A high-dynamic attack guidance and control method for a micro unmanned aerial vehicle with a tri-copter layout
By combining a tri-rotor micro-UAV with a linear time-invariant system dynamics model and nonlinear robust adaptive control, the problems of low guidance accuracy and flight instability of UAVs under high maneuverability are solved, achieving stable flight and precise guidance for high-dynamic attacks.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NORTHWESTERN POLYTECHNICAL UNIV
- Filing Date
- 2023-01-10
- Publication Date
- 2026-05-29
Smart Images
Figure CN116027806B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of micro unmanned aerial vehicle (UAV) control technology, and relates to a high-dynamic attack guidance and control method for a tri-rotor micro unmanned aerial vehicle. Background Technology
[0002] Micro-Aerial Vehicles (MAVs) are a new type of aircraft that integrates various advanced technologies such as flight mechanics, avionics, microcomputer electrical engineering, materials mechanics, and propulsion technology. Compared with traditional aircraft, their main characteristics are extremely small size, portability, ease of operation, good stealth, and high maneuverability. They have high research value and application prospects in both military and civilian fields. In the military, they can be used to perform various tasks such as search, tracking, detection, and military strikes. The military is also increasingly relying on micro-drone technology to monitor, reconnoiter, and strike potential threats to minimize harm to military personnel.
[0003] Current UAV attack guidance and control technologies mostly rely on the output of front-end environmental perception to plan flight paths or obstacle avoidance. However, due to high computational complexity and system payload limitations, guidance accuracy often fails to meet requirements, affecting the final attack hit accuracy. Furthermore, in near-ground scenarios, UAVs are susceptible to interference from external environmental and internal system uncertainties. The unique configuration of highly maneuverable micro-UAVs results in exceptionally complex controlled objects with severe coupling, exhibiting characteristics of multiple input variables, nonlinearity, underactuation, and strong coupling, often leading to instability during flight. Summary of the Invention
[0004] The purpose of this invention is to solve the problems in the prior art and provide a high-dynamic attack guidance and control method for a tri-rotor micro unmanned aerial vehicle (UAV), which can ensure stable flight of the UAV under the interference of uncertainties in the external environment and internal system.
[0005] To achieve the above objectives, the present invention employs the following technical solution:
[0006] A high-dynamic attack guidance and control method for a tri-rotor micro unmanned aerial vehicle (UAV) includes the following steps:
[0007] Establish a linear time-invariant system dynamic model for a micro unmanned aerial vehicle (UAV) and define the initial path. <x0>;
[0008] Based on the linear time-invariant system dynamics model, the initial path is... <x0>Update to obtain the path
[0009] Construct a parameter uncertainty function for a micro unmanned aerial vehicle (UAV) and a sliding mode variable structure controller based on a learning law;
[0010] Path-based Nonlinear robust adaptive control is achieved by using the uncertainty function of UAV parameters and a sliding mode variable structure controller based on the learning law.
[0011] Furthermore, the linear time-invariant system dynamic model of the micro-UAV is as follows:
[0012] x(k+1)=Ax(k)+Bu(k)
[0013] Where x(k+1) is the state of the UAV at time k+1, x(k) is the state of the UAV at time k, A is the inherent state matrix of the UAV, B is the inherent input matrix of the UAV, u(k) is the control input of the UAV at time k, u(k) satisfies the relation u(k)∈λ, and λ is the feasible state space of the UAV control input.
[0014] Furthermore, the initial path <x0>It includes a series of discrete state points, represented as:
[0015] <x0>={x0(1),x0(2),x0(3),…,x0(N)}
[0016] Where N represents the number of discrete state points.
[0017] Furthermore, the initial path <x0>The update process is as follows:
[0018] Substituting the N discrete state points sequentially into the linear time-invariant system dynamics model, we obtain the N initial control inputs that satisfy the linear time-invariant system dynamics model as follows:
[0019] {u0(1),u0(2),u0(3),…,u0(N)}
[0020] Determine whether all N initial control inputs satisfy u(k)∈λ. If any control input does not satisfy this condition, replace it to obtain N new initial control inputs:
[0021]
[0022] Substitute each of the new N initial control inputs into the linear time-invariant system dynamics model to determine the initial path. <x0>Update to obtain the path
[0023] Furthermore, the replacement method involves mapping the unsatisfied control inputs to the λ space, with the mapping relationship being:
[0024]
[0025] Where u0 is the initial control input that does not satisfy u(k)∈λ. This is the initial control input after the replacement.
[0026] Furthermore, the mathematical expression for the uncertainty function of the UAV parameters is:
[0027]
[0028] Where G(s,ε) represents the transfer function of parameter uncertainty, n p a represents the number of parameters for the drone. j and b i Both are functions with uncertain parameters, in the form of:
[0029]
[0030]
[0031] Where, n p Let r be the number of parameters for the UAV, and r be an empirical parameter.
[0032] Furthermore, the mathematical expression of the sliding surface of the learning law-based sliding mode variable structure controller is as follows:
[0033]
[0034] Where e is the system tracking error, To track the error rate, β is a specific positive constant, p>q>0 is an odd number, and parameter a can be defined as:
[0035]
[0036] Where α is the learning rate parameter, and α = b e -1, a0 is the initial value of the learning rate, which is a positive number, b>0, b≠1.
[0037] Furthermore, the nonlinear robust adaptive control is expressed as follows:
[0038]
[0039] Where x represents the real-time state of the drone during flight, measured by sensors. d For path The value of e is
[0040] Compared with the prior art, the present invention has the following beneficial effects:
[0041] This invention provides a high-dynamic attack guidance and control method for a tri-rotor micro unmanned aerial vehicle (UAV). By combining obstacle and target motion parameters with the UAV's own maneuverability constraints, high-dynamic trajectory planning is achieved. Then, a rolling time-domain iteration method is used to reduce computational complexity and system load while ensuring guidance accuracy. The nonlinear robust adaptive control method employed not only solves the complex object control problem but also ensures stable flight of the UAV under the interference of uncertainties in the external environment and internal system. Attached Figure Description
[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0043] Figure 1 This is a multi-view schematic diagram of the tri-rotor layout micro unmanned aerial vehicle of the present invention.
[0044] Figure 2 This is a diagram of a miniature photoelectric sensor mounted on a tri-rotor micro-drone according to the present invention.
[0045] Figure 3 This is an environmental image captured by the photoelectric sensor mounted on the tri-rotor micro-UAV of the present invention.
[0046] Figure 4 This is a flowchart of the high-dynamic attack guidance and control method for a tri-rotor micro unmanned aerial vehicle according to the present invention.
[0047] Among them: 10-thin-walled duct, 12-fixed rotor, 14-movable tail rotor, 16-UAV fuselage, 18-binocular camera, 20-data transmission interface;
[0048] Figure 1 (a) is a front view of a tri-rotor micro unmanned aerial vehicle. Figure 1 (b) is a left view of a tri-rotor micro unmanned aerial vehicle. Figure 1 (c) is a top view of a tri-rotor micro unmanned aerial vehicle. Figure 1 (d) is a three-dimensional schematic diagram of a tri-rotor micro unmanned aerial vehicle. Detailed Implementation
[0049] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.
[0050] Obviously, the described embodiments are only some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.
[0051] It should be noted that the terminals involved in the embodiments of this application may include, but are not limited to, mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers, personal computers (PCs), MP3 players, MP4 players, wearable devices (e.g., smart glasses, smartwatches, smart bracelets), smart home devices, and other smart devices.
[0052] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0053] The present invention will now be described in further detail with reference to the accompanying drawings:
[0054] See Figures 1 to 2 Based on the different methods of generating lift, micro-aircraft can be divided into micro-fixed-wing aircraft, micro-flapping-wing aircraft, and micro-rotor aircraft. Attack guidance and control technology for tri-rotor micro-rotor drones is an emerging military technology. As a type of micro-rotor aircraft, tri-rotor micro-rotor drones have the following advantages:
[0055] 1. It has high dynamic characteristics, which can meet the requirements of attack, and the thin-walled duct 10 on the outside of the rotor can improve the efficiency of the rotor system and increase the safety of use.
[0056] 2. Among these three types of drones, rotary-wing drones have achieved a high level of maturity in control algorithms and application, and their control efficiency is relatively high, making them a more practical form of micro-aircraft configuration.
[0057] 3. The tri-rotor configuration can improve maneuverability. The two fixed rotors 12 provide most of the lift and roll control force, while a movable tail rotor 14 provides pitch and yaw control force and a certain amount of lift. Since the torque of all three axes is directly generated by the rotor thrust, it is expected to further improve the aircraft's maneuverability.
[0058] 4. Compared with conventional multi-rotor drones, the tri-rotor layout is lighter in structure and more convenient to carry and transport.
[0059] This invention provides a high-dynamic attack guidance and control method for a tri-rotor micro-UAV. It utilizes a ground-based airborne platform and a tri-rotor micro-UAV based on that platform. The tri-rotor micro-UAV is stored and released by the ground platform. The tri-rotor micro-UAV carries a pair of miniature photoelectric sensors, including an image acquisition module and a transmission module. The paired sensors are mounted in parallel to acquire visible light images, such as… Figure 3 As shown, miniature optoelectronic sensors, along with ground station cloud and onboard computer, work together to guide the UAV in completing attack guidance and control.
[0060] See Figure 4 This invention provides a high-dynamic attack guidance and control method for a tri-rotor micro unmanned aerial vehicle, which mainly includes two steps: trajectory planning and nonlinear robust adaptive control.
[0061] Step 1: Flight path planning.
[0062] 1) The following is the dynamic model of the linear time-invariant system of the micro unmanned aerial vehicle:
[0063] x(k+1)=Ax(k)+Bu(k) (1)
[0064] Where x(k+1) is the state of the UAV at time k+1, x(k) is the state of the UAV at time k, A is the inherent state matrix of the UAV, B is the inherent input matrix of the UAV, and u(k) is the control input of the UAV at time k. The control input u(k) satisfies the relation u(k)∈λ, where λ is the feasible state space of the UAV control input.
[0065] 2) Construct the initial path <x0>Initial path <x0>It is pre-defined and consists of a series of discrete state points, which can be represented as: <x0>= {x0(1),x0(2),x0(3),…,x0(N)}, where N represents the number of discrete state points.
[0066] 3) Substitute these N discrete state points into formula (1) in sequence to obtain N initial control inputs that satisfy formula (1): {u0(1),u0(2),u0(3),…,u0(N)}.
[0067] 4) Determine whether all N initial control inputs {u0(1), u0(2), u0(3), ..., u0(N)} satisfy u(k) ∈ λ. If any control input does not satisfy this condition, replace it by mapping it to the λ space. The mapping relationship is as follows:
[0068]
[0069] Where u0 is the initial control input that does not satisfy u(k)∈λ. This is the initial control input after the replacement.
[0070] The new N initial control inputs after replacement can be represented as:
[0071] 5) Adjust the initial path based on the new N initial control inputs after replacement. <x0>Perform the update. The update method is to sequentially update... Substitute each control input into formula (1) to obtain the new path.
[0072] Step 2: Nonlinear robust adaptive control.
[0073] 1) The mathematical expression for the uncertainty function of the micro UAV parameters is constructed as follows:
[0074]
[0075] Where G(s,ε) represents the transfer function of parameter uncertainty, n p a represents the number of parameters for the drone. j and b i Both are functions with uncertain parameters, in the form of:
[0076]
[0077] Where, n p The number of drone parameters is denoted by r, which is an empirical parameter that can be selected from 0 to 50.
[0078] 2) Construct a sliding mode variable structure controller based on learning laws. Its mathematical expression for the sliding surface is:
[0079]
[0080] Where e is the system tracking error, To track the error rate, β is a specific positive constant, p>q>0 is an odd number, and the parameter a can be defined as...
[0081]
[0082] Where α is the learning rate parameter, and α = b e -1, a0 is the initial value of the learning rate, which is a positive number, b>0, b≠1.
[0083] Differentiating the above expression for the sliding surface, we can obtain
[0084]
[0085] Therefore, by combining the mathematical expression of the parameter uncertainty function of the micro UAV, the expression form of nonlinear robust adaptive control can be obtained as follows:
[0086]
[0087] Where x represents the real-time state of the drone during flight, measured by sensors. d The path obtained in step one The value of e is
[0088] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A high-dynamic attack guidance and control method for a tri-rotor micro unmanned aerial vehicle, characterized in that, Includes the following steps: Establish a linear time-invariant system dynamic model for a micro unmanned aerial vehicle (UAV) and define the initial path. ; Based on the linear time-invariant system dynamics model, the initial path is... Update to obtain the path ; Construct a parameter uncertainty function for a micro unmanned aerial vehicle (UAV) and a sliding mode variable structure controller based on a learning law; Path-based Nonlinear robust adaptive control is achieved by using the uncertainty function of UAV parameters and a sliding mode variable structure controller based on the learning law. The initial path The update process is as follows: Substituting the N discrete state points sequentially into the linear time-invariant system dynamics model, we obtain the N initial control inputs that satisfy the linear time-invariant system dynamics model as follows: {u0(1),u0(2),u0(3),…,u0(N)} Determine whether all N initial control inputs satisfy u(k)∈λ. If any control input does not satisfy this condition, replace it to obtain N new initial control inputs: { (1), (2), (3),…, (N)} Substitute each of the new N initial control inputs into the linear time-invariant system dynamics model to determine the initial path. Update to obtain the path ; The replacement method involves mapping the unsatisfactory control inputs to the λ space, with the mapping relationship being: Where u0 is the initial control input that does not satisfy u(k)∈λ. This is the initial control input after the replacement; The mathematical expression for the uncertainty function of the UAV parameters is: in, The transfer function representing parameter uncertainty, n p The number of drone parameters, and Both are functions with uncertain parameters, in the form of: Where, n p The number of drone parameters is r, which is an empirical parameter. The mathematical expression of the sliding surface of the learning law-based sliding mode variable structure controller is as follows: in, For system tracking error, To track the error rate, For a specific positive number, It is an odd number, and the parameter It can be defined as: in, Let be the learning rate parameter, and , The initial learning rate is a positive constant. .
2. The high-dynamic attack guidance and control method for a tri-rotor micro unmanned aerial vehicle according to claim 1, characterized in that, The linear time-invariant system dynamics model of the micro-UAV is as follows: Where x(k+1) is the state of the UAV at time k+1, x(k) is the state of the UAV at time k, A is the inherent state matrix of the UAV, B is the inherent input matrix of the UAV, u(k) is the control input of the UAV at time k, u(k) satisfies the relation u(k)∈λ, and λ is the feasible state space of the UAV control input.
3. A high-dynamic attack guidance and control method for a tri-rotor micro unmanned aerial vehicle according to claim 1, characterized in that, The initial path It includes a series of discrete state points, represented as: ={x0(1),x0(2),x0(3),…,x0(N)} Where N represents the number of discrete state points.
4. A high-dynamic attack guidance and control method for a tri-rotor micro unmanned aerial vehicle according to claim 1, characterized in that, The nonlinear robust adaptive control is expressed as follows: Where x represents the real-time state of the drone during flight, measured by sensors. d For path The value of e is e = x - .