CN111930012B - Closed-loop control method of magnetorheological actuator - Google Patents
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Abstract
The invention relates to the technical field of vibration control, in particular to a closed-loop control method of a magnetorheological actuator. The method comprises the steps of monitoring the change of the damping force/moment in real time through a damping force/moment monitoring system, selecting a corresponding control mode according to error information and a mapping relation between expected damping force/moment and actual damping force/moment through a logic switching module, summarizing a fuzzy control rule by utilizing expert experience, and reasoning increment of continuous and smooth control current/voltage, so that smoother control current/voltage is obtained. Compared with the traditional closed-loop control method of the magnetorheological actuator based on the Heaviside function, the damping force/moment of the magnetorheological actuator is controlled by smooth current/voltage, so that buffeting of the damping force/moment can be reduced, and the closed-loop control method has outstanding advantages of prolonging the service life of the magnetorheological actuator and improving the vibration suppression effect.
Description
Technical Field
The invention relates to the technical field of vibration control, in particular to a closed-loop control method of a magnetorheological actuator.
Background
The magneto-rheological actuator has real-time adjustable damping, wide dynamic range, high response speed, low power consumption and relatively simple structure, and has wide application prospect in the vibration and impact control fields of vehicle suspensions, civil bridges, engine suspensions, cannon recoil buffering and the like. As the core of the magneto-rheological vibration control system, the vibration system and the control strategy of the magneto-rheological actuator determine the vibration suppression capability of the structure.
The main problems and deficiencies in the prior art include: at present, the control modes of the magneto-rheological actuator are two. The most common method is to establish an inverse model of damping force-current/voltage according to the mapping relation of the damping force/moment, speed, displacement, acceleration, temperature, current and the like of the actuator, such as a polynomial model, a Bingham model, a Bouc-Wen model, a hyperbolic tangent algebraic model, a neural network model and the like. However, due to the influence of complex multi-field coupling effects such as electromagnetic fields, flow fields and temperature fields inside the magnetorheological actuator, the mechanical behavior of the magnetorheological actuator has the phenomena of hysteresis, temperature dependence, frequency dependence and the like, and the established mechanical model usually has certain errors. Mechanical model mismatch which is difficult to avoid by the magnetorheological actuator can not only cause the control performance to be seriously deviated from an initial design value, but also cause the stability and the reliability of a system to be difficult to evaluate, which is unacceptable in certain structural vibration control fields such as large-scale civil structures and bridge stay cables.
The common inverse model control of the damping force/moment-current/voltage of the magneto-rheological actuator is an open-loop control strategy, has certain errors, and can improve the control precision by adopting closed-loop control. The existing closed-loop control algorithm, such as a control method based on the Heaviside function proposed by scholars such as Dyke of the university of Holly, st.Louis, washington, USA, is a switching control method with only two modes, and the control method is realized by using a closed-loop control algorithm I max And I min Or V max And V min The damping force/moment is controlled continuously, so that there is a phenomenon that the damping force/moment buffets at high frequency (as shown in fig. 1). The high-frequency buffeting of the damping force/moment can not only bring certain impact to the structure, but also cause fatigue damage of the mechanical structure of the magnetorheological actuator, and greatly reduce the service life of the magnetorheological actuator.
Therefore, the method has important theoretical significance and engineering application value for improving the control performance and reliability of the vibration control system and prolonging the service life of the magnetorheological actuator by exploring a control strategy for controlling the smoothness of the current/voltage.
Disclosure of Invention
Aiming at the problems and the defects in the prior art, the invention provides a closed-loop control method of a magnetorheological actuator, which monitors the change of damping force/moment in real time through a damping force/moment monitoring system, selects a corresponding control mode according to error information and mapping relation between expected damping force/moment and actual damping force/moment through a logic switching module, and further summarizes a fuzzy control rule and deduces continuous and smooth increment of control current/voltage by utilizing expert experience so as to obtain smoother control current/voltage.
In order to achieve the purpose, the invention adopts the following technical scheme:
a closed-loop control method of a magneto-rheological actuator comprises a damping force/moment monitoring system, a switching logic module, a fuzzy controller, the magneto-rheological actuator, an actuator controller and a current driver, wherein the actuator controller comprises the switching logic module and the fuzzy controller;
the damping force/moment monitoring system is used for monitoring the actual damping force/moment value of the magnetorheological actuator at the current moment in real time and feeding the actual damping force/moment value back to the actuator controller; the switching logic module is used for judging the control mode of the actuator controller according to the distribution relation between the expected damping force/moment and the actual damping force/moment value of the magnetorheological actuator at the current moment monitored by the damping force/moment monitoring system in real time; the fuzzy controller is used for obtaining a corresponding continuous and smooth current/voltage control increment through a fuzzy control rule according to an error between the expected damping force/moment and the actual damping force/moment, and controlling the damping force/moment of the magnetorheological actuator through the current driver.
Furthermore, the damping force/moment monitoring system is used for monitoring the actual damping force/moment value of the magnetorheological actuator at the current moment in real time, and the actual damping force/moment value is realized through an actual physical force/moment sensor testing system or is realized through a virtual force/moment monitoring system.
Still further, the actual physical force/torque sensor test system: estimating the damping force/moment value at the current moment according to the measurable/predictable state of the magnetorheological actuator at the previous moment by utilizing a mapping model of the damping force/moment of the existing magnetorheological actuator on speed, displacement, acceleration, temperature and current factors, such as a polynomial model, a Bingham model, a Bouc-Wen model, a hyperbolic tangent algebraic model, a neural network model and the like; the virtual force/moment monitoring system reconstructs a damping force/moment value at the current moment according to the state of the vibration control system by utilizing an observer design technology (an augmented state and interference) in a modern control theory or a system identification module with a storage function.
Further, the switching logic module is configured to determine a control mode of the actuator controller according to a distribution relationship between an expected damping force/moment and an actual damping force/moment value of the magnetorheological actuator monitored by the damping force/moment monitoring system in real time at the current time, specifically:
if the desired damping force/moment is in the same quadrant as the actual damping force/moment, when controlling the current the control mode is:
I(k)＝I(k-1)+ΔI(k)
wherein I (k) represents the control current at the current moment, I (k-1) represents the control current at the previous moment, and Delta I (k) represents the corresponding continuous and smooth current control increment obtained by the fuzzy control rule;
when the voltage is controlled, the control mode is as follows:
U(k)＝U(k-1)+ΔU(k)
wherein U (k) represents the control voltage at the current moment, U (k-1) represents the control voltage at the previous moment, and delta U (k) represents the corresponding continuous and smooth voltage control increment obtained by the fuzzy control rule;
if the desired damping force/moment is in a different quadrant from the actual damping force/moment, when controlling the current the control mode is:
I(k)＝0；
when the voltage is controlled, the control mode is as follows:
U(k)＝0。
still further, the control current at the current moment needs to be limited in a control feasible region, namely I (k) is more than or equal to 0 and less than or equal to I max (ii) a The control voltage at the current moment needs to be limited in a control feasible region, namely U (k) is more than or equal to 0 and less than or equal to V max 。
Further, an error between the desired damping force/moment and the actual damping force/moment is defined as e (k) = F c (k)-F a (k) In which F is c (k) Indicating the desired damping force/moment at the present moment, F a (k) Representing the actual damping force/moment at the present moment.
Further, the fuzzy control rule is that the error between the expected damping force/moment and the actual damping force/moment is normalized from the natural universe of discourse [ -e ] min ,e max ]Mapping to the ambiguity Domain [ -1,1]And defining a fuzzy subset in the fuzzy domain to represent the fuzzy state of the input or the fuzzy state of the output.
Compared with the prior art, the invention has the following advantages:
aiming at the problems of low open-loop control precision of the existing magneto-rheological actuator and buffeting of the magneto-rheological actuator closed-loop dual-mode control method based on the Heaviside step function, the invention provides a magneto-rheological actuator closed-loop control method based on fuzzy control, which overcomes the defect of current/voltage control quantity jump of the Heaviside step function, and corrects the current/voltage value at the previous moment by an incremental method to obtain smooth control current/voltage, so that the buffeting of the damping force/moment of the magneto-rheological actuator is reduced.
Drawings
FIG. 1 shows experimental effects of a closed-loop control method for a magnetorheological actuator based on a Heaviside step function;
FIG. 2 is a schematic diagram of a closed loop control system for a magnetorheological actuator;
FIG. 3 is a membership function of a damping force/moment tracking error of a magnetorheological actuator;
FIG. 4 is a magnetorheological actuator closed-loop control routine.
Detailed Description
The invention is described in detail below with reference to the drawings and specific embodiments, which are provided for illustration only and are not meant to limit the invention.
As shown in fig. 2, the present invention discloses a closed-loop control method for a magnetorheological actuator, which includes a damping force/torque monitoring system, a switching logic module, a fuzzy controller, a magnetorheological actuator, an actuator controller and a current driver, wherein the actuator controller includes a switching logic module and a fuzzy controller;
the damping force/moment monitoring system is used for monitoring the actual damping force/moment value of the magnetorheological actuator at the current moment in real time and feeding the actual damping force/moment value back to the actuator controller; the damping force/moment monitoring system is used for monitoring the actual damping force/moment value of the magnetorheological actuator at the current moment in real time through an actual physical force/moment sensor testing system or a virtual force/moment monitoring system, and the virtual force/moment monitoring system is realized through a method of an extended state observer or a system identification module with a storage function.
The switching logic module is used for judging the control mode of the actuator controller according to the distribution relation between the expected damping force/moment and the actual damping force/moment value of the magnetorheological actuator monitored by the damping force/moment monitoring system in real time at the current moment, and specifically comprises the following steps:
if the desired damping force/moment is in the same quadrant as the actual damping force/moment, when controlling the current the control mode is:
I(k)＝I(k-1)+ΔI(k)
wherein I (k) represents the control current at the current moment, I (k-1) represents the control current at the previous moment, and Delta I (k) represents the current control increment which is obtained by the fuzzy rule and corresponds to continuous smoothness;
when the voltage is controlled, the control mode is as follows:
U(k)＝U(k-1)+ΔU(k)
wherein U (k) represents the control voltage at the current moment, U (k-1) represents the control voltage at the previous moment, and Delta U (k) represents the corresponding continuous and smooth voltage control increment obtained by the fuzzy rule;
if the desired damping force/moment is in a different quadrant from the actual damping force/moment, when controlling the current the control mode is:
I(k)＝0；
when the voltage is controlled, the control mode is as follows:
U(k)＝0。
the control current at the current moment needs to be limited in a control feasible region, namely, I (k) is more than or equal to 0 and less than or equal to I max (ii) a The control voltage at the current moment needs to be limited in a control feasible region, namely U (k) is more than or equal to 0 and less than or equal to V max 。
The fuzzy controller is used for obtaining a corresponding continuous and smooth current/voltage control increment according to an error between an expected damping force/moment and an actual damping force/moment through a fuzzy control rule, and controlling the damping force/moment of the magnetorheological actuator through the current driver, wherein the error between the expected damping force/moment and the actual damping force/moment is defined as e (k) = F c (k)-F a (k) In which F c (k) Indicating the desired damping force/force at the present momentMoment, F a (k) Representing the actual damping force/moment at the present moment; the fuzzy rule is that the error between the expected damping force/moment and the actual damping force/moment is normalized from the natural universe [ -e ] by using a normalization method min ,e max ]Mapping to the ambiguity Domain [ -1,1]And defining fuzzy subsets in the fuzzy domain to represent fuzzy states of the input or fuzzy states of the output.
The method specifically comprises the following steps:
(1) Defining the damping force/moment tracking error as e (k) = F c (k)-F a (k) Then the error is normalized from the natural discourse domain [ -e ] using a normalization method min ,e max ]Mapping to the ambiguity Domain [ -1,1]The same applies to mapping control current increments to the corresponding ambiguity domain.
(2) Defining fuzzy sets and membership functions of input and output variables, wherein the fuzzy controller is a single-input single-output system, and input and output variables need the same fuzzy subset number, generally five or seven, such as: { negative large (NB), negative Medium (NM), negative Small (NS), zero (ZE), positive Small (PS), positive Medium (PM), positive large (PB) }, membership functions for input variables as follows, and membership functions for output variables as follows, as shown in fig. 3.
(3) According to the normalized error information and the direction of the actual damping force/moment, 2 fuzzy control rules are designed as follows:
| e(k) | NB | NM | NS | ZE | PS | PM | PB |
| F a (k)>0 | NB | NM | NS | ZE | PS | PM | PB |
| F a (k)<0 | PB | PM | PS | ZE | NS | NM | NB |
(4) The current/voltage increment of the fuzzy control is mapped to a corresponding control natural domain.
(5) Limiting control current/voltage to 0, I using saturation clipping function max ]Or [0, V ] max ]Within the range.
Alternatively, the current control increment Δ I (k) may be implemented by a fuzzy controller, or a classical PID control strategy or an adaptive control strategy.
Aiming at the problems that the existing open-loop control precision of the magneto-rheological actuator is not high and the magneto-rheological actuator closed-loop bimodal control method based on the Heaviside step function shakes, the invention provides the magneto-rheological actuator closed-loop control method, which gets rid of the defect that the current/voltage control quantity of the Heaviside step function jumps, and corrects the current/voltage value at the previous moment by a smooth control method through an incremental method to obtain smooth control current/voltage, so that the shake of the damping force/moment of the magneto-rheological actuator is reduced. Compared with the traditional control method of the magnetorheological actuator based on the Heaviside step function, the method has higher control precision, can improve the performance and reliability of structural vibration control, and prolongs the service life of the magnetorheological actuator.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and scope of the present invention are intended to be covered thereby.
Claims (6)
1. The closed-loop control method of the magneto-rheological actuator is characterized by comprising a damping force/moment monitoring system, a switching logic module, a fuzzy controller, the magneto-rheological actuator, an actuator controller and a current driver, wherein the actuator controller comprises the switching logic module and the fuzzy controller;
the damping force/moment monitoring system is used for monitoring the actual damping force/moment value of the magnetorheological actuator at the current moment in real time and feeding the actual damping force/moment value back to the actuator controller; the switching logic module is used for judging the control mode of the actuator controller according to the distribution relation between the expected damping force/moment and the actual damping force/moment value of the magnetorheological actuator monitored by the damping force/moment monitoring system in real time; the fuzzy controller is used for obtaining a corresponding continuous and smooth current/voltage control increment through a fuzzy rule according to an error between an expected damping force/moment and an actual damping force/moment, and controlling the damping force/moment of the magnetorheological actuator through the current driver;
the switching logic module is used for judging the control mode of the actuator controller according to the distribution relation between the expected damping force/moment and the actual damping force/moment value of the magnetorheological actuator at the current moment monitored by the damping force/moment monitoring system in real time, and specifically comprises the following steps:
if the desired damping force/moment is in the same quadrant as the actual damping force/moment, when controlling the current the control mode is:
I(k)＝I(k-1)+ΔI(k)
wherein I (k) represents the control current at the current moment, I (k-1) represents the control current at the previous moment, and Delta I (k) represents the current control increment which is obtained by the fuzzy rule and corresponds to continuous smoothness;
when the voltage is controlled, the control mode is as follows:
U(k)＝U(k-1)+ΔU(k)
wherein U (k) represents the control voltage at the current moment, U (k-1) represents the control voltage at the previous moment, and delta U (k) represents the corresponding continuous and smooth voltage control increment obtained by the fuzzy rule;
if the desired damping force/moment is in a different quadrant from the actual damping force/moment, when controlling the current the control mode is:
I(k)＝0；
when the voltage is controlled, the control mode is as follows:
U(k)＝0。
2. the method of claim 1, wherein the damping force/torque monitoring system is configured to monitor the actual damping force/torque value of the magnetorheological actuator at the current moment in real time by an actual physical force/torque sensor test system or by a virtual force/torque monitoring system.
3. The method of claim 2, wherein the virtual force/torque monitoring system is a method by an extended state observer or a system identification module with memory function.
4. The method of claim 1, wherein the current time control current is limited to a control feasible region, i.e., 0 ≦ I (k) ≦ I max (ii) a The control voltage at the current moment needs to be limited to controlIn the production feasible region, namely U (k) is more than or equal to 0 and less than or equal to V max 。
5. The method of claim 1, wherein the error between the desired damping force/torque and the actual damping force/torque is defined as e (k) = F c (k)-F a (k) In which F is c (k) Indicating the desired damping force/moment at the present moment, F a (k) Representing the actual damping force/moment at the present moment.
6. The method of claim 1, wherein the fuzzy rule is a normalization of the error between the desired damping force/moment and the actual damping force/moment from the domain of nature [ -e ] using a normalization method min ,e max ]Mapping to the ambiguity Domain [ -1,1]And defining fuzzy subsets in the fuzzy domain to represent fuzzy states of the input or fuzzy states of the output.
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