CN113277046B - Simulated bat ray underwater vehicle depth control method based on centroid and tail fin - Google Patents
Simulated bat ray underwater vehicle depth control method based on centroid and tail fin Download PDFInfo
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Abstract
The invention relates to a method for controlling the depth setting of an imitated manta ray underwater vehicle based on a centroid and a tail fin, which aims at propelling the bionic underwater vehicle by a pectoral fin with strong model uncertainty, divides a depth setting task into three stages by adopting segmentation, and implements online correction on PID (proportion integration differentiation) coefficients by combining a fuzzy controller to realize the adjustment of the centroid structure and the tail fin so as to complete the task of swimming at the depth. In the depth setting process, a pectoral fin mechanism of the underwater vehicle is driven by a steering engine and always keeps a flapping state, and the steering engine outputs a constant amplitude value and a constant phase difference to generate thrust along an X axis of a carrier coordinate system. The pitching moment is generated by changing the back and forth movement of the metamorphic heart block and the up and down deflection of the tail fin, so that the depth fixing effect is achieved.
Description
Technical Field
The invention belongs to the field of motion control of an underwater vehicle, and relates to a method for controlling the fixed depth of an manta ray-simulated underwater vehicle based on a mass center and a tail fin, in particular to a method for controlling the fixed depth of the manta ray-simulated underwater vehicle based on the cooperative control of the mass center and the tail fin.
Background
The simulated bat ray underwater vehicle is a novel bionic underwater vehicle, adopts a pectoral fin propulsion mode, has the remarkable advantages of high efficiency, high maneuverability, high stability, low disturbance and the like, can realize excellent in-situ observation and adaptation to complex sea areas, simultaneously has good biological affinity, can be used in the fields of coral starfish disaster monitoring, marine ranching fish condition monitoring and the like, and has wide application prospect and theoretical research value.
The model of the pectoral fin propulsion bionic underwater vehicle is very complex, so far, no more accurate model of pectoral fin swing type propulsion fish exists, and the application of the traditional control method on the bionic vehicle is limited. At present, common depth control methods for the type of aircraft comprise a fuzzy control algorithm, a PID control algorithm and the like, but simple information processing of the fuzzy control algorithm can cause system control accuracy reduction and dynamic performance deterioration; the parameters in the PID control algorithm are constant values, do not change along with the system, and have no self-tuning characteristic. In published documents, there are few specific methods for depth control of pectoral fin bionic underwater vehicles, such as the following patents: a fish-shaped bionic underwater robot and a control method [ P ] CN111284663B thereof and the patent: bionic manta ray robot [ P ]. CN 112093018A.
Disclosure of Invention
Technical problem to be solved
In order to avoid the defects of the prior art, the invention provides a fixed-depth control method of a simulated manta ray underwater vehicle based on a centroid and a tail fin, and the method is the fixed-depth control method of the simulated manta ray underwater vehicle based on the coordination control of a centroid mechanism and the tail fin.
Technical scheme
A method for controlling the depth of an artificial bat ray underwater vehicle based on a centroid and a tail fin is characterized by comprising the following steps:
step 1, calculating depth deviation and depth deviation change rate: acquiring the current depth of the underwater vehicle as y and the current depth is positive downwards through a depth sensor; the reference depth set by the task is y d Then the depth deviation Δ y is:
Δy＝y-y d
and (3) deriving the depth deviation to obtain a depth deviation change rate v as:
wherein: t is the depth sensor information updating period of the underwater vehicle;
(1) rapid submergence/floatation: when delta y is larger than or equal to a, namely the current depth is far away from the target depth, the underwater vehicle is in a rapid submerging/floating stage, and a large fuzzy PID proportion coefficient setting range formed by combining the table 1 and the table 3 is adopted;
(2) starting and depth setting: when a is larger than or equal to delta y and larger than or equal to b, the current depth is close to the target depth, the underwater vehicle is in a prepared depth fixing stage, and a fuzzy PID proportional coefficient range setting range formed by combining a table 2 and a table 3 is adopted;
(3) keeping the depth to be fixed: when delta y is less than or equal to c, the current depth is approximately equal to the target depth without adjustment;
wherein: a, b and c are respectively depth large error, depth small error and depth error allowable values, and are set according to task requirements;
the setting range in the step (1) and the step (2) depends on the PID controller parameter corrected based on the fuzzy controller, namely the fuzzy controller simultaneously takes the depth deviation and the change rate of the depth deviation as input and corrects the PID controller parameter by using the fuzzy controller;
the discrete domain of depth deviation and the change rate of the depth deviation is { -3, -2, -1, 0, 1,2, 3}, and the fuzzy language value is { NB, NM, NS, ZE, PS, PM, PB }, i.e., { large negative, medium negative, small negative, zero, small positive, medium positive, large positive };
the value of Δ y as the abscissa E, v Performing table query as a value of the ordinate Ec, wherein the corrected PID parameters are:
wherein: k is a radical of p Is the original scale factor, k i For the original integral coefficient, k d Is the original differential coefficient; Δ k p The proportional coefficient setting adjustment, Δ k, is obtained by looking up a table for the fuzzy controller i Integral coefficient setting adjustment, Δ k, obtained for fuzzy controller look-up table d Obtaining a differential coefficient setting adjustment quantity for a fuzzy controller by looking up a table; k is a radical of pF For the adjustment quantity delta k of the proportional coefficient setting obtained by the look-up table of the fuzzy controller p Coefficient of proportionality to original k p Summed scaling factor, k iF Setting adjustment quantity delta k of integral coefficient obtained by looking up table through fuzzy controller i Integral coefficient, k, added to the original integral coefficient dF Setting adjustment quantity delta k of differential coefficient obtained by looking up table of fuzzy controller d Coefficient of differentiation from the original delta k d The added differential coefficients;
depth control quantity delta y calculated by PID controller for setting PID parameter by using fuzzy controller c Comprises the following steps:
wherein k is pF For adjusted proportionality coefficient, k iF For adjusted integral coefficient, k dF For the adjusted differential coefficient, Δ y is a deviation value from the current depth to the target depth, t is integration time, and dt is differentiation time;
the tables 1,2 and 3 are:
table 1: k is a radical of p Large range correction table for fuzzy control coefficient
Table 2: k is a radical of p Fuzzy control coefficient small range correction table
Table 3: k is a radical of i /k d Fuzzy control coefficient correction table
And step 3: for depth control quantity delta y c The control quantity after discretization can be obtained by discretization treatment and is made as follows:
wherein: Δ y n The depth deviation from the current depth to the target depth in the nth control period is shown, and T is a discretized time interval;
and 4, calculating an actuating mechanism value according to the control quantity:
calculating the position of the centroid block movement of the centroid mechanism:
M c ＝K M *Δy c *(M max -M min )
wherein: k M For conversion of control quantities into execution values, M max Is the upper limit of the movement of the mass center mechanism, M min Is the lower limit of the movement of the mass center mechanism, Δ y c For discrete controlled variables, M c The target position of the mass center mechanism needing to be moved;
calculating the amplitude of the tail fin action:
Q c ＝R*(Q max -Q min )*sin(P*Δy c )
wherein: p is the control quantity conversion coefficient, R is the output gain coefficient, Q max Upper limit of motion of the tail fin, Q min Lower limit of motion of the tail fin, Δ y c For discrete controlled quantities, Q c Is the target position of the tail fin action;
and 5: target position M of mass center mechanism needing to be moved c Control of the centroid mechanism of an underwater vehicle, target position Q of the skeg motion c Controlling the tail fin of an underwater vehicle and changing the bow of the flapping state of the pectoral fin of the vehiclePitching angles are formed, so that pitching moments with different sizes are generated, the depth deviation of the underwater vehicle reaches the error allowable range, and the depth fixing task is completed in the flapping process.
Advantageous effects
The simulated bat ray underwater vehicle depth control method based on the centroid and the tail fin provided by the invention is used for propelling the simulated underwater vehicle by aiming at the pectoral fin with strong model uncertainty, a depth setting task is divided into three stages by segmentation, and a fuzzy controller is combined to carry out online correction on PID coefficients, so that the adjustment of the centroid structure and the tail fin is realized, and the task of fixed-depth swimming is further completed. In the depth setting process, a pectoral fin mechanism of the underwater vehicle is driven by a steering engine and always keeps a flapping state, and the steering engine outputs a constant amplitude value and a constant phase difference to generate thrust along an X axis of a carrier coordinate system. The pitching moment is generated by changing the back and forth movement of the metamorphic heart block and the up and down deflection of the tail fin, so that the depth fixing effect is achieved.
The invention has the following beneficial effects:
1. the traditional PID control algorithm has certain limitation, and parameters in the control algorithm are constant values, do not change along with the system, and do not have self-tuning characteristics. The method divides the depth control of the aircraft into three working conditions according to the depth deviation, designs different fuzzy control rules to correct the PID parameters according to different working conditions, has parameter self-tuning, can be generally applicable to robot control without an accurate dynamic model, and has strong applicability and portability.
2. The obtained control quantity is subjected to linear calculation aiming at the mass center mechanism and nonlinear calculation aiming at the tail fin and then is used as the actuating mechanism, so that the combined action of the mass center mechanism and the tail fin can reach the maximum value of the mechanism under the condition of large depth deviation, the maximum output value reached by the vertical speed of the aircraft is obtained, the response is quicker, the speed of depth error convergence is greatly accelerated, and the like; and only tail fin adjustment is used under the condition that the depth deviation is between large depth deviation and small depth deviation, so that the power consumption is reduced, the aircraft has longer cruising ability, and the control precision can be improved and the overshoot can be reduced by virtue of the nonlinear calculation of the tail fin output.
Drawings
FIG. 1 is a schematic diagram of a depth control method of an underwater vehicle driven by pectoral fins according to the present invention;
FIG. 2 is a flowchart of the depth determination procedure of the present invention.
Fig. 3 is a simplified diagram of an simulated manta ray underwater vehicle, wherein flapping wing structures on two sides are symmetrical about a vehicle main body, and the numbering meanings in the diagram are as follows:
1,2 and 3 are fin ray units of an flapping wing framework of the aircraft;
4 is the aircraft head;
5 is an aircraft main body, and a center of mass block, an electronic bin and the like are coated in the aircraft main body;
6 is the flapping wing on the right side of the aircraft;
and 7 is the tail fin part of the aircraft.
Detailed Description
The invention will now be further described with reference to the following examples, and the accompanying drawings:
the invention adopts the technical scheme that current depth information is obtained through a depth sensor, different control strategies are adopted in three stages of quick submerging/floating, starting depth fixing and keeping depth fixing by using fuzzy PID control based on segmentation, the mass center structure and the tail fin are adjusted according to different working conditions, and finally the depth fixing control of an underwater vehicle in a flapping state is realized, and the specific steps are as follows:
step 1: and calculating the depth deviation and the depth deviation change rate.
The current depth of the underwater vehicle is obtained through the depth sensor and is y (positive downwards), and the reference depth set by the task is y d The depth deviation Deltay is
Δy＝y-y d (1)
The depth deviation is derived to obtain the change rate of the depth deviation v Is composed of
Where t is the depth sensor information update period for the underwater vehicle.
Step 2: a segmentation strategy is executed.
Based on the characteristics of complexity and parameter uncertainty of the pectoral fin propulsion bionic underwater vehicle model, an expert segmentation controller is designed. And after the real-time information of the depth deviation of the control system is obtained, executing an expert segmentation strategy.
The segment controller takes the depth deviation as input to divide the depth fixing task into the following three working conditions:
(1) rapid submergence/floatation: when the delta y is larger than or equal to a, namely the current depth is far away from the target depth, the underwater vehicle is in a rapid submerging/floating stage, and the fuzzy control tables are selected as a table 4 and a table 6. Under the working condition, the mass center mechanism and the tail fin act together according to the control quantity by the actuating mechanism. At the moment, the mass center mechanism and the tail fin generate larger pitching moment under the combined action of the control quantity, so that the fast submerging and floating are realized.
(2) Starting and depth setting: when a is larger than or equal to delta y and larger than or equal to b, the current depth is close to the target depth, the underwater vehicle is in a prepared depth fixing stage, and the fuzzy control tables are selected as a table 5 and a table 6. Under the working condition, only the tail fin part is adjusted according to the control quantity. At the moment, only the mass center system returns to the middle position, and only the tail fin is adjusted according to the control quantity, so that a smaller pitching moment is generated, and overshoot is reduced.
(3) Keeping the depth to be fixed: when Δ y ≦ c, the current depth is approximately equal to the target depth, no adjustment is required.
Wherein a, b and c are respectively set depth large error, depth small error and depth error allowable value.
And step 3: the PID controller parameters are modified based on the fuzzy controller.
The fuzzy controller takes the depth deviation and the change rate of the depth deviation as input at the same time, and corrects the PID controller parameters by utilizing the fuzzy controller, and a schematic diagram of the fuzzy controller is shown in FIG. 1.
The basic domain of depth deviation is Δ y ∈ [ - | y max |，|y max |]Wherein y is max Is the maximum value of the depth deviation; the domain of discourse of the depth deviation change rate is v ∈ [ - | v [ ] max |，|v max |]Wherein v is max Is the maximum value of the depth deviation ratio. Of depth deviation and rate of change of depth deviationThe discrete domain is { -3, -2, -1, 0, 1,2, 3}, and its fuzzy linguistic value is also { NB, NM, NS, ZE, PS, PM, PB }, where { NB, NM, NS, ZE, PS, PM, PB }, is { negative large, negative medium, negative small, zero, positive small, positive medium, positive large }.
The original PID parameters are self-tuned by the table look-up operation of the fuzzy controller according to different working conditions, and the value of the abscissa E is taken as the value of Delta y, v Performing table query as a value of the ordinate Ec, wherein the corrected PID parameters are:
in the formula (3), k p Is the original scale factor, k i For the original integral coefficient, k d Is the original differential coefficient; Δ k p The proportional coefficient setting adjustment, Δ k, is obtained by looking up a table for the fuzzy controller i Setting adjustment quantity, delta k, of integral coefficient obtained by looking up table of fuzzy controller d Obtaining a differential coefficient setting adjustment quantity for a fuzzy controller by looking up a table; k is a radical of pF For the adjustment quantity delta k of the proportional coefficient setting obtained by the look-up table of the fuzzy controller p Coefficient of proportionality to original k p Summed scaling factor, k iF Setting adjustment quantity delta k of integral coefficient obtained by looking up table through fuzzy controller i Integral coefficient, k, added to the original integral coefficient dF Setting adjustment quantity delta k of differential coefficient obtained by looking up table of fuzzy controller d Coefficient of differentiation from the original delta k d The added differential coefficients;
the fuzzy control tables of the embodiment are shown in attached tables 4, 5 and 6.
Depth control quantity delta y calculated by PID controller for setting PID parameter by using fuzzy controller c Comprises the following steps:
in the formula (4), k pF For adjusted proportionality coefficient, k iF For adjusted integral coefficient, k dF For adjusted differentiationThe coefficient, delta y is the deviation value from the current depth to the target depth, t is the integration time, and dt is the differentiation time;
discretization of the above formula can obtain a discretized control quantity system as follows:
Δ y in the formula (5) n And T is the discretization time interval for the depth deviation from the current depth to the target depth of the nth control period.
And 4, step 4: an actuator value is calculated based on the control amount.
The position of the movement of the mass center block of the mass center mechanism can be calculated according to the discretized control quantity, and the concrete formula is as follows:
M c ＝K M *Δy c *(M max -M min ) (6)
k in formula (6) M For conversion of control quantities into execution values, M max Is the upper limit of the movement of the mass center mechanism, M min Is the lower limit of the movement of the mass center mechanism, Δ y c For discrete controlled variables, M c The target position that needs to be moved for the centroid mechanism.
The amplitude of the tail fin action can be calculated according to the discretized control quantity, and the specific formula is as follows:
Q c ＝R*(Q max -Q min )*sin(P*Δy c ) (7)
in the formula (7), P is a control quantity conversion coefficient, R is an output gain coefficient, and Q max Upper limit of motion of the tail fin, Q min Lower limit of motion of the tail fin, Δ y c For discrete controlled quantities, Q c Is the target position of the tail fin action.
Such as M max Is 20, M min Is-20, M c Is 0, Q max Is 30, Q min Is 30, Q c At 5, indicating that the execution value of the centroid mechanism is 0, the centroid block should remain at zero position and not act; the tail fin execution value is 5, and the tail fin should be driven to the position of 5 degrees, and the starting fixed depth stage is carried out.
According to the reference mass center position and the reference tail fin deflection angle of the underwater vehicle which are adjusted and calculated according to the control quantity, the mass center mechanism and the tail fin are adjusted to enable the mass center and the tail fin of the underwater vehicle to reach the reference position, the size of the pitching attitude angle of the chest fin of the vehicle in the flapping state is changed, the underwater vehicle generates pitching moment to carry out depth adjustment, the depth deviation reaches the error allowable range, namely, the depth fixing process of the underwater vehicle in the chest fin flapping state is realized, the program flow chart is shown in figure 2, and the schematic diagram of the simulated manta ray underwater vehicle is shown in figure 3.
Attached Table 4:
specific example k p Large range correction table for fuzzy control coefficient
Attached table 5:
specific example k p Fuzzy control coefficient small range correction table
Attached table 6:
specific example k i /k d Fuzzy control coefficient correction table
Claims (1)
1. A method for controlling the depth of an artificial bat ray underwater vehicle based on a centroid and a tail fin is characterized by comprising the following steps:
step 1, calculating depth deviation and depth deviation change rate: acquiring the current depth of the underwater vehicle as y and the current depth is positive downwards through a depth sensor; the reference depth set by the task is y d Then the depth deviation is Δ y:
Δy＝y-y d
and (3) deriving the depth deviation to obtain a depth deviation change rate v as:
wherein: t is the depth sensor information updating period of the underwater vehicle;
step 2, taking the depth deviation as an input to divide the depth fixing task into the following three working conditions:
(1) rapid submergence/floatation: when delta y is larger than or equal to a, namely the current depth is far away from the target depth, the underwater vehicle is in a rapid submerging/floating stage, and a large fuzzy PID proportion coefficient setting range formed by combining the table 1 and the table 3 is adopted;
(2) starting and depth setting: when a is larger than or equal to delta y and larger than or equal to b, the current depth is close to the target depth, the underwater vehicle is in a prepared depth fixing stage, and a fuzzy PID proportional coefficient range setting range formed by combining a table 2 and a table 3 is adopted;
(3) keeping the depth to be fixed: when delta y is less than or equal to c, the current depth is approximately equal to the target depth without adjustment;
wherein: a, b and c are respectively depth large error, depth small error and depth error allowable values, and are set according to task requirements;
the setting range in the step (1) and the step (2) depends on the PID controller parameter corrected based on the fuzzy controller, namely the fuzzy controller simultaneously takes the depth deviation and the change rate of the depth deviation as input and corrects the PID controller parameter by using the fuzzy controller;
the discrete domain of depth deviation and the change rate of the depth deviation is { -3, -2, -1, 0, 1,2, 3}, and the fuzzy language value is { NB, NM, NS, ZE, PS, PM, PB }, i.e., { large negative, medium negative, small negative, zero, small positive, medium positive, large positive };
and performing table query by taking Δ y as the value of an abscissa E and v as the value of an ordinate Ec, wherein the corrected PID parameters are as follows:
wherein: k is a radical of p Is the original scale factor, k i For the original integral coefficient, k d Is the original differential coefficient; Δ k p The proportional coefficient setting adjustment, Δ k, is obtained by looking up a table for the fuzzy controller i Integral coefficient setting adjustment, Δ k, obtained for fuzzy controller look-up table d Obtaining a differential coefficient setting adjustment quantity for a fuzzy controller by looking up a table; k is a radical of pF For the adjustment quantity delta k of the proportional coefficient setting obtained by the look-up table of the fuzzy controller p Coefficient of proportionality to original k p Summed scaling factor, k iF Setting adjustment quantity delta k of integral coefficient obtained by looking up table through fuzzy controller i And the original integral coefficient k i Added integral coefficient, k dF Setting adjustment quantity delta k of differential coefficient obtained by looking up table of fuzzy controller d And the original differential coefficient k d The added differential coefficients;
depth control quantity delta y calculated by PID controller for setting PID parameter by using fuzzy controller c Comprises the following steps:
wherein k is pF For adjusted proportionality coefficient, k iF For adjusted integral coefficient, k dF For the adjusted differential coefficient, Δ y is a deviation value from the current depth to the target depth, t is integration time, and dt is differentiation time;
the tables 1,2 and 3 are:
TABLE 1 k p Large range correction table for fuzzy control coefficient
TABLE 2 k p Fuzzy control coefficient small range correction table
TABLE 3 k i /k d Fuzzy control coefficient correction table
And step 3: for depth control quantity delta y c The discretization treatment can obtain a discretized control quantity system as follows:
wherein: Δ y n The depth deviation from the current depth to the target depth in the nth control period is shown, and T is a discretized time interval; k is the current nth cycle.
And 4, calculating an actuating mechanism value according to the control quantity:
calculating the position of the centroid block movement of the centroid mechanism:
M c ＝K M *Δy c *(M max -M min )
wherein: k M For conversion of control quantities into execution values, M max Is the upper limit of the movement of the mass center mechanism, M min Is the lower limit of the movement of the mass center mechanism, Δ y c For discrete controlled variables, M c The target position of the mass center mechanism needing to be moved;
calculating the amplitude of the tail fin action:
Q c ＝R*(Q max -Q min )*sin(P*Δy c )
wherein: p is the control quantity conversion coefficient, R is the output gain coefficient, Q max Upper limit of motion of the tail fin, Q min Lower limit of motion of the tail fin, Δ y c For discrete controlled quantities, Q c Is the target position of the tail fin action;
and 5: target position M of mass center mechanism needing to be moved c Control of the centroid mechanism of an underwater vehicle, target position Q of the skeg motion c The tail fin of the underwater vehicle is controlled, and the pitching attitude angle of the pectoral fin of the vehicle in a flapping state is changed, so that pitching moments with different sizes are generated, the depth deviation of the underwater vehicle reaches an error allowable range, and a depth fixing task is completed in the flapping process.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07165180A (en) * | 1993-12-16 | 1995-06-27 | Nec Corp | Depth control system for submersible navigating body |
| CN102923286A (en) * | 2012-10-30 | 2013-02-13 | 北京航空航天大学 | Intelligent material IMPC-based manta ray-simulated underwater vehicle |
| CN105383654A (en) * | 2015-10-30 | 2016-03-09 | 哈尔滨工程大学 | Depth control device of autonomous underwater vehicle |
| JP2017206154A (en) * | 2016-05-19 | 2017-11-24 | 三菱重工業株式会社 | Underwater sailing body control device, underwater sailing system, underwater sailing body control method and program |
| JP2017208012A (en) * | 2016-05-20 | 2017-11-24 | 三菱重工業株式会社 | Control device, underwater vehicle, control method, and program |
| CN107571983A (en) * | 2017-07-12 | 2018-01-12 | 河海大学 | A kind of depth control method of three joint bionic machine fish |
| CN110758698A (en) * | 2019-11-28 | 2020-02-07 | 中国科学院自动化研究所 | Method, system and device for controlling gliding depth of bionic gliding dolphin |
| CN111930132A (en) * | 2020-07-22 | 2020-11-13 | 西北工业大学 | Track control method for underwater autonomous vehicle |
| US10935986B1 (en) * | 2019-11-28 | 2021-03-02 | Institute Of Automation, Chinese Academy Of Sciences | Gliding depth control method, system and device for biomimetic gliding robotic dolphin |
-
2021
- 2021-06-08 CN CN202110635058.3A patent/CN113277046B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07165180A (en) * | 1993-12-16 | 1995-06-27 | Nec Corp | Depth control system for submersible navigating body |
| CN102923286A (en) * | 2012-10-30 | 2013-02-13 | 北京航空航天大学 | Intelligent material IMPC-based manta ray-simulated underwater vehicle |
| CN105383654A (en) * | 2015-10-30 | 2016-03-09 | 哈尔滨工程大学 | Depth control device of autonomous underwater vehicle |
| JP2017206154A (en) * | 2016-05-19 | 2017-11-24 | 三菱重工業株式会社 | Underwater sailing body control device, underwater sailing system, underwater sailing body control method and program |
| JP2017208012A (en) * | 2016-05-20 | 2017-11-24 | 三菱重工業株式会社 | Control device, underwater vehicle, control method, and program |
| CN107571983A (en) * | 2017-07-12 | 2018-01-12 | 河海大学 | A kind of depth control method of three joint bionic machine fish |
| CN110758698A (en) * | 2019-11-28 | 2020-02-07 | 中国科学院自动化研究所 | Method, system and device for controlling gliding depth of bionic gliding dolphin |
| US10935986B1 (en) * | 2019-11-28 | 2021-03-02 | Institute Of Automation, Chinese Academy Of Sciences | Gliding depth control method, system and device for biomimetic gliding robotic dolphin |
| CN111930132A (en) * | 2020-07-22 | 2020-11-13 | 西北工业大学 | Track control method for underwater autonomous vehicle |
Non-Patent Citations (5)
| Title |
|---|
| A Method for Calculating the Amount of Movements to Estimate the self-position of Manta Robots;Takuya Imahama等;《Journal of Physics: Conference Series》;20181231;全文 * |
| Design and Locomotion Control of a Biomimetic Underwater Vehicle With Fin Propulsion;Chunlin Zhou等;《IEE/ASME TRANSACTIONS ON MECHATRONICS》;20120229;全文 * |
| Design and Locomotion Control of a Dactylopteridae-Inspired Biomimetic Underwater Vehicle With Hybrid Propulsion;Tiandong Zhang等;《IEE Transactions on Automation Science and Engineering》;20210408;全文 * |
| 仿生机器鱼胸/尾鳍协同推进闭环深度控制;李宗刚等;《机器人》;20200131;全文 * |
| 基于模糊PID的小型ROV定深运动控制仿真;綦声波等;《现代电子技术》;20200115;全文 * |
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