Lower limb exoskeleton robot time-independent control method based on vector field generator

Abstract

The invention discloses a lower limb exoskeleton robot time-independent control method based on a vector field generator, and relates to the technical field of lower limb exoskeleton robot control, in particular to a lower limb exoskeleton robot time-independent control method based on the vector field generator, and the method comprises the steps: mapping a target periodic motion into a limit cycle in a phase space; constructing a vector field generator, and driving a system state error to be converged to zero by a stable item and a periodic adjustment item; designing a control law based on the exoskeleton kinetic model, wherein the control law comprises estimated values of an inertia matrix, a Coriolis force matrix, a gravity item and a friction item; a control law is applied to converge and stabilize the exoskeleton joint state in a time-independent manner at a target limit cycle. The method can adapt to gait rhythm changes and active intentions of a user, global asymptotic stability is ensured through a vector field, and a self-intersection track in a joint space is effectively processed.

Description

Technical Field

[0001] This invention relates to the field of lower limb exoskeleton robot control technology, specifically a time-independent control method for lower limb exoskeleton robots based on a vector field generator. Background Technology

[0002] Lower limb exoskeleton robots, as an important human-machine collaborative assistive device, have broad application prospects in rehabilitation medicine, motor assistance, and weight-bearing handling. The performance of their control system directly affects the user experience and equipment efficiency. Currently, the control methods for lower limb exoskeleton robots mainly adopt traditional technical solutions such as time-indexed trajectory control (e.g., PID control), velocity field control, or limit loop control.

[0003] Time-dependent control methods achieve motion control through preset time-parameterized reference trajectories; however, this approach has significant limitations. When the user's gait rhythm changes or active intervention occurs, the time-parameterized control strategy cannot flexibly adapt to these changes, leading to antagonistic torques between the user and the machine, severely impacting the naturalness and safety of the interaction. Furthermore, external disturbances can disrupt the system's time synchronization, making the recovery process less smooth and natural.

[0004] While velocity field control methods reduce time dependence to some extent, they have inherent limitations when handling complex trajectories. When lower limb movements exhibit self-intersection in phase space projection, the velocity field method faces directional ambiguity, failing to clearly define the motion direction and leading to decreased control performance. This limitation severely restricts its application in complex gait patterns.

[0005] Limit cycle control methods can describe periodic motion, but existing technologies lack systematic design methods and struggle to accurately match natural gait patterns. Traditional limit cycle control typically relies on empirical adjustments and lacks rigorous theoretical guarantees, resulting in ineffective guarantees of convergence and stability in practical applications. Particularly in the presence of model uncertainties and external disturbances, the system's robustness is poor.

[0006] In summary, existing lower limb exoskeleton control methods are inadequate in terms of time dependence, ability to handle complex trajectories, and theoretical guarantees. A novel control method is needed that can adapt to gait changes, handle complex motion trajectories, and has strict stability guarantees. Summary of the Invention

[0007] The purpose of this invention is to provide a time-independent control method for a lower limb exoskeleton robot based on a vector field generator. By constructing a target limit cycle and a vector field generator, a model-based control law is designed to achieve global asymptotic convergence of the system state from any initial point to the target periodic motion. This method can adapt to changes in gait rhythm and user intentions, while ensuring autonomous resynchronization to the target trajectory under external disturbances.

[0008] To achieve the above objectives, the present invention provides a time-independent control method for a lower limb exoskeleton robot based on a vector field generator, comprising the following steps: Step 1: Constructing the Target Limit Cycle: This step maps the target's periodic motion (such as gait) into a closed trajectory in phase space, i.e., a limit cycle. The limit cycle is described by the phase parameter θ. ,in and These represent functions of joint position and velocity as a function of phase, respectively. The target state is defined as the point closest to the limit cycle from the system state (q, v). and introduce functions This is used to associate states with limit cycles, thereby establishing a time-independent reference frame.

[0009] Step 2: Vector Field Construction: This step involves designing a vector field generator to generate a vector field. This vector field consists of a stability term and a periodic adjustment term. Composition, that is The stabilizing term ensures the asymptotic stability of the system state error, while the periodic adjustment term adapts to the dynamic changes of periodic motion, together driving the system state error to converge to zero.

[0010] Step 3: Control Law Design: Based on the dynamic model of the lower limb exoskeleton robot, design the control law. Where (q, v) is the state vector of the exoskeleton system, representing joint position and velocity, respectively; , , , These are the estimated values ​​of the inertia matrix, Coriolis force matrix, gravity term, and friction term in the dynamic model, respectively. These estimates are obtained through model parameter identification or real-time observation. Control Law It is the output of the vector field generation, used to compensate for system dynamics.

[0011] Step 4: Control Implementation: Apply the above control law to the actuators (such as motors or hydraulic actuators) of the lower limb exoskeleton robot, causing the robot's joint states (q, v) to converge and stabilize in a time-independent manner to the target limit cycle. This means that the convergence process does not depend on time parameters, but is directly determined by the relative position of the system state and the limit cycle, enhancing robustness and adaptability.

[0012] Furthermore, in step one, the target state By calculating the phase parameters Confirmed. Specifically, The following relationship must be satisfied:

[0013] The min operation represents finding the point within the phase interval [0,T] that makes the system state (q,v) equal to the limit cycle. , The Euclidean distance between them is the smallest value, Scaling factor This represents the constraints. The target state is then obtained. Based on this, a time-independent error vector is constructed. This error is used in subsequent vector field design to ensure that control is independent of time reference.

[0014] Furthermore, the vector field generator constructed in step two Its function is to make the time-independent system state error asymptotically converge to zero. Vector field By combining the stabilizing term and the periodic adjustment term, a smooth vector flow is formed, which guides the system state to move along the direction of the limit loop tangent while correcting the radial deviation, thereby ensuring that the error converges exponentially in the phase space.

[0015] Furthermore, the control law in step three is a model-based control law, whose design is directly based on the dynamic equations of the exoskeleton system. The dynamic equations are typically expressed as follows: ,in , , , The parameters are obtained through offline parameter identification or online adaptive methods to ensure that the control law can accurately compensate for the nonlinear dynamics of the system.

[0016] Furthermore, the stability of this method is proven using the LaSalle invariant set theorem. Specifically, a Lyapunov function is constructed whose derivative is negative definite or semi-negative definite along the system trajectory. By analyzing the invariant sets of the system on the limit cycle, it is proven that starting from any initial state, the system state will globally and asymptotically converge to the target limit cycle, ensuring the global stability and reliability of the control method.

[0017] Furthermore, the target periodic motion is specifically gait motion (such as walking or running). Due to the time-independent nature of this method, it can adapt to changes in gait rhythm (such as increasing or decreasing speed) and user-initiated intentions (such as user-initiated changes in stride length or direction). The vector field automatically adjusts the target point based on the real-time system state, enabling the exoskeleton robot to move naturally in sync with the user.

[0018] Furthermore, the vector field is constructed in a complete phase space (i.e., position-velocity space). This construction method can explicitly define and stably track self-intersecting periodic trajectories (such as complex gait patterns) that appear in joint space or a two-dimensional phase plane projection. By using a high-dimensional phase space representation, the ambiguity in two-dimensional projection is avoided, ensuring the accuracy and smoothness of trajectory tracking.

[0019] Furthermore, when the system state deviates from the target limit cycle due to external disturbances (such as ground impacts or load changes) or user-initiated forces (such as users pushing or pulling the exoskeleton), the control law can drive the system state to autonomously resynchronize and converge back to the target limit cycle. The resynchronization process is achieved through the periodic adjustment term of the vector field, which calculates the optimal phase match between the state and the limit cycle in real time, ensuring rapid recovery without the need for external time signals.

[0020] Furthermore, the system is equipped with a controller (such as a microprocessor or embedded system) programmed to execute the time-independent control method as described in any one of claims 1 to 8. The system includes sensors (such as encoders or inertial measurement units) for real-time acquisition of joint states (q, v), actuators (such as motors) for applying control laws τ, and communication interfaces for connecting the components. The controller achieves autonomous motion control of the lower limb exoskeleton robot by calculating the vector field and control law in real time.

[0021] Furthermore, when executed by a processor, the program implements the steps of the method as described in any one of claims 1 to 8. The program includes code modules for constructing the target limit cycle, generating vector fields, calculating control laws, and outputting control results. The storage medium can be integrated into the exoskeleton robot's local controller or a remote server to ensure the method's deployability and repeatability.

[0022] This invention provides a time-independent control method for a lower limb exoskeleton robot based on a vector field generator, which has the following advantages: 1. Achieve true time-independent control, improving system adaptability and robustness. This invention maps the periodic motion of the target as a limit cycle in phase space and designs a control law based on a vector field generator, making the control process completely independent of time parameters. This means that the motion control of the lower limb exoskeleton robot is no longer constrained by a fixed time trajectory and can autonomously adapt to real-time changes in gait rhythm, such as sudden increases or decreases in the user's walking speed. When external disturbances (such as uneven ground or load changes) or user intervention (such as pausing or changing direction) occur, the system can automatically resynchronize to the target motion trajectory without replanning the time parameters. This time-independent characteristic significantly enhances the system's flexibility and robustness, avoids the rigid resistance problem common in traditional time-indexed control, and allows the exoskeleton to more naturally integrate with the user's movement intentions.

[0023] 2. Effectively handles complex trajectories and overcomes directional ambiguity in self-intersection cases.

[0024] Constructing a vector field in phase space allows this invention to explicitly handle self-intersecting periodic trajectories (such as complex swaying movements in gait) in joint space or two-dimensional planar projections. Traditional velocity field control is prone to directional ambiguity at trajectory self-intersection points, leading to chaotic control commands. However, this invention, through the definition of a high-dimensional phase space vector field, ensures the uniqueness and stability of trajectory tracking. The vector field generator can clearly guide the system state along the target limit cycle, and even if the trajectory intersects in a low-dimensional projection, it will not affect the control accuracy. This overcomes the limitations of existing methods in complex motion modes and provides a technical foundation for achieving a more natural and biomechanically sound gait in lower limb exoskeletons.

[0025] 3. It possesses theoretically guaranteed global stability, ensuring reliable system convergence.

[0026] Based on LaSalle's invariant set theorem, this invention provides a rigorous mathematical analysis of the closed-loop control system, proving that the system state converges globally asymptotically to the target limiting cycle from any initial point. This theoretical guarantee means that the control method is not only effective in practical applications but also remains robust in the presence of bounded model errors and external disturbances. Stability analysis ensures that the exoskeleton robot operates safely and reliably under various operating conditions (such as user weight differences or environmental changes), avoiding the risk of runaway or divergence. This theoretical rigor enhances the credibility of the control method, providing solid support for clinical applications and industrialization.

[0027] 4. Improve human-computer interaction compliance, and enhance user safety and comfort.

[0028] The control law design fully considers the influence of the user's active intentions. When the user applies force to change movement, the system treats the state change as a disturbance and smoothly adjusts the output torque, rather than generating an antagonistic response. This allows the exoskeleton to achieve natural pauses, resumptions, or speed changes in movement, significantly reducing the mechanical feel in human-computer interaction. Traditional time-dependent control often forces users to follow a fixed sequence, which can easily cause discomfort or danger. This invention achieves more compliant collaboration through a time-independent approach, improving wearability and overall safety, making it particularly suitable for elderly or rehabilitation users.

[0029] 5. Model-based control law design to achieve high-precision and adaptive tracking.

[0030] The control law directly integrates estimated parameters from the exoskeleton dynamics model (such as the inertia matrix, Coriolis force matrix, gravity term, and friction term), and outputs compensating torque through a vector field generator, enabling the system state to accurately track the target limit cycle. This model-based approach fully utilizes the characteristics of the physical system and reduces the hysteresis problem associated with pure feedback control. Simultaneously, online or offline estimation of model parameters allows the system to adapt to changes in different users or exoskeleton configurations, ensuring consistent control accuracy. Experimental verification shows that this method can be effectively deployed to a real prototype, achieving the expected motion performance and laying the foundation for industrial applications. Attached Figure Description

[0031] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating the construction process of the target limit cycle of the present invention. Figure 2 This is a flowchart of the vector field construction process of the present invention; Figure 3 This is a flowchart illustrating the design of the control law for this invention. Figure 4 This is a flowchart illustrating the control application and convergence process of this invention; Figure 5 This is a flowchart illustrating the stability verification process of the present invention. Detailed Implementation

[0033] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0035] Example 1: Claim 1 is the core and outline of the entire patented method, describing the complete technical process of a time-independent control method for a lower limb exoskeleton robot based on a vector field generator. Its core feature lies in "time independence," which is a key innovation that distinguishes it from traditional time trajectory tracking control methods.

[0036] This method comprises four distinct steps. The first step is "target limit cycle construction." Essentially, this step transforms the desired periodic movements of the lower limb exoskeleton (such as walking gait) from a time-domain trajectory into a closed geometric figure in phase space (typically referring to the state space composed of joint angles q and joint angular velocities v), i.e., a limit cycle Γ. This limit cycle is uniquely described by the phase parameter θ, and the actual state (q, v) of the system at any given time is mapped to the nearest point on this limit cycle, which is defined as the target state. This step completes the transformation from a time-dependent reference signal to a spatial geometry, laying the foundation for subsequent time-independent control.

[0037] The second step is "vector field construction." This is the core of the method's control strategy. A vector field generator is designed. The mathematical object, the generator, consists of two parts: a "stabilizing term" that drives the system state toward the limit cycle, and a "periodic adjustment term" that guides the state to move periodically along the limit cycle after it approaches the limit cycle. By combining these two elements, the vector field generator can generate a specific vector field. This vector field defines a direction and magnitude at each point in the phase space, and its fundamental function is to enable the error between the actual state and the target state of the system to converge asymptotically to zero.

[0038] The third step is "control law design." Based on the nonlinear dynamics model of the exoskeleton robot, a specific control torque τ is designed. This control law is a typical model-based control scheme, and its structure includes estimates of various parts of the system dynamics, such as the inertia matrix, Coriolis force matrix, gravity term, and friction term. The key to the design is to derive the required control torque from the desired dynamics (i.e., the error convergence law) defined in the vector field in the second step through the dynamics model. This allows the controller's output to directly serve the convergence path for achieving vector field planning.

[0039] The fourth step is "applying the control law". The designed control law is then applied to the drive system of the lower limb exoskeleton robot, ultimately enabling the robot's joint states to autonomously and stably converge and maintain on the ideal periodic motion defined by the target limit cycle in a way that does not explicitly depend on time references.

[0040] In summary, claim 1 constructs a complete technical solution framework from target definition (limit cycle), control strategy (vector field) to specific implementation (control law). Its core innovation lies in getting rid of the dependence on precise time synchronization through a geometric method.

[0041] Example 2: Claim 2 further refines and defines the characteristics of "step one" in claim 1, specifically explaining how to determine the target state from the current state of the system. The mathematical method is the key operation for achieving "time-independent" control.

[0042] The core of this claim lies in defining the phase parameter. The calculation method is as follows. Its formula describes the process of finding a phase value within a given parameter space. This makes the current system state (q, v) correspond to the point on the limit cycle. , The Euclidean distance (i.e., the square of the L2 norm) between them is minimized. This minimizes the distance. The value, that is, the target phase corresponding to the current state. In more intuitive terms, this means finding the closest point on the closed limit cycle trajectory to the robot's current position-velocity state. Once this closest point is found, its coordinates are naturally defined as the current target state. .

[0043] The importance of this mechanism lies in its complete abandonment of traditional time indexing. In traditional methods, the reference trajectory is a function of time, and the target state is predetermined at time t. However, in this patented method, the target state is dynamically changing, entirely determined by the system's current actual state. Regardless of the system's current position in phase space, the controller always attempts to pull it towards the point on the limit cycle with the closest geometric distance. This mechanism provides tremendous flexibility. For example, when the exoskeleton is subjected to external disturbances or the user actively changes their movement rhythm, the system state will deviate from the original limit cycle. At this time, the target point... It will immediately recalculate based on the new state, and the controller will adjust accordingly to move toward the new target point, thereby achieving autonomous "resynchronization" without resetting or adjusting any time counters.

[0044] Based on this target state, claim 2 further defines a time-independent error signal. This error signal will serve as the input for the subsequent design of the vector field controller. The goal of the entire control system is to drive this error, defined by geometric relationships, towards zero. Therefore, the target state determination mechanism detailed in claim 2 is the logical starting point and cornerstone for the formation and effective operation of the entire time-independent control loop.

[0045] Example 3: Claim 5 provides a theoretical guarantee for the stability of the control method, explicitly stating that its stability is proven using the LaSalle invariant set theorem, ensuring that the system state converges asymptotically globally from any initial point to the target limit cycle. This claim provides crucial support for the reliability and robustness of the method.

[0046] In control theory, after designing a control law for a nonlinear system, the stability of its closed-loop system must be rigorously analyzed; otherwise, the method will face significant risks in practical applications. LaSalle's invariant set theorem is a powerful tool for analyzing the stability of nonlinear systems, especially applicable to cases like this one, where the system asymptotically converges to a set (not just a point). The target limit cycle is precisely a closed set of trajectories. Proving this theorem typically involves several steps: first, a suitable Lyapunov function V needs to be constructed, which is a scalar function that usually characterizes some measure of the system's total energy or state error. By analyzing the rate of change of this function along the system trajectory over time, the stability of the system can be determined.

[0047] The core of this patented method lies in its designed vector field generator. The control law τ ensures that the rate of change of the constructed Lyapunov function V is always less than or equal to zero. This satisfies one of the conditions of LaSalle's theorem, indicating that the system's trajectory is bounded and tends towards an "invariant set." In this invariant set, the rate of change of the Lyapunov function is zero. Further analysis shows that the system state in this invariant set must satisfy the condition that it lies precisely on the target limit cycle. This means that, starting from any initial state in phase space, the system's trajectory will eventually be attracted and stabilized on the target limit cycle, without diverging or stabilizing at other points outside the cycle.

[0048] The conclusion of "global asymptotic convergence" is crucial. It means that the control method does not depend on a precise initial state and is highly inclusive of initial conditions. Regardless of the wearer's initial posture, the control system can guide them to the desired gait. This provides a solid theoretical basis for the practical application of the method, demonstrating that the scheme is not an empirical or only locally effective control strategy, but a complete control system with global stability that has been rigorously mathematically proven.

[0049] Example 4: Claim 6 clarifies the specific application scenario of the control method and the nature of the target periodic motion, specifying that the target periodic motion is a gait motion, and emphasizes that the method can adapt to changes in gait rhythm and the user's actively applied intentions. This reveals the close connection between the patented method and practical application needs.

[0050] Limiting the target motion to "gait motion" directly addresses the core application of lower limb exoskeleton robots—assisted walking. Gait is a typical periodic movement, but its periodicity is not static. Users naturally change their rhythm (e.g., walking fast, walking slowly) or actively apply movement intentions based on the environment (e.g., going upstairs, going downstairs). Traditional time-tracking controllers encounter difficulties in this scenario because they preset a fixed time rhythm. When the actual rhythm deviates from the preset rhythm, it can generate huge tracking errors and even lead to system instability.

[0051] The "time-independent" characteristic of this patent naturally solves this problem. Since the control target is a geometric shape (limit cycle) in phase space, rather than a function of time, the system is unconcerned about the specific time required to complete a gait cycle. The method's adaptability lies in the fact that when the user intends to increase their stride frequency, their joint angular velocity v will naturally increase, and in phase space, the state point will move to the vicinity of the region with a higher velocity value on the limit cycle. At this point, according to the mechanism of claim 2, the target point... The system will immediately update to this new region, and the controller will then drive the system to move along the limit loop, but the speed of movement is dominated by the user's intention. The entire process is smooth and autonomous, requiring no external commands to change the time scale of the reference trajectory. Similarly, for forces actively applied by the user (such as intentions to turn or change stride), these intentions will affect the selection of the target point by changing the system state (q, v), thereby achieving natural human-machine interaction and collaboration.

[0052] Therefore, claim 6 not only defines the scope of application, but also highlights the significant advantages of this control method over traditional methods in dealing with the uncertainty of human-computer interaction and responding to user intentions, making it particularly suitable for rehabilitation training or assisted walking scenarios that require a high degree of coordination with the user.

[0053] Example 5: Claim 8 emphasizes the robustness of the control method under non-ideal operating conditions, specifically describing the response capability of the control law when the system state deviates from the target limit cycle due to external disturbances or user-initiated actions. This characteristic is a key indicator for evaluating whether a control system can be put into practical application.

[0054] In practical use, lower limb exoskeleton robots inevitably experience various external disturbances, such as impacts from uneven ground or unexpected stumbles by the user. Simultaneously, the user's intentional movement is itself a planned "disturbance." These factors can all cause the system state to deviate from the preset ideal limit cycle. A fragile control system may be unable to recover after this deviation, or even become unstable. The claim in this patent that it "can autonomously resynchronize the system state and converge back to the target limit cycle" demonstrates its robustness.

[0055] This robustness is rooted in its entire control architecture. First, as described in claim 2, the target state It is calculated in real time and dynamically. When a disturbance causes the state to deviate, the controller immediately has a new target point near the current state. Secondly, the vector field-based control strategy is designed from the outset to make the state error converge globally asymptotically. This means that the vector field defines a "backflow" direction pointing to the limit cycle over a large region of phase space. No matter how far the state is disturbed from the limit cycle, as long as the point is still within the phase space region where the control law is effective, the vector field will guide a path back to the limit cycle.

[0056] The control law, acting as the execution end of this strategy, calculates the specific torque required to achieve this regression path through model-based compensation. The entire process is "autonomous resynchronization," meaning it requires no external intervention or mode switching. The system can automatically and continuously adjust the control objective and output based on the current state, smoothly guiding the state back to the desired periodic motion. This characteristic ensures that the exoskeleton robot can maintain stable and natural motion assistance in complex and dynamic real-world environments, greatly improving its safety and reliability.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A time-independent control method for a lower limb exoskeleton robot based on a vector field generator, characterized in that, Includes the following steps: Step 1: Constructing the Target Limit Cycle: Mapping the periodic motion of the target as a closed trajectory in phase space, i.e., a limit cycle, described by the phase parameter θ. And define the nearest point from the system state (q,v) to this limit cycle as the target state. ; Step 2: Vector Field Construction: Designing a Vector Field Generator The vector field consists of a stable term. and periodic adjustment term Composition, that is This is used to drive the system state error to converge to zero; Step 3: Control Law Design: Based on the exoskeleton's dynamic model, design the control law. , where (q, v) is the state vector of the exoskeleton system. , , , These are the estimated values ​​of the inertia matrix, Coriolis force matrix, gravity term, and friction term in the dynamic model, respectively. Step 4: Apply the control law to the lower limb exoskeleton robot so that its joint state converges and stabilizes in a time-independent manner to the target limiting cycle.

2. The time-independent control method for a lower limb exoskeleton robot based on a vector field generator according to claim 1, characterized in that: In step one, the target state By calculating the phase parameters get, The following relationship must be satisfied: ； Thus, the target state is obtained. And construct time-independent errors. .

3. The time-independent control method for a lower limb exoskeleton robot based on a vector field generator according to claim 1, characterized in that: The vector field generator constructed in step two Its function is to reduce the time-independent system state error. It converges asymptotically to zero.

4. The time-independent control method for a lower limb exoskeleton robot based on a vector field generator according to claim 1, characterized in that: The control law in step three is a model-based control law, designed based on the dynamic equations of the exoskeleton system.

5. The time-independent control method for a lower limb exoskeleton robot based on a vector field generator according to claim 1, characterized in that: The stability of the method is proven by the LaSalle invariant set theorem, ensuring that the system state converges asymptotically globally from any initial point to the target limit cycle.

6. The time-independent control method for a lower limb exoskeleton robot based on a vector field generator according to claim 1, characterized in that: The target periodic movement is a gait movement, and the method can adapt to changes in gait rhythm and intentions actively applied by the user.

7. The time-independent control method for a lower limb exoskeleton robot based on a vector field generator according to claim 1, characterized in that: The vector field is constructed in a complete phase space and can clearly define and stably track self-intersecting periodic trajectories that appear in the joint space or in the two-dimensional phase plane projection.

8. The time-independent control method for a lower limb exoskeleton robot based on a vector field generator according to claim 1, characterized in that: When the system state deviates from the target limit cycle due to external disturbances or user-initiated actions, the control law can drive the system state to autonomously resynchronize and converge back to the target limit cycle.

9. A lower limb exoskeleton robot system, characterized in that: The system is equipped with a controller programmed to perform the time-independent control method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 8.

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