CN114089637A - Multi-mode robust active disturbance rejection motion control method and system - Google Patents
Multi-mode robust active disturbance rejection motion control method and system Download PDFInfo
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Abstract
The invention relates to a multi-mode robust active disturbance rejection motion control method and a system. The method comprises the following steps which are executed in sequence: judging the current control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system; determining an ideal control instruction corresponding to the control modality of the controlled system according to the control modality of the controlled system; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction. The control method and the system can realize that the control method can correct an ideal control instruction to obtain an actual control instruction, inhibit transient response of a controlled system when different modal control rules are converted, realize control of a multi-modal system, improve control precision and stability of the controlled system, and enable the multi-modal controlled system to have stronger robustness.
Description
Technical Field
The invention relates to the technical field of motion control, in particular to a multi-mode robust active disturbance rejection motion control method and system.
Background
Motion control techniques are widely used in today's industry, such as motor motion control, numerically controlled machine tools, robotic control, etc. The motion control means controlling the amount of motion such as position/displacement, velocity, acceleration, and the like. Compared with other types of motion actuators, the motor adopted as the motion actuator has the advantages of simple structure, quick response, high precision and efficiency and the like, is beneficial to realizing high-performance motion control such as high speed or low speed, high precision and the like, and has wide application prospect in the fields of modern industry, civilian use, medical treatment, transportation, military use and the like.
Because the motion control system has the influence of factors such as friction, system parameter change, load disturbance force and the like, in particular to system nonlinear factors (such as signal measurement noise) and uncertain interference, the motion precision of the system is influenced to a great extent. Thus placing high demands on the performance of the motion controller.
At present, more and more advanced control algorithms are applied to the motion control research, and an iterative learning control algorithm, an adaptive robust control algorithm, a neural network control algorithm, an active disturbance rejection control algorithm and the like are common, wherein the active disturbance rejection control is regarded as a more effective technology.
In the prior art, the chinese invention patent "a fractional order active disturbance rejection motion control method based on an adjustable order filter" (publication No. CN108459507B, 25/05/2021) proposes a motion control method with flexible parameter adjustment and easy engineering implementation, and effectively improves the suppression capability of a motion control system on measurement noise and interference, but the model for the motion control method is relatively simple and does not consider the multi-modal characteristics.
The invention patent of china in the prior art "a design method for a multi-mode control system of an aircraft" (publication number CN104573182B, 12/08/2017) proposes a design method for a multi-mode control system of an aircraft, which can define and analyze the system in terms of functions, physics and software architecture, but does not give details about the design of a specific controller.
The paper "Adaptive tracking control for a class of systems with unknown switching nonlinearities" (ZHao X, ZHEN X, Niu B, et al. Adaptive tracking control for a class of systems with unknown functions [ J ]. Automatica, 2015, 2: 185-191.) used Adaptive backstepping techniques to construct state feedback controllers and Lyapunov functions to demonstrate their stability. The designed state feedback controller can ensure that all signals are bounded and the tracking error converges to a small neighborhood of the origin, but the controller design does not process system disturbance, and transient response of a controlled object can generate sudden change during mode switching, consume more energy and damage system execution mechanisms.
However, the active disturbance rejection motion control method in the related art does not consider the multi-modal characteristics of the system which are relatively complex and have practical engineering significance, and is difficult to suppress transient response sudden change of the execution mechanism, that is, the problems that the stability of the closed loop system is reduced and the control accuracy of the system is reduced due to sudden change of the control command during mode switching exist.
Disclosure of Invention
In view of this, the invention provides a multi-modal robust active disturbance rejection motion control method, which realizes control of a multi-modal system, improves control accuracy and stability of a controlled system, and enables the multi-modal controlled system to have stronger robustness.
The invention firstly provides a multi-modal robust active disturbance rejection motion control method, which comprises the following steps that are sequentially executed: judging the current control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system; determining an ideal control instruction corresponding to the control modality of the controlled system according to the control modality of the controlled system; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
In one possible embodiment, the method further comprises: establishing a multi-modal controlled system model according to the control target of the controlled system; and determining the control mode of the controlled system according to the controlled system model and the control target of the controlled system.
In one possible embodiment, the method further comprises: establishing a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models; wherein, the disturbance observer model is used for estimating the disturbance parameters of the controlled system; and the controller model is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameters.
In a possible embodiment, the determining an actual control command of the controlled system according to the controlled system information and the ideal control command includes: and correcting the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to the previous control mode to obtain a corrected actual control instruction.
In a possible implementation manner, establishing a multi-modal controlled system model according to the control target of the controlled system includes: establishing a controlled system model as follows:
wherein the content of the first and second substances,
representing the controlled system model includes
A dynamic equation
Representing the controlled system model includes
A number of different modalities;
indicating status information
The first derivative of (a);
a state information vector composed of state information representing a controlled system;
representing the system state vector obtained by using a radial basis function neural network algorithm RBFNN according to the state information vector
A continuous function of (a);
indicating status information
Corresponding disturbance parameters;
is shown as
Control input in each mode, and actual instructions of the control method;
and
and representing the state output of the controlled system.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes:
aiming at the controlled system model, designing
A robust auto-disturbance-rejection motion controller corresponding to each mode; for the
Then, the following disturbance observer model is established:
the following controller models were established:
wherein the content of the first and second substances,
representing the control command corresponding to the first dynamic equation in the controlled system model,
is a reference signal and represents a control target of a controlled system, and a tracking error is represented by
Is that the disturbance observer aims at the state information
The estimated parameters of the disturbance are used,
is an observation error, suppose
Is an estimate of the upper bound of the observation error,
the first derivative of (a) is,
is a hyperbolic tangent function; parameter(s)
Are all parameters which can be adjusted in the design process of the disturbance observer model,
there is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes:
for the
Then, the following disturbance observer model is established:
the following controller models were established:
wherein the content of the first and second substances,
representing the model of the controlled system
The tracking error is expressed as
Is a disturbanceDynamic watchers for status information
The estimated parameters of the disturbance are used,
is an observation error, suppose
Is an estimate of the upper bound of the observation error,
the first derivative of (a) is,
is a hyperbolic tangent function;
are all adjustable parameters in the design process of a disturbance observer and a controller model,
there is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes:
for the
Then, the following disturbance observer model is established:
the following controller models were established:
wherein the content of the first and second substances,
representing the model of the controlled system
The tracking error of the ideal control command corresponding to the dynamic equation is expressed as
That the disturbance observer is directed to the state information
The estimated parameters of the disturbance are used,
is an observation error, suppose
For switching errors, representing the difference between the ideal control command and the actual control command,
in order to actually control the command, it is,
estimated value of, parameter of
Are all adjustable parameters in the design process of a disturbance observer and a controller model,
in designing for disturbance observer modelsIntermediate amount, has no practical physical meaning.
In a possible embodiment, the determining an actual control command of the controlled system according to the controlled system information and the ideal control command includes: when the absolute value of the switching error is less than or equal to a first threshold value, taking the ideal control command as an actual control command; when the absolute value of the switching error is larger than a first threshold value, the ideal control command is sent
And correcting to obtain an actual control command.
In one possible embodiment, the ideal control command is used as the actual control command when the absolute value of the switching error is equal to or less than a first threshold, and the ideal control command is used when the absolute value of the switching error is greater than the first threshold
And correcting to obtain an actual control command, wherein the method comprises the following steps:
wherein
Is an adjustable positive parameter.
The invention also provides a multi-modal robust active disturbance rejection motion control system, which is used for executing the control method and comprises the following steps: the control mode judging module is used for judging the control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system; the ideal control instruction determining module is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the current control mode of the controlled system; and the instruction correction module is used for determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
In one possible embodiment, the multi-modal robust auto-disturbance-rejection motion control system further includes: the controlled system model establishing module is used for establishing a multi-modal controlled system model according to a control target of the controlled system; and determining the control mode of the controlled system according to the controlled system model and the control target of the controlled system.
In one possible embodiment, the multi-modal robust auto-disturbance-rejection motion control system further includes: the controller model and disturbance observer model building module is used for building a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models; wherein, the disturbance observer model is used for estimating the disturbance parameters of the controlled system; and the controller model is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameters.
In a possible implementation manner, the instruction modification module is further configured to modify the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to a previous control modality, so as to obtain a modified actual control instruction.
The multi-mode robust active disturbance rejection motion control method can judge the current control mode of a controlled system according to the information of the controlled system, and determine an ideal control instruction corresponding to the current control mode of the controlled system according to the current control mode of the controlled system; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction. The control method can correct an ideal control instruction to obtain an actual control instruction, inhibit transient response of the controlled system when control rules of different modes are converted, realize control of the multi-mode system, improve control precision and stability of the controlled system, and enable the multi-mode controlled system to have strong robustness.
Other features and aspects of the present invention will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram illustrating a multi-modal robust auto-disturbance-rejection motion control method according to an embodiment of the present invention.
Fig. 2 shows a flow chart of a multi-modal robust auto-disturbance-rejection motion control method according to an embodiment of the invention.
Fig. 3 shows a flow chart of a multi-modal robust auto-disturbance-rejection motion control method according to another embodiment of the present invention.
Fig. 4 shows a schematic diagram of a robust active disturbance rejection controller controlling a controlled system according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be noted that, in the case of no conflict, the features in the following embodiments and examples may be combined with each other; moreover, based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without any creative effort belong to the protection scope of the present invention.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
Fig. 1 is a schematic diagram illustrating a multi-modal robust auto-disturbance-rejection motion control method according to an embodiment of the present invention.
As shown in fig. 1, the actuator of the controlled object may feed back the motion state of the actuator to the controlled object, and the sensor may acquire the state information of the controlled object. The robust active-disturbance-rejection controller can construct a nominal controller according to a control target and a related control theory, estimate unknown disturbance of a system by using a nonlinear disturbance observer, and compensate the nominal controller to obtain the robust active-disturbance-rejection controller capable of realizing active disturbance rejection. The multi-modal robust active disturbance rejection motion control method can comprise a multi-modal switching mechanism, and the mechanism can judge the current mode of a controlled object according to the state information of the controlled object acquired by a sensor. The multi-mode robust active disturbance rejection motion control method can also be used for an event trigger mechanism under a multi-mode condition, the mechanism can determine an ideal control instruction corresponding to the current state according to state information of a controlled object acquired by a sensor, and the robust active disturbance rejection controller can correct the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to the previous control mode to obtain a corrected actual control instruction, and transmits the corrected actual control instruction to an execution mechanism through a control network, so that the motion control of the controlled object is realized, the execution mechanism is ensured not to generate sudden change within a certain threshold value, and the transient response of a controlled system is restrained when different control modes are converted. Fig. 2 shows a flow chart of a multi-modal robust auto-disturbance-rejection motion control method according to an embodiment of the invention.
The method comprises the following steps which are executed in sequence:
step S1: judging the current control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system;
step S2: determining an ideal control instruction corresponding to the control modality of the controlled system according to the control modality of the controlled system;
step S3: and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
Determining an ideal control instruction corresponding to the control modality of the controlled system according to the control modality of the controlled system by judging the control modality of the controlled system; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction. The control method can correct an ideal control instruction to obtain an actual control instruction, inhibit transient response of the controlled system when different modal control rules are converted, realize control of the multi-modal system, improve control precision and stability of the controlled system, and enable the multi-modal controlled system to have strong robustness.
In another possible embodiment, as shown in fig. 3, before step S1, step S0 may be further included.
Step S0 includes: establishing a multi-modal controlled system model according to the control target of the controlled system; determining a control mode of the controlled system according to the controlled system model and the control target of the controlled system;
establishing a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models; wherein, the disturbance observer model is used for estimating the disturbance parameters of the controlled system; and the controller model is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameters.
In a possible implementation manner, establishing a multi-modal controlled system model according to the control target of the controlled system includes: establishing a controlled system model as follows:
wherein
Representing the controlled system model includes
A dynamic equation;
representing the controlled system model includes
A number of different modalities;
indicating status information
The first derivative of (a);
a state information vector composed of state information representing a controlled system;
representing the system state vector obtained by using a radial basis function neural network algorithm RBFNN according to the state information vector
A continuous function of (a);
indicating status information
Corresponding disturbance parameters;
is shown as
Control input in each mode, and actual instructions of the control method;
and
and representing the state output of the controlled system.
In one possible embodiment, the controlled system may be, for example, a drone system, a robotic arm system, or the like, with motion control. For example, aiming at an unmanned aerial vehicle controlled system, a plurality of control modes of the unmanned aerial vehicle system can be determined according to control targets such as vertical rising, horizontal uniform flight, horizontal acceleration flight, horizontal deceleration flight, vertical falling and the like of the unmanned aerial vehicle system: a vertical rising mode, a horizontal constant-speed flight mode, a horizontal acceleration flight mode, a horizontal deceleration flight mode and a vertical falling mode. A multi-modal controlled system model of the drone may be established from the plurality of control targets of the drone.
In a possible implementation manner, when the controlled system is an unmanned aerial vehicle system, sufficient flight state information is acquired through actual flight experiments
Obtaining state information vector formed by state information of controlled system and obtaining enough airplane control input data (or called control instruction)
. For example, the flight status information of the drone system includes velocity, acceleration, angular velocity, and the like. Obtaining a fitting Function of the state vector of the controlled system by using a Radial Basis Function Neural Network (RBFNN) algorithm according to the state information vector
And the expression is used for establishing a fitting function database under different modes and establishing a controlled system model according to the function. In a possible embodiment, the function of the state vector of the system to be controlled
And storing the model into a database to prepare for subsequent controller model design.
And acquiring a large amount of motion control data of the controlled system through experiments to obtain a state information vector and control input data which are formed by state information of the controlled system. And obtaining a function of the state vector of the controlled system by using a radial basis function neural network RBFNN algorithm according to the state information vector, thereby establishing a multi-mode including system model. Therefore, the technical problems that a controlled system model designed in the related technology is relatively simple, the complex system multi-mode characteristics with practical engineering significance are not considered, the stability of the controlled system is poor and the control precision is low when the modes are switched are solved by establishing the multi-mode controlled system.
The RBFNN algorithm used in the process of establishing the multi-modal control system model is a commonly used algorithm with better approximation performance in the field, and is widely applied to neural network models in the fields of mode identification, nonlinear function approximation and the like. The invention obtains the function of the state vector of the controlled system by utilizing the radial basis function neural network according to the state information vector
And the specific implementation of the same is not limiting.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes:
aiming at the controlled system model, designing
A robust auto-disturbance-rejection motion controller corresponding to each mode;
for the
Then, the following disturbance observer model is established:
the following controller models were established:
wherein the content of the first and second substances,
representing the control command corresponding to the first dynamic equation in the controlled system model,
is a reference signal and represents a control target of a controlled system, and a tracking error is represented by
Is that the disturbance observer aims at the state information
The estimated parameters of the disturbance are used,
is an observation error, suppose
Is an estimate of the upper bound of the observation error,
is the first derivative of (a) is,
is a hyperbolic tangent function; parameter(s)
Are all adjustable parameters in the design process of a disturbance observer and a controller model,
there is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
Wherein, the function of the controlled system state vector in the disturbance observer model and the controller model
It can be directly obtained from the fitting function database, and the invention is not particularly limited in this regard.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes:
for the
Then, the following disturbance observer model is established:
the following controller models were established:
wherein the content of the first and second substances,
representing the control instruction corresponding to the ith dynamic equation in the controlled system model, and representing the tracking error as
Is a disturbanceObserver for state information
The estimated parameters of the disturbance are used,
is an observation error, suppose
Is an estimate of the upper bound of the observation error,
is the first derivative of (a) is,
is a hyperbolic tangent function;
are all adjustable parameters in the design process of a disturbance observer and a controller model,
there is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes:
for the
Then, the following disturbance observer model is established:
the following controller models were established:
wherein the content of the first and second substances,
representing the model of the controlled system
The tracking error of the ideal control command corresponding to the dynamic equation is expressed as
Is that the disturbance observer aims at the state information
The estimated parameters of the disturbance are used,
is an observation error, suppose
For switching errors, representing the difference between the ideal control command and the actual control command,
in order to actually control the command, it is,
is an estimate of, a parameter
Are all adjustable parameters in the design process of a disturbance observer and a controller model,
there is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
In this way, a controller model and a disturbance observer model are designed for the established multi-modal controlled system, and the technical problem that only the software system structure of the controller is defined in the related art but the specific controller design is not given in detail is solved.
In one possible implementation, the system state vector under different modes is established according to the controlled system model
Fitting function of
A database. A multi-mode switching mechanism is designed, when the system state information changes, in step S1, the current control mode of the controlled system is determined according to the controlled system information, that is, the controller of the current corresponding mode is started, and then the controller determines the ideal control instruction corresponding to the current control mode of the controlled system.
Therefore, different control modes correspond to different controllers, accuracy of control instructions output by the controllers is improved, and control precision is improved. In addition, when mode switching is considered in the design of the controller, transient response of a controlled system can generate sudden disturbance, and observation and estimation are carried out on the system disturbance, so that the technical problems that the system consumes more energy and can damage a system execution mechanism due to the disturbance are solved.
In a possible embodiment, the determining an actual control command of the controlled system according to the controlled system information and the ideal control command may include: and correcting the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to the previous control mode to obtain a corrected actual control instruction.
In a possible embodiment, the determining an actual control command of the controlled system according to the controlled system information and the ideal control command may include: when the absolute value of the switching error is less than or equal to a first threshold value, the ideal control command is sent
As an actual control instruction; when the absolute value of the switching error is larger than a first threshold value, the ideal control command is sent
And correcting to obtain an actual control command.
In one possible embodiment, the taking the ideal control command as an actual control command when the absolute value of the switching error is equal to or less than a first threshold, and correcting the ideal control command to obtain the actual control command when the absolute value of the switching error is greater than the first threshold includes:
wherein
Is an adjustable positive parameter.
Thus, when the absolute value of the switching error is less than or equal to the first threshold, the ideal control command is sent
As an actual control instruction; when the absolute value of the switching error is larger than a first threshold value, the ideal control command is sent
And correcting to obtain an actual control command. And suppressing the transient response of the controlled system when different control modes are switched. When the mode of the controlled system is switched, the ideal control instruction which is to be input into the system by the controller is corrected, specifically, an event trigger mechanism is set according to a given first threshold condition, and the actuator is ensured not to generate sudden change within a certain threshold.
In aIn a possible embodiment, the first threshold is set to
Wherein the content of the first and second substances,
is a control instruction corresponding to the previous control mode,
to be adjustable in experiments to adapt to the positive parameters of the corresponding control system. The invention is not right
Are subject to special restrictions.
The expression represents that an actual control instruction corresponding to the current control modality is obtained after an actual control instruction corresponding to the previous control modality and an ideal control instruction corresponding to the current control modality are weighted, and the actual control instruction is a modified control instruction.
In a possible implementation manner, an event trigger mechanism is arranged in the controller, and when the event trigger mechanism in the controller monitors that an ideal control instruction corresponding to a current control mode and an actual control instruction before switching of the control mode are too large in difference, an instruction correction mechanism is started, that is, the ideal control instruction is corrected to obtain an actual control instruction which is actually input to the controlled system, so that the fact that an actuator does not mutate within a certain threshold value is ensured, that is, transient response of the controlled system when switching of different control modes is suppressed.
Fig. 4 shows a schematic diagram of a robust active disturbance rejection controller controlling a controlled system according to an embodiment of the present invention. In one possible implementation, a multi-modal controlled system model is established according to the control targets of the unmanned aerial vehicle system. For example, the unmanned aerial vehicle system comprises control targets such as vertical ground ascending, horizontal uniform flight, horizontal acceleration flight, horizontal deceleration flight, vertical ground descending and the like, and a multi-mode control model of the unmanned aerial vehicle system is established according to the control targets. And determining a plurality of control modes of the controlled system according to the unmanned aerial vehicle system model and the control target of the unmanned aerial vehicle system, such as a horizontal uniform speed control mode, an accelerating ascending control mode, a decelerating descending control mode and other control modes of the unmanned aerial vehicle system.
As shown in fig. 4, the controlled system includes a sensor and an actuator, and the sensor is used for sensing state information of the controlled system. For example, when the controlled system is an unmanned aerial vehicle system, the sensor may sense information such as height, speed, acceleration, angular velocity, and the like of the unmanned aerial vehicle system; the actuator may be an electric motor for driving the movement of the drone. The unmanned aerial vehicle system comprises a robust active disturbance rejection controller and a controlled system. The robust active-disturbance-rejection controller in fig. 4 may obtain state information of the controlled system sensed by a sensor in the controlled system, and determine a control mode in which the controlled system is currently located according to the state information, for example, the robust active-disturbance-rejection controller determines that the unmanned aerial vehicle system is in a control mode that is vertical to the ground and accelerates and rises, and determines an ideal control instruction corresponding to the control mode that is currently located and accelerates and rises; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
In a possible implementation manner, the robust active disturbance rejection controller in fig. 4 may modify the ideal control command according to the ideal control command and an actual control command corresponding to a previous control modality, so as to obtain a modified actual control command. For example, when an event trigger mechanism in the robust active disturbance rejection controller detects that an ideal control instruction corresponding to a current control mode (an accelerated ascent control mode) and an actual control instruction corresponding to a control mode before switching of the control mode (for example, uniform horizontal flight) are too large in difference (namely, greater than a certain threshold), an instruction correction mechanism is started, that is, the ideal control instruction is subjected to finger-type correction to obtain an actual control instruction which is actually input to a controlled system, so that it is ensured that an execution mechanism does not generate sudden change within a certain threshold, that is, transient response of the controlled system when switching of different control modes is suppressed.
The invention also provides a multi-modal robust active disturbance rejection motion control system, which is used for executing the control method and comprises the following steps: the control mode judging module is used for judging the control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system; the ideal control instruction determining module is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the current control mode of the controlled system; and the instruction correction module is used for determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
In one possible embodiment, the multi-modal robust auto-disturbance-rejection motion control system further includes: the controlled system model establishing module is used for establishing a multi-modal controlled system model according to a control target of the controlled system; and determining the control mode of the controlled system according to the controlled system model and the control target of the controlled system.
In one possible embodiment, the multi-modal robust auto-disturbance-rejection motion control system further includes: the controller model establishing module is used for establishing a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models; wherein, the disturbance observer model is used for estimating the disturbance parameters of the controlled system; and the controller model is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameters. In a possible implementation manner, the instruction modification module is further configured to modify the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to a previous control modality, so as to obtain a modified actual control instruction.
According to the multi-mode robust active disturbance rejection motion control method, the control mode of the controlled system can be judged according to the information of the controlled system, and an ideal control instruction corresponding to the control mode of the controlled system is determined according to the control mode of the controlled system; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction. The control method can realize the control of the multi-mode system, inhibit the transient response of the controlled system when the control rules of different modes are converted, improve the control precision and the stability of the controlled system, and enable the multi-mode controlled system to have stronger robustness.
The above description is only for the specific 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 shall be subject to the protection scope of the claims.
Claims (10)
1. A multi-modal robust active disturbance rejection motion control method is characterized by comprising the following steps which are sequentially executed:
judging the current control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system;
determining an ideal control instruction corresponding to the control modality of the controlled system according to the control modality of the controlled system;
and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
2. The method according to claim 1, wherein the step of determining the current control mode of the controlled system further comprises:
establishing a multi-modal controlled system model according to the control target of the controlled system;
and determining the control mode of the controlled system according to the controlled system model and the control target of the controlled system.
3. The method according to claim 2, wherein the step of determining the current control mode of the controlled system further comprises:
establishing a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models;
wherein, the disturbance observer model is used for estimating the disturbance parameters of the controlled system;
and the controller model is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameters.
4. The method according to claim 2, wherein building a multi-modal controlled system model according to the control targets of the controlled system comprises:
establishing a controlled system model as follows:
wherein the content of the first and second substances,
representing the controlled system model includes
A dynamic equation;
indicating status information
The first derivative of (a);
5. The method of claim 3, wherein the establishing a controller model and a disturbance observer model comprises:
aiming at the controlled system model, designing
A robust auto-disturbance-rejection motion controller corresponding to each mode;
for the
When the temperature of the water is higher than the set temperature,
the following disturbance observer model is established:
the following controller models were established:
wherein the content of the first and second substances,
representing the control command corresponding to the first dynamic equation in the controlled system model,
is a reference signal and represents a control target of a controlled system, and a tracking error is represented by
Is that the disturbance observer aims at the state information
The estimated parameters of the disturbance are used,
is an observation error, suppose
Is an estimate of the upper bound of the observation error,
is the first derivative of (a) is,
is a hyperbolic tangent function; parameter(s)
Are all adjustable parameters in the design process of a disturbance observer and a controller model,
there is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
6. The method of claim 5, wherein the establishing a controller model and a disturbance observer model comprises:
for the
Then, the following disturbance observer model is established:
the following controller models were established:
wherein the content of the first and second substances,
representing the model of the controlled system
The tracking error is expressed as
That the disturbance observer is directed to the state information
The estimated parameters of the disturbance are used,
is an observation error, suppose
Is an estimate of the upper bound of the observation error,
the first derivative of (a) is,
is a hyperbolic tangent function
Are all adjustable parameters in the design process of a disturbance observer and a controller model,
there is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
7. The method of claim 6, wherein the establishing a controller model and a disturbance observer model comprises:
for the
Then, the following disturbance observer model is established:
the following controller models were established:
wherein the content of the first and second substances,
representing the model of the controlled system
The tracking error of the ideal control command corresponding to the dynamic equation is expressed as
Is that the disturbance observer aims at the state information
The estimated parameters of the disturbance are used,
is an observation error, suppose
For switching errors, representing the difference between the ideal control command and the actual control command,
in order to actually control the command, it is,
estimated value of, parameter of
Are all adjustable parameters in the design process of a disturbance observer and a controller model,
there is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
8. The method of claim 7, wherein determining the actual control commands of the controlled system according to the information of the controlled system and the ideal control commands comprises:
when the absolute value of the switching error is less than or equal to a first threshold value, taking the ideal control command as an actual control command;
and when the absolute value of the switching error is larger than a first threshold value, correcting the ideal control command to obtain an actual control command.
9. The method according to claim 8, wherein the ideal control command is used as the actual control command when the absolute value of the switching error is equal to or less than a first threshold, and the ideal control command is used when the absolute value of the switching error is greater than the first threshold
And correcting to obtain an actual control command, wherein the method comprises the following steps:
wherein
Is an adjustable positive parameter.
10. A multi-modal robust auto-disturbance-rejection motion control system, wherein the system is configured to perform the method of any of claims 1-9, the system comprising: the control mode judging module is used for judging the control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system; the ideal control instruction determining module is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the current control mode of the controlled system; and the instruction correction module is used for determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
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