An unmanned aerial vehicle adaptive sliding mode control method, system, device and medium

Abstract

This application discloses an adaptive sliding mode control method, system, device, and medium for a quadrotor UAV. A nonlinear kinematic and dynamic model of the quadrotor UAV is established, and the mass and disturbance forces of the quadrotor UAV are estimated in real time and incorporated into the position adaptive sliding mode controller. A desired pull control law for outer-loop position tracking is calculated, and a desired attitude angle is generated through decoupling. The generalized moment of inertia and disturbance torque of the quadrotor UAV are estimated in real time and incorporated into the attitude adaptive sliding mode controller. Using the desired attitude angle as the input command, a desired torque manipulation quantity for driving the UAV is calculated, thus coordinating the position and attitude control of the UAV with the desired pull control law. This effectively suppresses system chattering without sacrificing the robustness of sliding mode control, significantly improving the control accuracy and anti-interference capability of the quadrotor UAV under complex disturbances and parameter variations.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) flight control technology, and more specifically, to an adaptive sliding mode control method, system, device, and medium for UAVs. Background Technology

[0002] Quadrone drones, as a flight platform characterized by high flexibility, low cost, ease of operation, and no need for dedicated runways, have been widely used in various fields such as agricultural plant protection, geographic surveying, and industrial inspection. The flight control of these drones typically employs an inner-outer-outer-loop cascade control architecture. Based on the time-scale separation principle, the outer loop is responsible for position control, and the inner loop for attitude control. Currently, position and attitude controllers are generally designed using classic PID algorithms, which offer advantages such as simple structure, convenient debugging, and rapid system deployment.

[0003] However, classical PID control exhibits insufficient robustness when dealing with external disturbances during flight and internal disturbances caused by changes in the UAV's own dynamic characteristics. Because this type of controller lacks the ability to adapt to uncertainties, it struggles to maintain system control stability and trajectory tracking accuracy in complex environments, thus limiting its application in scenarios with higher performance requirements.

[0004] Sliding mode control, as a control method with strong robustness to system parameter uncertainties and external disturbances, has been widely studied and applied over the past few decades. However, traditional sliding mode controllers typically assume that the upper bound of the disturbance is known during design and rely on a large gain for suppression. This design approach easily introduces significant chattering, affecting the smoothness and control quality of the system, thus limiting its practical application value in UAV control systems that require high precision and low chattering. Summary of the Invention

[0005] The purpose of this application is to overcome the shortcomings of existing technologies and provide an adaptive sliding mode control method, system, device and medium for UAVs. This method effectively reduces chattering without sacrificing sliding mode control performance, improves control stability and robustness, and enhances the anti-interference ability and control accuracy of quadcopter UAVs in the face of complex and variable disturbances.

[0006] The objective of this application is achieved through the following technical solution:

[0007] In a first aspect, this application proposes an adaptive sliding mode control method for unmanned aerial vehicles (UAVs), the method comprising:

[0008] A nonlinear kinematic and dynamic model of a quadrotor UAV is established, which is subject to parameter perturbation, external force and torque disturbance.

[0009] Based on the nonlinear kinematics and dynamics model of a quadrotor UAV, the mass and interference force of the quadrotor UAV are estimated in real time through parameter adaptive adjustment technology.

[0010] Design a position adaptive sliding mode controller, add mass and disturbance force as compensation terms to the position adaptive sliding mode controller, calculate the desired tension control law for outer loop position tracking, and further decouple to generate the desired attitude angle required for inner loop attitude control;

[0011] Based on the nonlinear kinematics and dynamics model of a quadrotor UAV, the generalized moment of inertia and disturbance torque of the quadrotor UAV are estimated in real time through parameter adaptive adjustment technology.

[0012] The attitude adaptive sliding mode controller is designed, and the generalized moment of inertia and disturbance torque are added as compensation terms to the attitude adaptive sliding mode controller. The desired attitude angle is used as the input command, and the desired torque manipulation quantity for driving the UAV is calculated. Thus, together with the desired pull control law, the UAV's position and attitude are coordinated and controlled.

[0013] In one possible embodiment, the equations of the nonlinear kinematic and dynamic model are:

[0014] ;

[0015] in The initial mass of the drone. For the quality error of the drone, Here is the position acceleration vector. It is the total mass. It is the acceleration due to gravity. It is a unit vector. The virtual control input force is defined as follows: , For the force channel control input, This is the transformation matrix from the body coordinate system to the inertial coordinate system. The interference forces present during the translational movement of the drone. This represents the moment of inertia of the UAV in the ground coordinate system. For the error of the generalized rotational inertia matrix of the UAV, The attitude angular acceleration vector, For the Coriolis force and the centrifugal term, Attitude angular velocity, This is the input quantity for torque channel manipulation. This refers to the disturbance torque present during the rotational motion of the drone.

[0016] In one possible embodiment, the estimates of the mass and interference force of the quadcopter drone are:

[0017] ;

[0018] in, The time derivative of the quality estimator. An adaptive gain greater than 0 For linear sliding surface with positional error, Linear sliding surface for positional error transpose, Let the acceleration vector be the desired position. Design parameters for the sliding surface. This is the time derivative of the position tracking error. For position control gain, For estimating the mass of the drone, This is the time derivative of the disturbance force estimator.

[0019] In one possible embodiment, the position-adaptive sliding mode controller decouples the desired pull channel manipulation amount and the desired attitude angle based on the input desired position.

[0020] Based on the coordinate transformation relationship, at a given desired yaw angle Then, the desired tension control law Decoupling yields the desired attitude angle and the desired tension control amount;

[0021] The expected tension control law is: , This is an estimate of the interference force during the translational motion of the UAV;

[0022] The desired attitude angle is: ;

[0023] in , , and These are the components of the virtual control input force. For the desired pitch angle, For the desired roll angle, This represents the desired amount of tension control.

[0024] In one possible embodiment, the estimates of the generalized moment of inertia and the disturbance torque are:

[0025] ;

[0026] in, The time derivative of the estimator of the generalized rotational moment of inertia matrix. Adaptive gain for moment of inertia estimation Design parameters for the attitude sliding surface. This is the time derivative of the attitude angle tracking error. For attitude control gain, For attitude angle error linear sliding surface, The time derivative of the disturbance torque estimator. The adaptive gain for estimating the disturbance torque. Let be the angular acceleration of the desired attitude angle.

[0027] In one possible embodiment, the desired torque manipulation amount for: , This is a real-time estimate of the generalized rotational inertia matrix. This is a real-time estimate of the disturbance torque.

[0028] Secondly, this application proposes an adaptive sliding mode control system for unmanned aerial vehicles (UAVs), the system comprising:

[0029] The model building module is used to build the nonlinear kinematics and dynamics model of the quadcopter UAV. The nonlinear kinematics and dynamics model of the quadcopter UAV is subject to parameter perturbation, external forces and torque disturbances.

[0030] The first estimation module is used to estimate the mass and interference force of the quadrotor UAV in real time based on the nonlinear kinematics and dynamics model of the quadrotor UAV and through parameter adaptive adjustment technology.

[0031] The first generation module is used to design the position adaptive sliding mode controller. Mass and disturbance force are added as compensation terms to the position adaptive sliding mode controller to obtain the desired tension control law and desired attitude angle.

[0032] The second estimation module is used to estimate the generalized moment of inertia and disturbance torque of the quadrotor UAV in real time based on the nonlinear kinematics and dynamics model of the quadrotor UAV and through parameter adaptive adjustment technology.

[0033] The second generation module is used to design the attitude adaptive sliding mode controller. It adds the generalized moment of inertia and disturbance torque as compensation terms to the attitude adaptive sliding mode controller to obtain the desired torque manipulation amount.

[0034] Thirdly, this application also proposes a computer device including a processor and a memory, wherein the memory stores a computer program, which is loaded and executed by the processor to implement the UAV adaptive sliding mode control method as described in any of the first aspects.

[0035] Fourthly, this application also proposes a computer-readable storage medium storing a computer program that is loaded and executed by a processor to implement the UAV adaptive sliding mode control method as described in any of the first aspects.

[0036] The main solution and its various further alternatives described above can be freely combined to form multiple solutions, all of which are solutions that can be adopted and are claimed in this application; furthermore, the (non-conflicting alternatives) can also be freely combined with each other and with other alternatives. Those skilled in the art, after understanding the solution of this application, will realize from the prior art and common general knowledge that there are many combinations, all of which are technical solutions to be protected in this application, and will not be exhaustively listed here.

[0037] This application discloses an adaptive sliding mode control method, system, device, and medium for a quadrotor UAV. A nonlinear kinematic and dynamic model of the quadrotor UAV is established, and the mass and disturbance forces of the quadrotor UAV are estimated in real time and incorporated into the position adaptive sliding mode controller. A desired pull control law for outer-loop position tracking is calculated, and a desired attitude angle is generated through decoupling. The generalized moment of inertia and disturbance torque of the quadrotor UAV are estimated in real time and incorporated into the attitude adaptive sliding mode controller. Using the desired attitude angle as the input command, a desired torque manipulation quantity for driving the UAV is calculated, thus coordinating the position and attitude control of the UAV with the desired pull control law. This effectively suppresses system chattering without sacrificing the robustness of sliding mode control, significantly improving the control accuracy and anti-interference capability of the quadrotor UAV under complex disturbances and parameter variations. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of this application, 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 this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating an adaptive sliding mode control method for unmanned aerial vehicles (UAVs) proposed in an embodiment of this application is shown.

[0040] Figure 2 A block diagram of the control scheme for the UAV adaptive sliding mode control method proposed in this application is shown.

[0041] Figure 3 The diagram shows the position tracking effect of the adaptive sliding mode control method for UAVs.

[0042] Figure 4The diagram shows the quality and external disturbance estimation effects of the UAV adaptive sliding mode control method.

[0043] Figure 5 The diagram shows the 3D trajectory tracking effect of the UAV adaptive sliding mode control method. Detailed Implementation

[0044] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0045] Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] In existing technologies, position and attitude controllers are often implemented using classic PID algorithms, which allow for rapid debugging and deployment without complex control theory. However, for quadcopter UAVs experiencing external disturbances during flight and interference caused by changes in the UAV's own characteristics, classic PID algorithms cannot guarantee control stability and tracking accuracy, resulting in poor control performance. Sliding mode controllers are robust to uncertainties in system parameters and external disturbances under certain conditions. However, sliding mode controllers are often designed with disturbances assumed to be a quantity with a known upper bound and implemented using a high-gain approach, which can lead to chattering, a common problem in sliding mode control.

[0047] Therefore, in order to solve the above-mentioned technical problems, this application proposes an adaptive sliding mode control method, system, device and medium for unmanned aerial vehicles (UAVs), which can effectively reduce chattering without sacrificing sliding mode control performance and improve the robustness and control stability of the quadcopter UAV system to external interference.

[0048] Please refer to Figure 1 , Figure 1 This paper presents a flowchart illustrating an adaptive sliding mode control method for unmanned aerial vehicles (UAVs) according to an embodiment of this application. The method includes:

[0049] Step S1: Establish the nonlinear kinematics and dynamics model of the quadcopter UAV.

[0050] The nonlinear kinematics and dynamics model of a quadrotor UAV is affected by parameter perturbations, external forces, and torque disturbances. In order to construct a mathematical model that can truly reflect the characteristics of the quadrotor UAV system, this model is based on the Newton-Euler equations. It not only describes the basic kinematic relationships and dynamic laws of the UAV in space, but also actively quantifies the unavoidable parameter perturbations and complex external disturbances during flight into disturbance forces and disturbance torques acting on the system, and introduces them as intrinsic variables into the model equations.

[0051] The equations for the nonlinear kinematic and dynamic model are as follows:

[0052] ;

[0053] in The initial mass of the drone. For the quality error of the drone, Here is the position acceleration vector. It is the total mass. It is the acceleration due to gravity. It is a unit vector. The virtual control input force is defined as follows: , For the force channel control input, This is the transformation matrix from the body coordinate system to the inertial coordinate system. The interference forces present during the translational movement of the drone. This represents the moment of inertia of the UAV in the ground coordinate system. For the error of the generalized rotational inertia matrix of the UAV, The attitude angular acceleration vector, For the Coriolis force and the centrifugal term, Attitude angular velocity, This is the input quantity for torque channel manipulation. This refers to the disturbance torque present during the rotational motion of the drone.

[0054] According to the Newton-Euler equations, the dynamics and kinematics equations of a quadcopter UAV are expressed as follows: ,

[0055] in This represents the position of the UAV in the ground coordinate system. Let be the linear velocity of the UAV in the ground coordinate system. The linear velocity of the drone's motion. Let X be the linear acceleration of the UAV in the body coordinate system. To rotate the attitude angle of the drone, The rate of change of the UAV's rotational attitude angle. This is the transformation matrix from the inertial coordinate system to the body coordinate system. This is the transformation matrix between the UAV's angular velocity around its axis and its attitude angular rate. It is the acceleration due to gravity. The rotational attitude angular rate of the UAV. Let be the angular acceleration of the UAV in the body coordinate system. Here is the rotational inertia matrix of the UAV. For the force channel control input, For the quality of drones, The torque channel control input is specifically divided into: For the roll channel control input, For pitch channel control input, This is the input for yaw channel control. Let be the transformation matrix from the body coordinate system to the inertial coordinate system, expressed as: , The transformation matrix between the UAV's angular velocity around its axis and its attitude angular velocity is expressed as follows: Considering the effects of parameter perturbations and external disturbances, the dynamics and kinematic equations of the quadcopter UAV can be written as: ,in , , This represents the moment of inertia of the UAV in the ground coordinate system. and For the mass of the UAV and the error of the generalized rotational inertia matrix, and The interference forces and torques present during the translational and rotational motion of the UAV. The Coriolis force and the centrifugal term are expressed as follows:

[0056] .

[0057] Step S2: Based on the nonlinear kinematics and dynamics model of the quadrotor UAV, the mass and interference force of the quadrotor UAV are estimated in real time through parameter adaptive adjustment technology.

[0058] Based on the established dynamic model that includes explicit disturbance terms, key uncertainties in the position control loop are actively observed and identified. By designing adaptive parameter adjustment technology and utilizing measurable signals such as the UAV's real-time position tracking error, the total mass of the system (initial mass and unknown mass changes) and the equivalent disturbance force acting on the UAV's translational motion are estimated. This transforms quantities that are considered unknown disturbances in traditional control into estimated values ​​that can be quantified in real time, which is a prerequisite for achieving high-precision position control.

[0059] The estimates of the mass and interference force of the quadcopter UAV are as follows:

[0060] ;

[0061] in, The time derivative of the quality estimator. An adaptive gain greater than 0 For linear sliding surface with positional error, Linear sliding surface for positional error transpose, Let the acceleration vector be the desired position. Design parameters for the sliding surface. This is the time derivative of the position tracking error. For position control gain, For estimating the mass of the drone, The time derivative of the disturbance force estimator. For the design of linear sliding surface for positional error, For UAV position tracking error, For the desired position, This refers to the actual location.

[0062] The position controller outputs the desired attitude angle and the desired pull channel control amount based on the input desired position to ensure that the position tracking error asymptotically converges to 0. The current UAV position tracking error is defined as: ,in, The desired position is given. Introducing a linear sliding surface for positional error: The desired tensile force control law is: in, For estimating the mass of the drone, This is an estimate of the disturbance force during the UAV's translational motion. To reduce the impact of parameter perturbations and external disturbances on position tracking, an adaptive control law is defined for the above estimate: ,in .

[0063] Step S3: Design a position adaptive sliding mode controller, add mass and disturbance force as compensation terms to the position adaptive sliding mode controller, calculate the desired tension control law for outer loop position tracking, and further decouple to generate the desired attitude angle required for inner loop attitude control.

[0064] Based on real-time estimated mass and disturbance force information, a position-adaptive sliding mode controller with feedforward compensation capability is designed. This controller incorporates the estimated mass and disturbance force as key compensation terms into the calculation of the control law, thereby constructing a desired pull control law that can actively offset system uncertainties, ensuring that the UAV can accurately track the desired flight trajectory. Furthermore, through kinematic decoupling, the calculated spatial pull vector is transformed into commands that the inner-loop attitude control system can execute, namely the desired pitch and roll angles, thus achieving smooth and precise command transmission from outer-loop position control to inner-loop attitude control.

[0065] The position-adaptive sliding mode controller decouples the desired tension channel manipulation amount and the desired attitude angle based on the input desired position.

[0066] Based on the coordinate transformation relationship, at a given desired yaw angle Then, the desired tension control law Decoupling yields the desired attitude angle and the desired tension control amount;

[0067] The expected tension control law is: , This is an estimate of the interference force during the translational motion of the UAV;

[0068] The desired attitude angle is: ;

[0069] in , , and These are the components of the virtual control input force. For the desired pitch angle, For the desired roll angle, This represents the desired amount of tension control.

[0070] The stability of the position-adaptive sliding mode controller is analyzed using Lyapunov theory. Definitions are provided. , Consider the following Lyapunov function to account for the UAV mass estimation error and interference force estimation error: Taking the derivative of this expression, we get:

[0071] According to Lyapunov's stability theorem, the designed position controller is asymptotically stable.

[0072] The desired tension control law is obtained from the formula of the design desired tension control law. Then, according to the coordinate transformation relationship, we have: Given an additional desired yaw angle Then, according to this formula, the desired attitude angle can be obtained. and pulling force control amount As shown below:

[0073] .

[0074] Step S4: Based on the nonlinear kinematics and dynamics model of the quadrotor UAV, the generalized moment of inertia and disturbance torque of the quadrotor UAV are estimated in real time through parameter adaptive adjustment technology.

[0075] To address the key uncertainties in inner-loop attitude control, an adaptive observation system parallel to the position loop estimation was constructed. Based on the established unified model, a parameter adaptive adjustment technique specifically designed for rotational motion was employed. Utilizing real-time signals such as attitude tracking errors, the generalized moment of inertia matrix and the equivalent disturbance torque acting on the airframe, which may vary during actual flight, were accurately estimated online. This process transforms the changes in internal parameters affecting attitude stability and external torque disturbances into quantifiable estimates, providing a precise feedforward compensation basis for the inner-loop attitude controller.

[0076] The generalized moment of inertia and disturbance torque estimates are:

[0077] ;

[0078] The time derivative of the estimator of the generalized rotational moment of inertia matrix. Adaptive gain for moment of inertia estimation Design parameters for the attitude sliding surface. This is the time derivative of the attitude angle tracking error. For attitude control gain, For attitude angle error linear sliding surface, The time derivative of the disturbance torque estimator. The adaptive gain for estimating the disturbance torque. Let be the angular acceleration of the desired attitude angle. , For the designed attitude angle error linear sliding surface, For the desired attitude angle of the UAV, For the UAV attitude angle tracking error, This is an estimate of the disturbance torque caused by the rotational motion of the UAV.

[0079] The attitude controller determines the desired attitude angle based on the input. Output the desired torque manipulation amount. Define the UAV attitude angle tracking error at the current moment as: The linear sliding surface for attitude angle error is introduced as follows: .

[0080] The virtual torque control law is designed as follows: ,in This is an estimate of the generalized rotational inertia matrix of the UAV. This is an estimate of the disturbance torque caused by the UAV's rotational motion. To reduce the impact of parameter perturbations and external disturbances on attitude tracking, an adaptive control law of the following form is defined for the above estimate: ,in .

[0081] Step S5: Design an attitude adaptive sliding mode controller, add the generalized moment of inertia and disturbance torque as compensation terms to the attitude adaptive sliding mode controller, and use the desired attitude angle as the input command. Calculate the desired torque manipulation amount used to drive the UAV, thereby completing the coordinated control of the UAV's position and attitude together with the desired pull control law.

[0082] This step is the inner-loop execution terminal of the entire adaptive sliding mode control system, enabling high-precision and robust attitude tracking control. Using the outputs of previous steps as key inputs: First, it receives the generalized moment of inertia and disturbance torque estimated in real time. These key parameters are directly embedded into the attitude control law design as feedforward compensation terms, allowing the controller to actively counteract the effects of changes in moment of inertia and external torque disturbances, thus improving the inner-loop's anti-interference capability. Next, it receives the decoupled desired attitude angle, using this as the instruction target for attitude tracking. Based on these two types of inputs, the attitude adaptive sliding mode controller calculates and ultimately generates the desired torque manipulation quantity used to directly drive the UAV motors and generate the required body torque. The output desired torque manipulation quantity and the output desired pull control law together constitute the final control command acting on the complete dynamic model of the quadrotor UAV, thereby achieving close coordination between precise outer-loop position tracking and stable inner-loop attitude control, completing the entire closed loop from sensing uncertainty to generating anti-interference control force.

[0083] Desired torque control amount for: , This is a real-time estimate of the generalized rotational inertia matrix. This is a real-time estimate of the disturbance torque.

[0084] The stability of the attitude adaptive sliding mode controller is analyzed using Lyapunov theory. Definitions are provided. , To address the estimation errors of the generalized rotational inertia matrix and the disturbance torque of the UAV, consider the Lyapunov function: Define intermediate variables Taking the derivative of the Lyapunov function, we get:

[0085] According to Lyapunov's stability theorem, the designed attitude controller is asymptotically stable.

[0086] The desired pulling force output of the position adaptive sliding mode controller The desired torque manipulation amount output by the attitude adaptive sliding mode controller This is then applied to the control distributor to generate the control input for the actual mechanism.

[0087] Figure 2 A block diagram of the control scheme for the UAV adaptive sliding mode control method proposed in this application is shown. The system inputs a desired trajectory. The position adaptive sliding mode controller receives this desired trajectory and the actual flight state measured by sensors. By calculating the position tracking error and fusing real-time parameters from mass estimation and disturbance force estimation, it generates a desired pull control law for outer-loop position tracking. Due to the underactuated characteristics of the quadcopter UAV, the controller also needs to decouple the spatial pull command and calculate the desired attitude angle required for inner-loop attitude control. The attitude adaptive sliding mode controller receives this desired attitude angle and the actual flight state, and combines real-time data from generalized moment of inertia estimation and disturbance torque estimation to calculate the desired torque manipulation for stabilizing the attitude. This combination of generalized control quantities output from the position loop and attitude loop is then fed into a control distributor, which converts them into specific speed commands to drive the four motors based on the UAV's dynamic model. These commands ultimately act on the quadcopter UAV's physical model, causing it to produce corresponding flight motion.

[0088] The closed-loop feedback and adaptive mechanism is completed by flight state measurement, which continuously monitors the real state of the UAV and feeds it back to the controllers at all levels and all four parameter estimation modules. The estimation modules dynamically update their optimal estimates of mass, moment of inertia, disturbance force and disturbance torque based on system errors, forming an online correction closed loop that perceives and compensates for system uncertainties in real time.

[0089] In one possible embodiment, the effectiveness of the control strategy of this application is verified by taking the trajectory control of a quadcopter UAV as an example. The parameters of the quadcopter UAV are shown in Table 1:

[0090] Table 1

[0091]

[0092] initial position initial posture Designing the desired flight trajectory of a quadcopter drone for: Designing the mass of a quadcopter drone. The changes are as follows: Design of external interference for quadcopter unmanned aerial vehicle systems The changes are as follows: The position and attitude controller parameters for the quadcopter UAV, as well as the mass and external disturbance estimation parameters, are given in Table 2.

[0093] Table 2

[0094]

[0095] Figure 3 The diagram shows the position tracking effect of the UAV adaptive sliding mode control method. Figure 4 The diagram shows the quality and external disturbance estimation results of the UAV adaptive sliding mode control method. Figure 5 The diagram shows the three-dimensional trajectory tracking effect of the UAV adaptive sliding mode control method. Based on the above method, when there are changes in mass and external disturbances, the position and attitude adaptive sliding mode controller designed in this invention can track the desired trajectory changes well and make real-time estimates of mass and external disturbances.

[0096] Compared with the prior art, the embodiments of this application have the following beneficial effects:

[0097] First, complex coupling and external disturbances are uniformly abstracted into force / torque disturbances, and real-time estimation and compensation are performed through adaptive laws, which effectively improves the control accuracy and robustness of UAVs under parameter perturbations and complex external disturbances.

[0098] Secondly, by introducing the estimated disturbance as a feedforward compensation term into the controller, there is no need to rely on a large switching gain to suppress the disturbance. Thus, without making upper bound assumptions about the disturbance, the chattering problem inherent in sliding mode control is significantly reduced, while maintaining its excellent fast response performance.

[0099] Third, the asymptotic stability of the position and attitude controller was rigorously proven using Lyapunov stability theory. This ensures that the system converges throughout the adaptive control process, providing a theoretical guarantee for achieving accurate trajectory and attitude tracking.

[0100] Fourth, a clear hierarchical control structure of outer-loop position control and inner-loop attitude control is adopted. By using the desired attitude angle decoupled from the outer loop as the command for the inner loop, optimized control with close coordination and hierarchical decision-making between the position and attitude loops is achieved.

[0101] Fifth, it is not dependent on the specific source of the disturbance or a precise mathematical model, and has universal applicability to a variety of uncertainties. The designed adaptive law and controller have a clear structure, and the parameters are easy to adjust, making them convenient for application and implementation in engineering practice.

[0102] The following provides a possible implementation of an adaptive sliding mode control system for unmanned aerial vehicles (UAVs), which executes the various execution steps and corresponding technical effects of the UAV adaptive sliding mode control method shown in the above embodiments and possible implementations. The system includes:

[0103] The model building module is used to build the nonlinear kinematics and dynamics model of the quadcopter UAV. The nonlinear kinematics and dynamics model of the quadcopter UAV is subject to parameter perturbation, external forces and torque disturbances.

[0104] The first estimation module is used to estimate the mass and interference force of the quadrotor UAV in real time based on the nonlinear kinematics and dynamics model of the quadrotor UAV and through parameter adaptive adjustment technology.

[0105] The first generation module is used to design the position adaptive sliding mode controller. Mass and disturbance force are added as compensation terms to the position adaptive sliding mode controller to obtain the desired tension control law and desired attitude angle.

[0106] The second estimation module is used to estimate the generalized moment of inertia and disturbance torque of the quadrotor UAV in real time based on the nonlinear kinematics and dynamics model of the quadrotor UAV and through parameter adaptive adjustment technology.

[0107] The second generation module is used to design the attitude adaptive sliding mode controller. It adds the generalized moment of inertia and disturbance torque as compensation terms to the attitude adaptive sliding mode controller to obtain the desired torque manipulation amount.

[0108] This preferred embodiment provides a computer device that can implement the steps in any embodiment of the UAV adaptive sliding mode control method provided in this application. Therefore, it can achieve the beneficial effects of the UAV adaptive sliding mode control method provided in this application, as detailed in the preceding embodiments, which will not be repeated here.

[0109] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. Therefore, embodiments of this application provide a storage medium storing multiple instructions that can be loaded by a processor to execute the steps of any embodiment of the UAV adaptive sliding mode control method provided in this application.

[0110] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0111] Since the instructions stored in the storage medium can execute the steps in any of the UAV adaptive sliding mode control method embodiments provided in this application, the beneficial effects that any of the UAV adaptive sliding mode control methods provided in this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0112] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An adaptive sliding mode control method for unmanned aerial vehicles (UAVs), characterized in that, The method includes: A nonlinear kinematic and dynamic model of a quadrotor UAV is established, which is subject to parameter perturbation, external force and torque disturbance. Based on the nonlinear kinematics and dynamics model of a quadrotor UAV, the mass and interference force of the quadrotor UAV are estimated in real time through parameter adaptive adjustment technology. Design a position adaptive sliding mode controller, add mass and disturbance force as compensation terms to the position adaptive sliding mode controller, calculate the desired tension control law for outer loop position tracking, and decouple to generate the desired attitude angle required for inner loop attitude control; The position-adaptive sliding mode controller decouples the desired tension channel manipulation amount and the desired attitude angle based on the input desired position. Based on the coordinate transformation relationship, at a given desired yaw angle Then, the desired tension control law Decoupling yields the desired attitude angle and the desired tension control amount; The expected tension control law is: , For estimating the disturbance force of the UAV's translational motion, For estimating the mass of the drone, Let the acceleration vector be the desired position. It is the acceleration due to gravity. It is a unit vector. Design parameters for the sliding surface. This is the time derivative of the position tracking error. For position control gain, For linear sliding surface with positional error, The time derivative of the disturbance force estimator; The desired attitude angle is: ; in , , and These are the components of the virtual control input force. For the desired pitch angle, For the desired roll angle, The desired pulling force is determined by the generalized moment of inertia and disturbance torque of the quadrotor UAV, which are estimated in real time based on the nonlinear kinematics and dynamics model of the quadrotor UAV through parameter adaptive adjustment technology. An attitude adaptive sliding mode controller is designed, incorporating generalized moment of inertia and disturbance torque as compensation terms. The desired attitude angle is used as the input command, and the desired torque control quantity for driving the UAV is calculated. This, together with the desired pull control law, achieves coordinated control of the UAV's position and attitude. for: , This is a real-time estimate of the generalized rotational inertia matrix. This is a real-time estimate of the disturbance torque. Design parameters for the attitude sliding surface. This is the time derivative of the attitude angle tracking error. For attitude control gain, For attitude angle error linear sliding surface, For the Coriolis force and the centrifugal term, Attitude angular velocity.

2. The UAV adaptive sliding mode control method as described in claim 1, characterized in that, The equations for the nonlinear kinematic and dynamic model are as follows: ； in The initial mass of the drone. For the quality error of the drone, Let be the position acceleration vector. It is the total mass. It is the acceleration due to gravity. It is a unit vector. The virtual control input force is defined as follows: , For the force channel control input, This is the transformation matrix from the body coordinate system to the inertial coordinate system. The interference forces present during the translational movement of the drone. This represents the moment of inertia of the UAV in the ground coordinate system. For the error of the generalized rotational inertia matrix of the UAV, The attitude angular acceleration vector, For the Coriolis force and the centrifugal term, Attitude angular velocity, This is the input quantity for torque channel manipulation. This refers to the disturbance torque present during the rotational motion of the drone.

3. The UAV adaptive sliding mode control method as described in claim 2, characterized in that, The estimates of the mass and interference force of the quadcopter UAV are as follows: ； in, The time derivative of the quality estimator. An adaptive gain greater than 0 For linear sliding surface with positional error, Linear sliding surface for positional error transpose, Let the acceleration vector be the desired position. Design parameters for the sliding surface. This is the time derivative of the position tracking error. For position control gain, For estimating the mass of the drone, This is the time derivative of the disturbance force estimator.

4. The UAV adaptive sliding mode control method as described in claim 1, characterized in that, The generalized moment of inertia and disturbance torque estimates are: ； in, The time derivative of the estimator of the generalized rotational moment of inertia matrix. Adaptive gain for moment of inertia estimation Design parameters for the attitude sliding surface. This is the time derivative of the attitude angle tracking error. For attitude control gain, For attitude angle error linear sliding surface, The time derivative of the disturbance torque estimator. The adaptive gain for estimating the disturbance torque. Let be the angular acceleration of the desired attitude angle.

5. An adaptive sliding mode control system for unmanned aerial vehicles (UAVs), characterized in that, The system includes: The model building module is used to build the nonlinear kinematics and dynamics model of the quadcopter UAV. The nonlinear kinematics and dynamics model of the quadcopter UAV is subject to parameter perturbation, external forces and torque disturbances. The first estimation module is used to estimate the mass and interference force of the quadrotor UAV in real time based on the nonlinear kinematics and dynamics model of the quadrotor UAV and through parameter adaptive adjustment technology. The first generation module is used to design the position adaptive sliding mode controller. Mass and disturbance force are added as compensation terms to the position adaptive sliding mode controller to obtain the desired tension control law and desired attitude angle. The position adaptive sliding mode controller decouples to obtain the desired tension channel manipulation amount and desired attitude angle according to the input desired position. Based on the coordinate transformation relationship, at a given desired yaw angle Then, the desired tension control law Decoupling yields the desired attitude angle and the desired tension control amount; The expected tension control law is: , For estimating the disturbance force of the UAV's translational motion, For estimating the mass of the drone, Let the acceleration vector be the desired position. It is the acceleration due to gravity. It is a unit vector. Design parameters for the sliding surface. This is the time derivative of the position tracking error. For position control gain, For linear sliding surface with positional error, The time derivative of the disturbance force estimator; The desired attitude angle is: ; in , , and These are the components of the virtual control input force. For the desired pitch angle, For the desired roll angle, This represents the desired amount of tension control. The second estimation module is used to estimate the generalized moment of inertia and disturbance torque of the quadrotor UAV in real time based on the nonlinear kinematics and dynamics model of the quadrotor UAV and through parameter adaptive adjustment technology. The second generation module is used to design the attitude adaptive sliding mode controller. It incorporates the generalized moment of inertia and disturbance torque as compensation terms into the attitude adaptive sliding mode controller to obtain the desired torque control quantity. for: , This is a real-time estimate of the generalized rotational inertia matrix. This is a real-time estimate of the disturbance torque. Design parameters for the attitude sliding surface. This is the time derivative of the attitude angle tracking error. For attitude control gain, For attitude angle error linear sliding surface, For the Coriolis force and the centrifugal term, Attitude angular velocity.

6. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing a computer program, which is loaded and executed by the processor to implement the UAV adaptive sliding mode control method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which is loaded and executed by a processor to implement the UAV adaptive sliding mode control method as described in any one of claims 1-4.

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