Geometric model predictive control method, device and equipment for four-rotor unmanned aerial vehicle with suspension load and storage medium
By constructing a geometric model and a tracking error dynamic model, and combining physical constraints to design a geometric model predictive control method, the problem of reduced control performance of the quadrotor UAV's load-carrying system was solved, achieving accurate trajectory tracking for load transportation and improving system safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-03
AI Technical Summary
Existing technologies struggle to effectively address the multiple physical constraints of the quadcopter drone's load-bearing system, leading to decreased control performance and instability risks, thus limiting its application in complex environments.
A geometric model and a tracking error dynamic model of the UAV-suspension load system are constructed. A geometric model predictive control method is designed in combination with physical constraints. The system achieves effective trajectory tracking by optimizing the control input through rolling time-domain control and terminal constraint optimization.
Precise trajectory tracking control for quadcopter drone payload transportation was achieved in complex environments, improving the system's reliability and safety.
Smart Images
Figure CN121785364A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of aircraft control technology, and in particular to a geometric model predictive control method, device, equipment and storage medium for a quadcopter unmanned aerial vehicle with a suspended load. Background Technology
[0002] In recent years, quadcopter drones have gained widespread attention and application in fields such as material transportation, terrain mapping, and geological exploration in complex environments due to their advantages such as vertical takeoff and landing, stable hovering, and high maneuverability. In transportation missions, to improve efficiency and safety, the payload can be integrated with the drone through rigid connections or robotic arm gripping. However, in engineering practice, it is still more common to transport the payload by suspending it with cables.
[0003] Quadrone drones with suspended loads are highly underactuated and strongly coupled systems that must simultaneously cope with multiple physical constraints during actual operation, such as actuator saturation, attitude safety range, and load sway suppression. While existing geometric control methods can control the drone's attitude and load position, they are typically based on idealized models and struggle to systematically address these practical constraints. This can lead to decreased control performance or even instability during dynamic transport, limiting their application in scenarios with high reliability and safety requirements.
[0004] Model predictive control (MDC), as a control strategy capable of explicitly handling multiple constraints, has significant advantages in the control of complex systems. With the rapid development of computing hardware, MDC has demonstrated its potential in real-time control fields such as aerospace. Although various MDC schemes exist for single quadrotor UAVs, for quadrotor UAV-suspended load systems, the strong nonlinearity and additional degrees of freedom introduced by the load pose challenges to the design of traditional MDCs, including complex modeling, high computational burden, and difficulty in constraint integration. Existing methods often fail to fully coordinate the relationship between system dynamics and actual constraints, thus affecting the accuracy and robustness of trajectory tracking. Summary of the Invention
[0005] This application provides a geometric model predictive control method, apparatus, device, and storage medium for quadrotor drones with suspended loads, aiming to systematically solve the problem of accurate trajectory tracking control of quadrotor drones with suspended loads under multiple physical constraints.
[0006] In a first aspect, this application provides a geometric model predictive control method for a quadcopter unmanned aerial vehicle with a suspended load, comprising: S1. Construct the geometric model and tracking error dynamic model of the UAV-suspended load system; S2. Based on the physical constraints of the quadcopter UAV, set attitude constraints and system control inputs. Constraints; where control inputs , and These are the feedforward control term and the feedback control term, respectively. The constraint control inputs are the changes in force and torque, and the tracking output error is defined. And design the final optimization index. Terminal constraint set and terminal control domain, e x For load position error, This refers to the yaw angle error; S3, in time ,pass Calculate the optimal control input , It predicts the time domain, through Applying rolling time domain control , It is the model predictive control step size. It is a time variable. It's flight time. It is a moment Time refers to future moments The control input for the calculation It is the actual control input applied to the system. Is the system at any time The actual control input received, It is a moment Time refers to future moments Calculate the optimal control input; S4. In a single model predictive control step, determine the timing. If the condition is true, proceed to step S5; otherwise, proceed to step S6. S5. Based on the calculation Update system dynamics model Update the system state using the zero-order hold method Return to step S4, where , It is the sampling interval. It is the derivative of the system state vector. It is a system dynamics model function. It is a moment The system state vector, It is a moment The system control input, It is a moment The system state vector, It is a moment The system state vector, It is a moment System control inputs; S6, Update Update the tracking output error and execute step S7; S7. Determine the current situation. If the condition is met, return to step S3; otherwise, end the flight.
[0007] In one possible design, the geometric model of the quadrotor UAV-suspended load system in the inertial frame in step S1 is represented as follows: , in, It is the center of mass of the drone. It is the location of the load. It is the speed of the load. It is the direction of the cable pointing from the quadcopter drone to the load. It is the angular velocity of the cable. It is the attitude of a quadcopter drone. It is the angular velocity of the quadcopter drone. It's the quality of the quadcopter drone. It is the quality of the load. It is the cable length. It is the inertial matrix of a quadcopter drone. and It is the force and torque generated by the rotor. It is the acceleration of the quadcopter drone. It is a quantity related to the angular acceleration in the direction of the cable. It is gravitational acceleration. It is the unit vector in the vertical direction in the inertial frame. It is the cable direction vector. The cross product matrix, It is the angular acceleration of a quadcopter drone. It is the angular velocity of a quadcopter drone. The cross product matrix; The compact form of the geometric model is as follows: ,in It is a state vector. It is an input vector and , It represents the magnitude of the thrust, and T represents the matrix transpose. It is a system dynamics model function. It is the derivative of the system state vector. It is a moment The system state vector, It is a moment The system control input.
[0008] In one possible design, the tracking error dynamics model in step S1 is expressed as: , in, The acceleration representing the load position tracking error. Indicates the angular velocity in the direction of the cable. express Parallel to The amount, express The vertical component, Indicates the expected acceleration of the load. Angular acceleration representing the cable direction tracking error. This indicates the desired angular acceleration in the direction of the cable. Indicates attitude tracking error. Represents the trace operation of a matrix. This indicates the angular velocity tracking error of a quadcopter drone. This indicates the angular acceleration tracking error of a quadcopter drone. The cross product matrix represents the angular velocity tracking error of a quadcopter drone. This represents the desired angular acceleration of the quadcopter; The compact form of the tracking error dynamics model is as follows: Tracking error , Indicates the rate of change of tracking error. The function representing the tracking error dynamics model, Indicates time Tracking error, E x , E q and E R These represent the load position tracking error, cable direction tracking error, and UAV attitude tracking error, respectively.
[0009] In one possible design, the attitude constraint in step S2 is expressed as: , in It is the attitude matrix The third element of the third column, By Euler attitude angle Calculations show that It is an element of the attitude matrix. The lower limit threshold restricts the pitch and roll angles of quadcopter drones; System control input The input constraints are: and , in It is a moment The magnitude of thrust generated by the rotor It is the upper limit constraint value of thrust. It is a moment The torque generated by the rotor, It is the upper limit constraint value of the torque norm.
[0010] Feedforward control satisfies the following constraints: , in It is the thrust component in feedforward control. It is the upper limit constraint value of the feedforward thrust. It is the torque component in feedforward control. It is the upper limit constraint value of the feedforward torque.
[0011] The changes in force and torque satisfy the following constraints: , in It is a moment The magnitude of the thrust, It is the upper limit of the allowable variation in thrust. This is the upper limit of the allowable variation in torque. It is a moment The magnitude of the torque.
[0012] In one possible design, in step S2, the optimization index Designed as follows: , in To predict the time domain, This is the weight matrix. From time Predicted time The output tracking error, From time Predicted time The control input, From time Predicted end time in the time domain The output tracking error.
[0013] In one possible design, in step S2, the terminal constraint set is designed as follows: , in This is the terminal constraint set.
[0014] In one possible design, in step S2, the terminal control domain is designed as follows: , , , in For the desired thrust of the terminal controller, The feedback gain is the load position error. The feedback gain for cable orientation error. The feedback gain for the attitude error of the UAV. For load position tracking error, For cable direction tracking error, For the attitude tracking error of the UAV, The system matrix for angular velocity error dynamics, is the derivative of the dynamic system matrix for angular velocity error.
[0015] Secondly, this application provides a geometric model prediction control device for a quadcopter unmanned aerial vehicle with a suspended load, the device comprising: The system dynamics modeling module is configured to build the geometric model and tracking error dynamics model of the UAV-suspension load system; The constraint and index design module is configured to set attitude constraints and system control inputs based on the physical constraints of the quadcopter UAV. Constraints; where control inputs , and These are the feedforward control term and the feedback control term, respectively. The constraint control inputs are the changes in force and torque, and the tracking output error is defined. And design the final optimization index. Terminal constraint set and terminal control domain, e x For load position error, This refers to the yaw angle error; The rolling optimization solver module is configured to solve in time. ,pass Calculate the optimal control input , It predicts the time domain, through Applying rolling time domain control , It is the model predictive control step size. It is a time variable. It's flight time. It is a moment Time refers to future moments The control input for the calculation It is the actual control input applied to the system. Is the system at any time The actual control input received, It is a moment Time refers to future moments Calculate the optimal control input; The control step decision module is configured to make decisions at specific times within a single model prediction control step. If the condition is true, run the system status update module; otherwise, run the error and time update module. The system status update module is configured to update based on the calculated values. Update system dynamics model Update the system state using the zero-order hold method Return to the decision module within the execution control step, where , It is the sampling interval. It is the derivative of the system state vector. It is a system dynamics model function. It is a moment The system state vector, It is a moment The system control input, It is a moment The system state vector, It is a moment The system state vector, It is a moment The system control input.
[0016] The error and time update module is configured to update Update the tracking output error and run the control loop judgment module; The control loop judgment module is configured to judge at this time. If the condition is met, return to run the rolling optimization solution module; otherwise, end the flight.
[0017] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to perform the geometric model predictive control method for a quadcopter drone with suspended load as described in the first aspect and various possible designs of the first aspect.
[0018] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the geometric model predictive control method for a quadcopter unmanned aerial vehicle with suspended loads as described in the first aspect and various possible designs of the first aspect.
[0019] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the geometric model predictive control method for a quadcopter unmanned aerial vehicle with suspended loads as described in the first aspect and various possible designs of the first aspect.
[0020] The geometric model predictive control method, apparatus, device, and storage medium for quadrotor unmanned aerial vehicles with suspended loads provided in this application have at least the following beneficial effects: This application integrates geometric control and model predictive control. Targeting the underactuated and strongly coupled characteristics of the quadrotor UAV-suspended load system, a geometric dynamic model and a tracking error dynamic model are constructed. An MPC optimization control strategy with terminal constraints is designed to ensure the effectiveness and safety of quadrotor UAV load transport trajectory tracking control in complex environments. Attached Figure Description
[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0022] Figure 1 A flowchart of a geometric model predictive control method for a quadrotor unmanned aerial vehicle with suspended load provided in an embodiment of this application; Figure 2 3D trajectory diagram of the quadcopter drone and its payload provided in the embodiments of this application; Figure 3 The system status and tracking error diagram provided in the embodiments of this application; Figure 4 The geometric control input diagram provided for the embodiments of this application; Figure 5 This is a structural diagram of a geometric model prediction control device for a quadcopter drone with a suspended load, provided in an embodiment of this application.
[0023] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation
[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0025] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.
[0026] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.
[0027] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.
[0028] This application provides a geometric model predictive control method for a quadrotor unmanned aerial vehicle with a suspended load, the overall process of which is shown in the attached figure. Figure 1 As shown, the method includes the following steps S1 to S8.
[0029] S1. Construct the geometric model and tracking error dynamic model of the UAV-suspended load system.
[0030] In this embodiment, step S1 specifically determines the geometric model and the tracking error dynamics model as follows: The geometric model of the quadrotor UAV-suspended load system under inertial system is as follows: (1) in, It is the center of mass of the drone. It is the location of the load. It is the speed of the load. It is the direction of the cable pointing from the quadcopter drone to the load. It is the angular velocity of the cable. It is the attitude of a quadcopter drone. It is the angular velocity of the quadcopter drone. It's the quality of the quadcopter drone. It is the quality of the load. It is the cable length. It is the inertial matrix of a quadcopter drone. and It is the force and torque generated by the rotor. It is the acceleration of the quadcopter drone. It is a quantity related to the angular acceleration in the direction of the cable. It is gravitational acceleration. It is the unit vector in the vertical direction in the inertial frame. It is the cable direction vector. The cross product matrix, It is the angular acceleration of a quadcopter drone. It is the angular velocity of a quadcopter drone. The cross product matrix.
[0031] To achieve trajectory tracking of the load, the geometric model is transformed into: (2) in , , ,in An operator is defined as: for a 3D vector , .
[0032] Model (2) can be transformed into a compact form. , It is an input vector and .
[0033] The tracking error state can be defined as: (3) in and This is the desired state of the system. Operator is defined as ˇ .
[0034] Substituting (3) into (2) yields the tracking error dynamics model: (4) in, The acceleration representing the load position tracking error. Indicates the angular velocity in the direction of the cable. express Parallel to The amount, express The vertical component, Indicates the expected acceleration of the load. Angular acceleration representing the cable direction tracking error. This indicates the desired angular acceleration in the direction of the cable. Indicates attitude tracking error. Represents the trace operation of a matrix. This indicates the angular velocity tracking error of a quadcopter drone. This indicates the angular acceleration tracking error of a quadcopter drone. The cross product matrix represents the angular velocity tracking error of a quadcopter drone. This represents the desired angular acceleration of the quadcopter; , It is a matrix traces, It is a 3-dimensional vector with 2 degrees of freedom, when Time, always subscript The vector represents the first Each component, since attitude dynamics is feedback linearized, can be selected. In constructing the cost function in MPC, the attitude part in equation (4) can be rewritten as follows: ,in .
[0035] Therefore, the tracking error can be described as ,in , , The tracking error dynamics model (4) can be transformed into a compact form: (5) in Indicates the rate of change of tracking error. The function representing the tracking error dynamics model, Indicates time Tracking error, E x , E q and E R These represent the load position tracking error, cable direction tracking error, and UAV attitude tracking error, respectively.
[0036] Thus, we can obtain the two models mentioned in step S1 of this embodiment.
[0037] S2. Based on the physical constraints of the quadcopter UAV, set attitude constraints and system control inputs. Constraints, control input ,in , These are feedforward control terms and feedback control terms, respectively. Feedforward control and It is an open-loop solution, with constraints set for the changes in force and torque, and the tracking output error defined. And design the final optimization index. Terminal constraint set, terminal control domain.
[0038] In step S2, the attitude constraint is expressed as: , in It is the attitude matrix The third element of the third column, By Euler attitude angle Calculations show that It is an element of the attitude matrix. The lower limit threshold restricts the pitch and roll angles of quadcopter drones.
[0039] System control input The input constraints are: and , in It is a moment The magnitude of thrust generated by the rotor It is the upper limit constraint value of thrust. It is a moment The torque generated by the rotor, It is the upper limit constraint value of the torque norm.
[0040] Feedforward control satisfies the following constraints: , in It is the thrust component in feedforward control. It is the upper limit constraint value of the feedforward thrust. It is the torque component in feedforward control. It is the upper limit constraint value of the feedforward torque.
[0041] The changes in force and torque satisfy the following constraints: , in It is a moment The magnitude of the thrust, It is the upper limit of the allowable variation in thrust. This is the upper limit of the allowable variation in torque. It is a moment The magnitude of the torque.
[0042] The control input constraint is compactly represented as , .
[0043] In step S2, the optimization index Designed as follows: , in To predict the time domain, This is the weight matrix. From time Predicted time The output tracking error, From time Predicted time The control input, From time Predicted end time in the time domain The output tracking error.
[0044] In step S2, the terminal constraint set is designed as follows: , in This is the terminal constraint set.
[0045] In step S2, the terminal control domain is designed as follows: , , , in For the desired thrust of the terminal controller, The feedback gain is the load position error. The feedback gain for cable orientation error. The feedback gain for the attitude error of the UAV. For load position tracking error, For cable direction tracking error, For the attitude tracking error of the UAV, The system matrix for angular velocity error dynamics, is the derivative of the dynamic system matrix for angular velocity error.
[0046] S3, in time , It is flight time, through Calculate the optimal control input , It predicts the time domain, through Applying rolling time domain control , It is the MPC step size.
[0047] S4. In a single MPC step, determine the time. If the condition is true, proceed to step S5; otherwise, proceed to step S6.
[0048] S5. Based on the calculation Update system dynamics model Update the system state using the zero-order hold method , , This is the sampling interval; proceed to step S4.
[0049] S6, Update Time Update tracking output error Proceed to step S7.
[0050] S7. Determine the current situation. If the condition is true, proceed to step S3; otherwise, proceed to step S8.
[0051] S8. When the flight ends, the flight is over.
[0052] The following simulation examples will fully illustrate the feasibility and progressiveness of the method proposed in this application.
[0053] The geometric model of the quadrotor UAV-suspended load system in the inertial frame is shown in formula (1) above. , , , , , The spiral reference trajectory is as follows: (6) According to step S2, the attitude constraints of the quadcopter UAV-suspended load system are as follows: , It is the attitude matrix The corresponding elements in the equation are equivalent to Euler attitude angle constraints. The system's control constraints are Weight matrix , , The initial system output error is , .
[0054] Based on steps S3, S4, and S5, predict the time domain. With an MPC update frequency of 10Hz and a system dynamics update frequency of 50Hz, the optimal control input can be calculated. Applying rolling time domain control This leads to an update of the system dynamics model. and system status .
[0055] Update the output tracking error according to step S6. This enables trajectory tracking control.
[0056] Ultimately, trajectory tracking of the payload and the quadcopter UAV was achieved. The 3D trajectories of the payload and the UAV demonstrated the effective tracking performance of the proposed method. Figure 2 As shown, the load asymptotically tracks the reference trajectory while the quadcopter drone maintains a suitable position, and the load position error... and yaw angle error It asymptotically converges to 0. For example... Figure 3 As shown, Figure 3 It also showcased the Euler attitude angle. and cable swing angle Yaw angle From initial error Gradually converges to 0, Figure 4 The results demonstrate that all control inputs satisfy their respective constraints, illustrating the effectiveness of the designed geometric model predictive control method.
[0057] This application also provides a geometric model prediction control device for a quadcopter drone with a suspended load, such as... Figure 5 As shown, the geometric model prediction control device for a quadcopter UAV with a suspended load includes: System dynamics modeling module 501 is configured to construct the geometric model and tracking error dynamics model of the UAV-suspension load system; The constraint and index design module 502 is configured to set attitude constraints and system control inputs based on the physical constraints of the quadcopter UAV. Constraints; where control inputs , and These are the feedforward control term and the feedback control term, respectively. The constraint control inputs are the changes in force and torque, and the tracking output error is defined. And design the final optimization index. Terminal constraint set and terminal control domain, e x For the load position tracking error vector, This refers to the tracking error of the UAV's yaw angle. The rolling optimization solver module 503 is configured to perform optimization in time. ,pass Calculate the optimal control input , It predicts the time domain, through Applying rolling time domain control , It is the model predictive control step size. It is a time variable. It's flight time. It is a moment Time refers to future moments The control input for the calculation It is the actual control input applied to the system. Is the system at any time The actual control input received, It is a moment Time refers to future moments Calculate the optimal control input; The control step decision module 504 is configured to make a decision at a specific time during a single model prediction control step. If the condition is true, run the system status update module; otherwise, run the error and time update module. System status update module 505 is configured to update based on calculated data. Update system dynamics model Update the system state using the zero-order hold method Return to the decision module within the execution control step, where , It is the sampling interval. It is the derivative of the system state vector. It is a system dynamics model function. It is a moment The system state vector, It is a moment The system control input, It is a moment The system state vector, It is a moment The system state vector, It is a moment System control inputs; Error and time update module 506 is configured to update Update the tracking output error and run the control loop judgment module; The control loop judgment module 507 is configured to judge at this time. If the condition is met, return to run the rolling optimization solution module; otherwise, end the flight.
[0058] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.
[0059] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0060] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.
[0061] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.
[0062] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the geometric model predictive control method for a quadcopter drone with suspended load described in the above embodiments.
[0063] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the geometric model predictive control method for a quadcopter UAV with suspended load described in the above embodiments.
[0064] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.
[0065] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.
[0066] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.
[0067] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.
[0068] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.
[0069] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.
[0070] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.
[0071] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.
[0072] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.
[0073] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.
[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.
Claims
1. A geometric model predictive control method for a quadrotor unmanned aerial vehicle with a suspended load, characterized in that, The method includes: S1. Construct the geometric model and tracking error dynamic model of the UAV-suspended load system; S2. Based on the physical constraints of the quadcopter UAV, set attitude constraints and system control inputs. Constraints; where control inputs , and These are the feedforward control term and the feedback control term, respectively. The constraint control inputs are the changes in force and torque, and the tracking output error is defined. And design the final optimization index. Terminal constraint set and terminal control domain, e x For load position error, This refers to the yaw angle error; S3, in time ,pass Calculate the optimal control input , It predicts the time domain, through Applying rolling time domain control , It is the model predictive control step size. It is a time variable. It's flight time. It is a moment Time refers to future moments The control input for the calculation It is the actual control input applied to the system. Is the system at any time The actual control input received, It is a moment Time refers to future moments Calculate the optimal control input; S4. In a single model predictive control step, determine the timing. If the condition is true, proceed to step S5; otherwise, proceed to step S6. S5. Based on the calculation Update system dynamics model Update the system state using the zero-order hold method Return to step S4, where , It is the sampling interval. It is the derivative of the system state vector. It is a system dynamics model function. It is a moment The system state vector, It is a moment The system control input, It is a moment The system state vector, It is a moment The system state vector, It is a moment System control inputs; S6, Update Update the tracking output error and execute step S7; S7. Determine the current situation. If the condition is met, return to step S3; otherwise, end the flight.
2. The geometric model predictive control method for a quadrotor UAV with suspended load according to claim 1, characterized in that, In step S1, the geometric model of the quadrotor UAV-suspended load system in the inertial frame is represented as follows: in, It is the center of mass of the drone. It is the location of the load. It is the speed of the load. It is the direction of the cable pointing from the quadcopter drone to the load. It is the angular velocity of the cable. It is the attitude of a quadcopter drone. It is the angular velocity of the quadcopter drone. It's the quality of the quadcopter drone. It is the quality of the load. It is the cable length. It is the inertial matrix of a quadcopter drone. and It is the force and torque generated by the rotor. It is the acceleration of the quadcopter drone. It is the angular acceleration in the direction of the cable. It is gravitational acceleration. It is the unit vector in the vertical direction in the inertial frame. It is the cable direction vector. The cross product matrix, It is the angular acceleration of a quadcopter drone. It is the angular velocity of a quadcopter drone. The cross product matrix; The compact form of the geometric model is as follows: ,in It is a state vector. It is an input vector and , It represents the magnitude of the thrust, and T represents the matrix transpose. It is a system dynamics model function. It is the derivative of the system state vector. It is a moment The system state vector, It is a moment The system control input.
3. The geometric model predictive control method for a quadrotor UAV with suspended load according to claim 2, characterized in that, In step S1, the tracking error dynamic model is expressed as: in, The acceleration representing the load position tracking error. Indicates the angular velocity in the direction of the cable. express Parallel to The amount, express The vertical component, Indicates the expected acceleration of the load. Angular acceleration representing the cable direction tracking error. This indicates the desired angular acceleration in the direction of the cable. Indicates attitude tracking error. Represents the trace operation of a matrix. This indicates the angular velocity tracking error of a quadcopter drone. This indicates the angular acceleration tracking error of a quadcopter drone. The cross product matrix represents the angular velocity tracking error of a quadcopter drone. This represents the desired angular acceleration of the quadcopter; The compact form of the tracking error dynamics model is as follows: Tracking error , Indicates the rate of change of tracking error. The function representing the tracking error dynamics model, Indicates time Tracking error, E x , E q and E R These represent the load position tracking error, cable direction tracking error, and UAV attitude tracking error, respectively.
4. The geometric model predictive control method for a quadrotor UAV with suspended load according to claim 1, characterized in that, In step S2, the attitude constraint is expressed as: in It is the attitude matrix The third element of the third column, By Euler attitude angle Calculations show that It is an element of the attitude matrix. The lower limit threshold restricts the pitch and roll angles of quadcopter drones; System control input The input constraints are: and in It is a moment The magnitude of thrust generated by the rotor It is the upper limit constraint value of thrust. It is a moment The torque generated by the rotor, It is the upper limit constraint value of the torque norm; Feedforward control satisfies the following constraints: in It is the thrust component in feedforward control. It is the upper limit constraint value of the feedforward thrust. It is the torque component in feedforward control. It is the upper limit constraint value of the feedforward torque. The changes in force and torque satisfy the following constraints: in It is a moment The magnitude of the thrust, It is the upper limit of the allowable variation in thrust. This is the upper limit of the allowable variation in torque. It is a moment The magnitude of the torque.
5. The geometric model predictive control method for a quadcopter UAV with suspended load according to claim 1, characterized in that, In step S2, the optimization index Designed as follows: in To predict the time domain, This is the weight matrix. From time Predicted time The output tracking error, From time Predicted time The control input, From time Predicted end time in the time domain The output tracking error.
6. The geometric model predictive control method for a quadrotor UAV with suspended load according to claim 5, characterized in that, In step S2, the terminal constraint set is designed as follows: in This is the terminal constraint set.
7. The geometric model predictive control method for a quadcopter UAV with suspended load according to claim 1, characterized in that, In step S2, the terminal control domain is designed as follows: in For the desired thrust of the terminal controller, The feedback gain is the load position error. The feedback gain for cable orientation error. The feedback gain for the attitude error of the UAV. For load position tracking error, For cable direction tracking error, For the attitude tracking error of the UAV, The system matrix for angular velocity error dynamics, is the derivative of the dynamic system matrix for angular velocity error.
8. A geometric model predictive control device for a quadcopter unmanned aerial vehicle with a suspended load, characterized in that, The device includes: The system dynamics modeling module is configured to build the geometric model and tracking error dynamics model of the UAV-suspension load system; The constraint and index design module is configured to set attitude constraints and system control inputs based on the physical constraints of the quadcopter UAV. Constraints; where control inputs , and These are the feedforward control term and the feedback control term, respectively. The constraint control inputs are the changes in force and torque, and the tracking output error is defined. And design the final optimization index. Terminal constraint set and terminal control domain, e x For load position error, This refers to the yaw angle error; The rolling optimization solver module is configured to solve in time. ,pass Calculate the optimal control input , It predicts the time domain, through Applying rolling time domain control , It is the model predictive control step size. It is a time variable. It's flight time. It is a moment Time refers to future moments The control input for the calculation It is the actual control input applied to the system. Is the system at any time The actual control input received, It is a moment Time refers to future moments Calculate the optimal control input; The control step decision module is configured to make decisions at specific times within a single model prediction control step. If the condition is true, run the system status update module; otherwise, run the error and time update module. The system status update module is configured to update based on the calculated values. Update system dynamics model Update the system state using the zero-order hold method Return to the decision module within the execution control step, where , It is the sampling interval. It is the derivative of the system state vector. It is a system dynamics model function. It is a moment The system state vector, It is a moment The system control input, It is a moment The system state vector, It is a moment The system state vector, It is a moment System control inputs; The error and time update module is configured to update Update the tracking output error and run the control loop judgment module; The control loop judgment module is configured to judge at this time. If the condition is met, return to run the rolling optimization solution module; otherwise, end the flight.
9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the geometric model predictive control method for a quadcopter unmanned aerial vehicle with a suspended load as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the geometric model predictive control method for a quadcopter unmanned aerial vehicle with a suspended load as described in any one of claims 1-7.