CN110515298A - Based on the adaptive marine isomery multiple agent speed cooperative control method of optimization - Google Patents

Based on the adaptive marine isomery multiple agent speed cooperative control method of optimization Download PDF

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CN110515298A
CN110515298A CN201910514436.5A CN201910514436A CN110515298A CN 110515298 A CN110515298 A CN 110515298A CN 201910514436 A CN201910514436 A CN 201910514436A CN 110515298 A CN110515298 A CN 110515298A
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speed
indicate
velocity
unmanned device
noise
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CN110515298B (en
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陈旭
胡凯
邓志良
刘佳
刘云平
严飞
苗国英
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Nanjing University of Information Science and Technology
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Nanjing University of Information Science and Technology
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

The invention discloses a kind of based on adaptive marine isomery multiple agent speed cooperative control method is optimized, including following work step: establishing the kinematics model of waterborne, underwater unmanned device respectively；Access speed is co-variate, and according to navigating-following, method establishes joint formation structure, obtains waterborne, the underwater unified stable control signal of unmanned device；The velocity error that joint is formed into columns is obtained, and is normalized, the dispersion degree of its data is reflected by setting mean square velocity error, the level of noise is reflected with this；When noise level beyond can tolerance range, to noise amplification, eliminate；It sets the coordination control laws based on Integral Sliding Mode and real-time compensation is carried out to bounded velocity error.Firstly, the present invention improves the detection efficiency of noise and eliminates accuracy；Secondly, carrying out real-time compensation to velocity error, so that system has stronger robustness, the stability of system ensure that.

Description

Based on the adaptive marine isomery multiple agent speed cooperative control method of optimization
Technical field
The present invention relates to the Collaborative Control field of isomery multiple agent, more particularly, to a kind of adaptive based on optimizing Marine isomery multiple agent speed cooperative control method.
Background technique
In order to preferably develop marine resources and protection maritime rights and interests, need to design the heterogeneous system of a variety of robot compositions More efficient extensively explore is carried out to maritime environment.(for example the water surface, underwater united is formed by unmanned boat, underwater glider etc. Heterogeneous system can not only expand exploration area, can more improve working efficiency.) and want to make these functions relatively simple single Complex task is completed in intelligent body collaboration, it is necessary to study isomery multiple agent Collaborative Control.Cooperative control system has certain Redundancy, fault-tolerance and robustness, task completion is more efficient, and function is simplified, and cost also decreases, therefore cooperates with Control problem becomes the hot issue of MAS control technical field research.
In view of marine water surface wave, noise disturbance etc. influence, system control amount (such as speed, position etc.) can be made By different degrees of interference, especially when the influence of noise is beyond that can bear state, it is necessary to noise is handled in time, This requires control system to have higher robustness, can eliminate disturbance and compensation error in real time.And traditional coordination control strategy PID control, Artificial Potential Field, Behavior-based control method etc. still have several drawbacks, are not suitable for Heterogeneous Control System not only, and And the dynamic property of control object is not fully taken into account, it is limited to specific task environment and control parameter, control performance Also it is unable to get guarantee.In consideration of it, the above control method is not appropriate for using in heterogeneous system at sea.
The Chinese patent of Publication No. CN106054922 discloses a kind of unmanned plane-unmanned vehicle joint formation Collaborative Control Method, this method, which is built upon under a kind of ideal environment, carries out cluster formation control, not in view of the model of system is uncertain Property and external noise interference, also without propose solution noise disturbance measure, need to improve.
Summary of the invention
Goal of the invention: in order to overcome the shortcomings of background technique, the invention discloses a kind of seas adaptive based on optimization Upper isomery multiple agent speed cooperative control method.
Technical solution: the marine isomery multiple agent speed Collaborative Control side adaptive based on optimization of the present invention Method, including following work step:
(1) kinematics model of unmanned device waterborne and underwater unmanned device is established respectively；
(2) access speed is co-variate, and according to navigating-following, method establishes joint formation structure, obtains unmanned device waterborne With the unified stable control signal of underwater unmanned device；
(3) based on unified stable control signal, the velocity error that joint is formed into columns is obtained, and place is normalized Reason,
The dispersion degree for reflecting its data by setting mean square velocity error, the level of noise is reflected with this；
(4) when the level of noise beyond can tolerance range, by low noise power amplification feedback device to noise amplify；
(5) noise is eliminated by design sef-adapting filter；
(6) coordination control laws of the setting based on Integral Sliding Mode carry out real-time compensation to bounded velocity error.
Wherein, in step (1) unmanned device waterborne kinematics model expression formula are as follows:
Wherein,Indicate the speed of unmanned device waterborne,Indicate course angle, η=[x, y, ψ]TIndicate nothing waterborne The state vector of people's device, R (ψ) indicate to turn between earth inertial reference frame and unmanned device fixed reference frame waterborne Matrix, u, v are changed, r respectively indicates the control moment input of unmanned device waterborne, forward speed, transverse moving speed；
The kinematics model expression formula of underwater unmanned device are as follows:
Wherein,Indicate the speed of underwater unmanned device, θ, φ, ψ,Respectively indicate underwater unmanned device Pitch angle, roll angle, yaw angle, rate of pitch, angular velocity in roll, yaw rate, u indicate control moment input, V, w indicates the speed of underwater unmanned device, angular speed, Ω=[pqr] under earth axesTLocal coordinate is respectively indicated to be lauched The angular speed amount of unmanned device down.
Further, unmanned device waterborne described in step (2) and the unified stable control signal of unmanned device waterborne are as follows:
Wherein,Indicate the speed of underwater unmanned device, ev、vi、vi dThe speed for respectively indicating underwater unmanned device is missed Difference, actual speed, desired speed, waterborne, underwater unmanned device have with respective pilotage people, ej1、ej2、ej3It indicates to navigate Person-follower speed tracing error, V=[v ω]TIt include linear velocity v and angular velocity omega, l for generalized velocity vectorijIt indicates The standoff distance of follower and pilotage people,It is asymptotic stability design parameter.
Further, it normalizes and is expressed as follows described in step (3):
Wherein,Indicate that untreated speed error input signal, x indicate pretreated speed error input signal, λ For proportionality coefficient, after pretreatment is normalized to input signal, evaluated error is reduced；
The setting mean square velocity error is expressed as follows:
Wherein, xiIndicate velocity error, miIndicate mean square velocity error, u indicates that the average value of velocity error, μ are any normal Number can uniformly take μ=1 to reduce calculation amount.
Low noise power amplification feedback device described in step (4) includes amplifier, microprocessor, velocity sensor and electricity Source circuit, work step are as follows:
(4.1) the background noise output power that amplifier under multiple velocity amplitudes is measured by velocity sensor, establishes speed The mapping relations of value and output power are stored in microprocessor；
(4.2) it determines that the minimum background noise of amplifier goes out performance number, calculates other background noise output works of amplifier Multiple differences of the minimum background noise output power value of rate value and the amplifier, determine corresponding more under multiple velocity amplitude A value of magnification establishes the mapping relations of the two, is stored in microprocessor；
(4.3) microcomputer reads velocity amplitude determines real-time according to the mapping relations of multiple velocity amplitudes and multiple values of magnification The value of magnification input feedback network is carried out feedback amplification control, realizes the low noise function by the corresponding value of magnification of velocity amplitude Rate amplification feedback device exports stable local noise power within the scope of multi-speed angle value.
Wherein, the amplification factor setting of the amplifier are as follows:
xd=xi-xf
xf=Fx0
x0=Axd,
Wherein, xiIndicate radio-frequency input signals, xd、x0、xfRespectively indicate net input signal, the output letter of feedback amplifier Number, feedback signal, A, F are respectively the feedback amplification coefficient of the amplification coefficient of basic amplifier, feedback network.
Further, sef-adapting filter work step is as follows in step (5):
(5.1) initial value for assuming noise bounded and the sef-adapting filter is 0, is set according to least mean square algorithm Input signal vector and filter weight coefficient vector:
X (n)=[x (n), x (n-1) ... x (n-m+1]T,
W (n)=[ω0(n),ω1(n)…ωm-1(n)]T,
Reach minimum by the desired value of desired output speed and the difference square of sef-adapting filter reality output speed, adjusts Whole sef-adapting filter weight coefficient W value, lowest mean square formula are as follows:
Wherein, ydIndicate desired output speed, yiIndicate sef-adapting filter reality output speed；
(5.2) input signal x (n), desired output are d (n), then the output of the sef-adapting filter is y (n)=WT*y (n)；
(5.3) estimating speed error e (n) is calculated:
E (n)=d (n)-y (n)=d (n)-WT*x(n)；
(5.4) filter weight coefficient is adjusted again, that is, calculates the filter weight coefficient value at t+1 moment:
W (t+1)=+ 2 τ e (t) of W (t)
Wherein, τ is the gain constant that can control adaptive speed and stability, that is, considers adaptive speed and stabilization Property, for the ratio control between input and output in a range τ, λ indicates the maximum eigenvalue of correlation matrix R,
R=[x (n) x (n)T]。
T=t+1 is enabled, above step is repeated, reaches the target for eliminating noise.
Further, the implementation steps of step (6) are as follows:
(6.1) assume that each unmanned device can get expected and actual status information, and error bounded, setting description The form into columns desired speed of movement of unmanned device isActual speed is yi, definition status variable, that is, velocity error vector:
(6.2) Integral Sliding Mode surface function is designed:
Wherein, y·(t0) indicate initial time velocity error, kp、kiFor the coefficient greater than 0；
(6.3) the speed coordination control laws based on Integral Sliding Mode are designed:
Wherein, ρ indicates the boundary of error, and ξ indicates reference vector,η is positive definite matrix, ydFor desired speed to Amount, α, β indicate diagonal matrix, include design parameter α1、α2、β1、β2, sign () is sign function.
Wherein, the unmanned device waterborne is unmanned boat, and the underwater unmanned device is underwater glider.
The utility model has the advantages that compared with prior art, advantages of the present invention are as follows: firstly, the present invention is being to be uniformly controlled with speed On the basis of the joint of waterborne, the underwater unmanned device composition of amount is formed into columns, obtains velocity error and be normalized, pass through equal Fang Su Degree error can detect that noise level；When noise level beyond can tolerance range, recycle low noise power amplifier device and from Adaptive filter can amplify feedback and elimination to most Bounded Noise, which thereby enhance the detection efficiency of noise With elimination accuracy；Secondly, coordination control laws of the design based on Integral Sliding Mode carry out real-time compensation to velocity error, so that being System has stronger robustness, ensure that the stability of system, there are the more intelligence of marine isomery of bounded noise disturbance for suitable solution The problem of energy body speed Collaborative Control.
Detailed description of the invention
Fig. 1 is based on adaptive control system functional block diagram of the invention；
Fig. 2 is low noise power amplification feedback device structure chart of the invention；
Fig. 3 is sef-adapting filter structure chart of the present invention.
Specific embodiment
Further description of the technical solution of the present invention with reference to the accompanying drawings and examples.
Adaptive control system as shown in Figure 1 contains noise determining device, low noise power amplification feedback device, adaptive Answer filter, Integral Sliding Mode device.Noise determining device reflects the discrete journey of velocity error data by the way that mean square velocity error is arranged Degree, to judge whether there is noise disturbance, when mean square velocity error is greater than whether influence of the threshold value, that is, noise to speed amount exceeds Can tolerance range, then by noise signal input low noise power amplification feedback device amplify feedback, then pass through it is adaptive It answers filter to remove most Bounded Noise, finally designs the coordination control laws compensation error based on Integral Sliding Mode.
Using unmanned device (unmanned boat) waterborne and underwater unmanned device (underwater glider) as marine isomery in the present embodiment Multiple agent, the marine isomery multiple agent speed association adaptive based on optimization that above-mentioned adaptive control system is related to Same control method, including following work step:
(1) kinematics model of unmanned boat and underwater glider is established respectively；
Wherein, the kinematics model expression formula of unmanned boat are as follows:
Wherein,Indicate the speed of unmanned boat,Indicate course angle, η=[x, y, ψ]TIndicate the shape of unmanned boat State vector, R (ψ) indicate that the transition matrix between earth inertial reference frame and hull fixed reference frame, u, v, r divide It Biao Shi not the control moment input of hull, forward speed, transverse moving speed；
The kinematics model expression formula of underwater glider are as follows:
Wherein,Indicate underwater glider speed, θ, φ, ψ,Respectively indicate underwater glider Pitch angle, roll angle, yaw angle, rate of pitch, angular velocity in roll, yaw rate, u indicate control moment input, V, w indicates the speed of underwater glider, angular speed, Ω=[pqr] under earth axesTLocal coordinate is respectively indicated to be lauched The angular speed amount of lower aerodone.
(2) since the mathematical model of unmanned boat, underwater glider is different, to realize the collaboration of isomery multiple agent Control, needs to establish unified Controlling model.Access speed establishes joint collection as co-variate, and according to method of navigating-follow The formation structure of group, and obtain unmanned boat and the unified stable control signal of underwater glider；
The unmanned boat and the unified stable control signal of underwater glider are as follows:
Wherein,Indicate the speed of unmanned device, ev、vi、vi dRespectively indicate velocity error, the practical speed of unmanned device Degree, desired speed, waterborne, underwater unmanned device have with respective pilotage people, ej1、ej2、ej3Indicate pilotage people-follower Speed tracing error, V=[v ω]TIt include linear velocity v and angular velocity omega, l for generalized velocity vectorijIndicate follower and neck The standoff distance of boat person,It is asymptotic stability design parameter.
(3) based on unified stable control signal, the velocity error that joint is formed into columns is obtained, and place is normalized Reason is reflected the dispersion degree of its data by setting mean square velocity error, the level of noise is reflected with this；
In isomeric group control system, speed and velocity error are each inconsistent when unmanned boat, underwater glider move, and need Velocity error data are normalized:
Wherein,Indicate that untreated speed error input signal, x indicate pretreated speed error input signal, λ For proportionality coefficient, after pretreatment is normalized to input signal, evaluated error is reduced；
The setting mean square velocity error is expressed as follows:
Wherein, xiIndicate velocity error, miIndicate mean square velocity error, u indicates that the average value of velocity error, μ are any normal Number can uniformly take μ=1 to reduce calculation amount.
Any one system can all be influenced by different degrees of noise disturbance in reality, can be born when influence of noise exceeds State, it is necessary to which noise is handled.
It can be seen that the level of noise according to above-mentioned formula: if the value of the standard deviation is smaller, showing velocity error number According to concentrating near its average value, precision is higher, further illustrates that noise disturbance can bear the influence of the speed amount of unmanned device In range, without handling noise；If the value of the standard deviation is larger, i.e. when m is greater than a threshold value T, then show speed Error information relative distribution is spent, precision is lower, and further illustrating that noise disturbance influences to exceed on the speed amount of unmanned device can bear Range need to carry out noise to eliminate and compensate speed velocity error.
The spatial position as locating for unmanned boat in the heterogeneous system, underwater glider is different, and signal is passing through a distance After transmission, the signal power for reaching receiving device may be subjected to influence and reduce, it is therefore desirable to install low noise power amplification Feedback device amplifies noise power.
(4) when the level of noise beyond can tolerance range, by low noise power amplification feedback device to noise amplify；
As illustrated in fig. 2, it is assumed that immobilize without the input of external radio frequency electric signal and the gain of amplifier, the low noise Acoustical power amplifying device includes amplifier, velocity sensor, microprocessor, power circuit, while connecting feedback network, by it Be arranged on a public circuit plate.Direct-current input power supplying circuit powers to microprocessor and velocity sensor, velocity pick-up Device measured velocity value, microprocessor can read the velocity amplitude of sensor and transmit amplifier, amplifier and feedback network with penetrate Frequency circuit connection, and a metallic shield is added, influence and the internal electricity that generates of the shielding external electromagnetic wave to internal circuit Magnetic wave is to external radiation.
Work step is as follows:
(4.1) the background noise output power that amplifier under multiple velocity amplitudes is measured by velocity sensor, establishes speed The mapping relations of value and output power are stored in microprocessor；
(4.2) it determines that the minimum background noise of amplifier goes out performance number, calculates other background noise output works of amplifier Multiple differences of the minimum background noise output power value of rate value and the amplifier, determine corresponding more under multiple velocity amplitude A value of magnification establishes the mapping relations of the two, is stored in microprocessor；
(4.3) microcomputer reads velocity amplitude determines real-time according to the mapping relations of multiple velocity amplitudes and multiple values of magnification The value of magnification input feedback network is carried out feedback amplification control, realizes the low noise function by the corresponding value of magnification of velocity amplitude Rate amplification feedback device exports stable local noise power within the scope of multi-speed angle value.
Wherein, the amplification factor setting of the amplifier are as follows:
xd=xi-xf
xf=Fx0
x0=Axd,
Wherein, xiIndicate radio-frequency input signals, xd、x0、xfRespectively indicate net input signal, the output letter of feedback amplifier Number, feedback signal, A, F are respectively the feedback amplification coefficient of the amplification coefficient of basic amplifier, feedback network.
(5) noise is eliminated by design sef-adapting filter；
Wherein, sef-adapting filter as shown in Figure 3 is by desired output speed and sef-adapting filter reality output The desired value of the difference square of speed reaches minimum, adjusts sef-adapting filter weight coefficient W value with this.Input signal x (n) first, Then according to the output y (n) of filter and desired output d (n), estimating speed error e (n) is obtained, then adjusts filter power system Number calculates the filter weight coefficient value at t+1 moment, finally enables t=t+1, repeats above step, reaches the mesh for eliminating noise Mark.
Specific work steps is as follows:
(5.1) initial value for assuming noise bounded and the sef-adapting filter is 0, is set according to least mean square algorithm Input signal vector and filter weight coefficient vector:
X (n)=[x (n), x (n-1) ... x (n-m+1]T,
W (n)=[ω0(n),ω1(n)…ωm-1(n)]T,
Reach minimum by the desired value of desired output speed and the difference square of sef-adapting filter reality output speed, adjusts Whole sef-adapting filter weight coefficient W value, lowest mean square formula are as follows:
Wherein, ydIndicate desired output speed, yiIndicate sef-adapting filter reality output speed；
(5.2) input signal x (n), desired output are d (n), then the output of the sef-adapting filter is y (n)=WT*y (n)；
(5.3) estimating speed error e (n) is calculated:
E (n)=d (n)-y (n)=d (n)-WT*x(n)；
(5.4) filter weight coefficient is adjusted again, that is, calculates the filter weight coefficient value at t+1 moment:
W (t+1)=+ 2 τ e (t) of W (t)
Wherein, τ is the gain constant that can control adaptive speed and stability, that is, considers adaptive speed and stabilization Property, for the ratio control between input and output in a range τ, λ indicates the maximum eigenvalue of correlation matrix R, R=[x (n)x(n)T]。
Finally, enabling t=t+1, above step is repeated, reaches the target for eliminating noise.
(6) setting the coordination control laws based on Integral Sliding Mode to bounded velocity error carry out real-time compensation, realize there are The marine isomery multiple agent speed Collaborative Control target of boundary's noise disturbance.
Specific implementation step is as follows:
(6.1) assume that each unmanned device can get expected and actual status information, and error bounded, setting description The form into columns desired speed of movement of unmanned device isActual speed is yi, definition status variable, that is, velocity error vector:
(6.2) Integral Sliding Mode surface function is designed:
Wherein, ye(t0) indicate initial time velocity error, kp、kiFor the coefficient greater than 0；
(6.3) the speed coordination control laws based on Integral Sliding Mode are designed:
Wherein, ρ indicates the boundary of error, and ξ indicates reference vector,η is positive definite matrix, ydFor desired speed to Amount, α, β indicate diagonal matrix, include design parameter α1、α2、β1、β2, sign () is sign function.

Claims (9)

1. a kind of based on optimizing adaptive marine isomery multiple agent speed cooperative control method, it is characterised in that including with Lower work step:
(1) kinematics model of unmanned device waterborne and underwater unmanned device is established respectively；
(2) access speed is co-variate, and according to navigating-following, method establishes joint formation structure, obtains unmanned device and water waterborne The unified stable control signal of unmanned device down；
(3) based on unified stable control signal, the velocity error that joint is formed into columns is obtained, and be normalized, led to The dispersion degree that setting mean square velocity error reflects its data is crossed, the level of noise is reflected with this；
(4) when the level of noise beyond can tolerance range, by low noise power amplification feedback device to noise amplify；
(5) noise is eliminated by design sef-adapting filter；
(6) coordination control laws of the setting based on Integral Sliding Mode carry out real-time compensation to bounded velocity error.
2. the marine isomery multiple agent speed cooperative control method adaptive based on optimization according to claim 1, It is characterized by: in step (1) unmanned device waterborne kinematics model expression formula are as follows:
Wherein,Indicate the speed of unmanned device waterborne,Indicate course angle, η=[x, y, ψ]TIndicate unmanned device waterborne State vector, R (ψ) indicate the transition matrix between earth inertial reference frame and unmanned device fixed reference frame waterborne, U, v, r respectively indicate the control moment input of unmanned device waterborne, forward speed, transverse moving speed；
The kinematics model expression formula of underwater unmanned device are as follows:
Wherein,Indicate the speed of underwater unmanned device, θ, φ, ψ,Respectively indicate the pitching of underwater unmanned device Angle, roll angle, yaw angle, rate of pitch, angular velocity in roll, yaw rate, u indicate control moment input, and v, w indicate ground The speed of underwater unmanned device, angular speed, Ω=[pqr] under areal coordinate systemTRespectively indicate underwater unmanned device under local coordinate Angular speed amount.
3. the marine isomery multiple agent speed Collaborative Control side adaptive based on optimization according to claim 1 or 2 Method, it is characterised in that: unmanned device waterborne described in step (2) and the unified stable control signal of underwater unmanned device are as follows:
Wherein,Indicate the speed of unmanned device, ev、vi、vi dRespectively indicate the velocity error, actual speed, phase of unmanned device Hope speed, waterborne, underwater unmanned device has with respective pilotage people, ej1、ej2、ej3Indicate pilotage people-follower speed with Track error, V=[v ω]TIt include linear velocity v and angular velocity omega, l for generalized velocity vectorijIndicate the phase of follower and pilotage people Gauge is from qej=[ej1 ej2 ej3]TIt is asymptotic stability design parameter.
4. the marine isomery multiple agent speed Collaborative Control side adaptive based on optimization according to claim 1 or 3 Method, it is characterised in that: normalize and be expressed as follows described in step (3):
Wherein,Indicate untreated speed error input signal, x indicates pretreated speed error input signal, λ be than Example coefficient；
The setting mean square velocity error is expressed as follows:
Wherein, xiIndicate velocity error, miIndicate mean square velocity error, u indicates the average value of velocity error, and μ is arbitrary constant.
5. the marine isomery multiple agent speed Collaborative Control side adaptive based on optimization according to claim 1 or 4 Method, it is characterised in that: low noise power amplification feedback device described in step (4) includes amplifier, microprocessor, velocity pick-up Device and power circuit, work step are as follows:
(4.1) the background noise output power that amplifier under multiple velocity amplitudes is measured by velocity sensor, establish velocity amplitude and The mapping relations of output power are stored in microprocessor；
(4.2) it determines that the minimum background noise of amplifier goes out performance number, calculates other background noise output power values of amplifier With multiple differences of the minimum background noise output power value of the amplifier, corresponding multiple amplifications under multiple velocity amplitude are determined Value establishes the mapping relations of the two, is stored in microprocessor；
(4.3) microcomputer reads velocity amplitude determines real-time speed according to the mapping relations of multiple velocity amplitudes and multiple values of magnification It is worth corresponding value of magnification, by the value of magnification input feedback network, carries out feedback amplification control, realize the low noise power amplification Feedback device exports stable local noise power within the scope of multi-speed angle value.
6. the marine isomery multiple agent speed cooperative control method adaptive based on optimization according to claim 5, It is characterized by: the amplification factor of the amplifier is set are as follows:
xd=xi-xf
Xf=Fx0
x0=Axd,
Wherein, xiIndicate radio-frequency input signals, xd、x0、xfRespectively indicate the net input signal of feedback amplifier, output signal, anti- Feedback signal, A, F are respectively the feedback amplification coefficient of the amplification coefficient of basic amplifier, feedback network.
7. the marine isomery multiple agent speed Collaborative Control side adaptive based on optimization according to claim 1 or 6 Method, it is characterised in that: sef-adapting filter work step is as follows in step (5):
(5.1) initial value for assuming noise bounded and the sef-adapting filter is 0, is set and is inputted according to least mean square algorithm Signal vector and filter weight coefficient vector:
X (n)=[x (n), x (n-1) ... x (n-m+1]T,
W (n)=[ω0(n), ω1(n)…ωm-1(n)]T,
Reach minimum by the desired value of desired output speed and the difference square of sef-adapting filter reality output speed, adjustment is certainly Adaptive filter weight coefficient W value, lowest mean square formula are as follows:
Wherein, ydIndicate desired output speed, yiIndicate sef-adapting filter reality output speed；
(5.2) input signal x (n), desired output are d (n), then the output of the sef-adapting filter is y (n)=WT*y(n)；
(5.3) estimating speed error e (n) is calculated:
E (n)=d (n)-y (n)=d (n)-WT*x(n)；
(5.4) filter weight coefficient is adjusted again, that is, calculates the filter weight coefficient value at t+1 moment:
W (t+1)=+ 2 τ e (t) of W (t)
Wherein, τ is the gain constant that can control adaptive speed and stability, and λ indicates the maximum eigenvalue of correlation matrix R, R =[x (n) x (n)T]；
T=t+1 is finally enabled, above step is repeated, reaches the target for eliminating noise.
8. the marine isomery multiple agent speed Collaborative Control side adaptive based on optimization according to claim 1 or claim 7 Method, it is characterised in that: the implementation steps of step (6) are as follows:
(6.1) assume that each unmanned device can get expected and actual status information, and error bounded, setting describes nobody Device form into columns movement desired speed beActual speed is yi, definition status variable, that is, velocity error vector:
(6.2) Integral Sliding Mode surface function is designed:
Wherein, ye(t0) indicate initial time velocity error, kp、kiFor the coefficient greater than 0；
(6.3) the speed coordination control laws based on Integral Sliding Mode are designed:
Wherein, p indicates the boundary of error, and ξ indicates reference vector,η is positive definite matrix, ydIt is expected velocity vector, α, β It indicates diagonal matrix, includes design parameter α1、α2、β1、β2, sign () is sign function.
9. the marine isomery multiple agent speed cooperative control method adaptive based on optimization according to claim 1, It is characterized by: the unmanned device waterborne is unmanned boat, the underwater unmanned device is underwater glider.
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