CN113342013A - Course control method combining roll mechanism and flapping wing asymmetric phase difference - Google Patents
Course control method combining roll mechanism and flapping wing asymmetric phase difference Download PDFInfo
- Publication number
- CN113342013A CN113342013A CN202110635057.9A CN202110635057A CN113342013A CN 113342013 A CN113342013 A CN 113342013A CN 202110635057 A CN202110635057 A CN 202110635057A CN 113342013 A CN113342013 A CN 113342013A
- Authority
- CN
- China
- Prior art keywords
- unit
- phase difference
- omega
- course
- control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000007246 mechanism Effects 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000005096 rolling process Methods 0.000 claims abstract description 35
- 238000006243 chemical reaction Methods 0.000 claims abstract description 14
- 238000007667 floating Methods 0.000 claims abstract description 11
- 230000008878 coupling Effects 0.000 claims description 20
- 238000010168 coupling process Methods 0.000 claims description 20
- 238000005859 coupling reaction Methods 0.000 claims description 20
- 230000008859 change Effects 0.000 claims description 11
- 238000004891 communication Methods 0.000 claims description 11
- 238000012937 correction Methods 0.000 claims description 6
- 230000009189 diving Effects 0.000 claims description 6
- 230000004069 differentiation Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 5
- 239000011664 nicotinic acid Substances 0.000 abstract description 3
- 230000004044 response Effects 0.000 abstract description 3
- 238000005265 energy consumption Methods 0.000 abstract description 2
- 230000007774 longterm Effects 0.000 abstract description 2
- 241001331491 Myliobatis californica Species 0.000 description 17
- 241001471424 Manta birostris Species 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 210000000006 pectoral fin Anatomy 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 230000002195 synergetic effect Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003767 neural control Effects 0.000 description 1
- 230000001141 propulsive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000009182 swimming Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/04—Control of altitude or depth
- G05D1/06—Rate of change of altitude or depth
- G05D1/0692—Rate of change of altitude or depth specially adapted for under-water vehicles
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Feedback Control In General (AREA)
Abstract
The invention relates to a course control method combining a roll mechanism and an asymmetric phase difference of flapping wings, which comprises the steps of obtaining current course information through an attitude sensor and calculating a yaw value; according to the speed of the longitudinal plane of the current aircraft, the fuzzy PID control is utilized, when the speed of the gliding longitudinal plane of the aircraft is higher, the course control is carried out by adopting a rolling mechanism, and when the speed is lower, the navigation is carried out by adopting asymmetric flapping wing phase difference in the following submerged floating conversion stage. The power consumption can be reduced by cooperatively controlling the rolling mechanism and the flapping wings, the problem of large power consumption of the bionic aircraft during long-term flapping is solved, and the heading can be controlled by only rotating the rolling block which is a single mechanism when the longitudinal speed of the aircraft is high, so that the energy consumption is reduced; and when the longitudinal speed is reduced and the course of the roll mechanism cannot be effectively controlled, the asymmetric phase difference control of the flapping wings is adopted. The navigation device has the advantages that the response is faster and the maneuverability is higher in the whole navigation course control process, and the navigation device can be more suitable for sea areas with complex and variable sea conditions.
Description
Technical Field
The invention belongs to a method for controlling the course of a simulated manta ray aircraft, relates to a course control method combining a cross-rolling mechanism and an asymmetric phase difference of a flapping wing, and particularly relates to a method for controlling the course of the simulated manta ray aircraft by utilizing the synergistic action of the cross-rolling mechanism and the phase difference of the flapping wing.
Background
The underwater glider is a novel observation platform which does gliding movement in the ocean and collects and observes the ocean environment in the movement process, realizes the large-area multi-scale monitoring of the ocean by utilizing the self gravity and the buoyancy difference to drive the movement, and is suitable for the detection in the fields of ocean environment observation and military affairs. The simulated manta ray underwater vehicle combines gliding propulsion of a glider and flexible propulsion of the simulated marine organism manta ray on the basis of an underwater glider principle to realize gliding and flapping integration, has better maneuverability and higher concealment, can realize underwater observation of wide-area coarse dimensionality and fixed-point fine dimensionality, and is more suitable for various complex sea areas.
In order to solve the problem of yawing when an underwater vehicle executes tasks, the traditional underwater vehicle mostly depends on propeller propulsion, the propulsion speed is high, but the navigation attitude is difficult to flexibly change. And various methods for controlling the course of the underwater vehicle, such as a fuzzy control algorithm, a PID control algorithm and the like, are available, and most of the methods are designed based on kinematics and dynamics models established by the vehicle. The propulsion mode of the simulated manta ray aircraft is different from that of the traditional aircraft, and no more accurate model exists so far, so that the accurate course control is very difficult to realize by adopting the traditional method, and a new course control method needs to be designed. In the published documents, no example is provided for realizing the course control of the simulated bat ray aircraft by the synergistic action of a roll mechanism and a flapping wing phase difference.
Disclosure of Invention
Technical problem to be solved
Aiming at a sliding-flapping integrated simulated bat aircraft, a navigation-fixing task is divided into two working conditions according to the longitudinal speed to carry out control scheme design, and a fuzzy PID control algorithm is combined to adjust the rolling mechanism and a flapping wing system controlled by a CPG neural network, so that the navigation-fixing swimming task is completed.
In order to realize the task, the invention adopts the technical scheme that the current course information is obtained through the attitude sensor, and the yaw value is calculated; according to the speed of the longitudinal plane of the current aircraft, the fuzzy PID control is utilized, when the speed of the gliding longitudinal plane of the aircraft is higher, the course control is carried out by adopting a rolling mechanism, and when the speed is lower, the navigation is carried out by adopting asymmetric flapping wing phase difference in the following submerged floating conversion stage.
The invention realizes the basic principle of controlling the simulated bat underwater vehicle by adjusting the asymmetric phase difference of a roll mechanism and a flapping wing, and comprises the following steps:
taking the sliding process of the simulated bat ray aircraft as an example for analysis and explanation, when the simulated bat ray aircraft dives and glides at a certain speed, the transverse rolling mechanism is adjusted to deflect, and at the moment, the simulated bat ray aircraft deflects along the chord direction of the aircraft, so that the simulated bat ray aircraft presents a deflection state. Because an included angle exists between the aircraft body and the horizontal plane, the hydrodynamic force vertical to the surface of the aircraft generates a yawing moment which enables the aircraft to bypass a vertical shaft of the center of gravity, and the heading of the simulated bat aircraft changes. The course of the simulated bat ray aircraft can be corrected by utilizing the yawing moment.
When the speed of the longitudinal plane is low, the flapping wings on the two sides flap with asymmetric phase difference, and the posture adjustment of the simulated bat ray underwater vehicle can be realized. Research on the biological bat ray shows that the propulsive force generated by the flapping of the pectoral fins of the biological bat ray is opposite to the direction of wave transmission, and when the directions of the wave transmission generated by the flapping of the pectoral fins on two sides of the biological bat ray are opposite, a turning moment can be formed, so that turning is finished. Similarly, when flapping wings on two sides of the simulated bat ray underwater vehicle flap with asymmetric phase difference, fluctuation transmission in the same direction, different sizes and even different directions can be formed on two sides of the main body, so that a turning moment is formed, and the posture of the vehicle can be adjusted.
Technical scheme
A course control method combining asymmetric phase differences of a roll mechanism and a flapping wing is characterized in that: the left side of the aircraft comprises 2 steering engines which are named as a first steering engine 1 and a second steering engine 2 respectively, and the right side of the aircraft is named as a third steering engine 3 and a fourth steering engine 4; each steering engine is controlled by the output of a conversion formula to form a unit; the course control steps are as follows:
step 1: obtaining the current course angle of an underwater vehicle through an attitude sensor
The target course angle set by the task is
Yaw angle e:
step 2: and (3) deriving the yaw angle to obtain a course angle deviation change rate ec:
wherein t is the attitude sensor information updating time of the underwater vehicle;
and step 3: the calculated yaw angle and heading deviation change rate are subjected to blurring processing, and the blurred linguistic variables are expressed by NB, which is negative large, NM, which is negative medium, NS, which is negative small, ZO, PS, which is positive small, PM, which is positive medium, and PB, which are positive large. The fuzzy set is:
ec＝{NB,NM,NS,ZO,PS,PM,PB}，
e＝{NB,NM,NS,ZO,PS,PM,PB}，
and 4, step 4: giving the fuzzified yaw angle and course deviation change rate to a fuzzy rule table for table lookup, taking the ec value as a vertical coordinate and the e value as a horizontal coordinate for table query, performing self-tuning on the original PID parameter, and obtaining delta k according to the fuzzy rule tablep、ΔkiAnd kdThe corrected PID parameters are:
wherein k ispIs the original scale factor, kiFor the original integral coefficient, kdIs the original differential coefficient; Δ kpThe proportional coefficient setting adjustment, Δ k, is obtained by looking up a table for the fuzzy controlleriIntegral coefficient setting adjustment, Δ k, obtained for fuzzy controller look-up tabledObtaining a differential coefficient setting adjustment quantity for a fuzzy controller by looking up a table; k is a radical ofpFFor the adjustment quantity delta k of the proportional coefficient setting obtained by the look-up table of the fuzzy controllerpCoefficient of proportionality to original kpSummed scaling factor, kiFSetting adjustment quantity delta k of integral coefficient obtained by looking up table through fuzzy controlleriIntegral coefficient, k, added to the original integral coefficientdFSetting adjustment quantity delta k of differential coefficient obtained by looking up table of fuzzy controllerdCoefficient of differentiation from the original delta kdThe added differential coefficients;
fuzzy rule table 1:
kpfuzzy control coefficient correction table
Fuzzy rule table 2:
ki/kdfuzzy control coefficient correction table
And 5: the course control quantity yaw (t) calculated by the PID controller after the setting of the fuzzy controller is as follows:
dt is the differential time;
discretizing to obtain:
where yaw (t) is the control output of the controller at the current moment; t is a discrete time interval;
step 6: according to the method, a navigation task is divided into the following two conditions by taking the longitudinal speed v acquired by an aircraft from an attitude sensor as a judgment condition:
a) when v is larger than a, the underwater vehicle is in a rapid diving/floating stage, the course is controlled by adopting the rolling mechanism, and the control quantity of the deviation angle of the rolling block of the rolling mechanism is calculated according to the control quantity as follows:
in the formula: r is the angle of offset of the rolling block, AmaxThe deflection range of the rolling block is [ -A ] as the maximum deflection angle of the rolling blockmax Amax]Yaw (t) control quantity output of the controller, M is a control quantity conversion coefficient;
b) when v is less than or equal to a, the underwater vehicle is in a diving and floating conversion stage, and the course is controlled by changing the asymmetric phase difference of the flapping wings;
and 7: the phase difference variable quantity of the flapping wings at two sides, which is linearly converted by the following formula, of the controlled quantity is input into a CPG network, and the amplitude value R of the flapping wing is inputiFrequency viObtaining the angle theta of flapping of the flapping wings through the iterative computation of the CPG networkiThus realizing course control by using the asymmetric phase difference;
in the formula: yaw (t) is the control quantity output of the controller, and Q is the control quantity conversion coefficient. A is the expected phase difference during straight-stream.
To be the desired phase difference for the steering engines 1, 2,
for the desired phase difference of the steering engines 2, 3,
is the desired phase difference for steering engines 3, 4;
the CPG network is composed of a phase equation, an amplitude equation and an output equation:
θi＝ri(1+cosφi)
each equation is a phase equation, an amplitude equation and an output equation. In the formulaiRepresenting the phase, v, of the ith celliRepresenting the natural frequency, ωijRepresenting the coupling weight of the jth cell to the ith cell,
representing the desired phase difference; r isiDenotes the amplitude, aiNormal number, R, representing the rate of convergence of the control amplitudeiRepresenting a desired amplitude; thetaiRepresenting the output value.
The topological structure of the CPG network is as follows: the first unit 1 on the left communicates with the second unit 2, and the unit 3 on the right communicates with the unit 4; establishing the relation between the left side and the right side through the second unit 2 and the third unit 3; wherein the coupling weight omega of the jth unit to the ith unitijPresence of only omega12、ω13、ω23Three forms, which can take the value of omega12＝ω34＝4、ω23＝3。
The topological structure of the CPG network is as follows: the first unit 1 on the left communicates with the second unit 2, and the unit 3 on the right communicates with the unit 4; between the left and right sides by the first unit 1 and the third unit 3Contacting; wherein the coupling weight omega of the jth unit to the ith unitijPresence of only omega12、ω13、ω34Three forms, which can take the value of omega12＝ω34＝2、ω13＝1。
The topological structure of the CPG network is as follows: the first unit 1 on the left communicates with the second unit 2, and the unit 3 on the right communicates with the unit 4; establishing the relation between the left side and the right side through the first unit 1 and the third unit 3 and the second unit 2 and the fourth unit 4; wherein the coupling weight omega of the jth unit to the ith unitijPresence of only omega12、ω34、ω13、ω24Four forms, value omega12＝ω34＝4、ω13＝ω24＝2。
The topological structure of the CPG network is as follows: the first unit 1 on the left communicates with the second unit 2, and the unit 3 on the right communicates with the unit 4; establishing the relation between the left side and the right side through the second unit 2 and the third unit 3 and the first unit 1 and the fourth unit 4; wherein the coupling weight omega of the jth unit to the ith unitijPresence of only omega12、ω23、ω34、ω41Four forms, value omega12＝ω23＝ω41＝ω34＝3。
The topological structure of the CPG network is as follows: the first unit 1 on the left communicates with the second unit 2, and the unit 3 on the right communicates with the unit 4; establishing the relation between the left side and the right side through the first unit 1 and the fourth unit 4; wherein the coupling weight omega of the jth unit to the ith unitijPresence of only omega12、ω34、ω14Three forms, value is omega12＝ω34＝2、ω14＝1。
Advantageous effects
The invention provides a course control method combining a roll mechanism and an asymmetric phase difference of flapping wings, which comprises the steps of obtaining current course information through an attitude sensor and calculating a yaw value; according to the speed of the longitudinal plane of the current aircraft, the fuzzy PID control is utilized, when the speed of the gliding longitudinal plane of the aircraft is higher, the course control is carried out by adopting a rolling mechanism, and when the speed is lower, the navigation is carried out by adopting asymmetric flapping wing phase difference in the following submerged floating conversion stage.
The invention has the following beneficial effects:
1. for an object which is difficult to model by a simulated bat ray aircraft, a traditional PID control algorithm is difficult to effectively control, fuzzy rules divided by fuzzy control are limited, the control precision is reduced, and the combination of the fuzzy control algorithm and the fuzzy control algorithm has the characteristics of quick response, flexibility and the like of the fuzzy control and has the characteristic of high precision of the PID control.
2. The simulated bat ray aircraft has a great difference from the traditional propeller-propelled aircraft in the driving mode, and the traditional control method cannot be adopted. The invention provides a new idea for heading control of a bionic underwater vehicle integrating sliding and flapping, which belongs to the field of underwater vehicles with flapping wing layout and realizes fixed navigation by cooperative control of a roll mechanism and flapping wings.
3. The power consumption can be reduced by cooperatively controlling the rolling mechanism and the flapping wings, the problem of large power consumption of the bionic aircraft during long-term flapping is solved, and the heading can be controlled by only rotating the rolling block which is a single mechanism when the longitudinal speed of the aircraft is high, so that the energy consumption is reduced; and when the longitudinal speed is reduced and the course of the transverse rolling mechanism can not be effectively controlled, the asymmetric phase difference control of the flapping wings is adopted, and fluctuation transmission in the same direction, different sizes and even different directions is formed on the two sides of the main body, so that a turning moment is formed, the course of the simulated bat ray underwater vehicle is adjusted faster, the turning effect is more obvious, the maneuverability is higher, and the transverse rolling stability is better. The navigation device has the advantages that the response is faster and the maneuverability is higher in the whole navigation course control process, and the navigation device can be more suitable for sea areas with complex and variable sea conditions.
Drawings
FIG. 1 is a schematic view of a navigation principle of fuzzy PD control of an simulated bat ray aircraft according to the present invention;
FIG. 2 is a connection diagram of a CPG topology 1 constructed by the present invention;
FIG. 3 is a schematic connection diagram of CPG topology 2 constructed by the present invention;
FIG. 4 is a schematic connection diagram of CPG topology 3 constructed by the present invention;
FIG. 5 is a schematic connection diagram of CPG topology 4 constructed by the present invention;
FIG. 6 is a schematic connection diagram of CPG topology 4 constructed by the present invention;
FIG. 7 is a flowchart illustrating the present invention navigation process;
FIG. 8 is a graphical illustration of a course control curve for coordinated control of the roll mechanism and flapping wings of an aircraft in accordance with the present invention; the depth of t1-t2 has no obvious change, and the heading is controlled by adopting the asymmetric phase difference of the flapping wings; and the other sections adopt a rolling mechanism to control the course.
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
the invention obtains the current course angle and the current longitudinal speed of the vehicle through the attitude sensor, selects different execution mechanisms to adjust the course according to the longitudinal speed, and comprises the following specific steps:
1. obtaining the current course angle of an underwater vehicle through an attitude sensor
The target course angle set by the task is
Then yaw angle e, then
2. And (3) deriving the yaw angle to obtain a course angle deviation change rate ec, then:
wherein t is the attitude sensor information update time of the underwater vehicle.
Discretizing the above equation into:
wherein e (t) is the course angle deviation at the current moment, and e (t-1) is the course angle deviation at the last moment.
3. And fuzzifying the obtained yaw angle and heading deviation change rate, wherein the fuzzified linguistic variables are expressed by Negative Big (NB), Negative Middle (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Middle (PM) and Positive Big (PB). The fuzzy sets are as follows:
ec＝{NB,NM,NS,ZO,PS,PM,PB} (4)
e＝{NB,NM,NS,ZO,PS,PM,PB} (5)
giving the fuzzified yaw angle and course deviation change rate to a fuzzy rule table for table lookup, namely performing table query by taking an ec value as a vertical coordinate and an e value as a horizontal coordinate, and performing self-tuning on the original PID parameters, wherein the fuzzy rule table is shown in tables 3 and 4:
attached table 3:
kpfuzzy control coefficient correction table
Attached table 4:
ki/kdfuzzy control coefficient correction table
Deriving Δ k from fuzzy rule tablesp、ΔkiAnd kdThe corrected PID parameters are:
wherein k ispIs the original scale factor, kiFor the original integral coefficient, kdIs the original differential coefficient; Δ kpThe proportional coefficient setting adjustment, Δ k, is obtained by looking up a table for the fuzzy controlleriIs a dieIntegral coefficient setting adjustment quantity delta k obtained by fuzzy controller table look-updObtaining a differential coefficient setting adjustment quantity for a fuzzy controller by looking up a table; k is a radical ofpFFor the adjustment quantity delta k of the proportional coefficient setting obtained by the look-up table of the fuzzy controllerpCoefficient of proportionality to original kpSummed scaling factor, kiFSetting adjustment quantity delta k of integral coefficient obtained by looking up table through fuzzy controlleriIntegral coefficient, k, added to the original integral coefficientdFSetting adjustment quantity delta k of differential coefficient obtained by looking up table of fuzzy controllerdCoefficient of differentiation from the original delta kdThe added differential coefficients;
the course control quantity calculated by the PID controller after the fuzzy controller is set is as follows:
dt is the differentiation time.
Discretization can obtain:
where yaw (t) is the control output of the controller at the current moment; t is a discrete time interval.
The knowledge related to fluid power can be used, and under the condition that the longitudinal speed of the aircraft is low, the yaw moment generated by the roll angle is small, and the turning effect is not obvious. Based on the design, the rolling mechanism and the flapping wings cooperate to realize navigation of the aircraft. According to the method, a navigation task is divided into the following two conditions by taking the longitudinal speed v acquired by an aircraft from an attitude sensor as a judgment condition:
a) rapidly floating up and submerging: when v is larger than a, the underwater vehicle is in a rapid submerging/floating stage, the course is controlled by adopting the roll mechanism, the roll mechanism in the vehicle is adjusted to roll left and right according to the calculated control quantity, the roll angle of the vehicle is changed, and when the vehicle needs to turn left, the roll mechanism is high on the left and low on the right, so that the vehicle is in a left yaw attitude; when the aircraft needs to turn right, the rolling mechanism is low at the left and high at the right, so that the aircraft is in a right deflection attitude, and finally, a navigation task is finished.
Wherein, the offset angle of the rolling block of the rolling mechanism is calculated according to the control quantity, the deflection range of the rolling block is-45 degrees to 45 degrees, and the specific formula is as follows:
in the formula: r is the angle of offset of the rolling block, AmaxThe deflection range of the rolling block is [ -A ] as the maximum deflection angle of the rolling blockmax Amax]Yaw (t) control quantity output of the controller, and M is a control quantity conversion coefficient.
If the fuzzy PID calculates that the current theta (t) is 6, M is 0.1, namely the rolling mechanism block needs to move to the position of 39 degrees.
b) When diving and floating are switched: when v is less than or equal to a, the underwater vehicle is in a diving and floating conversion stage, the longitudinal speed is low, and the course is controlled by changing the asymmetric phase difference of the flapping wings;
in order to enable flapping wings to flap smoothly, an artificial CPG neural control network is constructed to realize the flapping control of the simulated bat ray aircraft, a CPG phase oscillator model which is connected simplest is adopted, the left pectoral fin of the simulated bat ray aircraft comprises 2 steering engines which are named as steering engines 1 and 2 respectively, and the right pectoral fin steering engines are named as steering engines 3 and 4. Each steering engine is controlled by a phase oscillator model, and the mutual connection among the steering engines is realized through a coupling item. The left pectoral fin unit 1 is connected with the unit 2; the right pectoral fin unit 3 is in communication with the unit 4; the connection between the left and right pectoral fins is established by pectoral fin intermediate units 1 and 3. The connection mode is shown in fig. 2, and the model mainly comprises a phase equation, an amplitude equation and an output equation, and specifically comprises the following steps:
wherein each equation is respectively a phase equation, an amplitude equation and an output equation. In the formulaiRepresenting the phase, v, of the ith celliRepresenting the natural frequency, ωijRepresenting the coupling weight of the jth cell to the ith cell,
representing the desired phase difference; r isiDenotes the amplitude, aiNormal number, R, representing the rate of convergence of the control amplitudeiRepresenting a desired amplitude; thetaiRepresenting the output value.
The mutual connection between the steering engines is realized by coupling terms, omegaijRepresenting the coupling term of the j-th cell to the i-th cell, in such a way that ωijPresence of only omega12、ω13、ω23Three forms, which can take the value of omega12＝ω34＝4、ω23＝3。
The control quantity obtained by fuzzy PID calculation is linearly converted into the phase difference variable quantity of the flapping wings at two sides by a formula (21) and is input into a CPG network, and the amplitude R of the set flapping wing is input at the same timeiFrequency viObtaining the angle theta of flapping of the flapping wings through the iterative computation of the CPG networkiTherefore, the course control is completed by using the asymmetric phase difference.
In the formula: yaw (t) is the control quantity output of the fuzzy PID controller, Q is the control quantity conversion coefficient, and A is the expected phase difference during straight-stream.
To be the desired phase difference for the steering engines 1, 2,
for the desired phase difference of the steering engines 2, 3,
is the desired phase difference for the steering engines 3, 4.
When the aircraft needsWhen the course is adjusted by turning left, the phase difference between the two fin rays on the left side of the flapping wing
Reducing or reversing the phase difference between the two right-hand fins
Increasing the left flapping wing propelling force to be smaller than the right flapping wing propelling force or the left flapping wing propelling force, and turning to finish left turning to reach the target course; when the aircraft needs to turn right to adjust navigation, the phase difference between the two fin rays on the left side of the flapping wing
Increasing the phase difference between the two right-hand fins
The left flapping wing propulsion is larger than the right flapping wing propulsion or the right flapping wing propulsion is larger, and the steering is completed, so that the right steering is completed to reach the target course;
if the aircraft needs to turn left to adjust the course, the amplitude value of the flapping wing set by the input is 30 degrees, the frequency is 0.3hz, A is 20, the value theta (t) obtained by fuzzy PID calculation is 6, Q is 5, and the sailing flapping wing can be adjusted to
The amplitude is 30 degrees, and the angle with the frequency of 0.3hz is used for fixed-flight flapping.
The specific program flow chart is shown in fig. 7, and the test course setting curve is shown in fig. 8.
When the CPG phase oscillator model topology adopts the graph of FIG. 3, the unit connection in the oscillator model is the unit 1 and the unit 2; unit 1 is connected with unit 3; unit 3 is connected to unit 4. OmegaijRepresenting the coupling of the jth cell to the ith cellTerm, the connection mode omegaijPresence of only omega12、ω13、ω34Three forms, which can take the value of omega12＝ω34＝2、ω13＝1。
When the CPG phase oscillator model topology adopts the graph of FIG. 4, the unit connection in the oscillator model is the unit 1 and the unit 2; unit 3 is connected with unit 4; unit 1 is connected with unit 3; unit 2 is connected to unit 4. OmegaijRepresenting the coupling term of the j-th cell to the i-th cell, in such a way that ωijPresence of only omega12、ω34、ω13、ω24Four forms, which can take the value of omega12＝ω34＝4、ω13＝ω24＝2。
When the CPG phase oscillator model topology adopts the graph of FIG. 5, the unit connection in the oscillator model is that the unit 1 is connected with the unit 2; unit 2 is connected with unit 3; unit 3 is connected with unit 4; unit 4 is connected to unit 1. OmegaijRepresenting the coupling term of the j-th cell to the i-th cell, in such a way that ωijPresence of only omega12、ω23、ω34、ω41Four forms, which can take the value of omega12＝ω23＝ω41＝ω34＝3。
When the CPG phase oscillator model topology adopts the graph of FIG. 6, the unit connection in the oscillator model is the unit 1 and the unit 2; unit 3 is connected with unit 4; unit 1 is connected to unit 4. OmegaijRepresenting the coupling term of the j-th cell to the i-th cell, in such a way that ωijPresence of only omega12、ω34、ω14Three forms, which can take the value of omega12＝ω34＝2、ω14＝1。
Claims (6)
1. A course control method combining asymmetric phase differences of a roll mechanism and a flapping wing is characterized in that: the left side of the aircraft comprises 2 steering engines which are named as a first steering engine (1) and a second steering engine (2) respectively, and the right side of the aircraft is named as a third steering engine (3) and a fourth steering engine (4); each steering engine is controlled by the output of a conversion formula to form a unit; the course control steps are as follows:
step 1: obtaining the current course angle of an underwater vehicle through an attitude sensor
The target course angle set by the task is
Yaw angle e:
step 2: and (3) deriving the yaw angle to obtain a course angle deviation change rate ec:
wherein t is the attitude sensor information updating time of the underwater vehicle;
and step 3: the calculated yaw angle and heading deviation change rate are subjected to blurring processing, and the blurred linguistic variables are expressed by NB, which is negative large, NM, which is negative medium, NS, which is negative small, ZO, PS, which is positive small, PM, which is positive medium, and PB, which are positive large. The fuzzy set is:
ec＝{NB,NM,NS,ZO,PS,PM,PB}，
e＝{NB,NM,NS,ZO,PS,PM,PB}，
and 4, step 4: giving the fuzzified yaw angle and course deviation change rate to a fuzzy rule table for table lookup, taking the ec value as a vertical coordinate and the e value as a horizontal coordinate for table query, performing self-tuning on the original PID parameter, and obtaining delta k according to the fuzzy rule tablep、ΔkiAnd kdThe corrected PID parameters are:
wherein k ispIs the original scale factor, kiFor the original integral coefficient, kdIs the original differential coefficient; Δ kpThe proportional coefficient setting adjustment, Δ k, is obtained by looking up a table for the fuzzy controlleriIntegral coefficient setting adjustment, Δ k, obtained for fuzzy controller look-up tabledObtaining a differential coefficient setting adjustment quantity for a fuzzy controller by looking up a table; k is a radical ofpFFor the adjustment quantity delta k of the proportional coefficient setting obtained by the look-up table of the fuzzy controllerpCoefficient of proportionality to original kpSummed scaling factor, kiFSetting adjustment quantity delta k of integral coefficient obtained by looking up table through fuzzy controlleriIntegral coefficient, k, added to the original integral coefficientdFSetting adjustment quantity delta k of differential coefficient obtained by looking up table of fuzzy controllerdCoefficient of differentiation from the original delta kdThe added differential coefficients;
fuzzy rule table 1:
kpfuzzy control coefficient correction table
Fuzzy rule table 2:
ki/kdfuzzy control coefficient correction table
And 5: the course control quantity yaw (t) calculated by the PID controller after the setting of the fuzzy controller is as follows:
dt is the differential time;
discretizing to obtain:
where yaw (t) is the control output of the controller at the current moment; t is a discrete time interval;
step 6: according to the method, a navigation task is divided into the following two conditions by taking the longitudinal speed v acquired by an aircraft from an attitude sensor as a judgment condition:
a) when v is larger than a, the underwater vehicle is in a rapid diving/floating stage, the course is controlled by adopting the rolling mechanism, and the control quantity of the deviation angle of the rolling block of the rolling mechanism is calculated according to the control quantity as follows:
in the formula: r is the angle of offset of the rolling block, AmaxThe deflection range of the rolling block is [ -A ] as the maximum deflection angle of the rolling blockmax Amax]Yaw (t) control quantity output of the controller, M is a control quantity conversion coefficient;
b) when v is less than or equal to a, the underwater vehicle is in a diving and floating conversion stage, and the course is controlled by changing the asymmetric phase difference of the flapping wings;
and 7: the phase difference variable quantity of the flapping wings at two sides, which is linearly converted by the following formula, of the controlled quantity is input into a CPG network, and the amplitude value R of the flapping wing is inputiFrequency viObtaining the angle theta of flapping of the flapping wings through the iterative computation of the CPG networkiThus realizing course control by using the asymmetric phase difference;
in the formula: yaw (t) is the control quantity output of the controller, and Q is the control quantity conversion coefficient. A is the expected phase difference during straight-stream.
To be the desired phase difference for the steering engines 1, 2,
for the desired phase difference of the steering engines 2, 3,
is the desired phase difference for steering engines 3, 4;
the CPG network is composed of a phase equation, an amplitude equation and an output equation:
θi＝ri(1+cosφi)
each equation is a phase equation, an amplitude equation and an output equation. In the formulaiRepresenting the phase, v, of the ith celliRepresenting the natural frequency, ωijRepresenting the coupling weight of the jth cell to the ith cell,
representing the desired phase difference; r isiDenotes the amplitude, aiNormal number, R, representing the rate of convergence of the control amplitudeiRepresenting a desired amplitude; thetaiRepresenting the output value.
2. The heading control method combining the asymmetric phase difference between the roll mechanism and the flapping wing of claim 1, wherein the topology of the CPG network is as follows: the first unit (1) on the left side is in communication with the second unit (2), and the unit (3) on the right side is in communication with the unit (4); establishing a connection between the left side and the right side through the second unit (2) and the third unit (3); wherein the coupling weight omega of the jth unit to the ith unitijPresence of only omega12、ω13、ω23Three forms, which can take the value of omega12＝ω34＝4、ω23＝3。
3. The heading control method combining the asymmetric phase difference between the roll mechanism and the flapping wing of claim 1, wherein the topology of the CPG network is as follows: the first unit (1) on the left side is in communication with the second unit (2), and the unit (3) on the right side is in communication with the unit (4); establishing a connection between the left side and the right side through the first unit (1) and the third unit (3); wherein the coupling weight omega of the jth unit to the ith unitijPresence of only omega12、ω13、ω34Three forms, which can take the value of omega12＝ω34＝2、ω13＝1。
4. The heading control method combining the asymmetric phase difference between the roll mechanism and the flapping wing of claim 1, wherein the topology of the CPG network is as follows: the first unit (1) on the left side is in communication with the second unit (2), and the unit (3) on the right side is in communication with the unit (4); establishing a relation between the left side and the right side through the first unit (1) and the third unit (3) and the second unit (2) and the fourth unit (4); wherein the coupling weight omega of the jth unit to the ith unitijPresence of only omega12、ω34、ω13、ω24Four forms, value omega12＝ω34＝4、ω13＝ω24＝2。
5. The heading control method combining the asymmetric phase difference between the roll mechanism and the flapping wing of claim 1, wherein the topology of the CPG network is as follows: the first unit (1) on the left side is in communication with the second unit (2), and the unit (3) on the right side is in communication with the unit (4); establishing a relation between the left side and the right side through the second unit (2) and the third unit (3) and the first unit (1) and the fourth unit (4); wherein the coupling weight omega of the jth unit to the ith unitijPresence of only omega12、ω23、ω34、ω41Four forms, value omega12＝ω23＝ω41＝ω34＝3。
6. The heading control method combining the asymmetric phase difference between the roll mechanism and the flapping wing of claim 1, wherein the topology of the CPG network is as follows: the first unit (1) on the left side is in communication with the second unit (2), and the unit (3) on the right side is in communication with the unit (4); establishing a connection between the left side and the right side through the first unit (1) and the fourth unit (4); wherein the coupling weight omega of the jth unit to the ith unitijPresence of only omega12、ω34、ω14Three forms, value is omega12＝ω34＝2、ω14＝1。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110635057.9A CN113342013B (en) | 2021-06-08 | 2021-06-08 | Course control method combining roll mechanism and flapping wing asymmetric phase difference |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110635057.9A CN113342013B (en) | 2021-06-08 | 2021-06-08 | Course control method combining roll mechanism and flapping wing asymmetric phase difference |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113342013A true CN113342013A (en) | 2021-09-03 |
| CN113342013B CN113342013B (en) | 2022-08-05 |
Family
ID=77474669
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110635057.9A Active CN113342013B (en) | 2021-06-08 | 2021-06-08 | Course control method combining roll mechanism and flapping wing asymmetric phase difference |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113342013B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB1470424A (en) * | 1973-08-24 | 1977-04-14 | Thomson Csf | System for measuring the velocity of a moving object |
| CN101916071A (en) * | 2010-08-04 | 2010-12-15 | 中国科学院自动化研究所 | CPG feedback control method of biomimetic robot fish movement |
| CN103383571A (en) * | 2013-08-13 | 2013-11-06 | 湖南航天机电设备与特种材料研究所 | Asymmetric four-rotor UAV (unmanned aerial vehicle) and control method thereof |
| CN104477357A (en) * | 2014-12-18 | 2015-04-01 | 北京航空航天大学 | Implementation method for rapid great pitch angle change motion of pectoral fin paddling type robotic fish |
| CN104881045A (en) * | 2015-06-17 | 2015-09-02 | 中国科学院自动化研究所 | Bionic robot fish three-dimensional tracking method based on embedded visual guidance |
| CN105785999A (en) * | 2016-04-27 | 2016-07-20 | 大连海事大学 | Unmanned surface vehicle course motion control method |
| CN105892475A (en) * | 2016-05-04 | 2016-08-24 | 中国海洋大学 | Underwater glider attitude control algorithm based on fuzzy PID |
| CN107192983A (en) * | 2017-07-04 | 2017-09-22 | 深圳潜行创新科技有限公司 | A kind of device, method and system for observing underwater vehicle relative position |
| CN109189075A (en) * | 2018-10-08 | 2019-01-11 | 哈尔滨工程大学 | A kind of fuzzy forgetting factor model-free adaption course heading control method in naval vessel |
| CN110217363A (en) * | 2019-05-20 | 2019-09-10 | 浙江大学 | A kind of six propellers based on FUZZY ALGORITHMS FOR CONTROL can descending aircraft |
-
2021
- 2021-06-08 CN CN202110635057.9A patent/CN113342013B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB1470424A (en) * | 1973-08-24 | 1977-04-14 | Thomson Csf | System for measuring the velocity of a moving object |
| CN101916071A (en) * | 2010-08-04 | 2010-12-15 | 中国科学院自动化研究所 | CPG feedback control method of biomimetic robot fish movement |
| CN103383571A (en) * | 2013-08-13 | 2013-11-06 | 湖南航天机电设备与特种材料研究所 | Asymmetric four-rotor UAV (unmanned aerial vehicle) and control method thereof |
| CN104477357A (en) * | 2014-12-18 | 2015-04-01 | 北京航空航天大学 | Implementation method for rapid great pitch angle change motion of pectoral fin paddling type robotic fish |
| CN104881045A (en) * | 2015-06-17 | 2015-09-02 | 中国科学院自动化研究所 | Bionic robot fish three-dimensional tracking method based on embedded visual guidance |
| CN105785999A (en) * | 2016-04-27 | 2016-07-20 | 大连海事大学 | Unmanned surface vehicle course motion control method |
| CN105892475A (en) * | 2016-05-04 | 2016-08-24 | 中国海洋大学 | Underwater glider attitude control algorithm based on fuzzy PID |
| CN107192983A (en) * | 2017-07-04 | 2017-09-22 | 深圳潜行创新科技有限公司 | A kind of device, method and system for observing underwater vehicle relative position |
| CN109189075A (en) * | 2018-10-08 | 2019-01-11 | 哈尔滨工程大学 | A kind of fuzzy forgetting factor model-free adaption course heading control method in naval vessel |
| CN110217363A (en) * | 2019-05-20 | 2019-09-10 | 浙江大学 | A kind of six propellers based on FUZZY ALGORITHMS FOR CONTROL can descending aircraft |
Non-Patent Citations (1)
| Title |
|---|
| YONG CAO，ET AL.: "CPG-fuzzy-based control of a cownose-ray-like fish robot", 《INDUSTRIAL ROBOT 》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113342013B (en) | 2022-08-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113341693B (en) | Course control method based on asymmetric phase difference and amplitude of flapping wings | |
| CN107168312B (en) | Space trajectory tracking control method for compensating UUV kinematic and dynamic interference | |
| CN110083061B (en) | Wheel type mobile robot control method | |
| CN113342012B (en) | Course control method of sliding and flapping integrated aircraft | |
| CN113325858A (en) | Simulated bat aircraft course control method based on flapping wing amplitude | |
| CN112147899B (en) | Underwater robot autonomous obstacle avoidance control method based on fuzzy sliding mode algorithm | |
| CN102385316A (en) | Deepening controlling method of underactuated automatic underwater vehicle based on neural network back stepping method | |
| Wang et al. | Development and control of underwater gliding robots: A review | |
| CN113341974B (en) | Gliding course control method of simulated manta ray underwater vehicle based on flapping wing bias | |
| Al Makdah et al. | Three-dimensional trajectory tracking of a hybrid autonomous underwater vehicle in the presence of underwater current | |
| CN113359785B (en) | Microminiature AUV underwater motion and hovering control method | |
| WO2023050582A1 (en) | Angle of attack-based bionic robotic fish fixed-depth control method and apparatus | |
| CN107315348A (en) | A kind of autonomous underwater vehicle posture control device and method based on punishment formula wavelet network | |
| CN113342013B (en) | Course control method combining roll mechanism and flapping wing asymmetric phase difference | |
| CN113277046B (en) | Simulated bat ray underwater vehicle depth control method based on centroid and tail fin | |
| CN113341973B (en) | Course control method based on asymmetric phase difference of flapping wings | |
| CN116048090A (en) | Sail navigation aid ship path tracking control method with energy consumption optimization effect | |
| CN113325857B (en) | Simulated bat ray underwater vehicle depth control method based on centroid and buoyancy system | |
| CN113342011A (en) | Gliding course control method of simulated bat aircraft based on rolling mechanism | |
| CN111506096B (en) | Tail-pushing type double-duct unmanned aerial vehicle fuzzy self-adaptive attitude control method | |
| Liu et al. | Research on key methods of autonomous sailboat’s position keeping control | |
| CN113848915A (en) | Polar ship course control method based on linear active disturbance rejection technology | |
| CN108820167A (en) | A kind of steerable system and region control position control method of biomimetic type underwater robot | |
| CN115755938B (en) | Method for controlling depth of simulated ray aircraft based on asymmetric fuzzy S-plane | |
| CN113805598B (en) | Navigation control method for under-actuated AUV (autonomous underwater vehicle) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |