CN108469728B - Decoupling control method for airborne LiDAR attitude angle compensation device - Google Patents
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Abstract
The invention relates to a decoupling control method for an airborne LiDAR attitude angle compensation device. In order to compensate the influence of the attitude angle change of the helicopter-mounted platform on the point cloud measured by the airborne laser radar, an attitude angle compensation device is designed, and the device is a three-axis turntable mechanism and has control coupling, so that a decoupling control method is provided. Firstly, a mathematical model of a dynamic system of the attitude angle compensation device is established, and the reversibility of the dynamic system of the attitude angle compensation device is proved. And secondly, establishing a neural network inverse system model of a dynamic system of the attitude angle compensation device, collecting neural network learning data, and performing off-line training on the neural network. The output of the neural network inverse system is then compared with the desired input value, and the difference is fed to a feedforward controller for modifying the control voltage signal of the attitude angle compensation means. And finally, a feedforward-feedback composite controller is formed by combining a fuzzy-PID controller adopting a closed-loop feedback loop, so that the real-time decoupling of the attitude angle compensation device control system is realized, the control precision and the anti-interference performance are improved, and the dynamic control performance is improved.
Description
Technical Field
The invention relates to a decoupling control method for realizing a control system of an airborne LiDAR attitude angle compensation device by adopting a neural network inverse system.
Background
Airborne LiDAR is a novel effective three-dimensional imaging technology and is widely applied to many fields such as topographic mapping, urban modeling and the like. In the working process of the airborne LiDAR, the attitude angle of the airborne platform can change in real time, the quality of the measured laser point cloud is influenced, and the follow-up three-dimensional imaging precision is reduced. Therefore, there is a need to design an onboard LiDAR attitude angle compensation device that can compensate in real time for the adverse effects of changes in the onboard platform three-axis attitude angle on onboard LiDAR measurements. However, the airborne LiDAR attitude angle compensation device is a complex multi-input multi-output strong coupling system, and decoupling control is required for improving control accuracy. The existing decoupling method mainly comprises a traditional decoupling method, a self-adaptive decoupling method and the like. The traditional decoupling adopts pre-compensation and is more suitable for a linear steady system; the self-adaptive decoupling is to identify a controlled object to realize the decoupling control of a parameter unknown or time-varying system, mainly solves the dynamic decoupling of a nonlinear and strong coupling system, but has complex algorithm and large calculation amount due to the requirement of an online identification system model, and is not suitable for a control system with quick response.
In order to solve the control coupling problem of the designed airborne LiDAR attitude angle compensation device, the dynamics characteristics of the attitude angle compensation device are analyzed, and a practical and effective neural network inverse system decoupling method is provided: firstly, establishing a neural network inverse system model of a dynamic system of the attitude angle compensation device; then, comparing the output of the neural network inverse system with the ideal input, and using the difference value as a control voltage signal of a feedforward controller for correcting the attitude angle compensation device; meanwhile, a neural network inverse system decoupling feedforward controller and a PID closed loop feedback controller are combined to form a feedforward-feedback composite controller, so that real-time decoupling of a multivariable coupling control system is realized, the control precision and the anti-interference performance are improved, and the dynamic control performance of the attitude angle compensation device is improved.
Disclosure of Invention
The decoupling control method of the airborne LiDAR attitude angle compensation device is characterized in that in the mechanical structure of the airborne laser radar attitude angle compensation device, a reflector (1) is supported by a cross formed by four mirror surface support rods (2) made of titanium alloy, a magnetic small hemispheroid universal bearing (3) is connected with an upright post (4), a spherical concave surface is arranged on the upright post, so that the magnetic small hemispheroid and the spherical concave surface attract each other through magnetism, and meanwhile, the magnetic small hemispheroid can flexibly rotate around an x axis and a y axis. The z axis adopts a vertical rotating shaft mode, and the lower end of the upright post (4) is arranged in a vertical rolling bearing (5). An x-axis direct-acting motor (6) and a y-axis direct-acting motor (7) are adopted to respectively drive the reflector (1) to swing up and down around the x axis and the y axis, and an x-axis grating displacement sensor (8) and a y-axis grating displacement sensor (9) are used for respectively measuring the actual movement displacement of the x-axis direct-acting motor (6) and the y-axis direct-acting motor (7). And a gear transmission mechanism (11) is driven by a z-axis rotating motor (10) to realize the rotation of the reflector (1) around the z axis, and an actual rotation angle of the z-axis rotating motor (11) is measured by a photoelectric axial angle encoder (12).
The attitude angle compensation device of the airborne laser radar is installed on an airborne platform, when three attitude angles of the airborne platform change, real-time change values of the three attitude angles are measured in real time by an airborne gyroscope, the measurement frequency of the real-time change values is 1000Hz, the real-time change values are input to an attitude angle compensation device controller (13), and then the attitude angle compensation device controller (13) respectively controls half values of a rolling angle and a pitch angle measured by reversely rotating a reflector (1) around an x axis and a y axis, so that disturbance compensation of the rolling angle and the pitch angle of the airborne platform can be respectively realized. In addition, the mirror (1) is controlled to reversely rotate around the z axis for measuring a double value of the yaw angle, so that the disturbance compensation of the yaw angle of the airborne platform can be realized. Wherein the damper (14) and the restoring spring (15) are used for adjusting the dynamic performance of the reflector (1) during rotation.
The rotation of the reflector (1) around the x axis and the y axis is around the magnetic small hemispheroid universal bearing (3) at the center, and the reflector center point (16) is superposed with the rotation center of the magnetic small hemispheroid universal bearing (3).
Wherein, through analysis, the state space equation of the control system of the attitude angle compensation device can be obtained as follows:
wherein, the control inputs of the three axes are respectively U = (C)U x ,U y ,U z ) The state variable is
. The attitude angle compensation device is a 3-input 6-output complex nonlinear system, and complex coupling exists between control and motion of rotating shafts.
The decoupling method of the neural network inverse system comprises the steps that output signals of three-axis rotation are used as input signals of the neural network inverse system, then the output of the neural network inverse system is compared with ideal input corner signals of three-axis rotation, the difference value of the output signals of the neural network inverse system is used as a correction signal of three-axis rotation control voltage, and the purposes of complete decoupling and improvement of three-axis rotation control accuracy are achieved by eliminating the control difference value. In the decoupling design of a control system of an attitude angle compensation device, firstly, obtaining training data input and output by the control system according to a triaxial dynamic equation (1) of the attitude angle compensation device; secondly, constructing a neural network inverse system structure and carrying out learning training; and finally, adding a neural network inverse system decoupling algorithm into the master control system.
The control system master controller can be divided into two parts, namely (1) a PID closed loop feedback controller and (2) a neural network inverse system decoupling feedforward controller, and the two parts form a feedforward-feedback composite controller. In the attitude angle compensating device control system, the ideal input angleθ g After passing through the PID controller, the output voltage is
=(
) The control voltage is applied to the control mechanism of the attitude angle compensation device, the output signal is angular velocity omega, and the actual output angle is obtained through integration
. Actual output angle
The signal measured by the angle measuring sensor is divided into three paths to return. First path and ideal input angleθ g Subtracting to obtain a difference value, scaling the difference valueθ -1 The adjustment obtains a relative value of the output angle error. The second path is processed by a neural network inverse system to obtain an actual output angle
The corresponding control voltage value is used for correcting the three-axis input control voltage (c) ((b))U x ,U y ,U z ) Therefore, the purpose of decoupling control is achieved, and the three-axis rotation control precision is improved. The third path is fed back to the input end and forms an angle with the ideal input endθ g Finding the difference value, and outputting a control signal (b) through a PID controllerU x , U y ,U z ) 。
Wherein the ideal input angleθ g Obtained by measurement of an airborne gyroscope, a control system master controller is programmed in an attitude angle compensation device controller (13) to realize actual output angle
The method is obtained by measuring actual movement displacements of an x-axis linear motion motor (6) and a y-axis linear motion motor (7) respectively by an x-axis grating displacement sensor (8) and a y-axis grating displacement sensor (9) and calculating according to the corresponding relation between the actual movement displacements and the rotation angle of the reflector (1).
The constructed neural network inverse system input layer has 9 variables, namely three-axis output angular motion information:
、
、
、
、
、
、
、
and
(ii) a Output layer 3 variables, i.e. three-axis drive motor control voltageU x ,U y ,U z . In addition, the neural network inverse system has 2 hidden layers in total. And determining the number of nodes of the hidden layer by adopting a combined experiment method, so that the output error of the neural network system is minimum and the calculation efficiency is optimal. The neural network inverse system adopts sigmoid function as transfer function in the input layer and hidden layer, and adopts linear function as transfer function in the output layer. According to the forward control model of the attitude angle compensation device, the input control voltages of motors of X, Y, Z three axes are respectively taken as amplitude valuesU x ,U y ,U z Periodically sampling a given voltageUAnd the angle rotated by three shafts of the three-shaft turntable
、
、
And then calculated according to a numerical derivation algorithm
、
、
To obtain a training data set
And
. To be provided with
In order to input the quantity to the neural network,
training for neural network output until the error of output value is less than 10 -6 And then, completing the neural network inverse system modeling of the attitude angle compensation device.
Drawings
Fig. 1 is a schematic mechanical structure diagram of an airborne laser radar attitude angle compensation device.
FIG. 2 is a decoupling control schematic diagram of an attitude angle compensation device based on a neural network inverse system.
Fig. 3 is a neural network inverse system model structure of the attitude angle compensation device.
Detailed Description
Fig. 1 is a mechanical structure of an airborne laser radar attitude angle compensation device, a reflector (1) is supported by a cross formed by four mirror surface support rods (2) made of titanium alloy, a magnetic small hemispheroid universal bearing (3) is connected with a stand column (4), a spherical concave surface is arranged on the stand column, the magnetic small hemispheroid and the spherical concave surface are attracted through magnetism, and meanwhile, the magnetic small hemispheroid can flexibly rotate around an x axis and a y axis. The z axis adopts a vertical rotating shaft mode, and the lower end of the upright post (4) is arranged in a vertical rolling bearing (5). An x-axis direct-acting motor (6) and a y-axis direct-acting motor (7) are adopted to respectively drive the reflector (1) to swing up and down around the x axis and the y axis, and an x-axis grating displacement sensor (8) and a y-axis grating displacement sensor (9) are used for respectively measuring the actual movement displacement of the x-axis direct-acting motor (6) and the y-axis direct-acting motor (7). And a z-axis rotating motor (10) is adopted to drive a gear transmission mechanism (11) to realize the rotation of the reflector (1) around the z axis, and an optoelectronic shaft angle encoder (12) is adopted to measure the actual rotation angle of the z-axis rotating motor (11). The airborne laser radar attitude angle compensation device is installed on an airborne platform, when three attitude angles of the airborne platform change, real-time change values of the three attitude angles are measured in real time by an airborne gyroscope, the measurement frequency of the real-time change values is 1000Hz, the real-time change values are input to an attitude angle compensation device controller (13), and then the attitude angle compensation device controller (13) respectively controls half values of a rolling angle and a pitch angle of a reflector (1) which are measured in a reverse rotation mode around an x axis and a y axis, so that disturbance compensation of the rolling angle and the pitch angle of the airborne platform can be respectively realized. In addition, the mirror (1) is controlled to reversely rotate around the z axis for measuring a double value of the yaw angle, so that the disturbance compensation of the yaw angle of the airborne platform can be realized. Wherein the damper (14) and the restoring spring (15) are used for adjusting the dynamic performance of the reflector (1) during rotation. The rotation of the reflector (1) around the x axis and the y axis is around the magnetic small hemispheroid universal bearing (3) at the center, and the reflector center point (16) is superposed with the rotation center of the magnetic small hemispheroid universal bearing (3).
Through analysis, the state space equation of the control system of the attitude angle compensation device can be obtained as follows:
wherein the three axes of control inputs are respectivelyU x ,U y ,U z The state variable is
. The attitude angle compensation device is a 3-input 6-output complex nonlinear system, and complex coupling exists between control and motion of rotating shafts.
Fig. 2 is a decoupling control schematic diagram of an attitude angle compensation device based on a neural network inverse system. The decoupling method of the neural network inverse system is to take the output signal of the three-axis rotation as the input signal of the neural network inverse system, and then the output of the neural network inverse system and the three-axis rotationAnd comparing the ideal input rotation angle signals, and taking the difference value as a correction signal of the three-axis rotation control voltage to achieve the aims of completely decoupling and improving the three-axis rotation control precision by eliminating the control difference value. In the decoupling design of a control system of an attitude angle compensation device, firstly, obtaining training data input and output by the control system according to a three-axis dynamic equation (1) of the attitude angle compensation device; secondly, constructing a neural network inverse system structure and carrying out learning training; and finally, adding a neural network inverse system decoupling algorithm into the master control system. In fig. 2, the control system master controller can be divided into two parts, (1) a PID closed-loop feedback controller, and (2) a neural network inverse system decoupling feedforward controller, which form a feedforward-feedback composite controller. In the attitude angle compensating device control system, the ideal input angleθ g After passing through the PID controller, the output voltage is U = (U x ,U y ,U z ) The control voltage is applied to the control mechanism of the attitude angle compensation device, the output signal is angular velocity omega, and the actual output angle is obtained through integration
. Actual output angle
The signal measured by the angle measuring sensor is divided into three paths to return. First path and ideal input angleθ g Subtracting to obtain a difference value, scaling the difference value by a scaling factorθ -1 The adjustment obtains a relative value of the output angle error. The second path is processed by a neural network inverse system to obtain an actual output angle
The corresponding control voltage value is multiplied by the relative value of the output angle error and the output control voltage value of the neural network inverse system, and the value is used for correcting the three-axis input control voltage (U x ,U y ,U z ) Therefore, the purpose of decoupling control is achieved, and the three-axis rotation control precision is improved. Third pathFeedback back to the input terminal at an angle to the ideal input terminalθ g Calculating the difference value, and outputting a control signal (through a PID controller)U x ,U y ,U z ). Ideal input angleθ g Obtained by measurement of an airborne gyroscope, a control system master controller is programmed in an attitude angle compensation device controller (13) to realize actual output angle
The method is characterized in that an x-axis grating displacement sensor (8) and a y-axis grating displacement sensor (9) are used for measuring actual movement displacement of an x-axis direct-acting motor (6) and a y-axis direct-acting motor (7) respectively, and then the actual movement displacement is obtained through calculation according to the corresponding relation between the actual movement displacement and the rotating angle of the reflector (1).
Fig. 3 is a neural network inverse system model structure of the attitude angle compensation device. The constructed neural network inverse system has 9 variables in the input layer, namely three-axis output angular motion information:
、
、
、
、
、
、
、
and
(ii) a Output layer 3 variables, i.e. three-axis drive motor control voltageU x ,U y ,U z ). In addition, the neural network inverse system has 2 hidden layers in total. And determining the number of nodes of the hidden layer by adopting a combined experiment method, so that the output error of the neural network system is minimum and the calculation efficiency is optimal. The input layer and the hidden layer of the neural network inverse system both adopt sigmoid functions as transfer functions, and the output layer adopts linear functions as transfer functions. According to the forward control model of the attitude angle compensation device, the input control voltages to the motors of X, Y, Z three axes are respectively taken as the amplitudes (U x ,U y ,U z ) Periodically sampling a given voltageUAnd the angle rotated by three shafts of the three-shaft turntable
、
、
And then calculated according to a numerical derivation algorithm
、
、
To obtain a training data set
And
. To be provided with
In order to input the quantity to the neural network,
training for neural network output until the error of output value is less than 10 -6 And then, completing the modeling of the neural network inverse system of the attitude angle compensation device.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (3)
1. A decoupling control method for an airborne LiDAR attitude angle compensation device is characterized in that a master controller of an attitude angle compensation device control system is divided into two parts, namely a PID closed-loop feedback controller and a neural network inverse system decoupling feedforward controller, wherein the two parts form a feedforward-feedback composite controller; in the attitude angle compensating device control system, the ideal input angleθ g After passing through the PID controller, the output voltage is U = (U x ,U y ,U z ) The control voltage is applied to the control mechanism of the attitude angle compensation device, the output signal is angular velocity omega, and the actual output angle is obtained through integrationθ(ii) a Actual output angleθThe signal measured by the angle measuring sensor is divided into three paths to return; first path and ideal input angleθ g Subtracting to obtain a difference value, scaling the difference valueθ -1 Adjusting to obtain a relative value of the output angle error; the second path is processed by a neural network inverse system to obtain an actual output angleθThe corresponding control voltage value is multiplied by the relative value of the output angle error and the output control voltage value of the neural network inverse system, and the value is used for correcting the three-axis input control voltage (U x ,U y ,U z ) Therefore, the purpose of decoupling control is achieved, and the three-axis rotation control precision is improved; the third path is fed back to the input end and forms an angle with the ideal input endθ g Calculating the difference value, and outputting a control signal (through a PID controller)U x ,U y ,U z ) (ii) a The closed-loop feedback control adopts PID double closed-loop control, and an angular speed feedback inner loop and an angular position feedback outer loop; the feedback signal of the control system comes from the collected signal of the sensor, and not only the angular displacement but also the angular velocity is needed.
2. The on-board LiDAR attitude angle compensation device decoupling control method of claim 1, the state space equation of the attitude angle compensation device control system being:
in the state space equation, the three axes of control inputs are respectivelyU x ，U y ，U z The state variable is
In which
The angles of the three axes of the three-axis turntable which are correspondingly rotated are obtained; the attitude angle compensation device is a 3-input 6-output complex nonlinear system, and complex coupling exists between control and motion of rotating shafts.
3. The decoupling control method for the airborne LiDAR attitude angle compensation device according to claim 1 or 2, wherein the constructed neural network inverse system has 9 variables in total at the input layer, namely three-axis output angular motion information:
(ii) a Output layer 3 variables, i.e. three-axis drive motor control voltageU x 、U y 、U z (ii) a The neural network reverse system has 2 hidden layers; determining the number of nodes of the hidden layer by adopting a combined experiment method, so that the output error of the neural network system is minimum and the calculation efficiency is optimal; the neural network inverse system adopts sigmoid function as transfer function in the input layer and hidden layer, and adopts linear function as transfer function in the output layer; according to the forward control model of the attitude angle compensation device, the input control voltages of the motors of X, Y, Z three axes are respectively taken as the amplitude valuesU x 、U y 、U z Periodically sampling a given voltageUAnd the angle rotated by three shafts of the three-shaft turntable
And then calculated according to a numerical derivation algorithm
To obtain a training data set
And
(ii) a To be provided with
The input quantity of the neural network is the input quantity of the neural network,
training for neural network output until the error of output value is less than 10 -6 And then, completing the neural network inverse system modeling of the attitude angle compensation device.
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