Pilot type unmanned aerial vehicle cluster cooperative trajectory tracking control method based on bat cells
Through the coordinating trajectory tracking control method of the UAV cluster designed by the fruit bat cell coding principle, the robustness and obstacle avoidance problems of the pilot drone cluster in complex environments is solved, and the coordinated control of the UAV cluster with high real-time and security is achieved.
Patent Information
- Application Number
- CN202510336058.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-20
- Publication Date
- 2025-07-11
AI Technical Summary
The existing pilot drone cluster collaborative trajectory tracking control algorithm is poorly robust in complex environments, relies on radio signals, and is difficult to achieve collision-free and obstacle avoidance, and lack of real-time performance.
The collaborative trajectory tracking control method of piloted drone clusters based on fruit bat cells is adopted. By designing a spatiotemporal decoupled heading controller, speed coordinator and non-periodic event triggering mechanism, combined with the social spatial cell coding principle of fruit bats, the high robustness and safe collaborative trajectory tracking of the drone cluster are achieved.
In complex environments, the robustness and real-time performance of the drone cluster is improved, the collision-free and obstacle avoidance are ensured, and the safety and reliability of the collaborative control system of the drone cluster is improved.
Smart Images
Figure CN120295328A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of cooperative trajectory tracking, and specifically, to a cooperative trajectory tracking control method for a leading-type unmanned aerial vehicle (UAV) cluster based on fruit bat cells. Background Art
[0002] In recent years, UAV clusters have been able to meet the requirements of multi-task functions through dynamic adaptive adjustment in complex environments, reaching a level that is difficult for a single intelligent agent to achieve. The cooperative spatial configurations of UAV clusters can be mainly divided into parallel type and leading type. Due to the correlation of positions between aircraft in the parallel cooperative structure, the tracking algorithm is more complex, requiring high computing resources and high communication resources; the leading cooperative system has become the most commonly used configuration method during the mission execution of UAV clusters due to its comprehensive advantages of low cost, stable performance, and easy implementation. Among them, the cooperative trajectory tracking control technology is one of the core technologies to further improve the performance of leading-type UAV clusters.
[0003] The existing cooperative trajectory tracking control algorithms for leading-type UAV clusters face the following main problems: First, the traditional trajectory tracking control algorithms have poor robustness in adapting to complex environments and are greatly affected by communication conditions; second, when using traditional control algorithms based on extended state observers, a periodic sampling strategy is often adopted, resulting in a large amount of redundant onboard communication resources and poor real-time performance; third, traditional preset performance control still has not solved the problem of incompatibility in realizing collision-free / obstacle avoidance in the cooperative control system of UAV clusters during long-distance cooperative operations.
[0004] The cooperative operation of UAV clusters has great similarities with the complex social behaviors generated by the cooperation of biological groups in nature. However, biological groups in nature can achieve orderly cooperation under the interference of complex environments. Therefore, if bionic technology can be used to realize the cooperative trajectory tracking control of leading-type UAV clusters, it will improve the robustness and reliability of the system under the conditions of complex environment interference and communication constraints, which is of great significance for enhancing the real-time and intelligent task execution capabilities of UAV clusters and enriching the cooperative navigation control theory of aircraft clusters. Summary of the Invention
[0005] Object of the Invention: To solve the problem that the existing cooperative trajectory tracking control method for leading-type UAV clusters has poor robustness in complex environments due to excessive dependence on radio signals, the present invention provides a cooperative trajectory tracking control method for a leading-type UAV cluster based on fruit bat cells.
[0006] Technical Solution: A cooperative trajectory tracking control method for a leading-type UAV cluster based on fruit bat cells, comprising the following steps:
[0007] (a1) Define the fruit bat head orientation cell as the azimuth angle of the leading UAV in the UAV swarm, and define the fruit bat position cell as the spatial information cognitive map of the leading UAV in the UAV swarm. According to the principle that the fruit bat head orientation cells are evenly distributed in a relatively fixed form at the horizontal 360° azimuth angle and the position cells provide the encoding of the spatial map, design a spatio-temporal decoupled UAV heading controller to control the heading of the UAV swarm;
[0008] (a2) Define the fruit bat target direction cell as the azimuth angle of the following UAV relative to the leading UAV in the horizontal position in the UAV swarm, and define the fruit bat target position cell as the distance of the following UAV relative to the leading UAV in the horizontal position. According to the fruit bat target direction cell and the target position cell, calculate the azimuth angle and target distance relative to the leading UAV in the horizontal position with the following UAV itself as the center, and design a velocity coordination regulator with spatio-temporal decoupling. Under the action of the heading controller and the velocity coordination regulator in step (a1), make the UAVs converge to the desired trajectory;
[0009] (a3) Define the fruit bat attitude joint tuning cell as the following UAV in the UAV swarm to jointly tune its own azimuth angle according to the azimuth angle of the leading UAV in the horizontal position, and define the fruit bat social position cell as the following UAV in the UAV swarm to jointly tune its distance from the leading UAV according to the distance of the leading UAV in the horizontal position. According to the attitude joint tuning cell, tune the azimuth angle of the leading UAV respectively by processing the fast-changing stimulus information, and the fruit bat social position cell encodes the position of the following UAV according to the spatial information of the leading UAV. Based on steps (a1) and (a2), formulate a cooperative trajectory strong tracking control strategy to achieve the cooperative trajectory tracking control of the leading UAV swarm.
[0010] Further, in step (a1), in the spatio-temporal decoupled UAV heading controller, introduce a composite deviation composed of the path following error and the heading control deviation, specifically including: calculating the heading control deviation e from the UAV position coordinates, heading angle, forward speed, angular velocity, and the desired heading angle and angular velocity ip ; dynamically representing the path following error θ from the UAV kinematic formula and the UAV path and heading control deviation ie ; calculating the composite deviation e ic ,
[0011] e ic =θ ie +arctan(ke ip )
[0012] where k is a preset parameter to enable the following UAVs in the swarm to accurately track the leading UAV.
[0013] Further, in step (a2), in the spatio-temporal decoupled speed coordination regulator, according to the true and desired projected arc lengths of the \(i\)-th and \(j\)-th drones along the desired curved path, the drone coordination error is calculated, and a speed coordination regulator that meets the time task is designed according to the drone coordination error.
[0014] Further, in step (a3), a trajectory strong tracking control strategy is formulated based on the aperiodic event-triggering mechanism, specifically including:
[0015] (b1) Design an estimator for the unknown system dynamics, perform low-pass filtering on the estimator, and ensure system convergence;
[0016] (b2) Based on the estimator, according to the position estimates of the \(i\)-th and \(j\)-th drones at time \(t\) and the triggering position of the \(j\)-th drone received at time \(t\), a position observer is designed; j、k At time \(t\), a position observer is designed.
[0017] (b3) Combine the position observer with the aperiodic event-triggering mechanism. When the event-triggering criterion is violated, the event will be instantaneously triggered. The \(i\)-th drone updates the sampled position information at time \(t\) and sends its sampled position information to neighboring drones; if the event-triggering criterion is not violated, the sampled value at time \(t\) will be maintained. i，k+1 At time \(t\), the sampled position information is updated, and its sampled position information is sent to neighboring drones; if the event-triggering criterion is not violated, the sampled value at time \(t\) will be maintained. i，k The sampled value at time \(t\) is maintained.
[0018] Further, in step (a2), while the drones converge to the required trajectory, it is necessary to ensure uniform motion forward.
[0019] Compared with the prior art, the bat cell-based leader-following UAV swarm cooperative trajectory tracking control method provided by the present invention has the following beneficial effects:
[0020] (1) By effectively encoding / decoding three-dimensional space information with the special and excellent performance of the cooperative mechanism of various types of three-dimensional space isotropic social space cells in the hippocampus of the bat's brain during high-speed flight, it can expand from the cognitive space map to the high-dimensional space motion mode in long-distance and large-scale natural and social activities, and finally perform complex navigation and trajectory prediction behaviors. Combining it with the leader-following UAV swarm hierarchical interactive high-robust cooperative trajectory tracking control model, the UAV swarm cooperative trajectory tracking control can still work normally when radio signals are denied and is not affected by complex environmental interference.
[0021] (2) By introducing compound deviation, collaborative error, and projection arc length into the collaborative trajectory tracking control model of the UAV swarm, the heading controller and speed collaborative regulator are further optimized to achieve high-real-time pose collaborative control with spatio-temporal decoupling. This can transcend the functional limitations imposed by the time function in traditional trajectory tracking methods, further improve the real-time performance of the trajectory tracking control system, and enhance the accuracy of the UAV swarm's pose.
[0022] (3) By introducing an unknown system dynamics estimator and a high-reliability collaborative real-time trajectory strong tracking control strategy based on the aperiodic event-triggered mechanism, spatio-temporal decoupled safe collaboration under collision-free and obstacle-avoidance conflict conditions is further realized, and the safety and reliability of the system are further improved. Description of the Drawings
[0023] Figure 1 It is a flowchart of the leader-following UAV swarm collaborative trajectory tracking control method based on fruit bat cells in this embodiment;
[0024] Figure 2 It is a schematic diagram of the collaborative trajectory tracking control model;
[0025] Figure 3 It is a schematic diagram of the trajectory tracking state of the follower UAV tracking the leader UAV; (a) is a schematic diagram of the trajectory tracking state of the follower UAV tracking the leader UAV in the x-axis direction; (b) is a schematic diagram of the trajectory tracking state of the follower UAV tracking the leader UAV in the y-axis direction.
[0026] Figure 4 It is a schematic diagram of the trajectory tracking error of the follower UAV tracking the leader UAV; (a) is a schematic diagram of the trajectory tracking error of the follower UAV tracking the leader UAV in the x-axis direction; (b) is a schematic diagram of the trajectory tracking error of the follower UAV tracking the leader UAV in the y-axis direction. Detailed Embodiment
[0029] The present invention will be further explained below with reference to the drawings and specific embodiments.
[0030] A leader-following UAV swarm collaborative trajectory tracking control method based on fruit bat cells, based on the encoding principle of the isotropic encoding social space cells of fruit bats, creates a robust collaborative trajectory tracking control model by designing a UAV heading controller, a speed collaborative regulator, and formulating a collaborative trajectory strong tracking control strategy, and realizes high-robust trajectory tracking control. As Figure 2 shown, the method includes the following steps:
[0031] (a1) Define the fruit bat head orientation cell as the azimuth angle of the leading UAV in the UAV cluster, and define the fruit bat position cell as the spatial information cognitive map of the leading UAV in the UAV cluster. According to the encoding principle that the fruit bat head orientation cells are evenly distributed in a relatively fixed form at the horizontal 360° azimuth angle and the position cells provide the spatial map, a spatio-temporally decoupled UAV heading controller is designed by introducing a composite deviation composed of the path following error and the heading control deviation of the leading UAV, which is used to control the heading of the UAV cluster;
[0032] (a2) Define the fruit bat target direction cell as the azimuth angle of the following UAV relative to the leading UAV in the horizontal position in the UAV cluster, and define the fruit bat target position cell as the distance of the following UAV relative to the leading UAV in the horizontal position. According to the fruit bat target direction cell and the target position cell, the azimuth angle and target distance relative to the leading UAV in the horizontal position are calculated with the following UAV itself as the center. By introducing the projected arc length and the UAV cooperation error, a velocity cooperation regulator with spatio-temporal decoupling is designed. Under the action of the heading controller and the velocity cooperation regulator in step (a1), the UAV converges to the required trajectory;
[0033] (a3) Define the fruit bat attitude joint tuning cell as the following UAV in the UAV cluster to jointly tune its own azimuth angle according to the azimuth angle of the leading UAV in the horizontal position, and define the social position cell as the following UAV in the UAV cluster to jointly tune its distance from the leading UAV according to the distance of the leading UAV in the horizontal position. According to the attitude joint tuning cell, the azimuth angle of the leading UAV is tuned by processing the fast-changing stimulus information respectively. The social position cell encodes the position of the following UAV according to the spatial information of the leading UAV. On the basis of steps (a1) and (a2), by designing an unknown system dynamics estimator, a strong tracking control strategy for the formulated cooperative trajectory is based on the aperiodic event-triggered mechanism to realize the cooperative trajectory tracking control of the leading UAV cluster.
[0034] In order to further improve the real-time performance and continuity of the cooperative trajectory tracking system of the leading UAV cluster, on the basis of the above-mentioned heading controller and velocity cooperation regulator, further optimization is carried out to realize a high real-time pose cooperation controller. See Figure 1 , specifically including the following two points:
[0035] 1. In step (a1), when designing the spatio-temporally decoupled UAV heading controller, a composite deviation composed of the path following error and the heading control deviation is introduced. The specific method includes:
[0036] The kinematic formula of the UAV is:
[0037]
[0038] where, x i 、yi and θ i represent the position and the heading angle in the rectangular coordinate system respectively; u i and ω i represent the forward speed and the turning speed of the UAV when u i > 0; v iw and ψ iw are the uncertain wind speed and wind direction; u0 and ω0 are the position and the forward speed of the leading UAV.
[0039] Let the path of the i-th UAV be denoted as p i = p(x i , y i ), then the path tracking error e ip of the i-th UAV is:
[0040] e ip = p i - p0 - d ip = p i - d ip (2)
[0041] where d ip is a constant specified by the user. When d ip > 0, there is a positive deviation between the evolved path and the desired path, and vice versa. p ix , p iy are the first-order partial derivatives of p i , and p ixx , p iyy are the second-order partial derivatives of p i .
[0042] The desired heading angle θ id and the angular velocity of the i-th UAV are:
[0043]
[0044]
[0045] The heading control deviation θ ie is defined by the controller as θ ie = θ i - θ id . According to the leading UAV satisfying θ 0e = 0. According to the UAV kinematic formula (1), and are the derivatives of p i and θ ie respectively, then the dynamic expression of the path following error of the i-th UAV is:
[0046]
[0047] To enable the following unmanned aerial vehicle to approach the preset path, a composite deviation e ie composed of the path following error θ ip and the heading control deviation e ic is introduced:
[0048] e ic = θ ie + arctan(ke ip ) (7)
[0049] where k is a preset parameter for enabling the following unmanned aerial vehicles in the cluster to accurately track the leading unmanned aerial vehicle.
[0050] According to equations (1) and (6), the time derivative of the composite deviation e ic is:
[0051]
[0052] Equation (8) is rewritten as:
[0053]
[0054] where represents the external disturbance, and the heading control rate r i for stabilizing the comprehensive deviation and achieving the spatial sub-goal is:
[0055]
[0056] 2. To further improve the continuity of the leading unmanned aerial vehicle cluster cooperative trajectory tracking system and ensure that the cooperative tracking control system avoids collision risks within the adjustable speed observation range, in step (a2), when designing the spatio-temporal decoupled speed cooperative regulator, according to the true and desired projected arc lengths of the i-th unmanned aerial vehicle and the j-th unmanned aerial vehicle along the desired curve path, the unmanned aerial vehicle cooperative error is calculated, and a speed cooperative regulator that meets the time task is designed according to the unmanned aerial vehicle cooperative error, as shown in Figure 2 , and the specific method includes:
[0057] To enable the unmanned aerial vehicle to converge to the required trajectory and ensure uniform forward movement, the unmanned aerial vehicle cooperative error e il (t) is:
[0058]
[0059] where l ij (t) and are the true and desired projected arc lengths of the i-th unmanned aerial vehicle and the j-th unmanned aerial vehicle along the desired curve path respectively. a ij represents the weight distribution between nodes i and j; a ij= 1 indicates that there is information interaction between nodes i and j; otherwise, a ij = 0. b i = 1 indicates that there is information interaction between the follower UAV i and the leader UAV; otherwise, b i = 0.
[0060] The triggering collaborative projection arc length error e il (t) can be expressed as
[0061]
[0062] The speed collaborative regulator u that satisfies the time task i is:
[0063]
[0064] where 0 < ρ i ≤ 1; k i2 is an unknown non - negative constant; represents the i - th row element of.
[0065] This step combines the speed collaborative regulator with the projection arc length to enable the UAV swarm to achieve continuous collaborative control.
[0066] In step (a3), using the aperiodic event - triggered mechanism, a strong tracking control strategy for the flight path is formulated, specifically including:
[0067] (b1) Design an estimator for the unknown system dynamics, perform a low - pass filtering operation on the estimator, and ensure system convergence. Specifically:
[0068] Design an estimator for the unknown system dynamics as:
[0069]
[0070] where, represents the estimated value of Δ iv .
[0071] Perform a low - pass filtering operation on e il and F i2 , that is:
[0072]
[0073] where κ iv > 0 represents the filtering parameter; and are the filtering variables of e il and F i2 .
[0074] (b2) Based on the estimator, a position observer is designed according to the position estimates of the \(i\)-th and \(j\)-th UAVs at time \(t\) and the received trigger position of the \(j\)-th UAV at time \(t\) to minimize the data transmission load, thereby avoiding resource waste caused by over-updating of the system; j，k The designed position observer at time \(t\) is as follows:
[0075] The designed position observer at time \(t\) j，k is specifically as follows:
[0076]
[0077] Among them, and respectively represent the position estimates of the \(i\)-th and \(j\)-th UAVs at time \(t\). and respectively represent the received trigger position of the \(j\)-th UAV at time \(t\) and its derivative. j，k at time \(t\).
[0078] (b3) Combine the position observer with an aperiodic event-triggering mechanism to reasonably arrange the transmission moments of speed and angular velocity, and improve the reliability of UAV swarm cooperative tracking.
[0079] The aperiodic event-triggering criterion is as follows:
[0080]
[0081] Among them, \(0 < \varepsilon\) i < 1 represents the event-triggering constant; \(\eta\) i is the positive triggering threshold; \(t\) i，k+1 represents the next triggering moment.
[0082] When the event-triggering criterion is violated, the event will be instantaneously triggered, and the \(i\)-th UAV updates its sampled position information at time \(t\) i，k+1 and sends its sampled position information to neighboring UAVs; if the event-triggering criterion is not violated, the sampled value at time \(t\) i，k will be maintained.
[0083] According to Equation (1) and following the physical property boundaries of the UAVs simultaneously, the forward speed \(u\) i and turning rate \(\omega\) i of the \(i\)-th UAV are:
[0084]
[0085] Among them, \(u\) min and \(u\) max respectively represent the lower and upper limits of the forward speed of the UAV; \(\omega\) max > 0 is allowed.
[0086] Such as Figure 3 And Figure 4 respectively represent the trajectory tracking state and trajectory tracking error in the simulation experiment data results. Among them, Figure 3 (a) is a schematic diagram of the trajectory tracking state of the follower UAV tracking the leader UAV in the x-axis direction. x0 represents the trajectory state of the leader UAV in the x-axis direction within the UAV cluster, and x1 to x4 respectively represent the trajectory tracking states of the follower UAV 1 to follower UAV 4 in the x-axis direction within the UAV cluster. Figure 3 (b) is a schematic diagram of the trajectory tracking state of the follower UAV tracking the leader UAV in the y-axis direction. y0 represents the trajectory state of the leader UAV in the y-axis direction within the UAV cluster, and y1 to y4 respectively represent the trajectory tracking states of the follower UAV 1 to follower UAV 4 in the y-axis direction within the UAV cluster. Figure 4 (a) is a schematic diagram of the trajectory tracking error of the follower UAV tracking the leader UAV in the x-axis direction. e1x to e4x respectively represent the trajectory tracking error states of the follower UAV 1 to follower UAV 4 in the x-axis direction within the UAV cluster. Figure 4 (b) is a schematic diagram of the trajectory tracking error of the follower UAV tracking the leader UAV in the y-axis direction. e1y to e4y respectively represent the trajectory tracking error states of the follower UAV 1 to follower UAV 4 in the y-axis direction within the UAV cluster.
Claims
1. A leader-following UAV swarm cooperative trajectory tracking control method based on fruit bat cells, characterized in that Including the following steps: (a1) Define the fruit bat head orientation cell as the azimuth angle of the leading drone in the drone swarm, and define the fruit bat position cell as the spatial information cognitive map of the leading drone in the drone swarm. According to the encoding principle that the fruit bat head orientation cells are evenly distributed in a relatively fixed form over the 360° horizontal azimuth angle and the position cells provide a spatial map, design a spatio-temporal decoupled drone heading controller for controlling the heading of the drone swarm; (a2) Define the fruit bat target direction cell as the azimuth angle of the following drone relative to the leading drone in the horizontal position in the drone swarm, and define the fruit bat target position cell as the distance of the following drone relative to the leading drone in the horizontal position. Calculate the azimuth angle and target distance relative to the leading drone in the horizontal position with the following drone itself as the center according to the fruit bat target direction cell and the target position cell, and design a speed coordination regulator with spatio-temporal decoupling. Under the action of the heading controller and the speed coordination regulator in step (a1), the drones converge to the required trajectory; (a3) Define the fruit bat attitude joint tuning cell as the following drone in the drone swarm coordinately tuning its own azimuth angle according to the azimuth angle of the leading drone in the horizontal position, and define the fruit bat social position cell as the following drone in the drone swarm coordinately tuning its distance from the leading drone according to the distance of the leading drone in the horizontal position. According to the attitude joint tuning cell, tune the azimuth angle of the leading drone respectively by processing fast-changing stimulus information, and the fruit bat social position cell encodes the position of the following drone according to the spatial information of the leading drone. Based on steps (a1) and (a2), formulate a cooperative trajectory strong tracking control strategy to achieve cooperative trajectory tracking control of the leading drone swarm.
2. The collaborative trajectory tracking control method for a leading drone swarm based on flying fox cells according to claim 1, characterized in that In step (a1), in the spatio-temporal decoupled UAV heading controller, a composite deviation composed of path following error and heading control deviation is introduced, specifically including: calculating the heading control deviation $e$ from the UAV position coordinates, heading angle, forward speed, angular velocity, and desired heading angle and angular velocity ip ; dynamically representing the path following error $\theta$ by the UAV kinematic formula, the UAV path, and the heading control deviation ie ; calculating the composite deviation $e$ ie , e ic = θ ie + arctan(ke ip ) Wherein, k is a preset parameter for enabling the following drones in the swarm to accurately track the leading drone.
3. The pilotless aircraft cluster cooperative trajectory tracking control method based on flying fox cells according to claim 1 or 2, characterized in that In step (a2), in the spatio-temporal decoupled speed coordination regulator, calculate the drone cooperation error according to the actual and expected projection arc lengths of the i-th drone and the j-th drone along the desired curve path, and design a speed coordination regulator that meets the time task according to the drone cooperation error.
4. The method for collaborative trajectory tracking control of a leading drone swarm based on flying fox cells according to claim 1 or 2, characterized in that In step (a3), formulate a trajectory strong tracking control strategy based on the aperiodic event-triggered mechanism, specifically including: (b1) Design an unknown system dynamics estimator, perform a low-pass filtering operation on the estimator, and ensure system convergence; (b2) Based on the estimator, design a position observer according to the position estimates of the i-th and j-th UAVs at time t and the trigger position of the j-th UAV received at time t j，k moment (b3) Combine the position observer with an aperiodic event-triggering mechanism. When the event-triggering criterion is violated, the event will be instantaneously triggered, and the $i$-th UAV updates its sampled position information at time $t$ i，k+1 and sends its sampled position information to neighboring UAVs; if the event-triggering criterion is not violated, the sampled value at time $t$ i，k will be maintained.
5. The collaborative trajectory tracking control method for a leading drone swarm based on flying fox cells according to claim 1, characterized in that In step (a2), while the drones converge to the required trajectory, it is necessary to ensure uniform forward movement.