Multi-stage vibration reduction method for flying manipulator

Abstract

The application discloses a kind of multistage vibration reduction methods of flight mechanical arm, it is related to unmanned aerial vehicle operation control technical field.The system includes multistage vibration reduction pedestal connected to the bottom of unmanned aerial vehicle and buffer piece arranged at the end of mechanical arm;The pedestal uses quasi-zero stiffness mechanism, and high-frequency vibration is isolated by positive and negative stiffness element parallel connection;The buffer piece is designed based on shape factor theory to absorb end impact.The method comprises the following steps: real-time acquisition of the end of mechanical arm multi-source vibration signal;Equivalent viscous damping state space model of flight mechanical arm based on complex damping characteristics is constructed;Optimal suppression force is solved by using Type-II (two type) fuzzy logic controller and multi-objective observer-teacher-learner optimization algorithm (MOOTLBO);Closed-loop feedback control is realized by adjusting magneto-rheological damper through damping-current mapping.The application can effectively suppress the 10-40Hz resonance frequency band jitter generated in contact type cleaning or brushing operation of flight mechanical arm, significantly improve brushing precision, and protect mechanical arm structure from fatigue damage.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) operation control and robot vibration reduction technology, and in particular to a multi-stage vibration reduction system and vibration suppression control method for a flying robotic arm. It is applicable to end-effector vibration suppression and structural vibration control when a flying robotic arm performs contact cleaning, painting, wiping and other operations on the bottom of bridge beams, walls or other high-altitude structural surfaces. Background Technology

[0002] With the development of drone platforms, robotic arm technology, and intelligent control With technological advancements, flying robotic arms are increasingly being applied to complex tasks such as high-altitude cleaning, bridge maintenance, wall painting, and structural inspection. Compared to traditional manual high-altitude operations, flying robotic arms offer advantages such as high mobility, a wide operating range, and lower safety risks, enabling them to perform contact-based tasks in locations difficult for humans to reach, such as the underside of bridge beams, building exteriors, and complex, irregularly shaped structural surfaces. However, during actual operation, flying robotic arms are simultaneously affected by disturbances from drone flight, robotic arm movement, and contact disturbances between the end effector and the work object. This makes their system dynamics more complex than those of ground-based fixed robotic arms, and ensures greater operational stability and end-effector control precision.

[0003] When a flying robotic arm performs cleaning or painting operations, the end effector brush head, cleaning head, or painting tool needs to maintain a certain contact force with the surface of the workpiece. Due to the limited rigidity of the drone platform itself and the coupling relationship between the robotic arm and the drone, high-frequency vibrations and impact vibrations are easily generated when the end effector contacts the surface. Especially under the combined effects of reciprocating friction of the brush head, uneven surface, airflow disturbance, and structural resonance of the robotic arm, the end effector of the flying robotic arm is prone to continuous vibration within a specific frequency band. This type of vibration not only leads to unstable contact between the brush head and the work surface, resulting in incomplete cleaning and uneven coating, but may also be transmitted to the robotic arm joints, connecting structures, and the drone body, causing attitude disturbances, decreased operational accuracy, and even structural fatigue damage.

[0004] Existing control methods for flying robotic arms primarily focus on UAV attitude stabilization, robotic arm trajectory tracking, or end-effector position control. Commonly used control methods include PID control, impedance control, and adaptive control. These methods are effective in low-frequency attitude adjustment or conventional trajectory tracking scenarios, but their response speed and suppression capabilities remain insufficient for high-frequency resonant vibrations generated during contact operations. Traditional PID control mainly relies on error feedback for adjustment. When the brush head disturbance has abrupt, nonlinear, and high-frequency characteristics, the controller is prone to problems such as response lag, difficulty in parameter tuning, and unstable vibration suppression effects. Simply relying on control algorithms for compensation makes it difficult to achieve graded isolation of UAV body vibration, robotic arm coupled vibration, and end-effector impact vibration at the structural level.

[0005] In terms of structural vibration reduction, existing methods typically employ single-stage passive vibration isolation structures such as springs, rubber pads, and dampers. While these structures can absorb impact energy to some extent, their stiffness and damping parameters are usually fixed, making it difficult to adapt to the dynamic changes in the flight robotic arm under different contact forces, operating postures, and vibration frequencies. Ordinary elastic support structures, if too stiff, can easily transmit high-frequency vibrations to the robotic arm and the drone body; if too stiff, they may lead to end-effector drift and insufficient load-bearing capacity. Quasi-zero stiffness vibration isolation mechanisms can achieve low equivalent stiffness near the working equilibrium position while maintaining a certain load-bearing capacity. However, existing quasi-zero stiffness structures are mostly used in ground equipment or large vibration isolation platforms, and their structural dimensions, layout, and control methods are difficult to directly adapt to the lightweight, highly maneuverable, and space-constrained requirements of drones.

[0006] Regarding end-effector cushioning, existing robotic arm end effectors typically only use ordinary rubber pads or flexible contact elements. Their design largely relies on experience-based selection and lacks parametric design tailored to the impact characteristics of contact operations. Rubber buffers of different shapes and sizes exhibit different nonlinear damping characteristics. If the buffer stiffness is too low, it may lead to unstable brush head contact; if the buffer stiffness is too high, it will be difficult to effectively absorb high-speed contact impacts. Therefore, how to rationally design the end-effector by combining the direction of the end-effector contact force, the structure of the working brush head, and the rubber shape factor is one of the key issues in improving the stability of contact operations of robotic arms.

[0007] In terms of damping modeling and active control, high-frequency vibrations at the end effector of a flight robotic arm typically exhibit significant structural damping characteristics. Complex damping models can effectively reflect the energy dissipation characteristics of structural materials under high-frequency vibrations; however, these models are primarily used for frequency domain analysis, and their direct application to time-domain control can lead to problems such as complex solutions or non-convergence of responses. While viscous damping models facilitate the construction of time-domain state-space equations, the determination of their damping coefficients often relies on experimental calibration, and their ability to characterize high-frequency structural loss characteristics is limited. Therefore, there is still room for improvement in how to transform complex damping characteristics into an equivalent viscous damping model suitable for time-domain control and combine it with vibration suppression control of the flight robotic arm.

[0008] Furthermore, semi-active damping devices such as magnetorheological dampers have the advantages of adjustable damping force, low energy consumption, and fast response, making them suitable for vibration suppression in flying robotic arms. However, existing semi-active damping control methods are mostly designed for vibration control of vehicles, building structures, or fixed platforms, and rarely specifically address the end-effector contact disturbances of flying robotic arms, the lightweight structure of UAV platforms, and the high-frequency resonant jitter characteristics. The vibration signals during the operation of flying robotic arms are uncertain and time-varying. If only a fixed threshold or a single feedback parameter is used for damping adjustment, it is difficult to balance jitter suppression effect and damper energy consumption.

[0009] Therefore, it is necessary to propose a multi-stage vibration reduction system and vibration suppression control method for a flying robotic arm. The system isolates high-frequency vibrations between the UAV body and the robotic arm at the structural level through a multi-stage vibration reduction base, absorbs the contact impact of the brush head through an end effector, and combines an equivalent viscous damping state-space model, type II fuzzy logic control, and a multi-objective optimization algorithm to adjust the semi-active magnetorheological damper in real time. This enables vibration suppression of the flying robotic arm during contact cleaning or painting operations, improving end effector stability and system safety. Summary of the Invention

[0010] To address the problems encountered by flying robotic arms during contact cleaning or painting operations on bridge beams, walls, or other high-altitude structural surfaces, including high-frequency end-effector vibration, unstable brush head contact, vibration transmission from the drone body, fatigue damage to the robotic arm structure, and insufficient high-frequency resonance suppression by traditional control methods, this invention provides a multi-stage vibration reduction system and vibration suppression control method for flying robotic arms. This solution utilizes a multi-stage vibration-damping base, end-effector buffer, and a semi-active magnetorheological damper working in synergy to reduce the vibration response of the flying robotic arm during contact operations from both structural vibration reduction and control vibration suppression perspectives, thereby improving end-effector stability and system safety.

[0011] To achieve the above objectives, the present invention provides the following technical solution:

[0012] A multi-stage vibration reduction method for a flying robotic arm includes the following steps:

[0013] S1, multi-source signal acquisition and preprocessing, wherein S1 includes:

[0014] S11, the end vibration signal and brush head disturbance force signal of the flying robotic arm are collected in real time by a sensor group set at the end of the flying robotic arm. The sensor group includes at least an inertial measurement unit. The inertial measurement unit is used to collect end acceleration signal and angular velocity signal, and obtain end velocity signal and end displacement signal by integral calculation or multi-sensor fusion.

[0015] S12, the end vibration signal is filtered to extract the vibration component reflecting the resonance characteristics of the robotic arm structure within the 10-40Hz resonant frequency band, and the vibration component is used as the control feedback input;

[0016] S2, constructing an equivalent dynamic state-space model, wherein S2 includes:

[0017] S21. Based on the equivalent mass, structural stiffness, complex damping loss factor, and brush head disturbance force of the flying robotic arm, a dynamic equation of the flying robotic arm containing complex damping terms is constructed to characterize the high-frequency damping loss characteristics of the flying robotic arm during contact cleaning or painting operations.

[0018] S22, using the equivalent energy dissipation criterion, the dynamic equation containing the complex damping term is transformed into a time-domain convergent equivalent viscous damped state-space model, which is as follows:

[0019] In the formula, X is a state vector containing end displacement and end velocity components, A is the system matrix, B is the control matrix, U is the damping force control vector, D is the disturbance matrix, and W is the brush head disturbance vector; wherein, the damping term in the system matrix A is composed of the equivalent viscous damping coefficient C. eq It is determined that the equivalent viscous damping coefficient satisfies:

[0020] In the formula, η is the complex damping loss factor, K is the structural stiffness, and ω is the vibration angular frequency;

[0021] S3, calculating the optimal inhibition force, wherein S3 includes:

[0022] S31, input the vibration components extracted in S12 into the Type-II fuzzy logic controller, and perform fuzzification processing on the end vibration deviation and deviation change rate through the interval type-II fuzzy set to generate the initial control weights;

[0023] S32, the MOOTLBO algorithm is introduced, with the optimization objectives of minimizing end-effector jitter amplitude and damper energy consumption. The initial control weights are adaptively optimized to calculate the optimal suppression force F for suppressing the end-effector vibration of the flight robotic arm. c ;

[0024] S4, closed-loop control of the actuator, wherein S4 includes:

[0025] S41, based on the optimal inhibition force F c The target driving current of the semi-active magnetorheological damper is calculated by using the mechanical mapping model of the semi-active magnetorheological damper.

[0026] S42, the target driving current is output to the semi-active magnetorheological damper set in the multi-stage vibration damping base to adjust the damping force of the semi-active magnetorheological damper, and in conjunction with the high viscoelastic buffer set at the end of the flight robotic arm, to achieve real-time cancellation of the brush head disturbance force.

[0027] S43, continuously collect vibration signals at the end of the flying robotic arm, and repeat steps S2 to S4 based on real-time feedback results until the vibration amplitude at the end of the flying robotic arm is within a preset allowable range.

[0028] The present invention also provides a multi-stage vibration reduction system for a flight robotic arm, which is used to implement the above-mentioned multi-stage vibration reduction method for a flight robotic arm, including a UAV body, a multi-stage vibration reduction base, a flight robotic arm, a sensor group, a semi-active magnetorheological damper, an end effector, and a controller.

[0029] The multi-stage vibration damping base is fixedly connected to the bottom of the UAV body. The multi-stage vibration damping base is configured as a quasi-zero stiffness vibration isolation mechanism. The quasi-zero stiffness vibration isolation mechanism includes a positive stiffness elastic component and a negative stiffness elastic component. The positive stiffness elastic component and the negative stiffness elastic component work together to make the equivalent combined stiffness of the multi-stage vibration damping base in the vertical working position approach zero, which is used to physically isolate the high-frequency vibration generated by the UAV body.

[0030] The flying robotic arm is connected to the multi-stage vibration damping base. The sensor group is located at the end and / or joints of the flying robotic arm to collect the end-effector vibration signal and brush head disturbance force signal in real time. The semi-active magnetorheological damper is located inside the multi-stage vibration damping base to receive the target driving current and adjust the damping force in real time. The end-effector buffer is located between the execution end of the flying robotic arm and the working brush head to absorb the nonlinear high-frequency impact generated during brush head operation. The controller is connected to the sensor group and the semi-active magnetorheological damper respectively, and executes the multi-stage vibration damping method of the flying robotic arm according to the end-effector vibration signal and brush head disturbance force signal collected by the sensor group, and outputs the target driving current to the semi-active magnetorheological damper to achieve closed-loop suppression of the end-effector vibration of the flying robotic arm.

[0031] Optionally, the multi-stage vibration damping base includes a base frame, a support seat, a first elastic element, a second elastic element, a horizontal guide rail, and a transmission component. The base frame is fixedly connected to the bottom of the UAV body, and the support seat is disposed within the base frame and connected to the flight robotic arm. The first elastic element is vertically disposed between the base frame and the support seat, and the first elastic element is configured as a positive stiffness elastic component to support the gravitational load of the UAV body and the flight robotic arm, and to provide basic support stiffness in the vertical direction.

[0032] Optionally, the horizontal guide rail is disposed within the base frame, and two second elastic elements are symmetrically disposed on both sides of the support seat and respectively disposed along the direction of the horizontal guide rail. The transmission component connects the support seat and the second elastic elements, and is used to convert the vertical displacement generated by the support seat relative to the base frame into the compressive displacement of the second elastic element along the direction of the horizontal guide rail. The second elastic element, the horizontal guide rail, and the transmission component together constitute a negative stiffness elastic component. When the flying robotic arm generates a vertical vibration displacement relative to the UAV body, the second elastic element is compressed in the horizontal direction and generates a negative stiffness effect to offset part of the positive stiffness provided by the first elastic element, so that the multi-stage vibration damping base exhibits quasi-zero stiffness characteristics in the vertical working position.

[0033] Optionally, the end effector is a cylindrical viscoelastic rubber component, which is disposed between the end effector of the flying robotic arm and the working brush head, and its compression direction is consistent with the working force direction of the working brush head. The cylindrical viscoelastic rubber component is made of one of nitrile rubber, silicone rubber, or polyurethane rubber.

[0034] Optionally, the shape factor S of the end buffer is configured to be 0.5-0.8, whereby the shape factor S is defined as the ratio of the pressure-bearing area to the free surface area of ​​the end buffer, and its calculation formula is as follows:

[0035] In the formula, D is the diameter of the end buffer and H is the thickness of the end buffer. The end buffer is used to generate nonlinear damping when the working brush head comes into contact with the work object, and works in conjunction with the multi-stage vibration damping base to absorb and attenuate the high-frequency impact energy during brush head operation.

[0036] Optionally, in step S22, the construction process of the equivalent viscous damping state-space model is as follows: First, based on the structural stiffness K and complex damping loss factor η of the flying robotic arm, its complex damping characteristics in the frequency domain are determined; second, using the equivalent energy dissipation criterion, the complex damping characteristics are converted into the equivalent viscous damping coefficient C in the time domain. eq C eq It is related to the complex damping loss factor η, structural stiffness K, and vibration frequency ω; finally, the C eq The matrix parameters in the state-space equation are constructed by combining the equivalent mass M of the robotic arm and the structural stiffness K with a matrix.

[0037] Optionally, in step S31, the specific process of generating initial control weights using a Type-II fuzzy logic controller is as follows: First, determine the input variables of the controller, which include the vibration displacement deviation and deviation change rate of the robotic arm end extracted in step S12; second, through a fuzzification process, map the input variables to an interval Type-II fuzzy set, which is characterized by a footprint uncertainty region composed of upper and lower membership functions, used to cover the signal disturbance uncertainty caused by contact nonlinearity during brush head operation; finally, perform rule matching through a fuzzy inference engine, and use a type reduction method to transform the Type-II fuzzy output into deterministic initial control weights, which are output to step S32 to guide the search direction of the multi-objective optimization algorithm.

[0038] Optionally, in step S32, the optimal inhibition force F is calculated using the MOOTLBO algorithm. c The specific process is as follows: First, a multi-objective optimization function J is constructed. The multi-objective optimization function takes minimizing both the vibration suppression objective J1 and the energy consumption optimization objective J2 as its optimization objectives, and is expressed as: minJ={J1,J2}

[0039] The vibration suppression target J1 is used to evaluate the jitter suppression effect of the end effector of the flight robotic arm in the 10-40Hz resonant frequency band, and its expression is:

[0040] In the formula, a i Let N be the acceleration value at the end of the flying robotic arm at the i-th sampling time, and N be the number of sampling points.

[0041] The energy consumption optimization target J2 is used to evaluate the control power consumption of the semi-active magnetorheological damper, and its expression is:

[0042] In the formula, I(t) is the driving current of the semi-active magnetorheological damper at time t, R is the equivalent resistance of the driving circuit, and T is the control time.

[0043] Secondly, the initial control weights generated by the Type-II fuzzy logic controller are used as the initial population distribution guide, and the MOOTLBO algorithm is used to iteratively optimize the control weights, damping force output parameters, and driving current parameters. The MOOTLBO algorithm includes a teacher phase, a learner phase, and an observer phase: in the teacher phase, the population is guided to update towards a better solution region based on the current best individual; in the learner phase, candidate solutions are updated through information interaction between individuals; in the observer phase, candidate solutions are evaluated according to the multi-objective optimization function J, and non-dominated solutions are retained.

[0044] Finally, a Pareto optimal solution set is formed based on the comprehensive evaluation results of the vibration suppression target J1 and the energy consumption optimization target J2. Then, based on the current contact cleaning or painting operation conditions of the flying robotic arm, the solution that satisfies the real-time vibration suppression requirement is selected from the Pareto optimal solution set as the optimal suppression force F. c The output is then sent to step S4.

[0045] Optionally, in step S4, the optimal inhibition force F is... c The specific process of mapping to damper target parameters is as follows: First, a mechanical characteristic model of the semi-active magnetorheological damper is established. This model characterizes the nonlinear mapping relationship between the damping force output and the yield stress, viscosity, and structural parameters of the magnetorheological fluid; second, the optimal damping force F is... c The input is fed into the inverse mapping operator of the mechanical property model to calculate the target damping coefficient and the target physical parameter values ​​related to the yield stress of the semi-active magnetorheological damper. Finally, the target physical parameter values ​​are linearized and corrected using a preset parameter compensation algorithm to eliminate the hysteresis effect of the damper during commutation, ensuring that the output parameters accurately correspond to the optimal suppression force F. c .

[0046] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the above-described multi-stage vibration reduction method for a flying robotic arm are implemented.

[0047] Compared with the prior art, the present invention has the following advantages and positive effects:

[0048] (1) By setting up a multi-level vibration damping base between the UAV body and the flying robotic arm and adopting a quasi-zero stiffness vibration isolation mechanism, the equivalent combined stiffness of the system near the vertical working position approaches zero, which can reduce the transmission of UAV body vibration to the end of the robotic arm and improve the stability of the flying robotic arm in the contact operation process.

[0049] (2) By setting an end buffer based on shape factor design, the present invention enables the end of the flying robotic arm to generate nonlinear damping when it comes into contact with the work object, absorbs the high-frequency impact energy generated during the brush head operation, and reduces the problems of brush head jumping, unstable contact and uneven work surface treatment.

[0050] (3) This invention introduces complex damping characteristics and converts them into an equivalent viscous damping state-space model that converges in the time domain, so that the high-frequency structural damping characteristics can be used for real-time control calculation of the flying robotic arm, thus improving the problem that traditional models are difficult to accurately characterize high-frequency damping losses.

[0051] (4) This invention uses a Type-II fuzzy logic controller to process the uncertainty in the end vibration signal and combines the MOOTLBO algorithm to comprehensively optimize the vibration suppression effect and damper energy consumption. It can adaptively adjust the output parameters of the semi-active magnetorheological damper according to different working conditions.

[0052] (5) The present invention forms a closed-loop feedback control through a sensor group, a controller and a semi-active magnetorheological damper, which enables the vibration of the end of the flying robotic arm in the 10-40Hz resonant frequency band to be suppressed in real time. This is beneficial to improve the accuracy of contact cleaning or painting operations and reduce the risk of fatigue damage to the robotic arm joints, connecting structures and the UAV body caused by vibration transmission. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly described below. It should be understood that the following drawings are only used for illustrative purposes to illustrate the technical solutions of the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0054] Figure 1 This is a schematic diagram of the overall structure of the multi-stage vibration reduction system for the flight robotic arm provided in an embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram of a multi-stage vibration damping base structure provided in an embodiment of the present invention, used to illustrate the connection relationship between the positive stiffness elastic component, the negative stiffness elastic component, the horizontal guide rail, the bearing seat, and the transmission component in the quasi-zero stiffness vibration isolation mechanism;

[0056] Figure 3 This is a schematic diagram of the shape factor structure of the end buffer provided in an embodiment of the present invention, used to illustrate the correspondence between the diameter, thickness, pressure area and free surface area of ​​the cylindrical viscoelastic rubber part;

[0057] Figure 4 The force-displacement characteristic curve of the quasi-zero stiffness vibration isolation mechanism provided in the embodiments of the present invention is used to show the relationship between the positive stiffness elastic component, the negative stiffness elastic component, and the total restoring force after their combination as a function of displacement.

[0058] Figure 5 The flowchart of the multi-stage vibration reduction method for a flight robotic arm provided in the embodiments of the present invention is used to illustrate the steps of multi-source signal acquisition and preprocessing, construction of equivalent dynamic state space model, calculation of optimal damping force, and closed-loop control of actuator.

[0059] Figure 6 The schematic diagram of the vibration suppression control system for the flight robotic arm provided in the embodiment of the present invention is used to illustrate the signal transmission relationship between the sensor group, controller, semi-active magnetorheological damper, flight robotic arm and feedback loop;

[0060] Figure 7 The time-domain simulation comparison diagram of vibration suppression effect provided in the embodiments of the present invention is used to show the comparison relationship between the vibration response without the method of the present invention and the vibration response after the method of the present invention is adopted.

[0061] Explanation of reference numerals in the attached drawings: 1. UAV body; 2. Multi-stage vibration damping base; 3. Flying robotic arm; 4. Working brush head; 5. Base frame; 6. Bearing seat; 7. First elastic element; 8. Second elastic element; 9. Horizontal guide rail; 10. Transmission component; 11. Semi-active magnetorheological damper; 12. Sensor group; 13. Controller; 14. End buffer; 15. Surface of the work object. Detailed Implementation

[0062] To further understand the technical solution of the present invention, the following is combined with... Figures 1 to 7 Specific embodiments of the present invention will be described below. It should be understood that the following embodiments are only for illustrating the present invention and are not intended to limit the scope of protection of the present invention; any equivalent substitutions or conventional improvements made by those skilled in the art to the structural form, installation position, control parameters and signal processing methods without departing from the technical concept of the present invention shall fall within the scope of protection of the present invention.

[0063] This embodiment provides a multi-stage vibration reduction system and vibration suppression control method for a flying robotic arm, used to suppress vibration when the flying robotic arm performs contact cleaning, brushing, or wiping operations on the bottom of bridge beams, walls, or other high-altitude structural surfaces. The system forms a multi-stage collaborative vibration reduction structure through an end effector, a semi-active magnetorheological damper, and a quasi-zero stiffness vibration isolation mechanism, and forms a closed-loop feedback control through a sensor group and a controller to reduce high-frequency vibrations generated during the contact between the working brush head and the work object.

[0064] like Figure 1 As shown, the multi-stage vibration reduction system of the flying robotic arm in this embodiment includes a drone body 1, a multi-stage vibration reduction base 2, a flying robotic arm 3, a working brush head 4, a semi-active magnetorheological damper 11, a sensor group 12, a controller 13, an end effector buffer 14, and a work surface 15. The multi-stage vibration reduction base 2 is fixedly connected to the bottom of the drone body 1, the flying robotic arm 3 is connected below the multi-stage vibration reduction base 2, and the working brush head 4 is located at the end effector of the flying robotic arm 3 for contacting the work surface 15 and performing cleaning or painting operations.

[0065] In this embodiment, the multi-stage vibration reduction system can be understood as a three-stage vibration reduction structure that functions sequentially along the vibration transmission path: the first stage is an end buffer 14 set between the execution end of the flying robotic arm 3 and the working brush head 4, used to absorb the local impact generated when the working brush head 4 comes into contact with the surface 15 of the work object; the second stage is a semi-active magnetorheological damper 11 integrated inside the multi-stage vibration reduction base 2, used to adjust the damping force in real time according to the target driving current output by the controller 13; the third stage is a quasi-zero stiffness vibration isolation mechanism also integrated inside the multi-stage vibration reduction base 2, used to isolate the high-frequency vibration transmission between the UAV body 1 and the flying robotic arm 3.

[0066] It should be noted that the "multi-level vibration reduction" in this embodiment is not limited to multiple vibration reduction components arranged in strict series along the same axis. Rather, it refers to setting vibration reduction units with different functions at the end contact position, the connection position between the robotic arm and the drone, and inside the integrated multi-level vibration reduction base 2, so that they can respectively complete the impact absorption, adjustable damping energy dissipation, and quasi-zero stiffness vibration isolation, thereby achieving a synergistic vibration reduction effect.

[0067] like Figure 2 As shown, the multi-stage vibration damping base 2 includes a base frame 5, a support seat 6, a first elastic element 7, a second elastic element 8, a horizontal guide rail 9, a transmission component 10, and a semi-active magnetorheological damper 11. The base frame 5 serves as the upper fixed end, which is fixedly connected to the bottom of the UAV body 1; the support seat 6 serves as the lower support end, which is disposed within or below the base frame 5 and connected to the flight robotic arm 3, for supporting the flight robotic arm 3 and its end effector.

[0068] The first elastic element 7 is vertically disposed between the base frame 5 and the bearing seat 6. The first elastic element 7 is configured as a positive stiffness elastic component to support the vertical load between the UAV body 1 and the flight robotic arm 3, and to provide basic support stiffness in the vertical direction. In one embodiment, the first elastic element 7 may be a helical compression spring, a rubber elastic column, or other elastic support member capable of providing vertical positive stiffness.

[0069] The second elastic element 8 is horizontally disposed within the base frame 5 and guided by the horizontal guide rail 9. Preferably, two second elastic elements 8 are provided and symmetrically arranged on both sides of the support seat 6. The transmission component 10 connects the support seat 6 and the second elastic element 8, and is used to convert the vertical displacement of the support seat 6 relative to the base frame 5 into the compressive displacement of the second elastic element 8 along the direction of the horizontal guide rail 9.

[0070] When the flying robotic arm 3 experiences vertical vibration displacement due to contact disturbance from the working brush head 4 or vibration of the UAV body 1, the support seat 6 subsequently experiences relative displacement. The transmission component 10 converts this vertical displacement into horizontal compression displacement of the second elastic element 8, causing the second elastic element 8, the horizontal guide rail 9, and the transmission component 10 to form a negative stiffness elastic assembly. This negative stiffness elastic assembly can offset part of the positive stiffness provided by the first elastic element 7, causing the multi-stage vibration damping base 2 to exhibit quasi-zero stiffness characteristics near the vertical working position.

[0071] The semi-active magnetorheological damper 11 is integrated inside the multi-stage vibration damping base 2, preferably located between the base frame 5 and the bearing seat 6, or located inside the base at a position that can dampen the relative vibration of the bearing seat 6. The semi-active magnetorheological damper 11 is signal-connected to the controller 13 to receive the target drive current output by the controller 13, and adjusts the damping force output in real time by changing the yield stress and viscosity of the magnetorheological fluid.

[0072] Through the above structural design, the multi-stage vibration damping base 2 is not merely a regular mechanical connector, but rather forms an integrated vibration damping base. This base integrates a quasi-zero stiffness vibration isolation mechanism and a semi-active magnetorheological damper 11. The quasi-zero stiffness vibration isolation mechanism reduces the equivalent transmitted stiffness between the UAV body 1 and the flight robotic arm 3, while the semi-active magnetorheological damper 11 provides adjustable damping force based on real-time vibration conditions, thereby achieving multi-stage coordinated vibration damping together with the end effector 14.

[0073] like Figure 3 As shown, the end effector 14 is disposed between the end effector of the flying robotic arm 3 and the working brush head 4. The end effector 14 is a cylindrical viscoelastic rubber component, and its compression direction is consistent with the working force direction of the working brush head 4. The end effector 14 can be made of one of nitrile rubber, silicone rubber, or polyurethane rubber.

[0074] The shape factor S of the end buffer 14 is defined as the ratio of the pressure area to the free surface area, and its calculation formula is as follows:

[0075]

[0076] Wherein, D is the diameter of the end buffer 14, and H is the thickness of the end buffer 14. Preferably, the shape factor S is configured to be 0.5-0.8.

[0077] When the working brush head 4 contacts the surface 15 of the work object, the end buffer 14 is compressed and generates a nonlinear damping effect. Under low-speed contact or small-amplitude disturbance conditions, the end buffer 14 can provide a flexible buffering effect, so that the working brush head 4 maintains relatively stable contact; under higher frequency or larger impact conditions, the end buffer 14 can improve local contact damping, absorb the high-frequency impact energy generated during the brush head operation, and reduce brush head jumping and contact instability.

[0078] like Figure 4 As shown, the force-displacement characteristics of the quasi-zero stiffness vibration isolation mechanism can be represented by the positive stiffness restoring force, the negative stiffness restoring force, and the total restoring force resulting from their combination. Specifically, the first elastic element 7 corresponds to the positive stiffness restoring force, while the negative stiffness elastic assembly formed by the second elastic element 8, the horizontal guide rail 9, and the transmission component 10 corresponds to the negative stiffness restoring force. After their combination, the system forms a quasi-zero stiffness region near the vertical working position, meaning that the total restoring force changes little within a small displacement range, thereby reducing the transmission of high-frequency, small-amplitude vibrations. When the displacement deviates from the working equilibrium position, the combined restoring force gradually increases to maintain the system's load-bearing capacity and reset capability.

[0079] The following is combined with Figure 5 The multi-stage vibration reduction method for the flight robotic arm provided in this embodiment is described below. The method includes the following steps:

[0080] S1, Multi-source signal acquisition and preprocessing.

[0081] When the flying robotic arm 3 performs contact cleaning or brushing operations, the sensor group 12 located at the end of the flying robotic arm 3 and / or at its joints collects end-effector vibration signals and brush head disturbance force signals in real time. The sensor group 12 includes at least an inertial measurement unit, which is used to collect end-effector acceleration signals and angular velocity signals, and obtain end-effector velocity signals and end-effector displacement signals through integral calculation or multi-sensor fusion.

[0082] The collected end-effector vibration signal is filtered, preferably using a bandpass filter to extract the vibration components within the 10-40Hz resonant frequency band that reflect the structural resonance characteristics of the flying robotic arm 3. These vibration components serve as feedback input to the subsequent controller 13, characterizing the real-time jitter state of the end-effector 3 during operation.

[0083] S2, construct an equivalent dynamic state-space model.

[0084] Based on the equivalent mass, structural stiffness, complex damping loss factor, and brush head disturbance force of the flying robotic arm 3, a dynamic equation containing complex damping terms is constructed to characterize the high-frequency damping loss characteristics of the flying robotic arm 3 during contact cleaning or painting operations.

[0085] To enable the complex damping model to be used for time-domain control calculations, this embodiment utilizes the equivalent energy dissipation criterion to transform the dynamic equations containing complex damping terms into a time-domain convergent equivalent viscous damped state-space model. The equivalent viscous damped state-space model is as follows:

[0086]

[0087] In the formula, X is the state vector containing the end displacement component and the end velocity component, A is the system matrix, B is the control matrix, U is the damping force control vector, D is the disturbance matrix, and W is the brush head disturbance vector.

[0088] The damping term in the system matrix A is determined by the equivalent viscous damping coefficient C. eq It is determined that the equivalent viscous damping coefficient satisfies:

[0089]

[0090] In the formula, η is the complex damping loss factor, K is the structural stiffness, and ω is the vibration angular frequency. Through this equivalent transformation method, the high-frequency damping characteristics of the structure can be introduced into the time-domain state-space control model, which facilitates the controller 13 to calculate the damping control quantity in real time.

[0091] S3, Solve for the optimal restraint force F c .

[0092] The controller 13 inputs the vibration components extracted in step S1 into the Type-II fuzzy logic controller, and performs fuzzification processing on the end vibration deviation and deviation change rate of the flying robotic arm 3 through the interval type-II fuzzy set to generate the initial control weights.

[0093] Based on this, the MOOTLBO algorithm is introduced, with the optimization objectives of minimizing end-effector jitter amplitude and damper energy consumption. The initial control weights are adaptively optimized to calculate the optimal suppression force F for suppressing the end-effector vibration of the flight robotic arm 3. c .

[0094] Specifically, a multi-objective optimization function J is constructed, wherein the multi-objective optimization function takes minimizing both the vibration suppression objective J1 and the energy consumption optimization objective J2 as its optimization objectives, and is expressed as follows:

[0095] minJ = {J1, J2}

[0096] The vibration suppression target J1 is used to evaluate the jitter suppression effect of the end effector of the flight robotic arm 3 in the 10-40Hz resonant frequency band, and its expression is:

[0097]

[0098] In the formula, a iLet N be the acceleration value at the end of the flying robotic arm 3 at the i-th sampling time, and N be the number of sampling points.

[0099] The energy consumption optimization target J2 is used to evaluate the control power consumption of the semi-active magnetorheological damper 11, and its expression is:

[0100]

[0101] In the formula, I(t) is the driving current of the semi-active magnetorheological damper 11 at time t, R is the equivalent resistance of the driving circuit, and T is the control time.

[0102] The MOOTLBO algorithm comprises a teacher phase, a learner phase, and an observer phase. In the teacher phase, the population is guided towards a better solution region based on the current best individual. In the learner phase, candidate solutions are updated through information exchange between individuals. In the observer phase, candidate solutions are evaluated according to the multi-objective optimization function J, and non-dominated solutions are retained. Finally, a Pareto optimal solution set is formed based on the comprehensive evaluation results of vibration suppression objective J1 and energy consumption optimization objective J2, and the solution satisfying the current operating conditions is selected from this Pareto optimal solution set as the optimal suppression force F. c .

[0103] S4, closed-loop control of the actuator.

[0104] The controller 13 obtains the optimal inhibition force F based on step S3. c Using the mechanical mapping model of the semi-active magnetorheological damper 11, the target driving current of the semi-active magnetorheological damper 11 is calculated inversely. The mechanical mapping model is used to characterize the nonlinear relationship between the damping force output and the yield stress, viscosity, and structural parameters of the magnetorheological fluid.

[0105] The target driving current is output to the semi-active magnetorheological damper 11, which adjusts the damping force output and works in conjunction with the end buffer 14 and the quasi-zero stiffness vibration isolation mechanism. The semi-active magnetorheological damper 11 changes the yield stress and damping characteristics of the magnetorheological fluid according to the target driving current, thereby producing a suppression effect that matches the end vibration of the flight robotic arm 3.

[0106] During the control process, sensor group 12 continuously collects the vibration signal at the end of the flying robotic arm 3 and feeds back the actual vibration response to controller 13. If the vibration amplitude at the end of the flying robotic arm 3 is not within the preset allowable range, the process returns to step S2 or step S3 to re-correct the model and solve for the optimal suppression force; if the vibration amplitude at the end of the flying robotic arm 3 is within the preset allowable range, the current control state is maintained or the current control cycle ends.

[0107] like Figure 6As shown, the control system principle in this embodiment is as follows: the desired vibration signal can be set to zero or a preset allowable vibration threshold. The actual vibration response is collected by the sensor group 12 and fed back to the controller 13. The controller 13 calculates the target damping parameters based on the error signal and outputs the target driving current to the semi-active magnetorheological damper 11 through the current driving unit. The semi-active magnetorheological damper 11 generates a suppressive force acting on the flying robotic arm 3 and the multi-stage vibration reduction base 2. The brush head disturbance force acts as an external disturbance input on the flying robotic arm 3, thereby forming a closed-loop vibration suppression process of "sensing-decision-execution-feedback".

[0108] like Figure 7 As shown, this embodiment also presents time-domain simulation comparison results of the vibration suppression effect. Under uncontrolled conditions, the vibration response at the end of the flying robotic arm 3 decays slowly and still maintains a relatively significant vibration amplitude within the control time. After adopting the multi-stage vibration reduction system and vibration suppression control method provided in this embodiment, the vibration amplitude at the end of the flying robotic arm 3 decays rapidly and approaches a stable state in a short period of time, indicating that the method can effectively reduce the high-frequency vibration response during contact operations.

[0109] In summary, this embodiment absorbs the local impact generated by the contact between the working brush head 4 and the surface 15 of the working object through the end buffer 14, achieves adjustable damping energy dissipation through the semi-active magnetorheological damper 11 integrated inside the multi-level vibration reduction base 2, reduces the equivalent transmission stiffness between the UAV body 1 and the flying robotic arm 3 through the quasi-zero stiffness vibration isolation mechanism, and achieves multi-level collaborative suppression of the end vibration of the flying robotic arm 3 by combining the closed-loop control based on the equivalent viscous damping state space model, the Type-II fuzzy logic controller and the MOOTLBO algorithm.

[0110] In other alternative embodiments, the first elastic element 7 can be selected from helical springs, rubber elastic elements or composite elastic elements with different stiffnesses according to the load size of the flying robotic arm 3; the second elastic element 8 can be selected from different preloads and installation angles according to the quasi-zero stiffness design requirements; the transmission component 10 can be a connecting rod, slider, wedge or a combination thereof; the semi-active magnetorheological damper 11 can be set as one or more according to the spatial dimensions of the multi-stage vibration damping base 2; the end buffer 14 can also be adjusted in diameter D, thickness H and shape factor S according to the size of the working brush head 4 and the material properties of the working object surface 15.

[0111] In other alternative embodiments, the sensor group 12 may include one or more combinations of an inertial measurement unit, an accelerometer, a displacement sensor, a force sensor, or an angular velocity sensor; the controller 13 may be an embedded controller, an onboard computing unit, or an external computing platform. The technical effects of this invention can be achieved as long as the acquisition of end-vibration signals, the construction of an equivalent dynamic state-space model, the solution of the optimal damping force, and the current control of the semi-active magnetorheological damper 11 can be realized.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, modifications or substitutions can be made to the structural form, control parameters, sensor configuration, damper arrangement, and algorithm implementation in the above embodiments without departing from the concept of the present invention; all equivalent changes should fall within the protection scope of the present invention.

Claims

1. A multi-stage vibration reduction method for a flying robotic arm, characterized in that, Includes the following steps: S1, multi-source signal acquisition and preprocessing, wherein S1 includes: S11, the end vibration signal and brush head disturbance force signal of the flying robotic arm are collected in real time by a sensor group set at the end of the flying robotic arm. The sensor group includes at least an inertial measurement unit. The inertial measurement unit is used to collect end acceleration signal and angular velocity signal, and obtain end velocity signal and end displacement signal by integral calculation or multi-sensor fusion. S12, the end vibration signal is filtered to extract the vibration component reflecting the resonance characteristics of the robotic arm structure within the 10-40Hz resonant frequency band, and the vibration component is used as the control feedback input; S2, constructing an equivalent dynamic state-space model, wherein S2 includes: S21. Based on the equivalent mass, structural stiffness, complex damping loss factor, and brush head disturbance force of the flying robotic arm, a dynamic equation of the flying robotic arm containing complex damping terms is constructed to characterize the high-frequency damping loss characteristics of the flying robotic arm during contact cleaning or painting operations. S22, using the equivalent energy dissipation criterion, the dynamic equation containing the complex damping term is transformed into a time-domain convergent equivalent viscous damped state-space model, which is as follows: In the formula, X is a state vector containing displacement and velocity components, A is the system matrix, B is the control matrix, U is the damping force control vector, D is the disturbance matrix, and W is the brush head disturbance vector; wherein the damping term in the system matrix A is determined by the equivalent viscous damping coefficient Ceq, and the equivalent viscous damping coefficient satisfies: S3, calculating the optimal inhibition force, wherein S3 includes: S31, input the vibration components extracted in S12 into the Type-II fuzzy logic controller, and perform fuzzification processing on the end vibration deviation and deviation change rate through the interval type-II fuzzy set to generate the initial control weights; S32, The MOOTLBO algorithm is introduced, with the optimization objectives of minimizing the end-effector jitter amplitude and minimizing the damper energy consumption. The initial control weights are adaptively optimized to calculate the optimal suppression force Fc for suppressing the end-effector vibration of the flight robotic arm. S4, closed-loop control of the actuator, wherein S4 includes: S41 calculates the target driving current of the semi-active magnetorheological damper by using the mechanical mapping model of the semi-active magnetorheological damper based on the optimal suppression force Fc. S42, the target driving current is output to the semi-active magnetorheological damper set in the multi-stage vibration damping base to adjust the damping force of the semi-active magnetorheological damper, and in conjunction with the high viscoelastic buffer set at the end of the flight robotic arm, to achieve real-time cancellation of the brush head disturbance force. S43, continuously collect vibration signals at the end of the flying robotic arm, and repeat steps S2 to S4 based on real-time feedback results until the vibration amplitude at the end of the flying robotic arm is within a preset allowable range.

2. A multi-stage vibration reduction system for a flight robotic arm, used to implement the multi-stage vibration reduction method for a flight robotic arm as described in claim 1, characterized in that, include: Unmanned aerial vehicle (UAV) airframe; A multi-stage vibration damping base is fixedly connected to the bottom of the UAV body. The multi-stage vibration damping base is configured as a quasi-zero stiffness vibration isolation mechanism. The quasi-zero stiffness vibration isolation mechanism includes a positive stiffness elastic component and a negative stiffness elastic component. The positive stiffness elastic component and the negative stiffness elastic component work together to make the equivalent combined stiffness of the multi-stage vibration damping base in the vertical working position approach zero, which is used to physically isolate the high-frequency vibration generated by the UAV body. A flying robotic arm is connected to the underside of the multi-stage vibration-damping base; A sensor array, located at the end and / or joints of the flying robotic arm, is used to collect the end vibration signal and brush head disturbance force signal of the flying robotic arm in real time. A semi-active magnetorheological damper is disposed inside the multi-stage vibration damping base to receive the target driving current and adjust the damping force in real time; An end effector is disposed between the end effector of the flying robotic arm and the working brush head. The end effector adopts a viscoelastic rubber structure based on shape factor design to absorb the nonlinear high-frequency impact generated during brush head operation. The controller is connected to the sensor group and the semi-active magnetorheological damper respectively. It is used to execute the multi-stage vibration reduction method of the flying manipulator based on the end vibration signal and brush head disturbance force signal collected by the sensor group, and output the target driving current to the semi-active magnetorheological damper to achieve closed-loop suppression of the end vibration of the flying manipulator.

3. The multi-stage vibration reduction system for a flying robotic arm according to claim 2, characterized in that, The multi-stage vibration damping base includes a base frame, a bearing seat, a first elastic element, a second elastic element, a horizontal guide rail, and a transmission component; The base frame is fixedly connected to the bottom of the UAV body, and the support seat is disposed in the base frame and connected to the flight robotic arm; The first elastic element is disposed vertically between the base frame and the bearing seat. The first elastic element is configured as a positive stiffness elastic component to support the gravity load of the UAV body and the flight robotic arm, and to provide basic support stiffness in the vertical direction. The horizontal guide rail is disposed within the base frame. There are two second elastic elements, which are symmetrically disposed on both sides of the bearing seat and respectively disposed along the direction of the horizontal guide rail. The transmission component is connected between the bearing seat and the second elastic element, and is used to convert the vertical displacement of the bearing seat relative to the base frame into the compressive displacement of the second elastic element along the direction of the horizontal guide rail; The second elastic element, together with the horizontal guide rail and the transmission component, constitutes a negative stiffness elastic component. When the flying robotic arm vibrates and displaces vertically relative to the UAV body, the second elastic element is compressed in the horizontal direction and generates a negative stiffness effect to offset part of the positive stiffness provided by the first elastic element, so that the multi-stage vibration damping base exhibits quasi-zero stiffness characteristics in the vertical working position.

4. The multi-stage vibration reduction system for a flying robotic arm according to claim 2, characterized in that, The end effector is a cylindrical viscoelastic rubber component, disposed between the end effector of the flying robotic arm and the working brush head, with its compression direction aligned with the working force direction of the working brush head. The cylindrical viscoelastic rubber component is made of one of nitrile rubber, silicone rubber, or polyurethane rubber. The shape factor S of the end effector is configured to be 0.5-0.8, defined as the ratio of the compression area to the free surface area of ​​the end effector, and its calculation formula is as follows: In the formula, D is the diameter of the end buffer and H is the thickness of the end buffer; the end buffer is used to generate nonlinear damping when the working brush head comes into contact with the working object, and works in conjunction with the multi-stage vibration damping base to absorb and attenuate the high-frequency impact energy during the brush head operation.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-stage vibration reduction method for the flying robotic arm as described in claim 1.

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