[0011] A further improvement of the technical solution of the present invention is that: the adaptive algorithm is based on model reference adaptive control (MRAC) or minimum mean square error (LMS) algorithm; The control parameter adjustment expression based on model reference adaptive control is: ; ; Where, is the adjustment amount of the control parameters based on the model reference adaptive control, which represents the correction value of the control parameters of the smart material layer at time t. is the adaptive gain, which is used to adjust the speed of control parameter update. is the error signal of the i-th sensor at time t, is the output of the reference model, is the output of the actual vibration state, Output Control parameters The partial derivative of represents the influence of the control parameters on the output, n is the number of sensors, which represents the total number of sensors involved in vibration monitoring; The control parameter adjustment expression based on the minimum mean square error algorithm is: ; Where, is the adjustment amount of the control parameters based on the minimum mean square error algorithm, which represents the correction value of the control parameters of the smart material layer at time t. is the step size parameter, which is used to adjust the speed of controlling parameter updates. is the input signal of the i-th sensor at time t, which represents the vibration data collected by the sensor.

[0012] A further improvement of the technical solution of the present invention is that the self-diagnosis module specifically includes: The self-diagnosis module uses the built-in monitoring program to continuously scan the operating status of each module in the system, collect the operating data of each module in real time, and check the status of the system communication link to ensure the integrity and timeliness of data transmission; Analyze the collected operating status data in real time, compare it with the pre-established benchmark model of the system's normal operating status, and identify abnormal fluctuations or data that deviates from the normal range; Based on the identified abnormal fluctuations or data that deviates from the normal range, the data source and location are located, and the alarm mechanism is automatically triggered to send an alarm signal. The alarm signal is sent to the shore-based monitoring center through the system's internal communication link and remote monitoring management platform to remind maintenance personnel to deal with it in a timely manner.

[0013] In a second aspect, a method for active low-frequency control of a ship based on smart materials and adaptive algorithms is implemented based on the above-mentioned active low-frequency control system of a ship based on smart materials and adaptive algorithms, and includes the following steps: Step 1: Through the sensor network deployed at key parts of the ship, the ship's vibration data, including acceleration, displacement and strain data, is collected in real time; Step 2: The collected ship vibration data is transmitted to the adaptive controller module via a wireless communication link, and the ship vibration data is pre-processed by filtering and amplifying to remove noise interference and improve signal quality; Step 3: The adaptive controller module receives the pre-processed ship vibration data, establishes a ship vibration model based on the ship's structural characteristics and historical data, determines the ideal vibration reference model, and calculates the error between the actual vibration state and the reference model, including vibration amplitude error, frequency deviation, and phase difference; Step 4: Adopting an adaptive algorithm, the control parameters of the smart material layer are adjusted in real time based on the calculated error, optimizing the magnitude and direction of the control force so that the actual vibration state gradually approaches the reference model. Step 5: The smart material layer generates corresponding control force based on the adjusted control parameters to offset the low-frequency vibration of the ship. At the same time, it continuously receives new ship vibration data, dynamically updates the ship vibration model and control parameters, and continuously actively controls the low-frequency vibration of the ship.

[0014] Due to the adoption of the above technical solution, the present invention has the following technical advancements compared to the prior art: The present invention provides a low-frequency active control system and method for ships based on intelligent materials and adaptive algorithms. By integrating intelligent materials and adaptive control algorithms, it can sense ship vibrations in real time and quickly generate corresponding control force to effectively offset vibrations. Compared with traditional passive control technology, the response speed is significantly improved, and it can respond to ship vibrations in a shorter time, thereby more effectively protecting the ship's structure and equipment, and improving the safety and comfort of ship navigation.

[0015] The present invention provides a low-frequency active control system and method for ships based on smart materials and adaptive algorithms. The adaptive control algorithm can calculate and adjust the control parameters of the smart material layer based on the ship vibration data collected by the sensor network, so that the actual vibration state of the ship gradually approaches the ideal reference model, ensuring the accuracy of the control process, and can more accurately offset the low-frequency vibration of the ship, reduce the impact of vibration on the ship's structure and equipment, and improve the operating efficiency and life of the ship. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 Schematic diagram of the system function modules of the present invention; Figure 2 Schematic diagram of the workflow of the present invention. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] Example 1, as Figure 1 、 Figure 2 As shown, the present invention provides a low-frequency active control system for ships based on smart materials and adaptive algorithms, including a remote monitoring and management platform, which is communicatively connected to a smart material layer module, a sensor network module, an adaptive controller module, a self-diagnosis module, and a power supply module; The remote monitoring and management platform transmits ship vibration data and control status to the shore-based monitoring center via wireless networks, enabling remote monitoring and management. This platform supports remote monitoring and management, improves management efficiency, facilitates timely detection and resolution of problems, and reduces operating costs. The smart material layer module is used to use smart materials to sense ship vibrations and generate corresponding control forces. It uses smart materials such as piezoelectric materials, shape memory alloys or magnetostrictive materials. When the ship vibrates at a low frequency, the smart materials can respond quickly and convert them into control forces to effectively offset the vibrations. The smart materials including piezoelectric materials, shape memory alloys or magnetostrictive materials are installed on the ship structure by bonding or embedding, and combined with the ship structure to form an integrated structure, including the bottom, deck or key bulkheads, to ensure a close fit. When the ship vibrates at a low frequency, the smart material layer generates electrical signals or Deformation: directly senses ship vibrations and converts vibration signals into processable electrical signals or deformation signals through the physical properties of smart materials. Based on the sensed vibration signals, the smart material layer module quickly responds through the internal mechanism of the smart material, adjusts the internal structure or redistributes the charge, and generates a control force that is opposite to the direction of the ship's vibration and matches the magnitude to offset the ship's low-frequency vibrations. The generated control force is directly applied to the ship's structure, interacting with the ship's low-frequency vibrations to offset the vibrations. Through the precise application of the control force, the ship's structure remains relatively stable during vibration, improving the comfort and safety of the ship. In addition, the internal mechanism of smart materials is: The internal mechanism of piezoelectric materials is the piezoelectric effect, which converts electrical signals into mechanical deformation through the inverse piezoelectric effect, generating a force opposite to the vibration direction; The internal mechanism of shape memory alloys is the shape memory effect, through which the material quickly recovers the predetermined shape under current or temperature stimulation, thereby generating control force; The internal mechanism of magnetostrictive materials is the magnetostrictive effect, which produces dimensional changes under the action of a magnetic field, thereby generating a control force; The sensor network module is used to collect ship vibration data in real time through sensors arranged at key parts of the ship, including acceleration sensors, displacement sensors and strain sensors. It can comprehensively monitor the ship's vibration status and capture the acceleration, displacement and strain data of the ship's low-frequency vibration. Acceleration sensors, displacement sensors and strain sensors are arranged at key parts of the ship including the bottom, deck and key bulkheads. The sensors are fixed by bonding or embedding to ensure that they fit closely with the ship structure to accurately sense vibration. At the same time, the power supply and communication lines are connected to the sensor, and the initialization settings are performed. The sampling frequency (100Hz) and range are set to put it in standby state. When the ship When low-frequency vibration occurs, the sensor senses and collects the corresponding physical quantities in real time to obtain ship vibration data. Among them, the acceleration sensor uses piezoelectric crystals or micro-electromechanical system technology to convert vibration acceleration into electrical signals. The displacement sensor measures the displacement change of the ship structure through capacitive or inductive principles. The strain sensor senses the strain of the ship structure based on the resistance change of the strain gauge. The collected ship vibration data is pre-processed by filtering and amplification to remove noise interference and improve signal quality. The pre-processed vibration data is transmitted wirelessly in real time to the remote monitoring and management platform, and then sent to the adaptive controller module for analysis and processing; The adaptive controller module is used to receive the ship vibration data collected by the sensor network, and use the adaptive algorithm to establish the ship vibration model, determine the reference model, calculate the error between the actual vibration state and the reference model, and adjust the control parameters of the smart material layer according to the error to make the actual vibration state approach the reference model. It receives the ship vibration data collected by the sensor network module, including acceleration, displacement and strain data, and extracts key features including vibration amplitude, frequency and phase. Then, it uses the adaptive algorithm to combine the ship structure characteristics and historical ship vibration data to establish the ship vibration model and determine the ideal ship vibration reference model. The vibration amplitude is determined by calculating the peak value of the acceleration data, the frequency analysis uses the fast Fourier transform method to extract the frequency component from the time domain signal, and the phase information is obtained by analyzing the waveform characteristics of the signal. The reference model represents the ideal vibration state of the ship in a state of no vibration or minimal vibration, and is used as a benchmark for the target vibration state. After the reference model is established, the error between the actual vibration state and the reference model is calculated, including vibration amplitude error, frequency deviation, and phase difference. An adaptive algorithm (based on the model reference adaptive control (MRAC) or the least mean square error (LMS) algorithm) is used to adjust the control parameters of the smart material layer in real time. By optimizing the control parameters, the actual vibration state of the ship gradually approaches the reference model, achieving effective suppression of the ship's low-frequency vibration. By receiving new ship vibration data transmitted by the sensor network module in real time, the ship vibration model is continuously updated, and the error between the actual vibration state and the reference model is recalculated. The control parameters are dynamically adjusted to adapt to the vibration changes of the ship under different operating conditions. In addition, the determination process of the ship vibration model and the ship vibration reference model is as follows: Based on the extracted key features including vibration amplitude, frequency and phase, combined with the ship structure characteristics and historical ship vibration data, a system identification method is used to establish a ship vibration model. Among them, the ship structure characteristic parameters are obtained through ship design drawings, material test reports, etc., and the historical vibration data comes from previous sensor records. The ship structure characteristics include the ship's size, shape, material properties and structural layout. The ship structure characteristics and historical ship vibration data are combined with the currently collected key feature data. The physical model fitting method is used to construct a ship vibration model that describes the ship's vibration behavior and reflects the dynamic characteristics of the ship's vibration under different operating conditions. Based on the established ship vibration model, the ideal ship vibration reference model is determined according to the ship's design requirements and operating conditions. The ideal reference model represents the ideal vibration state of the ship when there is no vibration or extremely low vibration, and is the target of vibration control. The determination of the ship vibration reference model needs to comprehensively consider the ship's structural safety, equipment operation requirements and crew comfort factors. The adaptive algorithm is based on model reference adaptive control (MRAC) or the least mean square error (LMS) algorithm. The adaptive control algorithm can calculate and adjust the control parameters of the smart material layer based on the ship vibration data collected by the sensor network, so that the actual vibration state of the ship gradually approaches the ideal reference model, ensuring the accuracy of the control process, and can more accurately offset the ship's low-frequency vibration, reduce the impact of vibration on the ship's structure and equipment, and improve the ship's operating efficiency and lifespan; The control parameter adjustment expression based on model reference adaptive control is: ; ; Where, is the adjustment amount of the control parameters based on the model reference adaptive control, which represents the correction value of the control parameters of the smart material layer at time t. is the adaptive gain, which is used to adjust the speed of control parameter update, and its value range is 0< <1, smaller It will make the system more stable, but the response speed will be slower. is the error signal of the i-th sensor at time t, which represents the difference between the actual vibration state and the reference model, is the output of the reference model, is the output of the actual vibration state, Output Control parameters The partial derivative of represents the influence of the control parameter on the output, which is used to determine the direction and amplitude of the control parameter adjustment. n is the number of sensors, indicating the total number of sensors involved in vibration monitoring. The control parameter adjustment expression based on the minimum mean square error algorithm is: ; Where, is the adjustment amount of the control parameters based on the minimum mean square error algorithm, which represents the correction value of the control parameters of the smart material layer at time t. is the step size parameter, which is used to adjust the speed of updating the control parameters. Its value range is , is the maximum eigenvalue of the input signal correlation matrix, the larger Makes parameters update faster, but may cause system instability, smaller It will make the system more stable, but the convergence speed will be slower. is the input signal of the i-th sensor at time t, representing the vibration data collected by the sensor. By integrating smart materials and adaptive control algorithms, it can sense ship vibrations in real time and quickly generate corresponding control forces to effectively offset vibrations. Compared with traditional passive control technologies, the response speed is significantly improved, and it can respond to ship vibrations in a shorter time, thereby more effectively protecting the ship's structure and equipment, and improving the safety and comfort of ship navigation; The self-diagnosis module is used to monitor the system's operating status in real time and automatically alarm when a fault occurs, thereby improving the system's reliability and safety, facilitating timely detection and handling of faults, and reducing maintenance costs. The self-diagnosis module uses a built-in monitoring program to continuously and comprehensively scan the operating status of each module in the system, collect the operating data of each module in real time, and perform status checks on the system's communication links to ensure the integrity and timeliness of data transmission. The collected operating status data is analyzed in real time and compared with a pre-established benchmark model of the system's normal operating status to identify abnormal fluctuations or data that deviates from the normal range. Based on the identified abnormal fluctuations or data that deviates from the normal range, the source and location of the data are located, and the alarm mechanism is automatically triggered to send an alarm signal. The alarm signal is sent to the shore-based monitoring center through the system's internal communication link and the remote monitoring and management platform to remind maintenance personnel to handle it in a timely manner; The power module is used to provide stable power support for the system. It uses solar panels or the ship's own power system to power the system, ensuring long-term stable operation of the system, reducing energy consumption and reducing ship operating costs.

[0020] Example 2, as Figure 1 、 Figure 2 As shown, based on Example 1, the present invention further provides a ship low-frequency active control method based on smart materials and adaptive algorithms, which is implemented based on the above-mentioned ship low-frequency active control system based on smart materials and adaptive algorithms, and includes the following steps: Step 1: Through the sensor network arranged in the key parts of the ship, the ship's vibration data, including acceleration, displacement and strain data, are collected in real time. Acceleration sensors, displacement sensors and strain sensors are arranged in key parts of the ship, such as the bottom, deck and key bulkheads. The sensors are fixed to the ship structure by bonding or embedding to ensure close fit with the ship structure to accurately sense vibration. The acceleration sensor uses piezoelectric crystals or micro-electromechanical system technology to convert vibration acceleration into electrical signals. The displacement sensor measures the displacement change of the ship structure through capacitive or inductive principles. The strain sensor senses the strain of the ship structure based on the resistance change of the strain gauge. When the ship generates low-frequency vibration, the sensor can sense and collect the corresponding physical quantities in real time to obtain ship vibration data; Step 2: The collected ship vibration data is transmitted to the adaptive controller module via a wireless communication link, and the ship vibration data is pre-processed by filtering and amplifying to remove noise interference and improve signal quality. The collected ship vibration data is transmitted to the adaptive controller module via a wireless communication link. The wireless transmission method can avoid the complexity and cost brought by wiring, while ensuring the real-time and reliability of data transmission. Before the data is transmitted to the adaptive controller module, the ship vibration data needs to be pre-processed. The pre-processing operation includes filtering and amplification. The filtering operation can remove noise interference and improve signal quality. The amplification operation can amplify the weak signal to an appropriate range to facilitate subsequent processing and analysis. Step 3: The adaptive controller module receives the pre-processed ship vibration data, establishes a ship vibration model based on the ship's structural characteristics and historical data, determines the ideal vibration reference model, and calculates the error between the actual vibration state and the reference model, including vibration amplitude error, frequency deviation and phase difference. The adaptive controller module receives the ship vibration data collected by the sensor network module, including acceleration, displacement and strain data, and extracts key features including vibration amplitude, frequency and phase. In combination with the ship's structural characteristics and historical ship vibration data, an adaptive algorithm is used to establish a ship vibration model. The ship vibration model can reflect the vibration characteristics of the ship under different working conditions and determine the ideal ship vibration reference model. The reference model represents the ideal vibration state of the ship in a state of no vibration or extremely low vibration, and is used as a benchmark for the target vibration state. The error between the actual vibration state and the reference model is calculated, including vibration amplitude error, frequency deviation and phase difference. Error calculation is a key link in vibration control. The adjustment direction and amplitude of the control parameters can be determined through error calculation. Step 4: Adopting an adaptive algorithm, based on the calculated error, the control parameters of the smart material layer are adjusted in real time, the magnitude and direction of the control force are optimized, and the actual vibration state gradually approaches the reference model. Adopting an adaptive algorithm based on model reference adaptive control (MRAC) or the least mean square error (LMS) algorithm, the control parameters can be adjusted in real time according to the error signal, so that the actual vibration state of the ship gradually approaches the reference model. Based on the calculated error, the control parameters of the smart material layer are adjusted in real time. By optimizing the control parameters, the magnitude and direction of the control force generated by the smart material layer are adjusted, so that the control force can more effectively offset the low-frequency vibration of the ship. Step 5: The smart material layer generates corresponding control force according to the adjusted control parameters to offset the low-frequency vibration of the ship. At the same time, it continuously receives new ship vibration data, dynamically updates the ship vibration model and control parameters, and continuously actively controls the low-frequency vibration of the ship. The smart material layer generates corresponding control force according to the adjusted control parameters. The generation of control force is based on the internal mechanism of the smart material, including the inverse piezoelectric effect of piezoelectric materials, the shape memory effect of shape memory alloys, and the magnetostrictive effect of magnetostrictive materials. The generated control force acts directly on the ship structure and interacts with the low-frequency vibration of the ship, thereby offsetting the vibration. Through the precise application of control force, the ship structure remains relatively stable during the vibration process, thereby improving the comfort and safety of the ship. While offsetting the vibration, it continuously receives new ship vibration data, dynamically updates the ship vibration model and control parameters, and the dynamic update mechanism enables the system to adapt to the vibration changes of the ship under different working conditions, thereby achieving continuous active control of the low-frequency vibration of the ship.

[0021] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A low-frequency active control system for ships based on intelligent materials and adaptive algorithms, including a remote monitoring and management platform, characterized by: The remote monitoring and management platform is communicatively connected to the intelligent material layer module, the sensor network module, the adaptive controller module, the self-diagnosis module and the power supply module; The smart material layer module is used to sense ship vibrations and generate corresponding control forces using smart materials; The sensor network module is used to collect ship vibration data in real time through sensors arranged at key parts of the ship; The adaptive controller module is used to receive ship vibration data collected by the sensor network, establish a ship vibration model using an adaptive algorithm, determine a reference model, calculate the error between the actual vibration state and the reference model, and adjust the control parameters of the smart material layer based on the error to make the actual vibration state approach the reference model; The self-diagnosis module is used to monitor the system operation status in real time and automatically alarm when a fault occurs; The power supply module is used to provide power support for the system.

2. The ship low-frequency active control system based on intelligent materials and adaptive algorithms according to claim 1 is characterized by: The smart material layer module specifically includes: Smart materials including piezoelectric materials, shape memory alloys or magnetostrictive materials are installed on the ship structure by bonding or embedding, and combined with the ship structure to form an integrated structure, including the bottom, deck or key bulkhead positions; When a ship experiences low-frequency vibration, the smart material layer generates electrical signals or deformations due to mechanical stress or temperature changes, directly sensing the ship's vibrations. Through the physical properties of the smart material, the vibration signals are converted into processable electrical or deformation signals. Based on the sensed vibration signals, the smart material's internal mechanism quickly responds by adjusting its internal structure or redistributing its charge, generating a control force that is opposite in direction and of the same magnitude as the ship's vibrations. The generated control forces are applied directly to the ship's structure, interacting with the ship's low-frequency vibrations and thus canceling them out.

3. The ship low-frequency active control system based on intelligent materials and adaptive algorithms according to claim 2 is characterized by: The internal mechanism of the smart material is: The internal mechanism of piezoelectric materials is the piezoelectric effect, which converts electrical signals into mechanical deformation through the inverse piezoelectric effect, generating a force opposite to the vibration direction; The internal mechanism of shape memory alloys is the shape memory effect, through which the material quickly recovers the predetermined shape under current or temperature stimulation, thereby generating control force; The internal mechanism of magnetostrictive materials is the magnetostrictive effect, which produces dimensional changes under the action of a magnetic field, thereby generating a control force.

4. The ship low-frequency active control system based on smart materials and adaptive algorithms according to claim 1 is characterized by: The sensor network module specifically includes: Arrange acceleration sensors, displacement sensors, and strain sensors at key locations on the ship, including the bottom, deck, and key bulkheads. At the same time, connect the sensors to power and communication lines, initialize them, set the sampling frequency and range, and place them in a standby state. When the ship generates low-frequency vibration, the sensor senses and collects the corresponding physical quantities in real time to obtain ship vibration data, and performs pre-processing operations such as filtering and amplification on the collected ship vibration data; The pre-processed vibration data is transmitted wirelessly to the remote monitoring and management platform in real time, and then sent to the adaptive controller module for analysis and processing.

5. The ship low-frequency active control system based on smart materials and adaptive algorithms according to claim 1 is characterized by: The adaptive controller module specifically includes: Receive ship vibration data collected from the sensor network module, including acceleration, displacement, and strain data, and extract key features including vibration amplitude, frequency, and phase. Then, use an adaptive algorithm to combine the ship's structural characteristics and historical ship vibration data to establish a ship vibration model and determine the ideal ship vibration reference model. The vibration amplitude is determined by calculating the peak value of the acceleration data. Frequency analysis uses the fast Fourier transform method to extract frequency components from the time domain signal. Phase information is obtained by analyzing the waveform characteristics of the signal. After establishing the reference model, the error between the actual vibration state and the reference model is calculated, including vibration amplitude error, frequency deviation, and phase difference. An adaptive algorithm is used to adjust the control parameters of the smart material layer in real time. By optimizing the control parameters, the actual vibration state of the ship gradually approaches the reference model. By receiving new ship vibration data transmitted by the sensor network module in real time, the ship vibration model is continuously updated, and the error between the actual vibration state and the reference model is recalculated, and the control parameters are dynamically adjusted to adapt to the vibration changes of the ship under different working conditions.

6. The ship low-frequency active control system based on smart materials and adaptive algorithms according to claim 5 is characterized by: The process of determining the ship vibration model and the ship vibration reference model is as follows: Based on the extracted key features including vibration amplitude, frequency, and phase, combined with the ship's structural characteristics and historical ship vibration data, a system identification method is used to establish a ship vibration model. The ship's structural characteristics include the ship's size, shape, material properties, and structural layout. The ship's structural characteristics and historical ship vibration data are combined with the currently collected key feature data, and a physical model fitting method is used to construct a ship vibration model that describes the ship's vibration behavior. On the basis of the established ship vibration model, the ideal ship vibration reference model is determined according to the design requirements and operating conditions of the ship. The ideal reference model represents the ideal vibration state of the ship when there is no vibration or extremely low vibration, and is the target of vibration control.

7. The ship low-frequency active control system based on smart materials and adaptive algorithms according to claim 6 is characterized by: The adaptive algorithm is based on model reference adaptive control or minimum mean square error algorithm; The control parameter adjustment expression based on model reference adaptive control is: ； ； Where, is the adjustment amount of the control parameters based on the model reference adaptive control, which represents the correction value of the control parameters of the smart material layer at time t. is the adaptive gain, is the error signal of the i-th sensor at time t, is the output of the reference model, is the output of the actual vibration state, Output Control parameters The partial derivative of represents the influence of the control parameters on the output, n is the number of sensors, which represents the total number of sensors involved in vibration monitoring; The control parameter adjustment expression based on the minimum mean square error algorithm is: ； Where, is the adjustment amount of the control parameters based on the minimum mean square error algorithm, which represents the correction value of the control parameters of the smart material layer at time t. is the step size parameter, is the input signal of the i-th sensor at time t, which represents the vibration data collected by the sensor.

8. The ship low-frequency active control system based on smart materials and adaptive algorithms according to claim 1 is characterized by: The self-diagnosis module specifically includes: The self-diagnosis module uses the built-in monitoring program to continuously and comprehensively scan the operating status of each module in the system, collect the operating data of each module in real time, and check the status of the system communication link; Analyze the collected operating status data in real time, compare it with the pre-established benchmark model of the system's normal operating status, and identify abnormal fluctuations or data that deviates from the normal range; Based on the identified abnormal fluctuations or data that deviates from the normal range, the data source and location are located, and the alarm mechanism is automatically triggered to send an alarm signal. The alarm signal is sent to the shore-based monitoring center through the system's internal communication link and remote monitoring management platform to remind maintenance personnel to deal with it in a timely manner.

9. A method for active low-frequency control of a ship based on smart materials and adaptive algorithms, implemented based on the active low-frequency control system of a ship based on smart materials and adaptive algorithms as described in any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Through the sensor network deployed at key parts of the ship, the ship's vibration data, including acceleration, displacement and strain data, is collected in real time; Step 2: The collected ship vibration data is transmitted to the adaptive controller module via a wireless communication link, and the ship vibration data is pre-processed by filtering and amplifying; Step 3: The adaptive controller module receives the pre-processed ship vibration data, establishes a ship vibration model based on the ship's structural characteristics and historical data, determines the ideal vibration reference model, and calculates the error between the actual vibration state and the reference model, including vibration amplitude error, frequency deviation, and phase difference; Step 4: Adopting an adaptive algorithm, the control parameters of the smart material layer are adjusted in real time based on the calculated error, optimizing the magnitude and direction of the control force so that the actual vibration state gradually approaches the reference model. Step 5: The smart material layer generates corresponding control force based on the adjusted control parameters to offset the low-frequency vibration of the ship. At the same time, it continuously receives new ship vibration data, dynamically updates the ship vibration model and control parameters, and continuously actively controls the low-frequency vibration of the ship.

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