CN103818525A - Fuzzy neural network PID (proportion integration differentiation) control system and fuzzy neural network PID control method for fin stabilizer - Google Patents
Fuzzy neural network PID (proportion integration differentiation) control system and fuzzy neural network PID control method for fin stabilizer Download PDFInfo
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Abstract
The invention provides a fuzzy neural network PID (proportion integration differentiation) control system and a fuzzy neural network PID control method for a fin stabilizer. After quantization, address mapping and CMAC storage are performed on a detected sea wave dip angle signal by a CMAC (cerebellar model articulation controller) neural network feed-forward control unit, by combining online learning of a fin angular displacement signal output by a fuzzy PID controller, CMAC operation is performed on the fin angular displacement signal and the quantized sea wave dip angle signal to obtain a fin stabilizer anti-interference offset angular displacement signal to be input into a fin stabilizer servodrive unit; a PLC (programmable logic controller) control unit is used for receiving signals detected by a velocity log and an angular velocity sensor, and the signals are output to the fuzzy PID controller after being fitted; the fin angular displacement signal processed by the fuzzy PID controller is sent to the fin stabilizer servodrive unit; the fin stabilizer is driven to rotate by the fin stabilizer servodrive unit according to the fin angular displacement signal and the offset angular displacement signal, and moreover, an angular displacement sensor is used for detecting a current fin angular displacement signal of the fin stabilizer, and the current fin angular displacement signal is sent onward to the fuzzy PID controller after the current fin angular displacement signal and the fin angular displacement signal sent by the PLC control unit are subtracted to drive the fin stabilizer to perform adaptive adjustment, and therefore, the adaptability of a control process is improved.
Description
Technical field
The present invention relates to Ship Steering Autopilot technical field, in particular to a kind of stabilizer Fuzzy Neural PID control system and method.
Background technology
When boats and ships navigate by water in water, owing to being subject to the impact of the factors such as wave, wind and current, naval vessel inevitably can produce various swaying, wherein remarkable with rolling, and impact is also maximum.The violent normal work of swaying navigability, safety and equipment to naval vessel, the easypro property fixing and occupant of goods all can have a great impact.For this reason, people are seeking to reduce the method for ship rolling always, and develop multiple ship rolling motion control setup and reduce ship rolling, mainly contain bilge keel, stabilizer and antirolling tank.Stabilizer is a kind of the most frequently used active stabilizer, and it is divided into retractable fin stabilizer and non-retractable formula stabilizer.It mainly comprises: the fin of airfoil type, the hydraulic actuator that turns fin and electric control system.By controlling the rotation of fin, make the create antagonism balancing torque of rolling of fin, to reach the object that reduces rolling.
At present, stabilizer be generally acknowledge both at home and abroad there is the ability of shaking that well subtracts, widely used ship stabilizer, be the important equipments such as naval vessels, large-scale ocean range vessel, fisheries administration ship, maritime patrol ship, ro-ro passenger ship, mammoth tanker, the fight capability to raising naval vessels, the safety of tjemplon, oil tanker visitor's traveling comfort all play good guarantee.
Ship Steering Autopilot control system of the prior art generally adopts PID control policy, and this control policy has the advantages such as simple in structure, good stability.But the rolling motion of boats and ships is nonlinear, only hour can be approximated to be linear system in roll angle.When the rolling damping of boats and ships or period of rolling or when wave is when changing with the angle of ship's navigation, be that boats and ships have uncertainty under sail, the control parameter of traditional PID controller is fixed, in the situation that ship parameter changes, its stabilizing efficiency will variation or system unstable.
Summary of the invention
The defect or the deficiency that exist for prior art, the object of the present invention is to provide a kind of stabilizer Fuzzy Neural PID control system, solution existing ship Control System of Ship Fin responds and causes not in time the unsettled defect of ship navigation under complicated water environment, and solves the problem to actuating device remote monitor and control inconvenience.
The object of another aspect of the present invention is also to provide a kind of stabilizer Fuzzy Neural PID control method.
Above-mentioned purpose of the present invention realizes by the technical characterictic of independent claims, and dependent claims develops the technical characterictic of independent claims with alternative or favourable mode.
For reaching above-mentioned purpose, the technical solution adopted in the present invention is as follows:
A kind of stabilizer Fuzzy Neural PID control system, comprise CMAC neural network feed forward control unit, fuzzy controller, PLC control unit, angular velocity sensor, angular-motion transducer, velocity log, stabilizer and stabilizer servodrive unit, described stabilizer servodrive unit is for rotating according to the signal driver stabilizer of input, wherein:
Described CMAC neural network feed forward control unit is for to detecting that wave slope of wave surface signal quantizes, after address mapping and CMAC storage, in conjunction with the fin angular displacement signal of fuzzy controller output described in on-line study, then through CMAC, computing obtains stabilizer disturbance rejection offset angle displacement signal and inputs described stabilizer servodrive unit the slope of wave surface signal by aforementioned fin angular displacement signal and after quantizing;
Described angular velocity sensor is arranged on boats and ships the angular velocity in roll signal in the time there is rolling for detection of boats and ships;
Described angular-motion transducer is arranged on the fin angular displacement signal for detection of stabilizer on the fin axle of stabilizer;
Described velocity log is for detection of the speed of a ship or plane signal of boats and ships;
Described velocity log and angular velocity sensor are connected to described PLC control unit, and PLC control unit receives angular velocity in roll signal and the speed of a ship or plane signal of boats and ships and carries out signal fitting, and the signal of exporting after matching is sent to described fuzzy controller;
Described fuzzy controller reasoning obtains ratio, integration, differential setting parameter, the fin angular displacement signal obtaining after fuzzy controller is processed is sent to described stabilizer servodrive unit, described stabilizer servodrive unit drives stabilizer to rotate according to this fin angular displacement signal and aforementioned stabilizer disturbance rejection offset angle displacement signal, simultaneously described angular-motion transducer detects the current fin angular displacement signal of stabilizer, the difference of the fin angular displacement signal that this fin angular displacement signal and PLC control unit send, it is fin angular transposition error signal, continue to send to fuzzy controller, drive the adjustment of stabilizer self adaptation, described fuzzy controller, angular-motion transducer, stabilizer and the control of stabilizer servodrive cell formation close loop negative feedback.
Further in embodiment, described CMAC neural network feed forward control unit comprises for by digitized slope of wave surface signal discrete quantization modules, for the signal after discrete being distributed to the address mapping module of a particular address, be used for the CMAC memory module of having distributed address discrete signal described in storing, for calculating CMAC functional operation module and the on-line study module of stabilizer disturbance rejection offset angle displacement signal, the incoming signal of described on-line study module is the output signal of described fuzzy controller, the output signal of on-line study module is stored in described CMAC memory module, the stabilizer disturbance rejection offset angle displacement signal of described CMAC functional operation module output is inputted described stabilizer servodrive unit.
Further in embodiment, described stabilizer servodrive unit comprises the servoamplifier, servovalve, fin rotation cylinder and the transmission device that connect successively, transmission device is connected with stabilizer, described servoamplifier is for amplifying the signal of input, then be sent to servovalve, servovalve work rear drive fin rotation cylinder rotates, and fin rotation cylinder drives stabilizer to rotate through transmission device.
According to improvement of the present invention, another aspect of the present invention also proposes a kind of stabilizer Fuzzy Neural PID control method, comprises the following steps:
1-1, ship rolling angle do not change, signal after first CMAC neural network feed forward control unit also gives each discrete slope of wave surface signal discreteization distributes an address, then by each signal storage to distributing in advance in the memory device of address, calculate stabilizer disturbance rejection offset angle displacement signal finally by crossing CMAC function, and this signal is sent to stabilizer servodrive unit, then enter following step 2;
1-2, in the time of boats and ships generation rolling motion, what utilization detected arrives ship rolling angular velocity signal and ship speed signal, carry out signal fitting calculating by PLC control unit, and the signal after matching is sent to fuzzy controller, fuzzy controller is exported fin angular displacement signal after processing, and is sent to stabilizer servodrive unit; And the described CMAC neural network feed forward control unit first signal by slope of wave surface signal discreteization and after giving each discrete distributes an address, then by each signal storage to distributing in advance in the memory device of address, obtain stabilizer disturbance rejection offset angle displacement signal and input described stabilizer servodrive unit through CMAC computing in conjunction with the slope of wave surface signal after fin angular displacement signal and the aforementioned quantification of fuzzy controller output described in on-line study; Then enter following step 3;
The stabilizer disturbance rejection offset angle displacement signal that step 2, stabilizer servodrive unit produce according to described 1-1 drives stabilizer to rotate in advance, then returns to step 1;
Step 3, stabilizer servodrive unit are according to the stabilizer disturbance rejection offset angle displacement signal of described fuzzy controller output fin angular displacement signal and aforementioned 1-2 generation; And
Step 4, detect the fin angular displacement signal of described stabilizer, and poor with the fin angular displacement signal of described PLC control unit output, obtain fin angular transposition error signal, and continue the described fuzzy controller of input with the adjustment of driving stabilizer self adaptation.
From the above technical solution of the present invention shows that, stabilizer Fuzzy Neural PID control system of the present invention and method, compared with prior art, its beneficial effect is:
1, CMAC neural network feed forward control is combined with fuzzy control, utilize on the one hand " concept " abstracting power and the Nonlinear Processing ability of fuzzy logic, utilize on the other hand the self-learning capability of neural network and the approximation capability of arbitrary function, by both combinations, the impact of compensation wave disturbance effect on roll angle, guaranteed the stability of boats and ships, stabilizing efficiency reaches more than 90%;
2, CMAC neural network feed forward control unit just provides and turns fin signal before boats and ships generation rolling, has greatly improved the speed of response of fin, and in fin rotation process, real-time learning offset angle displacement signal, has improved control accuracy;
3, CMAC neural network feed forward control unit is in fuzzy controller control process, constantly on-line study fin control signal, after CMAC function calculates, the Angular displacement control signal of fin in Compensation Fuzzy PID closed loop feedback control unit, obtain the more accurate angular displacement signal of fin, further improve the adaptivity of control process;
4, compared with traditional PID controller, fuzzy controller parameter parameter in control process can be adjusted in real time, along with the variation of sea situation, according to fuzzy rule, reasoning obtains real-time PID setting parameter to fuzzy controller, has improved adaptivity and the robustness of control process.
Accompanying drawing explanation
Fig. 1 is the system construction drawing of an embodiment of the present invention stabilizer Fuzzy Neural PID control system.
Fig. 2 is the exemplary structural representation in one of stabilizer servodrive unit in Fig. 1 embodiment.
Fig. 3 is the diagram of circuit of an embodiment of the present invention stabilizer Fuzzy Neural PID control method.
The specific embodiment
In order more to understand technology contents of the present invention, especially exemplified by specific embodiment and coordinate appended graphic being described as follows.
Figure 1 shows that the system architecture schematic diagram of an embodiment of the present invention stabilizer Fuzzy Neural PID control system, wherein, a kind of stabilizer Fuzzy Neural PID control system, comprise CMAC neural network feed forward control unit, fuzzy controller, PLC control unit, angular velocity sensor, angular-motion transducer, velocity log, stabilizer and stabilizer servodrive unit, described stabilizer servodrive unit is for rotating according to the signal driver stabilizer of input.
Described CMAC neural network feed forward control unit is for to detecting that wave slope of wave surface signal quantizes, after address mapping and CMAC storage, in conjunction with the fin angular displacement signal of fuzzy controller output described in on-line study, then through CMAC, computing obtains stabilizer disturbance rejection offset angle displacement signal and inputs described stabilizer servodrive unit the slope of wave surface signal by aforementioned fin angular displacement signal and after quantizing.
As shown in Figure 1, as optional embodiment, described CMAC neural network feed forward control unit comprises for by digitized slope of wave surface signal discrete quantization modules, for the signal after discrete being distributed to the address mapping module of a particular address, be used for the CMAC memory module of having distributed address discrete signal described in storing, for calculating CMAC functional operation module and the on-line study module of stabilizer disturbance rejection offset angle displacement signal, the incoming signal of described on-line study module is the output signal of described fuzzy controller, the output signal of on-line study module is stored in described CMAC memory module, the stabilizer disturbance rejection offset angle displacement signal of described CMAC functional operation module output is inputted described stabilizer servodrive unit.
Described angular velocity sensor is arranged on boats and ships the angular velocity in roll signal in the time there is rolling for detection of boats and ships.
Described angular-motion transducer is arranged on the fin angular displacement signal for detection of stabilizer on the fin axle of stabilizer.
Described velocity log is for detection of the speed of a ship or plane signal of boats and ships.
Described velocity log and angular velocity sensor are connected to described PLC control unit, and PLC control unit receives angular velocity in roll signal and the speed of a ship or plane signal of boats and ships and carries out signal fitting, and the signal of exporting after matching is sent to described fuzzy controller.
Described fuzzy controller reasoning obtains ratio (P), integration (I), differential (D) setting parameter, the fin angular displacement signal obtaining after fuzzy controller is processed is sent to described stabilizer servodrive unit, described stabilizer servodrive unit drives stabilizer to rotate according to this fin angular displacement signal and aforementioned stabilizer disturbance rejection offset angle displacement signal, simultaneously described angular-motion transducer detects the current fin angular displacement signal of stabilizer, the difference of the fin angular displacement signal that this fin angular displacement signal and PLC control unit send, it is fin angular transposition error signal, continue to send to fuzzy controller, drive the adjustment of stabilizer self adaptation.
Described fuzzy controller, angular-motion transducer, stabilizer and the control of stabilizer servodrive cell formation close loop negative feedback, make the adjustment of stabilizer self adaptation, until angular transposition error≤0.05rad of stabilizer makes boats and ships reach fast stabilized conditions.
As shown in Figure 2, as optional embodiment, described stabilizer servodrive unit comprises the servoamplifier, servovalve, fin rotation cylinder and the transmission device that connect successively, transmission device is connected with stabilizer, described servoamplifier is for amplifying the signal of input, then be sent to servovalve, servovalve work rear drive fin rotation cylinder rotates, and fin rotation cylinder drives stabilizer to rotate through transmission device.
Figure 3 shows that the stabilizer Fuzzy Neural PID control method of utilizing Fig. 1 embodiment to realize, wherein, a kind of stabilizer Fuzzy Neural PID control method, comprises the following steps:
1-1, ship rolling angle do not change, signal after first CMAC neural network feed forward control unit also gives each discrete slope of wave surface signal discreteization distributes an address, then by each signal storage to distributing in advance in the memory device of address, calculate stabilizer disturbance rejection offset angle displacement signal finally by crossing CMAC function, and this signal is sent to stabilizer servodrive unit, then enter following step 2;
1-2, in the time of boats and ships generation rolling motion, what utilization detected arrives ship rolling angular velocity signal and ship speed signal, carry out signal fitting calculating by PLC control unit, and the signal after matching is sent to fuzzy controller, fuzzy controller is exported fin angular displacement signal after processing, and is sent to stabilizer servodrive unit; And the described CMAC neural network feed forward control unit first signal by slope of wave surface signal discreteization and after giving each discrete distributes an address, then by each signal storage to distributing in advance in the memory device of address, obtain stabilizer disturbance rejection offset angle displacement signal and input described stabilizer servodrive unit through CMAC computing in conjunction with the slope of wave surface signal after fin angular displacement signal and the aforementioned quantification of fuzzy controller output described in on-line study; Then enter following step 3;
The stabilizer disturbance rejection offset angle displacement signal that step 2, stabilizer servodrive unit produce according to described 1-1 drives stabilizer to rotate in advance, then returns to step 1;
Step 3, stabilizer servodrive unit are according to the stabilizer disturbance rejection offset angle displacement signal of described fuzzy controller output fin angular displacement signal and aforementioned 1-2 generation; And
Step 4, detect the fin angular displacement signal of described stabilizer, and poor with the fin angular displacement signal of described PLC control unit output, obtain fin angular transposition error signal, and continue the described fuzzy controller of input with the adjustment of driving stabilizer self adaptation.
Although the present invention discloses as above with preferred embodiment, so it is not in order to limit the present invention.Persond having ordinary knowledge in the technical field of the present invention, without departing from the spirit and scope of the present invention, when being used for a variety of modifications and variations.Therefore, protection scope of the present invention is when being as the criterion depending on claims person of defining.
Claims (4)
1. a stabilizer Fuzzy Neural PID control system, it is characterized in that, comprise CMAC neural network feed forward control unit, fuzzy controller, PLC control unit, angular velocity sensor, angular-motion transducer, velocity log, stabilizer and stabilizer servodrive unit, described stabilizer servodrive unit is for rotating according to the signal driver stabilizer of input, wherein:
Described CMAC neural network feed forward control unit is for to detecting that wave slope of wave surface signal quantizes, after address mapping and CMAC storage, in conjunction with the fin angular displacement signal of fuzzy controller output described in on-line study, then through CMAC, computing obtains stabilizer disturbance rejection offset angle displacement signal and inputs described stabilizer servodrive unit the slope of wave surface signal by aforementioned fin angular displacement signal and after quantizing;
Described angular velocity sensor is arranged on boats and ships the angular velocity in roll signal in the time there is rolling for detection of boats and ships;
Described angular-motion transducer is arranged on the fin angular displacement signal for detection of stabilizer on the fin axle of stabilizer;
Described velocity log is for detection of the speed of a ship or plane signal of boats and ships;
Described velocity log and angular velocity sensor are connected to described PLC control unit, and PLC control unit receives angular velocity in roll signal and the speed of a ship or plane signal of boats and ships and carries out signal fitting, and the signal of exporting after matching is sent to described fuzzy controller;
Described fuzzy controller reasoning obtains ratio, integration, differential setting parameter, the fin angular displacement signal obtaining after fuzzy controller is processed is sent to described stabilizer servodrive unit, described stabilizer servodrive unit drives stabilizer to rotate according to this fin angular displacement signal and aforementioned stabilizer disturbance rejection offset angle displacement signal, simultaneously described angular-motion transducer detects the current fin angular displacement signal of stabilizer, the difference of the fin angular displacement signal that this fin angular displacement signal and PLC control unit send, it is fin angular transposition error signal, continue to send to fuzzy controller, drive the adjustment of stabilizer self adaptation, described fuzzy controller, angular-motion transducer, stabilizer and the control of stabilizer servodrive cell formation close loop negative feedback.
2. stabilizer Fuzzy Neural PID control system according to claim 1, it is characterized in that, described CMAC neural network feed forward control unit comprises for by digitized slope of wave surface signal discrete quantization modules, for the signal after discrete being distributed to the address mapping module of a particular address, be used for the CMAC memory module of having distributed address discrete signal described in storing, for calculating CMAC functional operation module and the on-line study module of stabilizer disturbance rejection offset angle displacement signal, the incoming signal of described on-line study module is the output signal of described fuzzy controller, the output signal of on-line study module is stored in described CMAC memory module, the stabilizer disturbance rejection offset angle displacement signal of described CMAC functional operation module output is inputted described stabilizer servodrive unit.
3. stabilizer Fuzzy Neural PID control system according to claim 1, it is characterized in that, described stabilizer servodrive unit comprises the servoamplifier, servovalve, fin rotation cylinder and the transmission device that connect successively, transmission device is connected with stabilizer, described servoamplifier is for amplifying the signal of input, then be sent to servovalve, servovalve work rear drive fin rotation cylinder rotates, and fin rotation cylinder drives stabilizer to rotate through transmission device.
4. the stabilizer Fuzzy Neural PID control method based on stabilizer Fuzzy Neural PID control system described in any one in claim 1-3, is characterized in that, comprises the following steps:
Step 1, receive after wave slope of wave surface in described CMAC neural network feed forward control unit, if:
1-1, ship rolling angle do not change, signal after first CMAC neural network feed forward control unit also gives each discrete slope of wave surface signal discreteization distributes an address, then by each signal storage to distributing in advance in the memory device of address, calculate stabilizer disturbance rejection offset angle displacement signal finally by crossing CMAC function, and this signal is sent to stabilizer servodrive unit, then enter following step 2;
1-2, in the time of boats and ships generation rolling motion, what utilization detected arrives ship rolling angular velocity signal and ship speed signal, carry out signal fitting calculating by PLC control unit, and the signal after matching is sent to fuzzy controller, fuzzy controller is exported fin angular displacement signal after processing, and is sent to stabilizer servodrive unit; And the described CMAC neural network feed forward control unit first signal by slope of wave surface signal discreteization and after giving each discrete distributes an address, then by each signal storage to distributing in advance in the memory device of address, obtain stabilizer disturbance rejection offset angle displacement signal and input described stabilizer servodrive unit through CMAC computing in conjunction with the slope of wave surface signal after fin angular displacement signal and the aforementioned quantification of fuzzy controller output described in on-line study; Then enter following step 3;
The stabilizer disturbance rejection offset angle displacement signal that step 2, stabilizer servodrive unit produce according to described 1-1 drives stabilizer to rotate in advance, then returns to step 1;
Step 3, stabilizer servodrive unit are according to the stabilizer disturbance rejection offset angle displacement signal of described fuzzy controller output fin angular displacement signal and aforementioned 1-2 generation; And
Step 4, detect the fin angular displacement signal of described stabilizer, and poor with the fin angular displacement signal of described PLC control unit output, obtain fin angular transposition error signal, and continue the described fuzzy controller of input with the adjustment of driving stabilizer self adaptation.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105652869A (en) * | 2016-01-04 | 2016-06-08 | 江苏科技大学 | CMAC and PID-based omnidirectional mobile robot and moving control method |
| CN105807614A (en) * | 2015-09-24 | 2016-07-27 | 哈尔滨工程大学 | Course generalized switching control method achieved through hovercraft executing mechanism |
| CN106043616A (en) * | 2016-06-03 | 2016-10-26 | 武汉理工大学 | Ship longitudinal dynamic stabilization method and device |
| CN106585915A (en) * | 2017-01-23 | 2017-04-26 | 哈尔滨工程大学 | Ship anti-rolling system double loop control based on fin and wing fin vector control |
| CN106842910A (en) * | 2016-11-17 | 2017-06-13 | 中国船舶科学研究中心（中国船舶重工集团公司第七0二研究所） | A kind of Ship Steering Autopilot sliding-mode control based on interference observer |
| CN107490958A (en) * | 2017-07-31 | 2017-12-19 | 天津大学 | A kind of Fuzzy Adaptive Control Scheme of series parallel robot in five degrees of freedom |
| CN110045612A (en) * | 2019-04-28 | 2019-07-23 | 哈尔滨理工大学 | A kind of contragradience self-adaptation control method of stabilizer hydraulic servo simulated experiment platform |
| CN113505437A (en) * | 2021-07-13 | 2021-10-15 | 哈尔滨理工大学 | Method for calculating effective projection area of marine fin stabilizer |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS61160389A (en) * | 1985-01-08 | 1986-07-21 | Mitsubishi Heavy Ind Ltd | Fin stabilizer control device |
| CN101727071A (en) * | 2009-11-13 | 2010-06-09 | 上海电力学院 | Concurrent control method of neural network model and quadratic mononeuron PID |
| CN201834187U (en) * | 2010-07-26 | 2011-05-18 | 上海派恩科技有限公司 | Electrohydraulic control system of fin stabilizer |
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS61160389A (en) * | 1985-01-08 | 1986-07-21 | Mitsubishi Heavy Ind Ltd | Fin stabilizer control device |
| CN101727071A (en) * | 2009-11-13 | 2010-06-09 | 上海电力学院 | Concurrent control method of neural network model and quadratic mononeuron PID |
| CN201834187U (en) * | 2010-07-26 | 2011-05-18 | 上海派恩科技有限公司 | Electrohydraulic control system of fin stabilizer |
Non-Patent Citations (4)
| Title |
|---|
| 于文博: "基于永磁同步电机的减摇鳍伺服系统设计研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》, no. 4, 15 October 2007 (2007-10-15) * |
| 李高云: "高海情下船舶减摇鳍智能控制系统研究", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 6, 15 June 2010 (2010-06-15) * |
| 肖蕙蕙: "基于CMAC(神经网络)与PID的并行控制", 《重庆工学院学报》, vol. 20, no. 2, 28 February 2006 (2006-02-28), pages 74 - 77 * |
| 金鸿章等: "CMAC与PID的复合控制在减摇鳍中的应用", 《舰船科学技术》, 15 January 2009 (2009-01-15) * |
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| CN105807614A (en) * | 2015-09-24 | 2016-07-27 | 哈尔滨工程大学 | Course generalized switching control method achieved through hovercraft executing mechanism |
| CN105652869A (en) * | 2016-01-04 | 2016-06-08 | 江苏科技大学 | CMAC and PID-based omnidirectional mobile robot and moving control method |
| CN106043616A (en) * | 2016-06-03 | 2016-10-26 | 武汉理工大学 | Ship longitudinal dynamic stabilization method and device |
| CN106842910A (en) * | 2016-11-17 | 2017-06-13 | 中国船舶科学研究中心（中国船舶重工集团公司第七0二研究所） | A kind of Ship Steering Autopilot sliding-mode control based on interference observer |
| CN106585915A (en) * | 2017-01-23 | 2017-04-26 | 哈尔滨工程大学 | Ship anti-rolling system double loop control based on fin and wing fin vector control |
| CN106585915B (en) * | 2017-01-23 | 2018-08-17 | 哈尔滨工程大学 | Ship rollstabilization system double loop control based on fin Yu wing fin vector controlled |
| CN107490958A (en) * | 2017-07-31 | 2017-12-19 | 天津大学 | A kind of Fuzzy Adaptive Control Scheme of series parallel robot in five degrees of freedom |
| CN107490958B (en) * | 2017-07-31 | 2020-06-19 | 天津大学 | Fuzzy self-adaptive control method of five-freedom-degree series-parallel robot |
| CN110045612A (en) * | 2019-04-28 | 2019-07-23 | 哈尔滨理工大学 | A kind of contragradience self-adaptation control method of stabilizer hydraulic servo simulated experiment platform |
| CN110045612B (en) * | 2019-04-28 | 2021-10-08 | 哈尔滨理工大学 | Backstepping self-adaptive control method of fin stabilizer hydraulic servo simulation experiment table |
| CN113505437A (en) * | 2021-07-13 | 2021-10-15 | 哈尔滨理工大学 | Method for calculating effective projection area of marine fin stabilizer |
| CN113505437B (en) * | 2021-07-13 | 2022-11-08 | 哈尔滨理工大学 | Method for calculating effective projection area of marine fin stabilizer |
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