CN115291622A - Obstacle avoidance unmanned aerial vehicle distributed formation fractional order sliding mode control method - Google Patents
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Abstract
The invention discloses an obstacle avoidance unmanned aerial vehicle distributed formation fractional order sliding mode control method based on an artificial potential field method and a cerebellum model neural network, which is used for unmanned aerial vehicle formation obstacle avoidance control and processing the influence of external interference and internal parameter uncertainty. The adverse effect of lumped interference is approximately estimated and compensated by introducing a cerebellum model neural network, and a self-adaptive fractional order sliding mode controller is designed based on an artificial potential field method. The method realizes the stability and good obstacle avoidance performance of the unmanned aerial vehicle formation closed-loop tracking control system, and verifies that the method has certain effectiveness and good performance through a simulation example.
Description
Technical Field
The invention relates to the field of control of aerial unmanned aerial vehicles, in particular to a distributed formation fractional order sliding mode control problem of an obstacle avoidance unmanned aerial vehicle considering lumped interference influence.
Background
In recent years, with the development of unmanned aerial vehicles becoming mature, a single unmanned aerial vehicle can not meet the requirements of some complex tasks gradually, and researchers begin to research the formation cooperative control of multiple unmanned aerial vehicles. Compared with a single unmanned aerial vehicle, the unmanned aerial vehicle formation has great advantages in military or civil fields such as loading and transportation, wildfire monitoring, disaster relief, battlefield reconnaissance, attack and the like. The unmanned aerial vehicle formation cooperative control also becomes more complex and difficult due to the influence of complex factors such as external interference, obstacles and uncertainty of internal parameters. Therefore, under the conditions of external interference, uncertainty of internal parameters and existence of obstacles, the rapid tracking control and obstacle avoidance problem of unmanned aerial vehicle formation causes extensive attention of scholars.
Aiming at the problem of obstacle avoidance of unmanned aerial vehicle formation, numerous scholars at home and abroad propose various algorithms. Patent CN112180954A invented an unmanned aerial vehicle obstacle avoidance method based on artificial potential field, and the main contribution is to provide additional transverse obstacle avoidance control force, so as to solve the adverse effect of local minimum value on unmanned aerial vehicle obstacle avoidance, but the method can not make unmanned aerial vehicle get back to the desired position immediately after avoiding the obstacle. Patent CN111290429A discloses an unmanned aerial vehicle formation and obstacle avoidance control method thereof based on a consistency algorithm and an artificial potential field method, and the influence of a local minimum value can be eliminated by introducing auxiliary traction acceleration information perpendicular to the moving direction of an obstacle. However, in the obstacle avoidance process, the unmanned aerial vehicle position tracking error and the speed tracking error based on the method fluctuate greatly, and the stability of formation is influenced. Furthermore, neither of the above two approaches takes into account the effect of lumped disturbances (external disturbances and internal parameter uncertainties) on the formation control system.
In view of the problem that the external interference and the parameter uncertainty affect the control performance of the nonlinear system, a great deal of research has been carried out by many scholars, and the research results show that the neural network can well estimate and compensate the external interference and the parameter uncertainty. Meanwhile, the improved artificial potential field method can solve the problem of local minimum values and realize formation cooperative obstacle avoidance. In addition, the designed fractional order sliding mode controller can ensure the stability and the global robustness of a closed-loop formation control system, and solves the problem that the traditional obstacle avoidance algorithm cannot return to the expected position immediately after avoiding the obstacle.
Disclosure of Invention
In view of the defects in the prior art, the invention provides a distributed formation fractional order sliding mode control method for an obstacle avoidance unmanned aerial vehicle, which mainly comprises the following steps:
wherein i is the number of unmanned aerial vehicles in the formation, i =1 i ，ψ i ，
Respectively representing the airspeed, the pitch angle, the course angle and x of the unmanned aerial vehicle i ，y i And z i Is the three-dimensional coordinate of the unmanned plane, m i Is the fuselage mass, g is the gravitational acceleration, and the control inputs to the system are
θ i Showing the roll angle, T i Is engine thrust, u xi ，u yi ，u zi Control inputs in the X, Y, Z-axis directions, respectively, n i Is the coefficient of dynamic load, D i Is the resistance;
step 2, converting the unmanned aerial vehicle dynamics model in the step 1 into a state space equation, and describing a nonlinear model considering lumped interference as follows:
wherein p is i ＝[x i ，y i ，z i ] T In the form of a position vector, the position vector,
is a velocity vector, d si For lumped interference, G and R i Can be obtained by the following formula:
G＝[0 0 -g] T
step 3, utilizing a cerebellum model neural network to realize the online estimation of the lumped interference, and the specific process is as follows:
the architecture of the cerebellum model neural network comprises an input space, an association memory space, a receiving domain space, a weight memory space and an output space. The specific introduction is as follows:
(1) Input space: for input I = [ ] 1 ，I 2 ，...，I q ] T ∈R q I is a continuous and q-dimensional input space;
(2) Associating memory space: several different elements are stacked into a block, and each block performs an accept domain basis function, here a gaussian function is used as the basis function, which can be expressed as:
wherein,
is the jth input I j The gaussian function corresponding to the k-th block,
and σ jk Respectively corresponding mean value and variance of the Gaussian function, and M is the number of blocks;
(3) Receiving a domain space: within this space, the multidimensional receive domain basis function is defined as:
wherein, L represents the L-th receiving domain base function, N is the number of the receiving domain base functions, and the multidimensional receiving domain base function is represented by a vector as follows:
wherein,
and σ can be represented by the following vector:
σ＝[σ 11 ，...，σ q1 ，σ 12 ，...，σ q2 ，...，σ 1M ，...，σ qM ] T
(4) Weight memory space: the N components of the weight memory space that receive each location of the domain space to a particular adjustable value can be represented as:
W＝[w 1 ，...，w L ，...，w N ] T
wherein w L Representing the connection weight of the L-th receiving domain basis function;
(5) An output space: the output of the entire cerebellar model neural network can be expressed as:
the output of the cerebellar model neural network can be expressed as:
cerebellum model neural network on-line approximation lumped interference d si The expression of (a) is:
wherein epsilon is an approximation error,
W *T and phi * The optimal parameter vectors of W and phi respectively,
and σ * Are respectively as
Optimal parameter vector of sum σ, d si * Is d si The optimal vector of (2);
the cerebellum model neural network estimation error can be expressed as:
wherein,
and
the estimated values of W and phi respectively;
in order to achieve a good estimate of the lumped interference, taylor expansion linearization technique is used to transform the nonlinear function into a partially linear form, i.e.:
wherein,
and
are respectively as
And σ, H is a higher order term, and has:
and is
And
is defined as:
according to the formula:
wherein the uncertainty term
Representing an approximation error term and assuming it is bounded, i.e., | | Δ | | < δ, δ being a positive constant, | | | Δ | | being the Euclidean norm of Δ；
The cerebellum model neural network self-adaptation law is obtained by a Lyapunov stability analysis method as follows:
wherein λ is max Is a matrix
L is the laplacian matrix, Λ is the adjacency matrix of the leader,
is the product of Crohn's disease, I 3 Is a 3 x 3 identity matrix, s is a fractional order global integral sliding mode as designed herein,
η σ ，ζ 1 ，ζ 2 and ζ 3 Are all normal numbers, W 0 ，
σ 0 Are each W * ，
σ * The initial estimate of (a);
step 4, designing a self-adaptive fractional order sliding mode controller based on an artificial potential field method;
firstly, it needs to be explained that the implementation of the control system of the present invention requires that the formation of unmanned aerial vehicles meets the designed formation configuration during the flight process, and therefore, the expected position of the ith unmanned aerial vehicle should meet:
wherein
A virtual leader position is represented by a virtual leader location,
representing a desired position of each drone relative to the virtual leader;
the graph theory is one of the important components in formation flight, and the invention adopts an undirected graph
Wherein
A set of nodes of the graph G is represented,
a set of edges is represented that is,
representing an adjacency matrix; in an undirected graph, we can get:
and a ij ＝a ji (ii) a When the ith unmanned aerial vehicle can receive the information of the jth unmanned aerial vehicle, there are (j, i) epsilon and a ij =1, otherwise, a ij =0; definition of
Is a degree matrix, wherein
Thus, the Laplacian matrix is
For a communication topology with one leader 0 and n followers, this can be represented as:
wherein
Representing a set of nodes; the adjacency matrix for the leader is Λ = diag { λ 1 ，...，λ n }. λ if the ith follower can receive the leader's information i > 0, otherwise, λ i ＝0；
An adaptive fractional order sliding mode controller u designed based on an artificial potential field method is composed of a track tracking controller and a collaborative obstacle avoidance controller, namely:
u＝u α +u β
wherein u is an adaptive fractional order sliding mode controller α For trajectory tracking controllers u β A cooperative obstacle avoidance controller;
(1) Trajectory tracking controller design
Position tracking error vector e of the ith unmanned aerial vehicle i (t) and velocity tracking error vector
Comprises the following steps:
wherein p is i (t) and
respectively actual position vector and expected position vector of the unmanned plane, v i (t) and
respectively an actual speed vector and an expected speed vector of the unmanned aerial vehicle, and t is time;
distributed coupled position tracking error of unmanned aerial vehicle formation
Can be described as:
wherein λ i Elements of the adjacency matrix Λ, a, being the leader ij Is a contiguous matrix
Element of (e) j (t) is a position tracking error vector of the jth unmanned aerial vehicle, j is the number of the unmanned aerial vehicle, and j = 1.
The above formula can be rewritten as:
wherein l ii And l ij Are all elements of the laplace matrix L;
thus, the distributed coupled position tracking error vector is:
wherein,
distributed coupled velocity tracking error vector from above
Comprises the following steps:
error vector for distributed coupled velocity tracking
Designing a fractional order global integral sliding mode:
wherein s is fractional order global integral sliding mode, D -α Representing distributed coupled velocity tracking error vectors
Is a fractional order, c is a normal number, and h (t) can be expressed as:
where k is a normal number, where k is,
h (0) is the initial value of h (t),
is the initial distributed coupled velocity tracking error vector,
is a fractional order integral value at time t =0;
and (5) obtaining the following by derivation of s:
in order to effectively reduce the chattering problem in the sliding mode control and improve the rate of tracking error convergence, the approach law used herein is:
wherein eta 1 ，η 2 ，
Are all design parameters and are all normal numbers, and tanh (·) is a hyperbolic tangent function;
according to the above, the trajectory tracking controller is designed to:
wherein,
lumped interference estimated for a cerebellar model neural network;
(2) Design of cooperative obstacle avoidance controller
Unlike the conventional artificial potential field method, the controller provided herein designs a virtual agent β on the surface of the obstacle, and keeps the speed of the drones in the formation consistent with the speed of the virtual agent β. In addition, a repulsion function is designed between the unmanned aerial vehicle and the virtual agent beta, and an obstacle avoidance prediction mechanism and a concave-convex function are introduced into the controller;
assume that the obstacle has a radius r o The center of the sphere is O β Thus, the state information of the virtual agent β can be derived by the following equation:
p i，β ＝τp i +(I-τ)O β ，v i，β ＝τPv i
wherein p is i，β ，v i，β Are respectively the ithThe position and speed of the virtual agent beta corresponding to the unmanned aerial vehicle,
I 3 is a 3 × 3 identity matrix, and | l | · | |, is the euclidean norm;
in formation flight, the ith drone not only shares detected obstacle information, but also receives obstacle information from neighboring drones. Comparing the obtained obstacle information, and selecting a pair of obstacle information, wherein the specific selection principle is as follows:
1) When the speed values of a plurality of pairs of obstacle information are different, according to the maximum speed value max (| | v) i，β | |) to select corresponding obstacle information;
2) When the speed values of a plurality of pairs of obstacle information are the same, according to the minimum distance value min (| | p) i -p i，β | |) to select corresponding obstacle information;
3) If the two conditions are not met, randomly selecting a pair of obstacle information;
therefore, the cooperative obstacle avoidance controller is designed as follows:
wherein,
represents a cooperative obstacle avoidance controller, belongs to β ，c p ，c v Are all normal numbers, p γ，β And v γ，β Is the position and velocity, k, of the virtual agent beta selected according to the above principles β For an obstacle avoidance prediction mechanism, whether the unmanned aerial vehicle needs to avoid the obstacle when detecting the obstacle is determined,
is a continuous lightSmooth concave-convex function, which can change the degree of influence of repulsion on the drone, k β And
can be obtained by the following formula:
wherein r is i Radius of unmanned plane, r d Is the detection radius of the unmanned aerial vehicle, r o Radius of obstacle, O β Is the center of the obstacle, d io Distance between unmanned aerial vehicle and obstacle, d io ＝||p i -O β ||，
Determining the maximum range of the unmanned aerial vehicle influenced by the repulsive force field,
and 5, verifying the stability of the unmanned aerial vehicle formation closed-loop control system.
According to the adaptive fractional order sliding mode controller, the stability of a formation closed-loop control system under the influence of lumped interference needs to be proved.
Defining a lyapunov function V:
deriving V as:
after equivalent transformation, the method is easy to obtain:
the above formula can be described as:
wherein χ and b are normal numbers, and are obtained by the following formula:
we can obtain from the above equation:
the following is obtained from the above equation: according to the Lyapunov stability condition, all error signals are bounded, and therefore the closed loop control system is asymptotically stable.
Compared with the prior art, the beneficial effects of the invention are embodied in the following aspects:
(1) The obstacle avoidance unmanned aerial vehicle distributed formation fractional order sliding mode control method based on the artificial potential field method and the cerebellum model neural network has the advantages of being high in tracking speed, control accuracy and the like, and ensures that formation can be quickly restored to an expected position after obstacle avoidance is completed;
(2) According to the distributed formation fractional order sliding mode control method of the obstacle avoidance unmanned aerial vehicle based on the artificial potential field method and the cerebellum model neural network, the adverse effect of lumped interference is considered;
(3) According to the distributed formation fractional order sliding mode control method of the unmanned aerial vehicle for avoiding the obstacles based on the artificial potential field method and the cerebellum model neural network, the problem of local minimum values can be solved, and formation obstacle avoidance can be successfully realized.
Drawings
In order to better embody the advantages of the method designed by the invention, aiming at the problem of obstacle avoidance of the unmanned aerial vehicle, the obstacle avoidance method of the traditional artificial potential field method is selected to be compared with the distributed formation fractional order sliding mode control method of the obstacle avoidance unmanned aerial vehicle designed by the invention, and the result shows that the method provided by the invention has better tracking performance and obstacle avoidance performance.
Fig. 1 is a communication topology diagram of formation of unmanned aerial vehicles according to the present invention;
FIG. 2 is a comparison graph of three-dimensional trajectory simulation;
FIG. 3 is a comparison graph of x-axis position tracking error simulation;
FIG. 4 is a comparison graph of position tracking error simulation in the y-axis;
FIG. 5 is a comparison graph of position tracking error simulation for the z-axis.
Detailed Description
The present invention will be explained in further detail below with reference to the drawings and embodiments. The specific embodiments described herein are merely illustrative of the invention and do not delimit the invention.
In order to enable people in the research field to better understand the implementation of the method, matlab2017a software is used for carrying out unmanned aerial vehicle formation tracking control and obstacle avoidance simulation so as to verify the reliability of the unmanned aerial vehicle formation tracking control and obstacle avoidance simulation. The simulation results of a formation consisting of one virtual leader and four identical drones are presented. The laplacian matrix L of the unmanned aerial vehicle formation communication topological graph is as follows:
herein, the initial velocity and position of the virtual navigator are respectively: v. of L (0)＝50m/s，
Flight path is [50t,200, 400 ]] T And m is selected. The maximum speed of the unmanned aerial vehicle is 60m/s, and the maximum acceleration is 10m/s 2 Acceleration of gravity g =9.8m/s 2 . The initial state of the unmanned aerial vehicle is shown in table 1, and each design parameter of the controller is selected as follows: k =0.01, η 1 ＝η 2 ＝1，
∈ β ＝1，c v ＝6，c p ＝5，c＝1，λ i ＝1，
r o ＝60，r d =100, the expected position of each drone relative to the virtual pilot is:
to simplify the shape of the obstacle, we assume the obstacle to be a radius r d =60m centered on O β ＝[1000，200，400] T m, a spherical object. Meanwhile, let the lumped interference be: d is a radical of si ＝0.12[sin0.5t，sin0.5t，sin0.5t] T The simulation step size is 0.1s.
Table 1 initial state of unmanned plane
The result shows that the distributed formation fractional order sliding mode control method for the obstacle avoidance unmanned aerial vehicle can realize the rapid tracking of the expected track within 0-5 s, and can immediately recover to the expected track after the obstacle avoidance is finished. Compared with the traditional artificial potential field method, the method provided by the invention not only considers the adverse effect of lumped interference, but also can solve the problem of local minimum value and realize obstacle avoidance. Through comparison of the simulation results, the effectiveness and feasibility of the distributed formation fractional order sliding mode control method for the obstacle avoidance unmanned aerial vehicle are verified, and the method is in line with expectation.
Finally, it is recognized that the invention is not limited to the specific embodiments described above, but rather is intended to cover all modifications, equivalents, improvements, and equivalents falling within the spirit and scope of the invention.
Claims (3)
1. A distributed formation fractional order sliding mode control method of an obstacle avoidance unmanned aerial vehicle based on an artificial potential field method and a cerebellum model neural network comprises the following steps:
step 1, establishing an ith unmanned aerial vehicle dynamics model:
wherein i is the number of the unmanned aerial vehicles in the formation, i =1, \ 8230, n, n is the number of the unmanned aerial vehicles in the formation, V i ，ψ i ，
Respectively representing the airspeed, pitch angle, course angle, x of the unmanned aerial vehicle i ，y i And z i Three-dimensional coordinates for unmanned aerial vehicles, m i Is the fuselage mass, g is the gravitational acceleration, and the control inputs to the system are
θ i Showing the roll angle, T i Is hairThrust of motive machine u xi ，u yi ，u zi Control inputs in the X-axis, Y-axis, Z-axis directions, n i Is the dynamic load coefficient, D i Is a resistance force;
step 2, converting the unmanned aerial vehicle dynamic model in the step 1 into a state space equation, and simultaneously considering modeling of lumped interference:
definition of p i ＝[x i ，y i ，z i ] T And
for the position vector and velocity vector of the ith drone, respectively, the nonlinear dynamical model considering lumped interference can be expressed as:
wherein d is si For lumped interference, G and R i Obtained by the following formula:
G＝[0 0 -g] T
and 3, estimating the lumped interference by using the cerebellum model neural network according to the step 2, wherein the specific steps are as follows:
the relation between the input and the output of the cerebellum model neural network is as follows:
wherein y is the output vector, I is the input vector, W is the connection weight vector of the receiving domain, phi is the multi-dimensional receiving domain basis function vector,
and σ are each a Gaussian functionA value and a variance;
cerebellum model neural network on-line approximation lumped interference d si The expression of (c) is:
where ε is the approximation error, W * ，φ * ，
σ * Are respectively W, phi,
optimum parameter vector of sigma, W *T Is W * Transpose of (d) si * Is d si The optimal vector of (2);
the cerebellum model neural network estimation error can be expressed as:
wherein,
are respectively W, phi, d si An estimated value of (d);
and (3) converting the nonlinear function into a partial linear form by using a Taylor expansion linearization technique, namely:
wherein,
and
are respectively as
And σ, H is a higher order term, and has:
and is
And
is defined as:
according to the formula:
wherein the uncertainty term
Representing an approximation error term and assuming that the approximation error term is bounded, namely | | | delta | is less than or equal to delta, delta is a normal number, and | | | delta | is an Euclidean norm of delta;
the cerebellum model neural network self-adaptation law is obtained by a Lyapunov stability analysis method as follows:
wherein λ is max Is a matrix
L is the laplacian matrix, a is the adjacency matrix of the leader,
is the product of Crohn's disease, I 3 Is a 3 x 3 identity matrix, s is a fractional order global integral sliding mode as designed herein,
η σ ，ζ 1 ，ζ 2 and ζ 3 Are all normal numbers, W 0 ，
σ 0 Are each W * ，
σ * The initial estimate of (a);
step 4, designing a self-adaptive fractional order sliding mode controller based on an artificial potential field method according to the step 2;
and 5, verifying the stability of the unmanned aerial vehicle formation closed-loop control system.
2. The obstacle avoidance unmanned aerial vehicle distributed formation fractional order sliding mode control method according to claim 1, characterized in that the step 4 of designing an adaptive fractional order sliding mode controller based on an artificial potential field method comprises the following steps:
an adaptive fractional order sliding mode controller designed based on an artificial potential field method is composed of a track tracking controller and a collaborative obstacle avoidance controller, namely:
u＝u α +u β
wherein u is an adaptive fractional order sliding mode controller α For trajectory tracking controllers u β A cooperative obstacle avoidance controller;
(1) Trajectory tracking controller design
Position tracking error vector e of ith unmanned aerial vehicle i (t) and velocity tracking error vector
Comprises the following steps:
wherein p is i (t) and
respectively actual position vector and expected position vector of the unmanned plane, v i (t) and
respectively an actual speed vector and an expected speed vector of the unmanned aerial vehicle, and t is time;
distributed coupled position tracking error of unmanned aerial vehicle formation
Can be described as:
wherein λ i Elements of the adjacency matrix Λ, a, being the leader ij To follow up a neighbor matrix
Element of (e) j (t) is a position tracking error vector of a jth unmanned aerial vehicle, j is the number of the unmanned aerial vehicle, and j =1, \ 8230;, n;
the above formula can be rewritten as:
wherein l ii And l ij Are all elements of the laplace matrix L;
thus, the distributed coupled position tracking error vector may be described as:
wherein,
distributed coupled velocity tracking error vector from above
Comprises the following steps:
error vector for distributed coupled velocity tracking
Designing a fractional order global integral sliding mode:
wherein s is a fractional order global integral sliding mode, D -α Representing distributed coupled velocity tracking error vectors
Is given by a fractional integral of (a), α ∈ (0, 1) is the fractional order, c is a positive number, and h (t) can be expressed as:
where k is a normal number, where k is,
h (0) is the initial value of h (t),
is the initial distributed coupling velocity tracking error vector,
is a fractional order integral value at time t =0;
in order to effectively reduce the buffeting problem in sliding mode control and improve the rate of tracking error convergence, the approach law used herein is as follows:
wherein eta 1 ，η 2 ，
Are all normal numbers, and tanh (·) is a hyperbolic tangent function;
according to the above, the trajectory tracking controller is designed to:
wherein,
lumped interference estimated for a cerebellar model neural network;
(2) Design of cooperative obstacle avoidance controller
Different from the traditional artificial potential field method, the controller provided by the invention designs a virtual intelligent body beta on the surface of the barrier, keeps the speed of the unmanned aerial vehicle in formation consistent with that of the virtual intelligent body beta, designs a repulsion function between the unmanned aerial vehicle and the virtual intelligent body beta, and introduces an obstacle avoidance prediction mechanism and a concave-convex function into the controller;
assume that the obstacle has a radius r o The center of the sphere is O β The state information of the virtual agent β can therefore be derived from the following equation:
p i，β ＝τp i +(I 3 -τ)O β ，v i，β ＝τPv i
wherein p is i，β ，v i，β Respectively the position and the speed of the virtual agent beta corresponding to the ith unmanned aerial vehicle,
I 3 is a 3 × 3 identity matrix, and | | · | | is an euclidean norm;
in formation flight, the ith unmanned aerial vehicle not only needs to share detected obstacle information, but also receives obstacle information from adjacent unmanned aerial vehicles, and a pair of obstacle information is selected by comparing the obtained obstacle information, wherein the specific selection principle is as follows:
1) When the speed values of a plurality of pairs of obstacle information are different, according to the maximum speed value max (| | v) i，β | |) to select corresponding obstacle information;
2) When the speed values of a plurality of pairs of obstacle information are the same, according to the minimum distance value min (| | p) i -p i，β | |) to select corresponding obstacle information;
3) If the two conditions are not met, randomly selecting a pair of obstacle information;
therefore, the cooperative obstacle avoidance controller is designed as follows:
wherein i is the number of the unmanned aerial vehicle,
a cooperative obstacle avoidance controller is shown,
c p ，c v are all normal numbers, p γ，β And v γ，β Is the position and velocity, k, of the virtual agent beta selected according to the above principles β For an obstacle avoidance prediction mechanism, which determines whether the unmanned aerial vehicle needs to avoid an obstacle when detecting an obstacle, ρ (z) is a continuous smooth concave-convex function which can change the influence degree of repulsion on the unmanned aerial vehicle, k β And ρ (z) can be obtained by the following formula:
wherein r is i Radius of unmanned plane, r d Radius of detection for unmanned aerial vehicle, r o Radius of obstacle, O β Is the center of the obstacle, d io Is the distance between the drone and the obstacle, d io ＝||p i -O β ||，
Determining the maximum range of the unmanned aerial vehicle influenced by the repulsive force field,
3. the obstacle avoidance unmanned aerial vehicle distributed formation fractional order sliding mode control method according to claim 1, wherein the process of verifying the stability of the unmanned aerial vehicle formation closed-loop control system in step 5 is as follows:
defining the Lyapunov function V as:
deriving V as:
after equivalent transformation, it is easy to obtain:
the above formula can be described as:
wherein χ and b are normal, and can be obtained by the following formula:
the above inequality equation can be rewritten as:
the following is obtained from the above equation: according to the Lyapunov stability condition, all error signals are bounded, and therefore the closed loop control system is asymptotically stable.
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| CN116414148A (en) * | 2023-03-15 | 2023-07-11 | 华中科技大学 | Distributed rotor unmanned aerial vehicle cooperative control method, device and system |
| CN116700355A (en) * | 2023-08-04 | 2023-09-05 | 南京航空航天大学 | Fixed wing unmanned aerial vehicle fault-tolerant control method for facing tracking of unmanned aerial vehicle |
| CN117608318A (en) * | 2024-01-23 | 2024-02-27 | 北京航空航天大学 | Unmanned aerial vehicle formation obstacle avoidance control method and system based on bird-like phototaxis |
| CN117873078A (en) * | 2023-12-28 | 2024-04-12 | 哈尔滨理工大学 | Unmanned surface vessel formation fault-tolerant control method based on artificial potential function |
| CN118012129A (en) * | 2024-02-01 | 2024-05-10 | 辽宁工业大学 | Unmanned aerial vehicle cluster distributed formation control method with preset convergence precision |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116414148A (en) * | 2023-03-15 | 2023-07-11 | 华中科技大学 | Distributed rotor unmanned aerial vehicle cooperative control method, device and system |
| CN116414148B (en) * | 2023-03-15 | 2023-12-05 | 华中科技大学 | Distributed rotor unmanned aerial vehicle cooperative control method, device and system |
| CN116700355A (en) * | 2023-08-04 | 2023-09-05 | 南京航空航天大学 | Fixed wing unmanned aerial vehicle fault-tolerant control method for facing tracking of unmanned aerial vehicle |
| CN116700355B (en) * | 2023-08-04 | 2023-11-07 | 南京航空航天大学 | Fixed wing unmanned aerial vehicle fault-tolerant control method for facing tracking of unmanned aerial vehicle |
| CN117873078A (en) * | 2023-12-28 | 2024-04-12 | 哈尔滨理工大学 | Unmanned surface vessel formation fault-tolerant control method based on artificial potential function |
| CN117608318A (en) * | 2024-01-23 | 2024-02-27 | 北京航空航天大学 | Unmanned aerial vehicle formation obstacle avoidance control method and system based on bird-like phototaxis |
| CN117608318B (en) * | 2024-01-23 | 2024-04-09 | 北京航空航天大学 | Unmanned aerial vehicle formation obstacle avoidance control method and system based on bird-like phototaxis |
| CN118012129A (en) * | 2024-02-01 | 2024-05-10 | 辽宁工业大学 | Unmanned aerial vehicle cluster distributed formation control method with preset convergence precision |
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