Large antenna compound control method and system

Abstract

The invention discloses a large antenna compound control method and system, and relates to the technical field of antenna control. According to the method, the axial angle encoder data of the large-scale antenna at the current moment are firstly obtained, then the state of the large-scale antenna is estimated through a continuous Kalman filtering algorithm based on the axial angle encoder data, and a final control signal is obtained by combining proportional-integral control, feedforward control and optimal feedback control so as to control the large-scale antenna. According to the method, the state of the large-scale antenna is accurately estimated through a continuous Kalman filtering algorithm, proportional integral control, feed-forward control and optimal feedback control are combined, and high-precision pointing and tracking control of the large-scale antenna is achieved through the feed-forward control and the optimal feedback control on the basis of the proportional integral control. The composite control strategy improves the servo bandwidth of the system, shortens the adjustment time of the system, reduces the overshoot of the system, improves the control effect of the large-scale antenna, and remarkably improves the dynamic response performance of the large-scale antenna.

Description

Technical Field

[0001] The present invention relates to the field of antenna control technology, and in particular to a large-scale antenna composite control method and system. Background Art

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

[0003] In the field of antenna control, small antennas usually use the traditional proportional integral (PI) control method due to their simple structure and relatively low precision requirements. On-site commissioning personnel adjust the controller parameters through trial and error, which is simple and easy to implement and can meet the use requirements of small antennas in general environments.

[0004] However, with the continuous advancement of science and technology, the demand for large antennas is increasing. At present, large antennas play a vital role in modern communications, aerospace, astronomical observation and other fields. However, with the increase of antenna aperture and operating frequency, its control accuracy faces many challenges.

[0005] Large antennas have complex structures and low resonant frequencies, which not only limits the improvement of servo bandwidth, but also makes large antennas susceptible to structural deformation during dynamic response, limiting the dynamic response speed and control accuracy of large antennas, resulting in reduced pointing accuracy.

[0006] Although the traditional PI controller shows good control effect 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 error-based PI control is also difficult to meet the tracking 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 large antenna composite control method and system to address the above technical problems.

[0008] The present invention adopts the following technical solutions: The invention provides a large-scale antenna composite control method. The invention first obtains shaft angle encoder data of the large-scale antenna at the current moment; then, based on the continuous motion model of the large-scale antenna, the state of the large-scale antenna at the next moment is estimated by a continuous Kalman filtering algorithm according to the shaft angle encoder data, so as to obtain the estimated state of the large-scale antenna including the estimated position, estimated speed and estimated acceleration; then, according to the error between the target position and the estimated position of the large-scale antenna, a proportional integral control signal is determined by a proportional integral control algorithm; and according to the target speed, estimated speed and estimated acceleration of the large-scale antenna, based on the response characteristics of the large-scale antenna speed tracking, a feedforward control signal for compensating for the follow-up error caused by the target speed change is determined; then, according to the current estimated state of the large-scale antenna, a performance index optimization problem is constructed and solved by an optimal feedback control algorithm to determine the feedback control signal; finally, the proportional integral control signal, the feedforward control signal and the feedback control signal are merged to obtain the final control signal, and the position and speed of the large-scale antenna are controlled.

[0009] The present invention provides a large-scale antenna composite control system, comprising: An acquisition module is used to obtain the shaft angle encoder data of the large antenna at the current moment; The state estimation module is used to estimate the state of the large antenna at the next moment based on the continuous motion model of the large antenna through a continuous Kalman filter algorithm according to the shaft encoder data, and obtain the estimated state of the large antenna including the estimated position, estimated velocity and estimated acceleration; A proportional-integral control module, used for determining a proportional-integral control signal through a proportional-integral control algorithm according to an error between a target position and an estimated position of the large antenna; A feedforward control module, for determining a feedforward control signal for compensating for a tracking error caused by a change in the target speed based on a response characteristic of the large antenna speed tracking according to the target speed, the estimated speed and the estimated acceleration of the large antenna; The optimal feedback control module is used to 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, 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.

[0010] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the large antenna composite control method is implemented.

[0011] The present invention provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the large antenna composite control method when executing the program.

[0012] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects: The present invention first obtains the shaft angle encoder data of the large antenna at the current moment, and then estimates the state of the large antenna through a continuous Kalman filtering algorithm based on the shaft angle encoder data, and 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, and finally adds the optimal feedback control, and combines the various items to obtain the final control signal to control the large antenna.

[0013] The present invention uses a continuous Kalman filter algorithm to accurately estimate the state of the large antenna, uses a proportional integral control signal to quickly track the position of the large antenna, and only extracts the speed signal of the target to add to the feedforward controller, which can significantly reduce the tracking error caused by the relative motion of the large antenna, while not increasing the random error too much, thereby improving the control response speed of the large antenna, and then combined with the optimal feedback control to achieve 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 dynamic response performance of the large antenna. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A schematic flow chart of a large antenna composite control method provided by the present invention; Figure 2 A schematic diagram of the hardware composition of a large antenna control system provided by the present invention; Figure 3 A schematic diagram of a large antenna control process provided by the present invention; Figure 4 A logic block diagram is provided for the present invention; Figure 5 A test data schematic diagram provided by the present invention Figure 1 ; Figure 6 A test data schematic diagram provided by the present invention Figure 2 ; Figure 7 A schematic diagram of a large-scale antenna composite control system provided by the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0016] At present, the complex structure of large antennas makes them susceptible to structural deformation during dynamic response. Wind loads in the working environment, especially rapidly changing gusts, pose a serious threat to the pointing stability of large antennas. The structural deformation and electric axis offset of large antennas caused by gusts will significantly reduce the sensitivity and resolution of large antennas, shorten the stable observation time, and thus affect the accuracy of data transmission and the success rate of observation missions.

[0017] Under the action of wind load, the antenna structure will deform, which will further reduce the sensitivity and resolution of large antennas, shorten the stable observation time, and seriously affect the performance of large antennas. However, traditional control methods are often unable to cope with such complex environmental factors. For example, although the PI controller shows good control effects in simple systems, it is difficult to find suitable parameters to meet the system performance indicators when faced with the high precision requirements of large antennas and complex environmental interference.

[0018] Under the demanding tracking accuracy requirements, harsh working environment (influence of wind load) and boundary condition restrictions (resonant frequency of antenna structure), the traditional antenna control method has low control accuracy. To this end, the present invention establishes a nonlinear quantitative characterization model of antenna pointing error under interference conditions, that is, the continuous motion equation of large antennas. On this basis, the PI control and feedforward (FF) control technologies are integrated to design a large antenna composite control system based on a high-order state optimal estimator and an optimal feedback controller. This system can not only break through the difficulty of gust disturbance pointing compensation, but also effectively solve the problem of reduced sensitivity, resolution and stable observation time caused by deformation of large antenna structures in complex environments, thereby achieving accurate compensation and control of antenna structure deformation, and significantly improving the pointing accuracy and tracking performance of large antennas in complex environments.

[0019] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0020] Figure 1 The present invention is a schematic flow chart of a large antenna composite control method, which specifically includes the following steps: S101: Acquire the shaft encoder data of the large antenna at the current moment.

[0021] S102: Based on the continuous motion model of the large antenna, the state of the large antenna at the next moment is estimated through a continuous Kalman filter algorithm according to the shaft encoder data, and an estimated state of the large antenna including an estimated position, an estimated velocity and an estimated acceleration is obtained.

[0022] S103: Determine a proportional-integral control signal through a proportional-integral control algorithm according to an error between the target position and the estimated position of the large antenna.

[0023] S104: Determine a feedforward control signal for compensating for a tracking error caused by a change in the target speed according to the target speed, the estimated speed and the estimated acceleration of the large antenna and based on a response characteristic of the large antenna speed tracking.

[0024] S105: According to the estimated state of the current large antenna, a performance index optimization problem is constructed and solved through an optimal feedback control algorithm to determine a feedback control signal; the proportional integral control signal, the feedforward control signal and the feedback control signal are integrated to obtain a final control signal, and the position and speed of the large antenna are controlled.

[0025] For the convenience of explanation, the following description is only based on the server as the execution subject. The server mentioned in the present invention can be a server set up on the business platform, or a device such as a desktop computer, a notebook computer, etc. that can execute the solution of the present invention.

[0026] In actual working environments, gust disturbance is one of the main factors affecting the pointing accuracy of large antennas. Gusts can cause the electric axis of large antennas to deviate from the target, resulting in relative motion errors. Since the electric axis is much more sensitive to wind loads than the mechanical axis, it is impossible to accurately estimate the disturbance of the wind load on the electric axis by relying solely on the axis angle encoding data of the mechanical axis. In addition, the combined effect of nonlinear factors such as wind loads, gravity loads, and temperature loads will aggravate the inconsistency between the electric axis and the mechanical axis of large antennas, further reducing the control effect.

[0027] With the continuous development of control theory, the emergence of optimal state estimators and optimal feedback controllers has provided new ideas for solving the control problem of large antennas. Large antennas can refer to large-aperture radio telescope antennas, etc.

[0028] The optimal state estimator can use the input and output data and noise statistical characteristics of the system to accurately estimate the internal state of the system and provide accurate feedback information for the controller. The optimal feedback controller can design the optimal control strategy based on the preset performance indicators, so that the system can achieve 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 the fields of deep space exploration, astronomical observation, etc.

[0029] The large antenna composite control method based on optimal state estimator and optimal feedback controller is implemented from both hardware and software aspects.

[0030] Regarding the basic components of the hardware, in one or more embodiments of the present invention, in the control system of a large antenna, due to the large size, large inertia and limited structural stiffness of the large antenna, in order to meet the pointing accuracy requirements of the large antenna, multiple motors (e.g., more than 4 motors) can be used to work simultaneously to eliminate gaps. Since multiple motors are used to work simultaneously, the speeds and positions of the multiple motors are often required to be synchronized with high precision, and the synchronization accuracy directly affects the reliability of the system and the performance of the antenna.

[0031] Based on this, the POWERLINK bus can be used as the communication link, the programmable computer controller (PCC) as the core, and a multi-motor control system with redundant main and standby machines can be used to achieve rapid feedback of speed and position and precise positioning.

[0032] Figure 2 This is a schematic diagram of the hardware composition of a large antenna control system in the present invention. In the control system composition, the host can be selected as the main site, and a redundant system can be provided. A POWERLINK bus control module can be added, and multiple drivers and an I / O controller can be added to the bus control module. These devices are connected together to form a POWERLINK ring network. This lays a physical foundation for the implementation of POWERLINK.

[0033] Among them, the main site is the core of the entire network control and is used to realize the network topology (MN, Mininet) function of the network. The cycle period can be set to 2ms and the communication rate can be set to gigabit. Since POWERLINK does not allow two active hosts in the same network, that is, only one of the two hosts can be online at the same time, only when one host is offline can the other host be allowed to work online. The main site address and redundant system address can be set accordingly.

[0034] For software control methods, the composite control methods for large antennas include optimal estimators, optimal controllers, PI controllers, and feedforward controllers, such as Figure 3 As shown, Figure 3 The figure is a schematic diagram of a large antenna control process in the present invention. Figure 3 The “+” in the equation represents positive feedback, and the “-” represents negative feedback.

[0035] In one or more embodiments of the present invention, the large antenna is discussed on the assumption that it is a continuous linear time-invariant system. Compared with the discretized mathematical model that discretizes the continuous-time system at a fixed sampling interval and describes it with a differential equation, which has approximate errors, the continuous mathematical model uses a continuous-time differential equation to describe the system state changes, which can accurately reflect the dynamic characteristics of the system.

[0036] 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 is a system constant; is the noise high-order transfer matrix; and are all random interferences of the system and obey a Gaussian distribution with a mean of 0. 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 indicates the transpose.

[0037] For large antennas, the above mathematical model can be used to represent it, but it should be noted that the influence of the high-order transfer matrix of noise needs to be considered, which depends on the specific application scenario and system design. At the same time, in addition to the random interference within 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, it is necessary to calibrate the system regularly and introduce compensation terms in 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.

[0038] Furthermore, in control systems, it is often necessary to estimate the internal state of the system, because it may be difficult or expensive to directly measure the system state. 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 the subsequent acquisition of relevant information such as the speed of the antenna and the implementation of the control algorithm. According to the continuous motion equation of the large antenna, the state of the large antenna at the next moment is preliminarily predicted to obtain the preliminary predicted state, and then the Kalman gain is determined by the continuous Kalman filtering algorithm with the shaft angle encoder data as the observation data to correct and update the preliminary predicted state.

[0039] Kalman filter is a very effective state estimation method, which can make the best estimate of the system state by using the input and output data and noise statistical characteristics of the system. In the continuous motion state, Kalman filter can be updated as:

[0040] Time prediction: ; ; Measurement Update: ; ; ; In the formula, To predict the current state, To predict the amount to be integrated for the current state, is the amount to be integrated of the control input vector, is the prediction covariance matrix at the current moment, is the prediction covariance matrix to be integrated at the current moment, is the process noise, is the Kalman gain, is the measurement noise covariance matrix, To control the output vector, is the identity matrix.

[0041] In one or more embodiments of the present invention, a Kalman filter algorithm may be used to estimate the state of a large antenna, where the state may include position, velocity, and acceleration.

[0042] In addition, in one or more embodiments of the present invention, when the continuous motion model of the large antenna includes noise items corresponding to wind speed and temperature shock, the server can obtain the wind speed sensor data and temperature shock sensor data of the large antenna at the current moment in addition to the shaft angle encoder data of the large antenna at the current moment. Thus, the shaft angle encoder data, wind speed sensor data and temperature shock sensor data are used as observation data through a continuous Kalman filter algorithm to determine the Kalman gain, so as to correct and update the preliminary prediction state.

[0043] Therefore, in the scenario of large antenna state estimation, the Kalman filter algorithm continuously adjusts the state estimation by combining the system's dynamic model and the sensor's measurement data 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 large antennas.

[0044] 1) By avoiding discretization errors, continuous Kalman filtering can more accurately estimate the state of large antennas, thereby achieving more precise control and improving the pointing accuracy and tracking performance of large antennas. The proof is as follows: According to the object motion equation mentioned above, it can be defined as follows after discretization: .

[0045] in, is the state transfer matrix, and T is the sampling period.

[0046] According to Taylor's formula, when T is very small, It can be approximated as: .

[0047] Therefore, this approximation may make the model inaccurate in high-frequency or fast dynamic systems, inevitably leading to estimation errors. The continuous Kalman filter directly processes continuous signals, avoids discretization errors, and can estimate the system state more accurately.

[0048] 2) The continuous Kalman filter can process continuous signals in real time and has better adaptability to the dynamic changes of large antennas. This is reflected in the dynamic equation of its error covariance matrix, which is proved as follows: ; This equation shows that It is continuously updated and can respond quickly to dynamic changes in the system. In contrast, the update frequency of the discrete Kalman filter is limited by the sampling period T, and the error covariance matrix cannot be adjusted in real time, thus affecting the dynamic response speed of large antennas.

[0049] 3) The continuous Kalman filter has good numerical stability. 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: As mentioned above, in the continuous Kalman filter, the error covariance matrix The dynamic equation is: .

[0050] To prove its stability, first, consider Symmetry and positive definiteness.

[0051] Symmetry: Assumption is symmetrical, that is .

[0052] Then, for any t>0, we have: .

[0053] because and is symmetrical, the right side of the above formula is zero, so Remain unchanged. ,but This holds for all t>0.

[0054] Positive definiteness: Consider The Lyapunov function of : .

[0055] Where tr( ) represents the trace of the matrix. is symmetric, so its trace is equal to the sum of all its eigenvalues.

[0056] ,because ,but .

[0057] because is positive definite, and usually has a negative eigenvalue. So we have .

[0058] Through the above derivation, it is proved that , that is, the error covariance matrix of the continuous Kalman filter The trace of is decreasing, which shows that the continuous Kalman filter has good numerical stability.

[0059] Based on the model of the large antenna, a composite control of the large antenna can be further formed according to the PI controller, the feedforward controller and the optimal controller.

[0060] 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), thereby reducing the error and making 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 extracted electric axis (axis angle encoder data) with the deviation of the target position or self-tracking path and the electric axis coordinates (corrected axis angle encoder value) to generate a feedforward control signal. This signal is directly added to the servo inner loop speed loop, significantly reducing the tracking error caused by the relative motion between the target and the electric 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 indicator, 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 by the Lyapunov function to ensure that the system is stable in the Lyapunov sense. The optimal feedback controller can effectively suppress noise and interference and improve the robustness of antenna control.

[0061] Among them, the PI controller is one of the main controllers of the antenna system. It occupies a very important position in various antenna controls and is also the most commonly used and effective controller.

[0062] In one or more embodiments of the present invention, the proportional integral control signal may be determined by a proportional integral control algorithm according to the error between the target position and the estimated position of the large antenna using the following formula: .

[0063] in, 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 For time.

[0064] If there is a positive definite matrix P , the continuous linear time-invariant system established above can be constructed with the following Lyapunov function: In the formula, is the independent variable.

[0065] The derivative is: .

[0066] Substituting the PI controller control rate into the above formula, we get: .

[0067] To obtain stability in the sense of Lee, we need to prove that is negative, that is ,but .

[0068] Since the matrix P is a positive definite matrix, for the PI controller, if we find a suitable and , then the control system is stable in the Lyapunov sense.

[0069] However, with the emergence of large-aperture radio telescope antennas and the use of higher frequency bands, it is difficult for PI controllers to meet system requirements in various complex environments. and , it is difficult to meet the usage requirements, but it can serve as the basis for various high-order controls.

[0070] For the FF controller, the FF controller adds the processed signal to the servo inner loop speed loop with a relatively high bandwidth. The control effect also depends to a large extent on the degree of understanding of the control object model. If the control object model is accurately known, the FF controller can achieve output equal to input or error zero, that is, to achieve the so-called complete invariance. Achieving complete invariance is not only difficult, but also often produces the opposite effect, because at this time only the tracking error caused by the relative motion between the target position or self-tracking path and the electric axis is minimized. However, due to the introduction of the FF controller, the noise and other interference of this branch directly enters the servo inner loop with a relatively high bandwidth, and the increase in random errors may not necessarily lead to a good overall effect.

[0071] In the implementation of the FF controller, the 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. This can significantly reduce the tracking error caused by the relative motion between the target and the electric axis, while not increasing the random error too much, thereby improving the control response speed of large antennas.

[0072] Specifically, in one or more embodiments of the present invention, a feedforward compensator may be designed based on the system model characteristics of a large antenna. Assume that the transfer function of the system is G ( s ), the transfer function of a system based on a large antenna G ( s ) The transfer function of the feedforward compensator is designed as H ( s ), then the feedforward control signal It can be expressed as: .

[0073] in, is the target position of the large antenna, that is, is the velocity signal of the large antenna; FIndicates that a filtering operation is performed on large antenna position information.

[0074] By understanding the transfer function of the system G ( s ), a suitable feedforward compensator can be designed H ( s ), so that the feedforward control signal can effectively offset 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 G ( s ), so that the feedforward control signal can directly offset the characteristics of the system and make the output closer to the ideal response.

[0075] In addition, in one or more embodiments of the present invention, the influence of wind speed and temperature on the change of antenna speed can be predicted based on the wind speed sensor data and temperature and shock sensor data of the large antenna at the current moment, so that a feedforward compensator can be set according to the influence of wind speed and temperature on the change of antenna speed, so as to help the feedforward controller better compensate for the error caused by the target speed change.

[0076] Finally, for the optimal controller, in one or more embodiments of the present invention, a linear quadratic regulator (LQR) optimal control may be used, which is applicable to linear systems. LQR designs an optimal feedback controller by minimizing a quadratic performance index.

[0077] In the sense of Kalman filtering, the performance index of LQR is usually composed of weighted quadratic forms of state variables and control inputs, in the form of: .

[0078] In the formula, is the discretized state variable A vector of two-dimensional angles, i.e., a vector representation of the azimuth and elevation angles of a large antenna. Weight matrix M and N are symmetric semi-positive definite matrices and symmetric positive definite matrices respectively, that is, M = M T , N = N T >0.

[0079] And, the weight matrix M and N , which are used to adjust the relative importance of state variables and control inputs. M andN , the state of a large antenna can be quickly stabilized to zero, and the amplitude and rate of change of the control input are within an acceptable range.

[0080] Considering that LQR can obtain the optimal feedback control law .

[0081] in, K is the feedback gain matrix obtained by solving the algebraic Riccati equation. This optimal feedback control law can make the performance index J Minimum, thus achieving optimal control of large antennas.

[0082] For the stability analysis of the optimal feedback control, consider the above linear time-invariant system and assume that a linear optimal controller is designed with the control rate as shown above. Therefore, a closed-loop system can be expressed as: , choose a positive definite quadratic function as the Lyapunov function: .

[0083] Where P is a positive definite matrix.

[0084] Compute the derivative of the Lyapunov function along the trajectory of the closed-loop system: .

[0085] Substitute the state equation of the closed-loop system into: .

[0086] In the LQR design, P is the solution of the algebraic Riccati equation: .

[0087] because , can be obtained ,therefore .

[0088] where Q is a positive definite matrix.

[0089] In summary, .

[0090] This shows is negative definite, therefore, the optimal controller is stable in the Li sense.

[0091] The stability proof of the above large antenna state estimation, PI controller, feedforward controller and feedback controller ensures the reliability and effectiveness of the composite control strategy in practical applications: 1. Improve tracking accuracy: On the basis of stability, the system can track the target position more accurately and reduce errors.

[0092] 2. Improve servo bandwidth: A stable system can respond to input changes more quickly, improving the servo bandwidth of the system.

[0093] 3. Shorten the adjustment time: Stability ensures that the system can quickly return to a stable state after being disturbed, shortening the adjustment time.

[0094] 4. Reduce overshoot: Through stability analysis and control strategy optimization, the overshoot of the system can be effectively reduced and excessive oscillation can be avoided.

[0095] 5. Enhanced wind resistance: The influence of interference is taken into account in the stability proof, so that the system can better resist wind disturbance and maintain stable performance in practical applications.

[0096] In summary, the combination and application of the composite controller greatly improves the tracking accuracy of large antennas in noisy environments, increases the servo bandwidth, shortens the adjustment time, reduces the overshoot, and has a strong ability to resist wind disturbance. 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.

[0097] based on Figure 1 The large antenna composite control method shown in the present invention first obtains the shaft angle encoder data of the large antenna at the current moment, and then estimates the state of the large antenna through a continuous Kalman filtering algorithm based on the shaft angle encoder data, and 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, and finally adds the optimal feedback control, and combines each item to obtain the final control signal to control the large antenna.

[0098] The present invention proposes a large-scale 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 real-time acquisition of shaft angle encoder and other sensor data, combined with continuous Kalman filter algorithm to build a state estimation model, the dynamic characteristics of large antennas can be accurately reconstructed. On this basis, in-depth research and integration of typical PI control, feedforward compensation mechanism and optimal feedback control strategy 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 overshoot. Experimental results show that this method exhibits excellent anti-interference ability under complex interference conditions, and provides a more superior solution for high-precision pointing and tracking of large antennas under complex working conditions.

[0099] The present invention uses a continuous Kalman filter algorithm to accurately estimate the state of a large antenna, uses a proportional integral control signal to quickly track the position of the large antenna, and only extracts the speed signal of the target to add to the feedforward controller, which can significantly reduce the tracking error caused by the relative motion of the large antenna, while not increasing the random error too much, thereby improving the control response speed of the large antenna, and then combined with the optimal feedback control to achieve 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 antenna's ability to resist wind disturbance in complex environments.

[0100] 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: 1) Improved the servo bandwidth of large antennas, shortened the adjustment time of large antennas, and reduced the overshoot of large antennas; 2) High-precision synchronization of speed and position of large antennas, high-power multiple motors is achieved.

[0101] When applying the large antenna composite control method provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0102] Based on one or more of the above embodiments, the present invention is applied, such as Figure 4 As shown, Figure 4 It is a logic block diagram of the present invention.

[0103] First, hardware initialization can be performed, including: Motor and drive configuration: Start the multi-motor control system, ensure that all motors (more than 2) are connected to the main controller via the POWERLINK bus, and complete the initialization configuration.

[0104] Sensor calibration: Calibrate shaft angle encoders, wind speed sensors, etc. to ensure the accuracy and reliability of sensor data.

[0105] Redundant system check: Verify the switching function of the primary and standby redundant systems to ensure that the standby controller can take over seamlessly when the primary controller fails.

[0106] Then perform software initialization, which may include: Parameter loading: Load system parameters from the configuration file, including motor parameters, controller gains (PI controller, LQR gain matrix, etc.), initial covariance matrix of the Kalman filter, etc.

[0107] Algorithm initialization: Initialize the PI controller, feedforward controller (FF), Kalman filter and LQR controller to ensure that each algorithm module is in standby state.

[0108] Communication link establishment: The communication link between the main controller and each motor driver and sensor is established through the POWERLINK bus to ensure real-time data transmission.

[0109] After that, the method of the present invention can be executed to generate the final control model, and the final control signal can be 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.

[0110] Furthermore, fault detection and processing can be performed, including: Fault detection: Real-time detection of system faults, such as motor failure, communication interruption, sensor abnormality, etc.

[0111] Fault handling: Execute corresponding handling strategies according to the fault type, such as switching redundant systems, degrading operating modes, etc.

[0112] The pseudo code of the steps executed by this method is as follows: " / / Initialize hardware function void Hardware_Init() { / / Initialize the motor and driver Initialize_Motors(); / / Calibrate the sensor Calibrate_Sensors(); / / Check the redundant system Check_Redundancy_System(); } / / Initialize software function void Software_Init() { / / Load system parameters Load_System_Parameters(); / / Initialize the controller Initialize_PI_Controller(); Initialize_Feedforward_Controller(); Initialize_Kalman_Filter(); Initialize_LQR_Controller(); / / Establish a communication link Establish_Communication_Link(); } / / Real-time control loop function void Real_Time_Control_Loop() { while (true) { / / Data collection Collect_Data(); / / State estimation Estimate_State(); / / Error calculation Calculate_Error(); / / Control signal generation Generate_Control_Signal(); / / Motor drive Drive_Motors(); / / Monitoring and adjustment Monitor_and_Adjust(); / / Fault detection and handling Detect_and_Handle_Fault(); } } / / Data collection function void Collect_Data() { / / Collect the encoder data encoder_data = Get_Encoder_Data(); / / Collect wind speed sensor data wind_speed_data = Get_Wind_Speed_Data(); / / Collect other sensor data other_sensor_data = Get_Other_Sensor_Data(); } / / State estimation function void Estimate_State() { / / Use Kalman filter to estimate the state state_estimate = Kalman_Filter(encoder_data, wind_speed_data, other_sensor_data); } / / Error calculation function void Calculate_Error() { / / Calculate the error between the target position and the current estimated position error = target_position - state_estimate.position; } / / Control signal generation function void Generate_Control_Signal() { / / PI control pi_control_signal = PI_Controller(error); / / Feedforward control ff_control_signal = Feedforward_Controller(target_velocity); / / Optimal feedback control lqr_control_signal = LQR_Controller(state_estimate); / / Control signal fusion final_control_signal = Combine_Controls(pi_control_signal, ff_control_signal, lqr_control_signal); } / / Motor drive function void Drive_Motors() { / / Send control signals to the motor driver Send_Control_Signal_To_Motors(final_control_signal); } / / Feedback and adjustment function void Monitor_and_Adjust() { / / Monitor system performance Monitor_System_Performance(); / / Dynamically adjust controller parameters Adjust_Controller_Parameters(); } / / Fault detection and handling function void Detect_and_Handle_Fault() { / / Detect system failure fault = Detect_Fault(); if (fault) { / / Execute processing strategy according to fault type Handle_Fault(fault); } } / / Entry function int main() { / / Initialize hardware and software Hardware_Init(); Software_Init(); / / Enter the real-time control loop Real_Time_Control_Loop(); return 0; }” The present invention also provides an embodiment of the large antenna composite control method provided by the present invention, which is applied to a 70-meter large-caliber antenna system. In multiple mission executions, the operation is stable and reliable, and the tracking accuracy meets the performance index requirements. Tests show that the actual tracking control accuracy of each axis of the antenna does not exceed 2.5" under multi-source uncertain environmental factors. Figure 5 This is a test data diagram of the present invention. Figure 1 , Figure 5 The AZ-axis tracking error is shown in the figure, with an accuracy of 2.15″. Figure 6 This is a test data diagram of the present invention. Figure 2 , Figure 6 The EL axis tracking error is shown in Figure 1, with an accuracy of 2.5″.

[0113] The above is a large-scale 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-scale antenna composite control system, such as Figure 7 shown.

[0114] Figure 7A schematic diagram of a large-scale antenna composite control system provided by the present invention includes: An acquisition module 201 is used to acquire the shaft encoder data of the large antenna at the current moment; The state estimation module 202 is used to estimate the state of the large antenna at the next moment based on the continuous motion model of the large antenna and the shaft encoder data through a continuous Kalman filter algorithm to obtain an estimated state of the large antenna including an estimated position, an estimated velocity and an estimated acceleration; A proportional-integral control module 203, configured to determine a proportional-integral control signal through a proportional-integral control algorithm according to an error between a target position and an estimated position of the large antenna; A feedforward control module 204, for determining a feedforward control signal for compensating for a tracking error caused by a change in the target speed based on a response characteristic of the large antenna speed tracking according to the target speed, the estimated speed and the estimated acceleration of the large antenna; The optimal feedback control module 205 is used to 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; merge 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.

[0115] For the specific definition of the large-scale antenna composite control system, please refer to the definition of the large-scale antenna composite control method above, which will not be repeated here. Each module in the above-mentioned large-scale antenna composite control system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be 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 the above modules.

[0116] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A large antenna composite control method is provided.

[0117] 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 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 achieve the above Figure 1 A large antenna composite control method is provided.

[0118] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0119] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, 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, they should be considered to be within the scope of the present invention.

Claims

1. A large antenna composite control method, characterized in that: include: Get the current axis encoder data of the large antenna; Based on the continuous motion model of the large antenna, the state of the large antenna at the next moment is estimated through a continuous Kalman filter algorithm according to the shaft encoder data, and the estimated state of the large antenna including the estimated position, estimated velocity and estimated acceleration is obtained; 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; According to the target speed, estimated speed and estimated acceleration of the large antenna, a feedforward control signal for compensating for a tracking error caused by a change in the target speed is determined based on a response characteristic of the large antenna speed tracking; According to the estimated state of the current large antenna, the performance index optimization problem is constructed and solved through the optimal feedback control algorithm to determine the feedback control signal; the proportional integral control signal, the feedforward control signal and the feedback control signal are fused to obtain the final control signal, and the position and speed of the large antenna are controlled.

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 continuous motion model based on the large antenna estimates the state of the large antenna at the next moment through a continuous Kalman filter algorithm according to the shaft encoder data, specifically including: Obtain the current wind speed sensor data and temperature and shock sensor data of the large antenna; According to the continuous motion equation of the large antenna, a preliminary prediction is made on the state of the large antenna at the next moment to obtain a preliminary predicted state; The Kalman gain is determined by a continuous Kalman filter algorithm using the shaft angle encoder data, wind speed sensor data and temperature and vibration sensor data as observation data to correct and update the preliminary prediction state.

3. The large antenna composite control method according to claim 1, characterized in that: The method of determining a proportional integral control signal by a proportional integral control algorithm according to an error between a target position and an estimated position of the large antenna specifically includes: The proportional-integral control signal is determined by the proportional-integral control algorithm according to the error between the target position and the estimated position of the large antenna using the following formula: ； in, 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 For time.

4. The large antenna composite control method according to claim 1, characterized in that: The method of constructing and solving a performance index optimization problem based on the estimated state of the current large antenna by an optimal feedback control algorithm to determine a feedback control signal specifically includes: With the goal of minimizing the state error and control cost of the large antenna, a linear quadratic performance indicator is constructed based on the estimated state and control input of the current large antenna; The problem is solved with the goal of minimizing the linear quadratic performance index, and a feedback control law is obtained, and a feedback control signal is generated according to the feedback control law.

5. A large antenna composite control system, characterized in that: include: An acquisition module is used to obtain the shaft angle encoder data of the large antenna at the current moment; The state estimation module is used to estimate the state of the large antenna at the next moment based on the continuous motion model of the large antenna through a continuous Kalman filter algorithm according to the shaft encoder data, and obtain the estimated state of the large antenna including the estimated position, estimated velocity and estimated acceleration; A proportional-integral control module, used for determining a proportional-integral control signal through a proportional-integral control algorithm according to an error between a target position and an estimated position of the large antenna; A feedforward control module is used to determine a feedforward control signal for compensating for a tracking error caused by a change in the target speed based on a response characteristic of the large antenna speed tracking according to the target speed, the estimated speed and the estimated acceleration of the large antenna; The optimal feedback control module is used to 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, 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.

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 according to any one of claims 1 to 4 is implemented.

7. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 4 when executing the program.

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