Satellite attitude controller parameter self-tuning method
By optimizing the PID parameters of the satellite attitude controller using the particle swarm optimization algorithm, and combining the fitness function and flywheel physical constraints, the problem of difficulty in manually setting the parameters of the satellite attitude controller is solved, and efficient and accurate satellite attitude adjustment is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- AEROSPACE XINGYUN TECH CO LTD
- Filing Date
- 2026-03-27
- Publication Date
- 2026-06-23
AI Technical Summary
In existing technologies, the PID parameter setting of satellite attitude controllers relies on human experience and manual trial and error, which makes it impossible to obtain an accurate optimal solution, affecting the accuracy and efficiency of satellite Earth observation.
By obtaining the desired and actual pointing of the satellite, the control torque is determined. The particle swarm optimization algorithm combined with the fitness function is used for iterative updates to optimize the PID parameters, including proportional, integral, and derivative gains. The physical constraints of the flywheel and the weights of different imaging modes are taken into account to achieve self-tuning.
It improves the accuracy and efficiency of setting satellite attitude controller parameters, ensuring high precision and stability of satellite attitude adjustment, and adapting to the needs of different imaging modes.
Smart Images
Figure CN122260966A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of aerospace technology, and in particular to a method for self-tuning parameters of an attitude controller for an Earth observation satellite. Background Technology
[0002] Earth observation satellites are used for acquiring Earth information, environmental monitoring, and national defense. Their core mission effectiveness depends on whether the high-resolution imaging payload can continuously, stably, and accurately point to the target observation area. To achieve this, the satellite must be able to adjust its attitude according to control commands, ensuring that its optical or radar imaging payloads are pointed at the target area as it travels along its orbit. Essentially, this requires that the three Euler angles in the satellite's body coordinate system remain zero in a stable state, ensuring that the satellite's body coordinate system always coincides with the orbital coordinate system, thus achieving Earth orientation.
[0003] Currently, one approach to achieving this high-precision pointing control involves controlling a reaction flywheel mounted on the satellite using a PID algorithm that combines proportional, integral, and derivative calculations. The PID controller calculates the control torque based on the deviation between the desired and actual pointing; the flywheel outputs this control torque by changing its rotational speed, thereby precisely adjusting the satellite's attitude.
[0004] However, setting the PID parameters is crucial, and determining these parameters relies on human experience and manual trial and error. Manual parameter tuning is also prone to getting trapped in local optima. Therefore, it is impossible to obtain the exact optimal solution for the PID parameters.
[0005] Obtaining accurate and optimal solutions for PID parameters to improve parameter setting efficiency is a pressing technical problem that needs to be solved. Summary of the Invention
[0006] In view of the above problems, the present invention provides a method and apparatus for self-tuning the parameters of the attitude controller of an Earth observation satellite to overcome or at least partially solve the above problems.
[0007] In a first aspect, the present invention provides a method for self-tuning satellite attitude controller parameters, comprising: The desired and actual pointing of the satellite are obtained, and the control torque on the flywheel is determined. The satellite is an Earth observation satellite. Based on the control torque, the initial PID parameters are obtained; The initial PID parameters are used as simulation parameters for the PID controller to simulate satellite attitude control. A simulation method for a PID controller based on flywheel physical constraints is proposed, which constructs a fitness function for the simulation parameters of the PID controller under different satellite imaging modes. Based on the fitness function and the initial PID parameters, the target PID parameters corresponding to the optimal fitness in each satellite Earth imaging mode are determined through iterative updates using the particle swarm optimization algorithm.
[0008] Preferably, obtaining the desired and actual pointing of the satellite and determining the control torque on the flywheel includes: Obtain the satellite's expected and actual pointing; Determine the deviation between the expected direction and the actual direction; Based on the aforementioned deviation, the control torque on the flywheel is determined.
[0009] Preferably, the PID controller simulation method based on flywheel physical constraints constructs a fitness function for the PID controller simulation parameters under different satellite imaging modes, including: The data collected included the satellite's adjustment time when its attitude stabilized, the satellite's overshoot, the steady-state error between the satellite's attitude and the desired direction when its attitude stabilized, the satellite's control energy consumption, the satellite's integral saturation penalty, and the flywheel load penalty. Based on the aforementioned adjustment time, overshoot, steady-state error, control energy consumption, integral saturation penalty, and flywheel load penalty, the fitness function of the PID controller simulation parameters under different satellite imaging modes is constructed using the following formula:
[0010] in, The first weight for the adjustment time, The second weight of the overshoot is... This is the third weight of the steady-state error. This is the fourth weight for controlling energy consumption. The fifth weight of the integral saturation penalty, The sixth weight of the flywheel load penalty, For fitness value, Adjustments are made based on the different satellite Earth imaging modes.
[0011] Preferably, the integral saturation penalty for the satellite is determined as follows: Determine the corresponding integral limit based on the initial PID parameters; Based on the aforementioned integral limit, the maximum integral limit is selected; Based on the maximum integral limit, an integral saturation penalty is determined.
[0012] Preferably, the corresponding integral limit is determined based on the initial PID parameters, specifically calculated according to the following formula:
[0013] in, The maximum torque of the flywheel, For safety reasons, The integral gain is a parameter in the PID control, which includes proportional gain, integral gain, and derivative gain. This is the integral limit value; Based on the maximum integral limit, the integral saturation penalty is determined, including: By changing the integral gain, the proportion of the maximum integral limit value is recorded; Based on the aforementioned ratio, an integral saturation penalty is determined.
[0014] Preferably, the satellite Earth imaging modes include: an agile imaging mode, a high-precision stable mode, and an energy-saving and safe mode. Based on the fitness function and the initial PID parameters, iterative updates are performed using a particle swarm optimization algorithm to determine the target PID parameters corresponding to the optimal fitness in each satellite imaging mode, including: In agile imaging mode, the first weight of the adjustment time is increased and the second weight of the overshoot is decreased to obtain a first optimal fitness value; Based on the first optimal fitness value, determine the first target PID parameters; In high-precision stable mode, the second weight of the overshoot and the third weight of the steady-state error are increased to obtain a second optimal fitness value; Based on the second optimal fitness value, determine the second target PID parameters; In the energy-saving and safe mode, the sixth weight of the flywheel load penalty is increased to obtain the third optimal fitness value; Based on the third optimal fitness value, the third objective PID parameters are determined.
[0015] Preferably, the PID parameters include: proportional gain, integral gain, and derivative gain. After iteratively updating the target PID parameters corresponding to the optimal fitness for each satellite imaging mode using a particle swarm optimization algorithm based on the fitness function and the initial PID parameters, the method further includes: Each set of PID parameters is replaced by the position vector of a particle. In three-dimensional space, the proportional gain of each set of PID parameters is equivalent to the X-axis data of the particle, the differential gain is equivalent to the Y-axis data of the particle, and the integral gain is equivalent to the Z-axis data of the particle. Historical motion trajectories of target particles corresponding to target PID parameters are plotted to demonstrate the process of finding target particles in a particle swarm through animation.
[0016] Preferably, historical motion trajectories are plotted for the target particles corresponding to the target PID parameters to demonstrate the process of finding the target particles in the particle swarm through animation, including: Obtain the first objective PID parameters corresponding to the individual's historical best fitness and the second objective PID parameters corresponding to the global best fitness during the iteration process; Based on the first target PID parameters and the second target PID parameters, the position vectors of the corresponding particles are obtained; In three-dimensional space, the position vectors of each particle are marked to obtain the historical trajectory of the target particle, so as to show the process of finding the target particle in the particle swarm through animation.
[0017] Preferably, in three-dimensional space, the position vectors of each particle are marked to obtain the historical motion trajectory of the target particle, so as to display the process of finding the target particle in the particle swarm in the form of animation, including: In three-dimensional space, random numbers are generated for each particle, and the range of the random numbers satisfies [0,1]. The velocity of each particle is determined based on the random number and the learning factor. Based on the velocity, the position of each particle is updated, and the historical trajectory of the target particle is plotted to demonstrate the process of finding the target particle in the particle swarm through animation.
[0018] Preferably, after determining the target PID parameters corresponding to the optimal fitness for each satellite imaging mode through iterative updates using a particle swarm optimization algorithm based on the fitness function and the initial PID parameters, the method further includes: Load the Earth scene, select the satellite to perform the Earth imaging mission, and the orbit extrapolation model; Based on the target PID parameters, the attitude changes of the satellite during the Earth imaging mission are shown through an orbit extrapolation model.
[0019] Secondly, the present invention also provides a satellite attitude controller parameter self-tuning device, comprising: The acquisition module is used to acquire the desired and actual pointing of the satellite and determine the control torque on the flywheel, wherein the satellite is an Earth observation satellite; The module is used to obtain initial PID parameters based on the control torque; The simulation module is used to simulate satellite attitude control by using the initial PID parameters as simulation parameters for the PID controller. A module is built for PID controller simulation methods based on flywheel physical constraints, and a fitness function is constructed for PID controller simulation parameters under different satellite imaging modes; The determination module is used to determine the target PID parameters corresponding to the optimal fitness in each satellite imaging mode by iteratively updating the fitness function and the initial PID parameters using a particle swarm optimization algorithm.
[0020] Thirdly, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the first aspect.
[0021] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.
[0022] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages: This invention provides a self-tuning method for satellite attitude controller parameters, comprising: acquiring the desired and actual pointing of the satellite and determining the control torque on the flywheel; obtaining initial PID parameters based on the control torque; using the initial PID parameters as simulation parameters for the PID controller to simulate satellite attitude control; constructing a fitness function for the PID controller simulation parameters under different satellite imaging modes based on a PID controller simulation method with flywheel physical constraints; iteratively updating the fitness function and the initial PID parameters using a particle swarm optimization algorithm to determine the target PID parameters corresponding to the optimal fitness in each satellite imaging mode; by using a particle swarm optimization algorithm combined with the fitness function for iterative updating, the optimal solution for the PID parameters can be obtained, thereby improving the accuracy and efficiency of parameter setting. Attached Figure Description
[0023] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This invention illustrates a flowchart of the satellite attitude controller parameter self-tuning method in an embodiment of the present invention. Figure 2 A schematic diagram showing the position of the target particle corresponding to the global optimal solution in an embodiment of the present invention is provided. Figure 3 This diagram illustrates the attitude changes of a satellite during an Earth imaging mission in an embodiment of the present invention. Figure 4 A schematic diagram of the satellite attitude controller parameter self-tuning device in an embodiment of the present invention is shown; Figure 5 A schematic diagram of the structure of a computer device for implementing a satellite attitude controller parameter self-tuning method in an embodiment of the present invention is shown. Detailed Implementation
[0024] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.
[0025] Example 1: Embodiments of the present invention provide a method for self-tuning satellite attitude controller parameters, such as... Figure 1 As shown, it includes: S101, obtain the desired and actual pointing of the satellite and determine the control torque on the flywheel; the satellite is an Earth observation satellite. S102, based on the control torque, obtain the initial PID parameters; S103 uses the initial PID parameters as simulation parameters for the PID controller to simulate satellite attitude control; S104, A simulation method for a PID controller based on flywheel physical constraints, which constructs a fitness function for PID controller simulation parameters under different satellite imaging modes; S105, based on the fitness function and initial PID parameters, uses the self-managing group algorithm for iterative updates to determine the target PID parameters corresponding to the optimal fitness for each satellite imaging mode.
[0026] First, S101, obtain the desired and actual pointing of the satellite, and determine the control torque on the flywheel, including: Obtain the satellite's expected and actual pointing; Determine the deviation between the expected direction and the actual direction; Based on this deviation, the control torque on the flywheel is determined.
[0027] Specifically, the satellite's actual pointing is its current pointing, i.e., its current attitude, while its desired pointing is the target attitude that needs to be adjusted to.
[0028] The deviation between the expected pointing and the actual pointing is specifically the attitude error e(t). Therefore, based on the attitude error... It can calculate the control torque on the flywheel.
[0029] Specifically, the formula for calculating the control torque is as follows:
[0030] in, For PID parameters, For proportional gain, For integral gain, For differential gain, This represents the attitude error.
[0031] Next, execute S102 to obtain the initial PID parameters based on the control torque.
[0032] The initial PID parameters can be obtained according to the formula described above. To obtain the optimal PID parameters, a particle swarm optimization algorithm is used to search for the optimal solution.
[0033] S103 uses the initial PID parameters as simulation parameters for the PID controller to simulate satellite attitude control.
[0034] Specifically, the initial PID parameters are injected into the satellite attitude control simulation model to simulate the satellite attitude control.
[0035] Next, S104 is executed, which is a PID controller simulation method based on flywheel physical constraints. The fitness function of the PID controller simulation parameters under different satellite imaging modes is constructed.
[0036] Specifically, the data collected includes the adjustment time when the satellite attitude tends to stabilize, the satellite overshoot, the steady-state error between the satellite attitude and the desired direction when the satellite attitude tends to stabilize, the satellite control energy consumption, the satellite integral saturation penalty, and the flywheel load penalty. Based on settling time, overshoot, steady-state error, control energy consumption, integral saturation penalty, and flywheel load penalty, the fitness function corresponding to each set of PID parameters, which incorporates the flywheel physical constraints, is constructed using the following formula:
[0037] in, To adjust the first weight of time, The second weight for overshoot, As the third weight of steady-state error, To control energy consumption as the fourth weighting, The fifth weight for integral saturation penalty, The sixth weight for flywheel load penalty, For fitness value, Adjustments are made based on the different satellite Earth imaging modes.
[0038] The adjustment time is specifically determined by first recording the time series data of the attitude error, and then adjusting according to the time elapsed until the data tends to stabilize. Overshoot is the ratio of the maximum deviation during the adjustment process to the initial attitude error. Steady-state error refers to the steady-state error between the satellite's attitude and the desired pointing direction when the satellite's attitude tends to stabilize. Satellite control energy consumption specifically refers to the total energy consumption of the flywheel. Flywheel load penalty refers to the performance degradation of the flywheel due to load fluctuations, specifically manifested as increased energy loss, decreased control accuracy, and shortened system lifespan.
[0039] The integral saturation penalty for satellites is determined as follows: Determine the corresponding integral limit based on the initial PID parameters; Based on the integral limit, select the maximum integral limit; Based on the maximum integral limit, an integral saturation penalty is determined.
[0040] Specifically, the integral limit is determined based on the initial PID parameters, and is calculated using the following formula:
[0041] in, The maximum torque of the flywheel, For a safety margin, a number between 0.3 and 0.6 should be selected. This refers to the integral gain in a PID controller. PID parameters include proportional gain, integral gain, and derivative gain. This is the integral limit value; Based on the maximum integral limit, the integral saturation penalty is determined, including: By changing the integral gain, the percentage of time that the maximum integral limit is reached is recorded; Based on this ratio, an integral saturation penalty is determined.
[0042] First, the integral limit is dynamically calculated based on the differences in proportional gain among different groups of PID parameters. Then, a maximum integral limit is obtained in each iteration of the particle swarm optimization algorithm. The maximum integral limit is fixed for each simulation. With continuous iteration, the integral term (integral gain) in the PID parameters changes dynamically. If the satellite attitude change contains errors that continue to increase, the integral term (integral gain) will grow larger and larger until it reaches or exceeds its maximum value. The ratio of this time to the total time is the proportion of the maximum integral limit reached. This proportion is recorded, and by evaluating it, the integral saturation penalty can be obtained. When the penalty is high, the corresponding set of PID parameters is discarded; when the penalty is low, the corresponding set of PID parameters is retained.
[0043] Next, it is necessary to calculate the settling time when the satellite attitude tends to stabilize, the satellite overshoot, the steady-state error between the satellite attitude and the desired direction when it tends to stabilize, the satellite control energy consumption, and the flywheel load penalty. The fitness value of this set of PID parameters is then calculated using the aforementioned fitness function. This fitness function is used to select the optimal solution.
[0044] By actively preventing integral saturation in the particle swarm optimization algorithm, control space is reserved for Billy gain and differential gain, ensuring that the maximum torque generated by the integral term (integral gain) will not exceed the flywheel capacity. This prevents the risk of ignoring physical constraints during parameter tuning and avoids potential deterioration or even instability of control performance due to exceeding physical limits in practical applications.
[0045] Next, S105 is executed, which uses the particle swarm optimization algorithm to iteratively update the initial PID parameters based on the fitness function to determine the target PID parameters corresponding to the optimal fitness for each satellite imaging mode.
[0046] Specifically, the satellite imaging modes include: agile imaging mode, high-precision stabilization mode, and energy-saving and safe mode.
[0047] Among them, the Agile Imaging Mode sacrifices some shaking to improve imaging speed. The High-Precision Stabilization Mode sacrifices some speed for ultimate stability and accuracy. The Energy-Saving Safety Mode allows the Particle Swarm Optimization Algorithm to prioritize safety control strategies that minimize component wear.
[0048] In agile imaging mode, the first weight of adjustment time is increased and the second weight of overshoot is decreased to obtain the first optimal fitness value; Based on the first optimal fitness value, determine the first target PID parameters; In high-precision stable mode, the second weight of overshoot and the third weight of steady-state error are increased to obtain the second optimal fitness value; Based on the second optimal fitness value, determine the second target PID parameters; In energy-saving and safe mode, the sixth weight of the flywheel complex penalty is increased to obtain the third optimal fitness value; Based on the third optimal fitness value, the third objective PID parameters are determined.
[0049] By adapting the fitness adjustment scheme to various satellite imaging modes, the target PID parameters corresponding to different satellite imaging modes are obtained.
[0050] In the process of obtaining the optimal fitness, the individual's historical optimal fitness is updated to the current fitness by judging whether the current fitness is greater than the individual's historical optimal fitness. In the process of continuous iteration, if the current fitness is greater than the global optimal fitness, the global optimal fitness is updated to the current fitness.
[0051] This approach gradually approaches the optimal fitness while simultaneously providing a fast and accurate understanding of the target PID parameters. The optimal fitness is then determined, and the target PID parameters are identified.
[0052] Finally, based on the target PID parameters, the satellite attitude is adjusted to make the satellite reach the desired pointing direction.
[0053] Specifically, based on the target PID parameters, the corresponding control torque is output to control the flywheel, thereby adjusting the satellite's attitude so that the satellite reaches the desired orientation.
[0054] In the particle swarm optimization algorithm, each set of PID parameters is replaced by the position vector of a particle. Specifically:
[0055] in, For any set of PID parameters, This refers to the proportional gain in this set of PID parameters. This refers to the integral gain in this set of PID parameters. This is the derivative gain in this set of PID parameters.
[0056] In three-dimensional space, the proportional gain in each set of PID parameters is equivalent to the X-axis data of the particle, the differential gain is equivalent to the Y-axis data of the particle, and the integral gain is equivalent to the Z-axis data of the particle. Historical motion trajectories of target particles corresponding to target PID parameters are plotted to demonstrate the process of finding target particles in a particle swarm through animation.
[0057] The process of finding the target particle in a particle swarm includes: Obtain the first objective PID parameters corresponding to the individual's historical best fitness and the second objective PID parameters corresponding to the global best fitness during the iteration process; Based on the PID parameters of the first target and the PID parameters of the second target, the position vectors of the corresponding particles are obtained; In three-dimensional space, the position vectors of each particle are marked to obtain the historical trajectory of the target particle, so as to show the process of finding the target particle in the particle swarm through animation.
[0058] The specific annotation process is as follows: In three-dimensional space, a random number is generated for each particle, and the range of the random number is [0,1]. The velocity of each particle is determined based on random numbers and a learning factor; Based on this velocity, the positions of each particle are updated, and the trajectory of the target particle is plotted to demonstrate the process of finding the target particle in the particle swarm through animation.
[0059] like Figure 2 The image shows the position of the target particle corresponding to the global optimal solution. The motion trajectory of this target particle can be represented by color to indicate the performance of the PID parameters corresponding to the particle on the trajectory. A red-yellow-green gradient is used to display different performance levels; for example, red represents poor performance, and green represents excellent performance. This allows users to see which regions have better PID parameter performance through color. The particle positions are continuously updated during the iteration process, realizing the process of the animated diamond finding the target particle in the particle swarm.
[0060] In addition to demonstrating the process of finding target particles in a particle swarm, it can also demonstrate the visualization process of controlling satellite attitude through a PID controller.
[0061] Therefore, the method also includes: Load the Earth scene, select the satellite to perform the Earth imaging mission, and the orbit extrapolation model; Based on the target PID parameters, an orbital extrapolation model is used to demonstrate the attitude changes during the satellite imaging mission, such as... Figure 3 As shown.
[0062] Specifically, based on the target PID parameters, Euler angle parameters are calculated, and the satellite's orbit and attitude are displayed in real time through the simulation engine. Based on the orbit extrapolation model, the satellite's orbit is calculated, providing basic orbital data for the simulation. A mission planning algorithm is used to generate the satellite's Earth observation mission. Through the method of mutual transformation between the satellite's body coordinate system and the orbital centroid coordinate system, the pitch, yaw, and roll angles generated during the satellite's Earth imaging mission controlled by the PID controller are demonstrated.
[0063] By selecting the target area and extrapolating the start and end times of the satellite orbit in the Earth scene, as well as the extrapolation step size, the shooting time window of the satellite Earth imaging mission is calculated; by selecting a time window and starting the satellite attitude controller, the entire process of attitude determination, Earth orientation, target acquisition, and target staring during the satellite Earth imaging mission is displayed.
[0064] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages: This invention provides a self-tuning method for satellite attitude controller parameters, comprising: acquiring the desired and actual pointing of the satellite and determining the control torque on the flywheel; obtaining initial PID parameters based on the control torque; using the initial PID parameters as simulation parameters for the PID controller to simulate satellite attitude control; constructing a fitness function for the PID controller simulation parameters under different satellite imaging modes based on a PID controller simulation method with flywheel physical constraints; iteratively updating the fitness function and the initial PID parameters using a particle swarm optimization algorithm to determine the target PID parameters corresponding to the optimal fitness in each satellite imaging mode; by using a particle swarm optimization algorithm combined with the fitness function for iterative updating, the optimal solution for the PID parameters can be obtained, thereby improving the accuracy and efficiency of parameter setting.
[0065] Example 2 Based on the same inventive concept, the present invention also provides a satellite attitude controller parameter self-tuning device, such as... Figure 4 As shown, it includes: The acquisition module 401 is used to acquire the desired and actual pointing of the satellite and determine the control torque on the flywheel, wherein the satellite is an Earth observation satellite; Module 402 is used to obtain initial PID parameters based on the control torque; Simulation module 403 is used to simulate satellite attitude control by using the initial PID parameters as simulation parameters of the PID controller. Module 404 is constructed for a PID controller simulation method based on flywheel physical constraints, and a fitness function for PID controller simulation parameters based on different satellite imaging modes is constructed. The determination module 405 is used to determine the target PID parameters corresponding to the optimal fitness in each satellite imaging mode by iteratively updating the fitness function and the initial PID parameters using a particle swarm optimization algorithm.
[0066] In one alternative implementation, the acquisition module 401 is used for: Obtain the satellite's expected and actual pointing; Determine the deviation between the expected direction and the actual direction; Based on the aforementioned deviation, the control torque on the flywheel is determined.
[0067] In one alternative implementation, the construction module 403 is configured to: The data collected included the satellite's adjustment time when its attitude stabilized, the satellite's overshoot, the steady-state error between the satellite's attitude and the desired direction when its attitude stabilized, the satellite's control energy consumption, the satellite's integral saturation penalty, and the flywheel load penalty. Based on the aforementioned adjustment time, overshoot, steady-state error, control energy consumption, integral saturation penalty, and flywheel load penalty, the fitness function of the PID controller simulation parameters under different satellite imaging modes is constructed using the following formula:
[0068] in, The first weight for the adjustment time, The second weight of the overshoot is... This is the third weight of the steady-state error. This is the fourth weight for controlling energy consumption. The fifth weight of the integral saturation penalty, The sixth weight of the flywheel load penalty, For fitness value, Adjustments are made based on the different satellite Earth imaging modes.
[0069] In one alternative implementation, the construction module 403 is configured to: The integral saturation penalty for satellites is determined as follows: Determine the corresponding integral limit based on the initial PID parameters; Based on the aforementioned integral limit, the maximum integral limit is selected; Based on the maximum integral limit, an integral saturation penalty is determined.
[0070] In one alternative implementation, the construction module 403 is configured to: Based on the initial PID parameters, the corresponding integral limit is determined, specifically by the following calculation formula:
[0071] in, The maximum torque of the flywheel, For safety reasons, The integral gain is a parameter in the PID control, which includes proportional gain, integral gain, and derivative gain. This is the integral limit value; Based on the maximum integral limit, the integral saturation penalty is determined, including: By changing the integral gain, the proportion of the maximum integral limit value is recorded; Based on the aforementioned ratio, an integral saturation penalty is determined.
[0072] In one optional implementation, the satellite Earth imaging mode includes: an agile imaging mode, a high-precision stable mode, and an energy-saving and safe mode. The determination module 404 is used for: In agile imaging mode, the first weight of the adjustment time is increased and the second weight of the overshoot is decreased to obtain a first optimal fitness value; Based on the first optimal fitness value, determine the first target PID parameters; In high-precision stable mode, the second weight of the overshoot and the third weight of the steady-state error are increased to obtain a second optimal fitness value; Based on the second optimal fitness value, determine the second target PID parameters; In the energy-saving and safe mode, the sixth weight of the flywheel load penalty is increased to obtain the third optimal fitness value; Based on the third optimal fitness value, the third objective PID parameters are determined.
[0073] In one optional implementation, the PID parameters include: proportional gain, integral gain, and derivative gain, and further include: a first visualization module for: Each set of PID parameters in the iteration process is replaced by the position vector of a particle. In three-dimensional space, the proportional gain in each set of PID parameters is equivalent to the X-axis data of the particle, the differential gain is equivalent to the Y-axis data of the particle, and the integral gain is equivalent to the Z-axis data of the particle. Historical motion trajectories of target particles corresponding to target PID parameters are plotted to demonstrate the process of finding target particles in a particle swarm through animation.
[0074] In one alternative implementation, the first visualization module is used for: Obtain the first objective PID parameters corresponding to the individual's historical best fitness and the second objective PID parameters corresponding to the global best fitness during the iteration process; Based on the first target PID parameters and the second target PID parameters, the position vectors of the corresponding particles are obtained; In three-dimensional space, the position vectors of each particle are marked to obtain the historical trajectory of the target particle, so as to show the process of finding the target particle in the particle swarm through animation.
[0075] In one alternative implementation, the first visualization module is used for: In three-dimensional space, random numbers are generated for each particle, and the range of the random numbers satisfies [0,1]. The velocity of each particle is determined based on the random number and the learning factor. Based on the velocity, the position of each particle is updated, and the historical trajectory of the target particle is plotted to demonstrate the process of finding the target particle in the particle swarm through animation.
[0076] In one alternative implementation, a second visualization module is further included, for: Load the Earth scene, select the satellite to perform the Earth imaging mission, and the orbit extrapolation model; Based on the target PID parameters, the attitude changes of the satellite during the Earth imaging mission are shown through an orbit extrapolation model.
[0077] Example 3 Based on the same inventive concept, embodiments of the present invention provide a computer device, such as... Figure 5 As shown, it includes a memory 504, a processor 502, and a computer program stored in the memory 504 and executable on the processor 502. When the processor 502 executes the program, it implements the steps of the above-described satellite attitude controller parameter self-tuning method.
[0078] Among them, Figure 5 In this document, a bus architecture (represented by bus 500) is used. Bus 500 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 502 and memory represented by memory 504. Bus 500 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 506 provides an interface between bus 500 and receiver 501 and transmitter 503. Receiver 501 and transmitter 503 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 502 is responsible for managing bus 500 and general processing, while memory 504 can be used to store data used by processor 502 during operation.
[0079] Example 4 Based on the same inventive concept, embodiments of the present invention provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described satellite attitude controller parameter self-tuning method.
[0080] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages is for the purpose of disclosing the best mode of implementation of the invention.
[0081] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.
[0082] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various inventive aspects, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are explicitly recited in each embodiment. Rather, as reflected in each embodiment, inventive aspects lie in fewer than all features of the single foregoing disclosed embodiment. Therefore, the claims, following the detailed description, are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.
[0083] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.
[0084] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the specific implementation, any of the claimed embodiments can be used in any combination.
[0085] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the satellite attitude controller parameter self-tuning device or computer device according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
[0086] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.
Claims
1. A method for self-tuning satellite attitude controller parameters, characterized in that, include: The desired and actual pointing of the satellite are obtained, and the control torque on the flywheel is determined. The satellite is an Earth observation satellite. Based on the control torque, the initial PID parameters are obtained; The initial PID parameters are used as simulation parameters for the PID controller to simulate satellite attitude control. A simulation method for a PID controller based on flywheel physical constraints is proposed, which constructs a fitness function for the simulation parameters of the PID controller under different satellite imaging modes. Based on the fitness function and the initial PID parameters, the target PID parameters corresponding to the optimal fitness in each satellite imaging mode are determined through iterative updates using the particle swarm optimization algorithm.
2. The method as described in claim 1, characterized in that, Obtain the desired and actual pointing of the satellite, and determine the control torque on the flywheel, including: Obtain the satellite's expected and actual pointing; Determine the deviation between the expected direction and the actual direction; Based on the aforementioned deviation, the control torque on the flywheel is determined.
3. The method as described in claim 1, characterized in that, A PID controller simulation method based on flywheel physical constraints is proposed, which constructs a fitness function for the PID controller simulation parameters under different satellite imaging modes, including: The data collected included the satellite's adjustment time when its attitude stabilized, the satellite's overshoot, the steady-state error between the satellite's attitude and the desired direction when its attitude stabilized, the satellite's control energy consumption, the satellite's integral saturation penalty, and the flywheel load penalty. Based on the aforementioned adjustment time, overshoot, steady-state error, control energy consumption, integral saturation penalty, and flywheel load penalty, the fitness function of the PID controller simulation parameters under different satellite imaging modes is constructed using the following formula: in, The first weight for the adjustment time, The second weight of the overshoot is... This is the third weight of the steady-state error. This is the fourth weight for controlling energy consumption. The fifth weight of the integral saturation penalty, The sixth weight of the flywheel load penalty, For fitness value, Adjustments are made based on the different satellite imaging modes.
4. The method as described in claim 3, characterized in that, The integral saturation penalty for satellites is determined as follows: Determine the corresponding integral limit based on the initial PID parameters; Based on the aforementioned integral limit, the maximum integral limit is selected; Based on the maximum integral limit, an integral saturation penalty is determined.
5. The method as described in claim 4, characterized in that, Based on the initial PID parameters, the corresponding integral limit is determined, specifically by the following calculation formula: in, The maximum torque of the flywheel, For safety reasons, The integral gain is a parameter in the PID control, which includes proportional gain, integral gain, and derivative gain. This is the integral limit value; Based on the maximum integral limit, the integral saturation penalty is determined, including: By changing the integral gain, the proportion of the maximum integral limit value is recorded; Based on the aforementioned ratio, an integral saturation penalty is determined.
6. The method as described in claim 1, characterized in that, The satellite imaging modes include: agile imaging mode, high-precision stable mode, and energy-saving and safe mode. Based on the fitness function and the initial PID parameters, the target PID parameters corresponding to the optimal fitness in each satellite imaging mode are determined through iterative updates using a particle swarm optimization algorithm, including: In agile imaging mode, the first weight of the adjustment time is increased and the second weight of the overshoot is decreased to obtain a first optimal fitness value; Based on the first optimal fitness value, determine the first target PID parameters; In high-precision stable mode, the second weight of the overshoot and the third weight of the steady-state error are increased to obtain a second optimal fitness value; Based on the second optimal fitness value, determine the second target PID parameters; In the energy-saving and safe mode, the sixth weight of the flywheel load penalty is increased to obtain the third optimal fitness value; Based on the third optimal fitness value, the third objective PID parameters are determined.
7. The method as described in claim 1, characterized in that, The PID parameters include: proportional gain, integral gain, and derivative gain. After iteratively updating the target PID parameters corresponding to the optimal fitness for each satellite imaging mode using the particle swarm optimization algorithm based on the fitness function and the initial PID parameters, the following is also included: Each set of PID parameters in the iteration process is replaced by the position vector of a particle. In three-dimensional space, the proportional gain in each set of PID parameters is equivalent to the X-axis data of the particle, the differential gain is equivalent to the Y-axis data of the particle, and the integral gain is equivalent to the Z-axis data of the particle. Historical motion trajectories of target particles corresponding to target PID parameters are plotted to demonstrate the process of finding target particles in a particle swarm through animation.
8. The method described in claim 7, characterized in that, Historical motion trajectories of the target particles corresponding to the target PID parameters are plotted to demonstrate the process of finding the target particles in the particle swarm through animation, including: Obtain the first objective PID parameters corresponding to the individual's historical best fitness and the second objective PID parameters corresponding to the global best fitness during the iteration process; Based on the first target PID parameters and the second target PID parameters, the position vectors of the corresponding particles are obtained; In three-dimensional space, the position vectors of each particle are marked to obtain the historical trajectory of the target particle, so as to show the process of finding the target particle in the particle swarm through animation.
9. The method described in claim 8, characterized in that, In three-dimensional space, the position vectors of each particle are labeled to obtain the historical trajectory of the target particle. This is then used to demonstrate the process of finding the target particle in a particle swarm through animation, including: In three-dimensional space, random numbers are generated for each particle, and the range of the random numbers satisfies [0,1]. The velocity of each particle is determined based on the random number and the learning factor. Based on the velocity, the position of each particle is updated, and the historical trajectory of the target particle is plotted to demonstrate the process of finding the target particle in the particle swarm through animation.
10. The method as described in claim 1, characterized in that, After determining the target PID parameters corresponding to the optimal fitness for each satellite imaging mode through iterative updates using the particle swarm optimization algorithm based on the fitness function and the initial PID parameters, the method further includes: Load the Earth scene, select the satellite to perform the Earth imaging mission, and the orbit extrapolation model; Based on the target PID parameters, the attitude changes of the satellite during the Earth imaging mission are shown through an orbit extrapolation model.