A large antenna composite control method and system

Abstract

The present invention discloses a composite control method and system for a large antenna, relating to the technical field of antenna control. The present invention first obtains the shaft angle encoder data of the large antenna at the current moment, then estimates the state of the large antenna through a continuous Kalman filtering algorithm based on the shaft angle encoder data, and then combines proportional-integral control, feedforward control, and optimal feedback control to obtain the final control signal for controlling the large antenna. The present invention accurately estimates the state of the large antenna through a continuous Kalman filtering algorithm, combines proportional-integral control, feedforward control, and optimal feedback control, and on the basis of proportional-integral control, through feedforward control and optimal feedback control, realizes high-precision pointing and tracking control of the large antenna. This composite control strategy improves the servo bandwidth of the system, shortens the system adjustment time, reduces the system overshoot, improves the control effect of the large antenna, and significantly enhances the dynamic response performance of the large antenna.

Description

Technical Field

[0001] The present invention relates to the technical field of antenna control, and particularly relates to a composite control method and system for a large antenna. Background Art

[0002] With the rapid development of modern communication, aerospace, and astronomical observation technologies, the performance requirements for antennas are getting higher and higher. Especially in large antenna systems, their control accuracy is directly related to the accuracy of data transmission and the success or failure of observation tasks.

[0003] In the field of antenna control, due to their simple structure and relatively low accuracy requirements, small antennas usually adopt the traditional Proportional Integral (PI) control method. Field debuggers adjust the controller parameters through the trial and error method. This method is simple and easy to implement, and can meet the usage requirements of small antennas in general environments.

[0004] However, with the continuous progress of technology, the demand for large antennas is increasing day by day. Currently, large antennas play a crucial role in modern communication, aerospace, and astronomical observation and other fields. However, with the increase in antenna aperture and the improvement of operating frequency, their control accuracy faces many challenges.

[0005] The structure of large antennas is complex and the resonance frequency is low, which not only limits the improvement of the servo bandwidth, but also makes large antennas vulnerable to structural deformation during the dynamic response process, limiting the dynamic response speed and control accuracy of large antennas, resulting in a decrease in pointing accuracy.

[0006] Although the traditional PI controller shows good control effects in simple systems (such as small antennas), it is difficult to find suitable parameters to meet the system performance indicators when facing the high-precision requirements of large antennas. At the same time, the PI control based on error is also difficult to meet the follow-up accuracy required for high-precision control of large antennas. In summary, the existing large antenna control methods have low control accuracy, resulting in low pointing accuracy of large antennas. Summary of the Invention

[0007] Based on this, it is necessary to provide a composite control method and system for a large antenna in view of the above technical problems.

[0008] The present invention adopts the following technical solutions:

[0009] The present invention provides a composite control method for a large antenna. First, the present invention obtains the shaft angle encoder data of the large antenna at the current moment; then, based on the continuous motion model of the large antenna, the state of the large antenna at the next moment is estimated by a continuous Kalman filtering algorithm according to the shaft angle encoder data, and an estimated state of the large antenna including an estimated position, an estimated speed, and an estimated acceleration is obtained; thereafter, according to the error between the target position and the estimated position of the large antenna, a proportional-integral control signal is determined by a proportional-integral control algorithm; and according to the target speed, the estimated speed, and the estimated acceleration of the large antenna, based on the response characteristics of the large antenna speed tracking, a feedforward control signal for compensating the follow-up error caused by the change of the target speed is determined; then, according to the estimated state of the current large antenna, a performance index optimization problem is constructed and solved by an optimal feedback control algorithm to determine a feedback control signal; finally, the proportional-integral control signal, the feedforward control signal, and the feedback control signal are fused to obtain a final control signal, and the position and speed of the large antenna are controlled.

[0010] The present invention provides a composite control system for a large antenna, including:

[0011] An acquisition module, configured to acquire the shaft angle encoder data of the large antenna at the current moment;

[0012] A state estimation module, configured to estimate the state of the large antenna at the next moment by a continuous Kalman filtering algorithm according to the shaft angle encoder data based on the continuous motion model of the large antenna, and obtain an estimated state of the large antenna including an estimated position, an estimated speed, and an estimated acceleration;

[0013] A proportional-integral control module, configured to determine a proportional-integral control signal by a proportional-integral control algorithm according to the error between the target position and the estimated position of the large antenna;

[0014] A feedforward control module, configured to determine a feedforward control signal for compensating the follow-up error caused by the change of the target speed based on the response characteristics of the large antenna speed tracking according to the target speed, the estimated speed, and the estimated acceleration of the large antenna;

[0015] An optimal feedback control module, configured to construct and solve a performance index optimization problem by an optimal feedback control algorithm according to the estimated state of the current large antenna to determine a feedback control signal; fuse the proportional-integral control signal, the feedforward control signal, and the feedback control signal to obtain a final control signal, and control the position and speed of the large antenna.

[0016] The present invention provides a computer-readable storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned composite control method for a large antenna is implemented.

[0017] The present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned large antenna composite control method is implemented.

[0018] The above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects:

[0019] The present invention first obtains the shaft angle encoder data of the large antenna at the current moment, then estimates the state of the large antenna through a continuous Kalman filtering algorithm based on the shaft angle encoder data, then determines the proportional-integral control signal through a proportional-integral control algorithm based on the error between the target position and the estimated position of the large antenna, and determines the feedforward control based on the target speed of the large antenna. Finally, the optimal feedback control is added, and the combination of each item obtains the final control signal to control the large antenna.

[0020] The present invention accurately estimates the state of the large antenna through a continuous Kalman filtering algorithm, quickly tracks the position of the large antenna through a proportional-integral control signal, extracts only the speed signal of the target and adds it to the feedforward controller, which can significantly reduce the follow-up error caused by the relative movement of the large antenna, and at the same time does not increase the random error too much, so as to improve the control response speed of the large antenna. Then, combined with the optimal feedback control, high-precision pointing and tracking control of the large antenna are realized. This composite control strategy improves the servo bandwidth of the system, shortens the system adjustment time, reduces the system overshoot, improves the control effect of the large antenna, and significantly improves the dynamic response performance of the large antenna. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0022] Figure 1 is a schematic flow chart of a large antenna composite control method provided by the present invention;

[0023] Figure 2 is a schematic diagram of the hardware composition of a large antenna control system provided by the present invention;

[0024] Figure 3 is a schematic flow chart of a large antenna control provided by the present invention;

[0025] Figure 4 is a schematic diagram of a logic block provided by the present invention;

[0026] Figure 5 is a schematic diagram of test data provided by the present invention Figure 1 ;

[0027] Figure 6 A schematic diagram of test data provided by the present invention Figure 2 ;

[0028] Figure 7 A schematic diagram of a large antenna composite control system provided by the present invention Specific implementation manners

[0029] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention

[0030] Currently, the structure of large antennas is complex, making large antennas vulnerable to structural deformation during the dynamic response process. For wind loads in the working environment, especially rapidly changing gusts, it poses a serious threat to the pointing stability of large antennas. The structural deformation and electrical axis offset of large antennas caused by gusts will significantly reduce the sensitivity and resolution of large antennas, shorten the stable observation duration, and thus affect the accuracy of data transmission and the success rate of observation tasks

[0031] Under the action of wind loads, the antenna structure will deform, which will further reduce the sensitivity and resolution of large antennas, shorten the stable observation duration, and seriously affect the performance of large antennas. However, traditional control methods often seem inadequate when dealing with such complex environmental factors. For example, although the PI controller shows good control effects in simple systems, it is difficult to find appropriate parameters to meet the system performance indicators when facing the high-precision requirements of large antennas and complex environmental disturbances

[0032] Under the harsh requirements of follow-up accuracy, severe working environment (influence of wind loads), and boundary condition limitations (antenna structure resonance frequency), the control accuracy of traditional antenna control methods is relatively low. In response to this, the present invention establishes a non-linear quantitative characterization model of antenna pointing error under interference conditions, that is, the continuous motion equation of large antennas. On this basis, by integrating PI control and feedforward (FF) control technologies, a large antenna composite control system based on a high-order state optimal estimator and an optimal feedback controller is designed. This system can not only break through the problem of gust disturbance pointing compensation, but also effectively solve the problems of reduced sensitivity, resolution, and stable observation duration caused by structural deformation of large antennas in complex environments, thereby achieving precise compensation and control of antenna structural deformation, and significantly improving the pointing accuracy and tracking performance of large antennas in complex environments

[0033] The technical solutions provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0034] Figure 1 The following is a schematic flowchart of a large antenna composite control method in the present invention, which specifically includes the following steps:

[0035] S101: Obtain the shaft angle encoder data of the large antenna at the current moment.

[0036] S102: Based on the continuous motion model of the large antenna, estimate the state of the large antenna at the next moment through the continuous Kalman filtering algorithm according to the shaft angle encoder data, and obtain the estimated state of the large antenna including the estimated position, estimated speed, and estimated acceleration.

[0037] S103: Determine the proportional-integral control signal through the proportional-integral control algorithm according to the error between the target position and the estimated position of the large antenna.

[0038] S104: Determine the feedforward control signal for compensating the follow-up error caused by the change of the target speed based on the response characteristics of the large antenna speed tracking according to the target speed, estimated speed, and estimated acceleration of the large antenna.

[0039] S105: Construct and solve the performance index optimization problem through the optimal feedback control algorithm according to the estimated state of the current large antenna, and determine the feedback control signal; fuse the proportional-integral control signal, feedforward control signal, and feedback control signal to obtain the final control signal, and control the position and speed of the large antenna.

[0040] For the sake of convenience of description, only the server will be taken as the execution entity for description below. The server mentioned in the present invention can be a server set up on the service platform, or a device such as a desktop computer or a notebook computer that can execute the solution of the present invention.

[0041] In the actual working environment, gust disturbance is one of the main factors affecting the pointing accuracy of large antennas. Gusts will cause the electrical axis of the large antenna to deviate from the target, resulting in relative motion errors. Since the electrical axis is much more sensitive to wind loads than the mechanical axis, it is impossible to accurately estimate the disturbance amount of the wind load on the electrical axis only relying on the shaft angle encoding data of the mechanical axis. In addition, the combined action of non-linear factors such as wind loads, gravity loads, and temperature loads will exacerbate the inconsistency between the electrical axis and the mechanical axis of the large antenna, further reducing the control effect.

[0042] With the continuous development of control theory, the emergence of the optimal state estimator and the optimal feedback controller provides a new idea for solving the large antenna control problem. The large antenna can refer to a large-aperture radio telescope antenna, etc.

[0043] The optimal state estimator can utilize the input and output data of the system and the noise statistical characteristics to accurately estimate the internal state of the system and provide accurate feedback information for the controller. The optimal feedback controller, on the other hand, can design the optimal control strategy according to the preset performance index, enabling the system to achieve the optimal suppression of interference while meeting the performance requirements. Based on this background, the present invention proposes a composite control method for large antennas, aiming to achieve high-precision pointing and tracking control of large antennas in complex environments and provide strong support for the technological development in fields such as deep space exploration and astronomical observation.

[0044] The composite control method for large antennas based on the optimal state estimator and the optimal feedback controller is implemented from both hardware and software aspects.

[0045] Regarding the basic composition of the hardware, in one or more embodiments of the present invention, in the control system of the large antenna, due to the large volume, large inertia, and limited structural stiffness of the large antenna, in order to meet the pointing accuracy requirements of the large antenna, multiple motors (such as more than 4 motors) can be used to work simultaneously for backlash elimination. Since multiple motors work simultaneously, it is often required that the speeds and positions of multiple motors can be synchronized with high precision, and their synchronization accuracy directly affects the reliability of the system and the performance of the antenna.

[0046] Based on this, the POWERLINK bus can be used as the communication link, and the programmable computer (Programmable Computer Controller, PCC) can be used as the core. At the same time, a multi-motor control system with master-slave redundancy is adopted, which can achieve fast feedback and precise positioning of speed and position.

[0047] Figure 2 This is a schematic diagram of the hardware composition of a large antenna control system in the present invention. In the composition of the control system, the host can be selected as the main station, and a redundant system is equipped. A POWERLINK bus control module is added, and multiple drivers and 1 I / O controller are added in the bus control module. These devices are connected together to form a POWERLINK ring network, laying a physical foundation for the implementation of POWERLINK.

[0048] Among them, the main station, as the core of the entire network control, is used to implement the network topology (MN, Mininet) function of the network. The cycle period can be set to 2 ms, and the communication rate is set to gigabit. Since POWERLINK does not allow 2 active hosts in the same network, that is, only one of the two hosts can be online at the same time, and only when one host goes offline is it allowed for another host to work online. Correspondingly, the main station address and the redundant system address can be set.

[0049] For the control method of software, the composite control method of a large antenna includes an optimal estimator, an optimal controller, a PI controller, and a feedforward controller, as Figure 3 shown, Figure 3 which is a schematic diagram of a large antenna control process in the present invention. Figure 3 In , the "+" represents positive feedback, and the "-" represents negative feedback.

[0050] In one or more embodiments of the present invention, it is discussed under the assumption that the large antenna is a continuous linear time-invariant system. Compared with the discretized mathematical model that discretizes the continuous-time system at fixed sampling intervals and describes it with a difference equation, there is an approximation error. The continuous mathematical model uses a continuous-time differential equation to describe the system state change and can accurately reflect the dynamic characteristics of the system.

[0051] Therefore, if the state model is known, the mathematical model of the continuous motion equation of the controlled object can be expressed as: , . In the formula, is the state vector of the system; is the control input vector; is the control output vector of the system; A , B and C are system constants; is the noise high-order transfer matrix; and are both random interferences of the system and follow a Gaussian distribution with a mean of 0, and their covariance matrix is , , is the Dirac delta function, is the process noise covariance matrix, is the measurement noise covariance matrix, and the subscript T represents the transpose.

[0052] For a large antenna, it can be represented by the above mathematical model, but it should be noted that the influence of the noise high-order transfer matrix needs to be considered, which depends on the specific application scenario and system design. At the same time, in addition to the random interference inside the system, the external environment will also interfere with the antenna system, such as electromagnetic interference, thermal noise, etc. These interferences need to be described by the noise terms in the model, and measures should be taken to suppress them in the system design. Therefore, in order to reduce the difference between the model and the actual system, the system needs to be calibrated regularly, and a compensation term should be introduced into the model to correct these differences. Based on this, in one or more embodiments of the present invention, the continuous motion model of the large antenna includes noise terms corresponding to wind speed and temperature.

[0053] Further, in a control system, it is often necessary to estimate the internal state of the system because directly measuring the system state may be difficult or costly. In one or more embodiments of the present invention, the server may first obtain the shaft angle encoder data of the large antenna at the current moment. The shaft angle encoder can obtain the position angle information of the antenna, which is crucial for subsequently obtaining relevant information such as the speed of the antenna and the implementation of the control algorithm. Based on the continuous motion equation of the large antenna, a preliminary prediction of the state of the large antenna at the next moment is made to obtain a preliminary predicted state, and then through the continuous Kalman filtering algorithm, with the shaft angle encoder data as the observation data, the Kalman gain is determined to correct and update the preliminary predicted state.

[0054] The Kalman filter is a very effective state estimation method that can utilize the input-output data and noise statistical characteristics of the system to optimally estimate the system state. In a continuous motion state, the Kalman filter can be updated as follows:

[0055] Time prediction:

[0056] ;

[0057] ;

[0058] Measurement update:

[0059] ;

[0060] ;

[0061] ;

[0062] In the formula, is the state prediction at the current moment, is the integral component of the state prediction at the current moment, is the integral component of the control input vector, is the prediction covariance matrix at the current moment, is the integral component of the prediction covariance matrix at the current moment, is the process noise, is the Kalman gain, is the measurement noise covariance matrix, is the control output vector, is the identity matrix.

[0063] In one or more embodiments of the present invention, the Kalman filtering algorithm can be used to estimate the state of the large antenna, and the said state may include position, speed, and acceleration.

[0064] In addition, in one or more embodiments of the present invention, when the continuous motion model of the large antenna includes noise terms corresponding to wind speed and temperature shock, in addition to obtaining the shaft angle encoder data of the large antenna at the current moment, the server can also obtain the wind speed sensor data and temperature shock sensor data of the large antenna at the current moment. Then, through the continuous Kalman filtering algorithm, using the shaft angle encoder data, wind speed sensor data, and temperature shock sensor data as observation data, the Kalman gain is determined to correct and update the preliminary prediction state.

[0065] Thus, in the scenario of large antenna state estimation, the Kalman filtering algorithm continuously adjusts the state estimation by combining the dynamic model of the system and the measurement data of the sensors to minimize the estimation error. This real-time state estimation provides accurate feedback information for subsequent control strategies, enabling the composite control method to effectively improve the control accuracy and anti-interference ability of the large antenna.

[0066] 1) Avoiding discretization error, the continuous Kalman filter can more accurately estimate the state of the large antenna, thus achieving more precise control, improving the pointing accuracy and tracking performance of the large antenna, as proven below:

[0067] According to the object motion equation described above, after discretization, it can be defined as: .

[0068] Among them, is the state transition matrix, and T is the sampling period.

[0069] According to Taylor's formula, when T is very small, can be approximated as: .

[0070] Therefore, this approximation may make the model inaccurate in high-frequency or fast dynamic systems, inevitably resulting in estimation errors. The continuous Kalman filter directly processes continuous signals, avoiding discretization errors and being able to more accurately estimate the system state.

[0071] 2) The continuous Kalman filter can process continuous signals in real time and has better adaptability to the dynamic changes of the large antenna. This is reflected in the dynamic equation of its error covariance matrix, as proven below:

[0072] ;

[0073] This equation shows that is continuously updated and can quickly respond to system dynamic changes. In contrast, the update frequency of the discrete Kalman filter is limited by the sampling period T and cannot adjust the error covariance matrix in real time, thus affecting the dynamic response speed of the large antenna.

[0074] 3) The numerical stability of continuous Kalman filtering is relatively good, and it can avoid numerical problems caused by discretization in the composite control of large antennas, thereby enhancing the stability and reliability of the entire large antenna. The proof is as follows:

[0075] As can be seen from the foregoing, in continuous Kalman filtering, the dynamic equation of the error covariance matrix is: .

[0076] To prove its stability, first, consider the symmetry and positive definiteness of

[0077] Symmetry: Assume that is symmetric, that is, .

[0078] Then, for any t > 0, we have: .

[0079] Since and are symmetric, the right side of the above equation is zero. Therefore, remains unchanged. Since , then holds for all t > 0.

[0080] Positive definiteness: Consider the Lyapunov function of : .

[0081] where tr( ) represents the trace of the matrix. Since is symmetric, its trace is equal to the sum of all its eigenvalues.

[0082] , since , then .

[0083] Because is positive definite and usually has negative eigenvalues. Therefore, it can be obtained that .

[0084] Through the above derivation, it is proved that , that is, the trace of the error covariance matrix of continuous Kalman filtering is decreasing, which indicates that continuous Kalman filtering has good numerical stability.

[0085] Based on the model of the large antenna, the composite control of the large antenna can be further formed according to the PI controller, the feedforward controller, and the optimal controller.

[0086] In one or more embodiments of the present invention, the main task of the PI controller is to adjust the control signal according to the error signal of the large antenna (i.e., the difference between the expected value and the actual value), so as to reduce the error and make the large antenna point close to the expected value. Specifically, the PI controller achieves this goal through two parts: proportional and integral. The feedforward controller superimposes the deviation of the extracted electrical axis (shaft angle encoder data) from the target position or the self-tracking path and the electrical axis coordinates (the corrected shaft angle encoded value) to generate a feedforward control signal. This signal is directly added to the servo inner-loop speed loop, significantly reducing the follow-up error caused by the relative movement between the target and the electrical axis, while avoiding excessive increase in random errors, thereby improving the control response speed of the large antenna. The optimal feedback controller adjusts the control strategy in real time based on the state estimation information provided by the continuous Kalman filter. By minimizing a performance index, the optimal feedback controller is designed to quickly stabilize the system state to zero, while keeping the amplitude and change rate of the control input within an acceptable range. The stability of the closed-loop system is analyzed through the Lyapunov function to ensure that the system is stable in the sense of Lyapunov. The optimal feedback controller can effectively suppress noise and interference, improving the robustness of the antenna control.

[0087] Among them, the PI controller is one of the main controllers of the antenna system, occupies a very important position in various antenna controls, and is also the most commonly used and effective controller at present.

[0088] In one or more embodiments of the present invention, the proportional-integral control signal can be determined according to the error between the target position and the estimated position of the large antenna through the following formula by the proportional-integral control algorithm: .

[0089] Among them, is the proportional-integral control law, is the error between the target position and the estimated position of the large antenna, is the proportional control signal, is the integral control signal, t is the time.

[0090] If there is a positive definite matrix P , for the previously established continuous linear time-invariant system, the following Lyapunov function can be constructed: . In the formula, is the independent variable.

[0091] Taking the derivative, we can get: .

[0092] Substituting the control law of the PI controller into the above formula, we get: .

[0093] If we want to obtain stability in the sense of Lyapunov, it is necessary to prove is negative definite, that is , then .

[0094] Since the matrix P is a positive definite matrix, therefore, for the PI controller, if appropriate and are found, then the control system is stable in the sense of Lyapunov.

[0095] However, with the emergence of large large-aperture radio telescope antennas and the use of higher frequency bands, in the face of various complex environments, it has become very difficult for the PI controller to find and that meet the system requirements, and it is difficult to meet the usage requirements, but it can be used as the basis for various high-order controls.

[0096] For the FF controller, the FF controller adds the processed signal to the servo inner loop speed loop with a relatively high bandwidth, and the control effect also depends to a large extent on the understanding of the control object model. If the control object model is accurately known, the FF controller can achieve an output equal to the input or an error of zero, that is, achieve the so-called perfect invariance. Achieving perfect invariance is not only difficult but also often has the opposite effect, because at this time only the follow-up error caused by the relative movement of the target position or the self-tracking path and the electric axis is minimized. However, since the introduction of the FF controller allows noise and other interferences on this branch to directly enter the servo inner loop with a relatively high bandwidth, increasing the random error may not necessarily result in a good overall effect.

[0097] In the implementation of the FF controller, a method of approximately achieving invariance in the low-frequency band is adopted. Only the speed signal of the target is extracted and added to the FF controller, which can significantly reduce the follow-up error caused by the relative movement of the target and the electric axis, and at the same time will not increase the random error too much, so as to improve the control response speed of the large antenna.

[0098] Specifically, in one or more embodiments of the present invention, a feedforward compensator can be designed according to the system model characteristics of the large antenna. Assume that the transfer function of the system is G ( s ), based on the transfer function of the system of the large antenna G ( s ), the transfer function of the designed feedforward compensator is H ( s ), then the feedforward control signal can be expressed as: .

[0099] Among them, is the target position of the large antenna, that is, is the speed signal of the large antenna; FIndicates filtering operation on the position information of the large antenna.

[0100] By understanding the transfer function of the system G ( s ), a suitable feedforward compensator can be designed H ( s ), such that the feedforward control signal can effectively cancel out certain characteristics of the system, thereby improving the performance of the system. For example, if the transfer function of the system G ( s ) is known, the feedforward compensator H ( s ) can be designed as the inverse function of G ( s ), so that the feedforward control signal can directly cancel out the characteristics of the system, making the output closer to the ideal response.

[0101] In addition, in one or more embodiments of the present invention, the influence of wind speed and temperature on the antenna speed change can also be predicted based on the wind speed sensor data and temperature shock sensor data of the large antenna at the current moment, so as to set the feedforward compensator according to the influence of wind speed and temperature on the antenna speed change, to help the feedforward controller better compensate for the error caused by the target speed change.

[0102] Finally, for the optimal controller, in one or more embodiments of the present invention, linear quadratic regulator (LQR) optimal control can be adopted, which is applicable to linear systems. LQR designs the optimal feedback controller by minimizing a quadratic performance index.

[0103] In the sense of Kalman filtering, the performance index of LQR is usually composed of a weighted quadratic form of state variables and control inputs, and the form is: .

[0104] In the formula, is the vector of state variables after discretization for two-dimensional angles, that is, the vector representation of the azimuth and elevation angles of the large antenna. The weight matrices M and N are symmetric semi-positive definite matrix and symmetric positive definite matrix respectively, that is M = M T , N = N T >0.

[0105] And, the weight matrices M and N , are respectively used to adjust the relative importance of state variables and control inputs. By selecting appropriate M andN It can quickly stabilize the state of the large antenna to zero and keep the amplitude and change rate of the control input within an acceptable range.

[0106] Considering that LQR can obtain the optimal feedback control law .

[0107] Among them, K is the feedback gain matrix obtained by solving the algebraic Riccati equation. This optimal feedback control law can minimize the performance index J and thus achieve the optimal control of the large antenna.

[0108] For the stability analysis of the optimal feedback control, considering the aforementioned linear time-invariant system, assuming that a linear optimal controller is designed and the control law is as shown above. Therefore, a closed-loop system can be expressed as: , select a positive definite quadratic function as the Lyapunov function: .

[0109] Among them, P is a positive definite matrix.

[0110] Calculate the derivative of the Lyapunov function along the trajectory of the closed-loop system: .

[0111] Substitute the state equation of the closed-loop system into:

[0112] .

[0113] In the LQR design, P is the solution of the algebraic Riccati equation: .

[0114] Since , it can be obtained that , so .

[0115] Among them, Q is a positive definite matrix.

[0116] In summary, it can be obtained that .

[0117] This shows that is negative definite, so the optimal controller is stable in the sense of Lyapunov.

[0118] The above stability proofs of the large antenna state estimation, PI controller, feedforward controller and feedback controller ensure the reliability and effectiveness of the composite control strategy in practical applications:

[0119] 1. Improve the servo accuracy: On the basis of stability, the system can track the target position more accurately and reduce errors.

[0120] 2. Improve the servo bandwidth: A stable system can respond faster to input changes, thus increasing the servo bandwidth of the system.

[0121] 3. Shorten the adjustment time: Stability ensures that the system can quickly return to a stable state after being disturbed, shortening the adjustment time.

[0122] 4. Reduce the overshoot: Through stability analysis and optimization of control strategies, the overshoot of the system can be effectively reduced, avoiding excessive oscillations.

[0123] 5. Enhance the wind disturbance resistance ability: The influence of disturbances is considered in the stability proof, enabling the system to better resist wind disturbances and maintain stable performance in practical applications.

[0124] In summary, the combination and application of the composite controller have greatly improved the follow-up accuracy of large antennas in a noisy environment, increased the servo bandwidth, shortened the adjustment time, reduced the overshoot, and have a strong wind disturbance resistance ability. Finally, the server can add and fuse the obtained proportional-integral control signal, feedforward control signal, and feedback control signal to obtain the final control signal, and control the position and speed of the large antenna.

[0125] Based on Figure 1 the large antenna composite control method shown above, the present invention first obtains the shaft angle encoder data of the large antenna at the current moment, then estimates the state of the large antenna through the continuous Kalman filtering algorithm based on the shaft angle encoder data, determines the proportional-integral control signal through the proportional-integral control algorithm based on the error between the target position and the estimated position of the large antenna, determines the feedforward control based on the target speed of the large antenna, and finally adds the optimal feedback control. The combination of each item obtains the final control signal to control the large antenna.

[0126] The present invention proposes a large antenna composite control method and system based on multi-source data fusion and composite control architecture, aiming to solve the problem of high-precision pointing and tracking control in complex environments. By collecting shaft angle encoder and other sensor data in real time, and combining the continuous Kalman filtering algorithm to construct a state estimation model, the accurate reconstruction of the dynamic characteristics of the large antenna is realized. On this basis, in-depth research and integration of typical PI control, feedforward compensation mechanism, and optimal feedback control strategies are carried out to construct a multi-dimensional composite control framework. This architecture significantly improves the dynamic response performance of the system through frequency-domain servo bandwidth expansion and nonlinear disturbance suppression, while effectively shortening the adjustment time and reducing the overshoot. Experimental results show that this method exhibits excellent disturbance resistance ability under complex interference conditions, providing a more excellent solution for the high-precision pointing and tracking of large antennas under complex working conditions.

[0127] The present invention accurately estimates the state of a large antenna through a continuous Kalman filtering algorithm, quickly tracks the position of the large antenna through a proportional-integral control signal, and only extracts the velocity signal of the target and adds it to the feedforward controller, which can significantly reduce the follow-up error caused by the relative movement of the large antenna, and at the same time will not increase the random error too much, so as to improve the control response speed of the large antenna, and then combined with the optimal feedback control, realizes the high-precision pointing and tracking control of the large antenna. This composite control strategy improves the servo bandwidth of the system, shortens the system adjustment time, reduces the system overshoot, improves the control effect of the large antenna, and significantly improves the wind disturbance resistance ability of the antenna in a complex environment.

[0128] The large antenna composite control method based on the optimal state estimator and the optimal feedback controller of the present invention has the following characteristics:

[0129] 1) It improves the servo bandwidth of the large antenna, shortens the adjustment time of the large antenna, and reduces the overshoot of the large antenna;

[0130] 2) It realizes the high-precision synchronization of the speeds and positions of multiple high-power motors of the large antenna.

[0131] When applying the large antenna composite control method provided by the present invention, it is not necessary to execute according to the Figure 1 sequence of each step shown. The specific execution sequence of each step can be determined according to needs, and the present invention does not limit this.

[0132] Based on one or more of the above embodiments, when the present invention is applied, as Figure 4 shown, Figure 4 it is a schematic logic block diagram in the present invention.

[0133] First, hardware initialization can be performed, including:

[0134] Motor and driver configuration: Start the multi-motor control system, ensure that all motors (more than 2) are connected to the main controller through the POWERLINK bus, and complete the initialization configuration.

[0135] Sensor calibration: Calibrate the shaft angle encoder, wind speed sensor, etc., to ensure the accuracy and reliability of the sensor data.

[0136] Redundant system check: Verify the switching function of the primary and standby redundant systems to ensure that the standby controller can seamlessly take over in case of a main controller failure.

[0137] Then software initialization can be performed, which may include:

[0138] Parameter loading: Load system parameters from the configuration file, including motor parameters, controller gains (PI controller, LQR gain matrix, etc.), the initial covariance matrix of the Kalman filter, etc.

[0139] Algorithm initialization: Initialize the PI controller, feedforward controller (FF), Kalman filter, and LQR controller to ensure that each algorithm module is in a standby state.

[0140] Communication link establishment: Establish a communication link between the main controller and each motor driver and sensor through the POWERLINK bus to ensure real-time data transmission.

[0141] After that, the method of the present invention can be executed to generate the final control model, and the final control signal is sent to each motor driver to drive the motor to adjust the position and speed of the antenna. The system performance can also be monitored in real time, and the controller parameters can be dynamically adjusted as needed.

[0142] Furthermore, fault detection and handling can also be performed, including:

[0143] Fault detection: Real-time detection of system faults, such as motor faults, communication interruptions, sensor abnormalities, etc.

[0144] Fault handling: According to the type of fault, execute corresponding handling strategies, such as switching to a redundant system, downgrading the operating mode, etc.

[0145] The pseudo-code for the execution steps of this method is as follows:

[0146] " / / Initialize hardware function

[0147] void Hardware_Init() {

[0148] / / Initialize motors and drivers

[0149] Initialize_Motors();

[0150] / / Calibrate sensors

[0151] Calibrate_Sensors();

[0152] / / Check redundancy system

[0153] Check_Redundancy_System();

[0154] }

[0155] / / Initialize software function

[0156] void Software_Init() {

[0157] / / Load system parameters

[0158] Load_System_Parameters();

[0159] / / Initialize the controller

[0160] Initialize_PI_Controller();

[0161] Initialize_Feedforward_Controller();

[0162] Initialize_Kalman_Filter();

[0163] Initialize_LQR_Controller();

[0164] / / Establish the communication link

[0165] Establish_Communication_Link();

[0166] }

[0167] / / Real-time control loop function

[0168] void Real_Time_Control_Loop() {

[0169] while (true) {

[0170] / / Data acquisition

[0171] Collect_Data();

[0172] / / State estimation

[0173] Estimate_State();

[0174] / / Error calculation

[0175] Calculate_Error();

[0176] / / Control signal generation

[0177] Generate_Control_Signal();

[0178] / / Motor drive

[0179] Drive_Motors();

[0180] / / Monitoring and adjustment

[0181] Monitor_and_Adjust();

[0182] / / Fault detection and handling

[0183] Detect_and_Handle_Fault();

[0184] }

[0185] }

[0186] / / Data acquisition function

[0187] void Collect_Data() {

[0188] / / Collect shaft angle encoder data

[0189] encoder_data = Get_Encoder_Data();

[0190] / / Collect wind speed sensor data

[0191] wind_speed_data = Get_Wind_Speed_Data();

[0192] / / Collect other sensor data

[0193] other_sensor_data = Get_Other_Sensor_Data();

[0194] }

[0195] / / State estimation function

[0196] void Estimate_State() {

[0197] / / Estimate the state using the Kalman filter

[0198] state_estimate = Kalman_Filter(encoder_data, wind_speed_data, other_sensor_data);

[0199] }

[0200] / / Error calculation function

[0201] void Calculate_Error() {

[0202] / / Calculate the error between the target position and the current estimated position

[0203] error = target_position - state_estimate.position;

[0204] }

[0205] / / Control signal generation function

[0206] void Generate_Control_Signal() {

[0207] / / PI control

[0208] pi_control_signal = PI_Controller(error);

[0209] / / Feedforward control

[0210] ff_control_signal = Feedforward_Controller(target_velocity);

[0211] / / Optimal feedback control

[0212] lqr_control_signal = LQR_Controller(state_estimate);

[0213] / / Control signal fusion

[0214] final_control_signal = Combine_Controls(pi_control_signal, ff_control_signal, lqr_control_signal);

[0215] }

[0216] / / Motor drive function

[0217] void Drive_Motors() {

[0218] / / Send the control signal to the motor driver

[0219] Send_Control_Signal_To_Motors(final_control_signal);

[0220] }

[0221] / / Feedback and adjustment function

[0222] void Monitor_and_Adjust() {

[0223] / / Monitor system performance

[0224] Monitor_System_Performance();

[0225] / / Dynamically adjust controller parameters

[0226] Adjust_Controller_Parameters();

[0227] }

[0228] / / Fault detection and handling function

[0229] void Detect_and_Handle_Fault() {

[0230] / / Detect system faults

[0231] fault = Detect_Fault();

[0232] if (fault) {

[0233] / / Execute handling strategy according to fault type

[0234] Handle_Fault(fault);

[0235] }

[0236] }

[0237] / / Entry function

[0238] int main() {

[0239] / / Initialize hardware and software

[0240] Hardware_Init();

[0241] Software_Init();

[0242] / / Enter the real-time control loop

[0243] Real_Time_Control_Loop();

[0244] return 0;

[0245] }”

[0246] The present invention also provides an embodiment of applying the large antenna composite control method provided by the present invention, which is applied to a 70-meter large-aperture antenna system. During multiple mission executions, it operates stably and reliably, and the tracking accuracy meets the performance index requirements. When testing it under multi-source uncertain environmental factors, the actual tracking control accuracy of each axis of the antenna does not exceed 2.5″. Figure 5 Schematic diagram of a kind of test data in the present invention Figure 1 , Figure 5 The tracking error of the AZ axis is shown, and its accuracy is 2.15″. Figure 6 Schematic diagram of a kind of test data in the present invention Figure 2 , Figure 6 The tracking error of the EL axis is shown, and its accuracy is 2.5″.

[0247] The above is the large antenna composite control method provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding large antenna composite control system, as Figure 7 shown.

[0248] Figure 7 Schematic diagram of a large antenna composite control system provided by the present invention, including:

[0249] An acquisition module 201, configured to acquire the shaft angle encoder data of the large antenna at the current moment;

[0250] A state estimation module 202, configured to estimate the state of the large antenna at the next moment based on the continuous motion model of the large antenna and through a continuous Kalman filtering algorithm according to the shaft angle encoder data, and obtain an estimated state of the large antenna including an estimated position, an estimated speed, and an estimated acceleration;

[0251] A proportional-integral control module 203, configured to determine a proportional-integral control signal through a proportional-integral control algorithm according to the error between the target position and the estimated position of the large antenna;

[0252] A feedforward control module 204, configured to determine a feedforward control signal for compensating the follow-up error caused by the change of the target speed based on the response characteristics of the large antenna speed tracking according to the target speed, the estimated speed, and the estimated acceleration of the large antenna;

[0253] An optimal feedback control module 205, configured to construct and solve a performance index optimization problem through an optimal feedback control algorithm according to the estimated state of the current large antenna, and determine a feedback control signal; fuse the proportional-integral control signal, the feedforward control signal, and the feedback control signal to obtain a final control signal, and control the position and speed of the large antenna.

[0254] For the specific limitations of the large antenna composite control system, reference can be made to the limitations of the large antenna composite control method in the above text, which will not be elaborated here. Each module in the above large antenna composite control system can be implemented in whole or in part by software, hardware, and their combinations. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0255] The present invention also provides a computer-readable storage medium storing a computer program, which can be used to execute the above Figure 1 provided large antenna composite control method.

[0256] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 1 provided large antenna composite control method.

[0257] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memories. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. The volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0258] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded by the present invention.

Claims

1. A large antenna composite control method, characterized in that, Including: Obtain the shaft angle encoder data of the large antenna at the current moment; Based on the continuous motion model of the large antenna, estimate the state of the large antenna at the next moment through the continuous Kalman filtering algorithm according to the shaft angle encoder data, and obtain the estimated state of the large antenna including the estimated position, estimated speed and estimated acceleration; Determine the proportional-integral control signal through the proportional-integral control algorithm according to the error between the target position and the estimated position of the large antenna; Based on the response characteristics of the large antenna speed tracking, determine the feedforward control signal for compensating the follow-up error caused by the change of the target speed according to the target speed, estimated speed and estimated acceleration of the large antenna; According to the estimated state of the large antenna at the current moment, construct and solve the performance index optimization problem through the optimal feedback control algorithm to determine the feedback control signal; fuse the proportional-integral control signal, the feedforward control signal and the feedback control signal to obtain the final control signal, and control the position and speed of the large antenna.

2. The large antenna composite control method according to claim 1, characterized in that The continuous motion model of the large antenna includes noise terms corresponding to wind speed and temperature; The estimating the state of the large antenna at the next moment through the continuous Kalman filtering algorithm according to the shaft angle encoder data based on the continuous motion model of the large antenna specifically includes: Obtain the wind speed sensor data and temperature shock sensor data of the large antenna at the current moment; Preliminarily predict the state of the large antenna at the next moment according to the continuous motion equation of the large antenna to obtain the preliminary predicted state; Determine the Kalman gain with the shaft angle encoder data, wind speed sensor data and temperature shock sensor data as the observation data through the continuous Kalman filtering algorithm to correct and update the preliminary predicted state.

3. The large antenna composite control method according to claim 1, characterized in that, The determining the proportional-integral control signal through the proportional-integral control algorithm according to the error between the target position and the estimated position of the large antenna specifically includes: Determine the proportional-integral control signal through the proportional-integral control algorithm according to the error between the target position and the estimated position of the large antenna by the following formula: ； wherein, is the proportional-integral control law, is the error between the target position and the estimated position of the large antenna, is the proportional control signal, is the integral control signal, t is the time.

4. The large antenna composite control method according to claim 1, characterized in that, The constructing and solving the performance index optimization problem through the optimal feedback control algorithm according to the estimated state of the large antenna at the current moment to determine the feedback control signal specifically includes: With the goal of minimizing the state error and control cost of the large antenna, construct a linear quadratic performance index according to the estimated state and control input of the large antenna at the current moment; Solve with the goal of minimizing the linear quadratic performance index to obtain the feedback control law, and generate the feedback control signal according to the feedback control law.

5. A large antenna composite control system, characterized in that, Including: An acquisition module for acquiring the shaft angle encoder data of the large antenna at the current moment; A state estimation module for estimating the state of the large antenna at the next moment through the continuous Kalman filtering algorithm according to the shaft angle encoder data based on the continuous motion model of the large antenna, and obtaining the estimated state of the large antenna including the estimated position, estimated speed and estimated acceleration; A proportional-integral control module for determining the proportional-integral control signal through the proportional-integral control algorithm according to the error between the target position and the estimated position of the large antenna; A feedforward control module, configured to determine a feedforward control signal for compensating the follow-up error caused by the change of the target speed based on the response characteristics of the large antenna speed tracking according to the target speed, estimated speed and estimated acceleration of the large antenna; An optimal feedback control module, configured to construct and solve a performance index optimization problem through an optimal feedback control algorithm according to the estimated state of the current large antenna to determine a feedback control signal; fuse the proportional-integral control signal, the feedforward control signal and the feedback control signal to obtain a final control signal, and control the position and speed of the large antenna.

6. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 4 is implemented.

7. A computer device, characterized in that, It includes a memory, a processor and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in any one of claims 1 to 4 is implemented.

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