CN112631305B - Anti-collision and anti-interference control system for multi-unmanned ship formation - Google Patents
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Abstract
The invention discloses an anti-collision anti-interference multi-unmanned ship formation control system, which comprises a model reconstruction module, a unmanned ship information network topology module, a position prediction control module, a bow swing angle prediction control module, and an uncertainty and disturbance estimation module, wherein the model reconstruction module is used for reconstructing a kinematics and dynamics model established by a controlled unmanned ship module, the interaction information network topology module is used for acquiring unmanned ship information interacted with unmanned ship existence information in unmanned ship formation and sending the unmanned ship information to the position prediction control module, the position prediction control module is used for acquiring navigation information and interaction information of an unmanned ship, calculating a longitudinal speed control input and a reference bow swing angle sequence, inputting the longitudinal speed control input and the reference bow swing angle sequence to the bow swing angle prediction control module, calculating a bow swing angle speed control input value, and inputting the bow swing angle control input value to the bow swing angle prediction control module of the unmanned ship kinematics and dynamics model module, and the uncertainty and the estimated value of a time-varying sea current disturbance unknown function. The system can improve the disturbance resistance and control accuracy of the unmanned ship under a complex marine environment, and can prevent collision between unmanned ships in formation.
Description
Technical Field
The invention relates to the technical field of multi-unmanned ship formation motion control, in particular to an anti-collision and anti-interference control system for multi-unmanned ship formation.
Background
Unmanned ships are important tools for human awareness, development and protection of the ocean, and are important manifestations of the national ocean technology level. The cooperativity is a sign for measuring the intelligent degree of the unmanned ship and is also a necessary requirement for intelligent development of the ship. The operation efficiency can be obviously improved by the cooperation of multiple unmanned ships, and the complementary advantages and the large-scale effect are formed. In the key technical field of unmanned ship coordination, the formation control technology is an important component, and the exploration of the unmanned ship formation control technology has important significance.
Aiming at the problem of unmanned ship formation motion control, some feasible technical schemes exist. For example, chinese patent CN111506079a proposes a novel virtual structure formation control method of an unmanned ship considering obstacle avoidance, which constructs a reference track by a virtual structure method and parameterizes a basic track, thereby ensuring that formation can be maintained at any time. Aiming at the condition that an obstacle exists in the environment, the artificial potential field method is utilized to adjust the basic track so as to generate a reference track for avoiding the obstacle, and the anti-collision of the unmanned ship in the motion process is realized. Chinese patent CN107015562a discloses an optimized formation tracking control method based on distributed model predictive control, which is to build an under-actuated unmanned ship motion model and a tracking error model, and perform state prediction according to neighbor information and the error model, build a model predictive algorithm, and implement formation optimized tracking control.
Through observation, the current unmanned ship formation motion control method is found to have the following defects:
1. most of the existing unmanned ship formation methods focus on control robustness and stability, and do not comprehensively consider the actual state constraint, the execution mechanism constraint, the formation collision avoidance constraint and the optimization performance indexes of the unmanned ship, such as energy optimization and control input smoothness. If the control method is designed by neglecting the factors, the optimality and engineering applicability of the control method are necessarily reduced;
2. the existing unmanned ship formation control method based on optimization mostly depends on a fixed and accurate unmanned ship mathematical model to design a controller. The actual unmanned ship dynamic system in the complex ocean environment inevitably has model uncertainty and disturbance of ocean time-varying wind wave current, so that the control performance is reduced by the design of the control method based on a fixed mathematical model without considering the model uncertainty and the disturbance of the time-varying wind wave current in the actual ocean environment.
Disclosure of Invention
The invention provides an anti-collision anti-interference control system for multi-unmanned ship formation.
The invention adopts the following technical means: a multi-unmanned ship formation anti-collision anti-interference control system comprises a model reconstruction module, an interaction information network topology module, a position prediction control module, a bow swing angle prediction control module and an uncertainty and disturbance estimation module;
the model reconstruction module is used for acquiring the kinematics and dynamics model of the controlled unmanned ship established by the controlled unmanned ship module, and reconstructing the kinematics and dynamics model to acquire the position information p of the controlled unmanned ship under the earth coordinate system i Velocity information q in the earth coordinate system i Yaw angle information in the earth coordinate systemYaw rate r in hull coordinate system i ；
The interactive information network topology module is used for acquiring the position information p of the controlled unmanned ship in the earth coordinate system, which is interacted with the existence information of the controlled unmanned ship in the unmanned ship formation i Velocity information q in the earth coordinate system i Yaw angle information in the earth coordinate systemYaw rate r in hull coordinate system i And the position information p of the controlled unmanned ship with information interaction with the controlled unmanned ship in the earth coordinate system i And velocity information q in the earth coordinate system i Sending to a position prediction control module;
the position prediction control module is used for acquiring the position information p of the controlled unmanned ship under the earth coordinate system i Velocity information q in the earth coordinate system i Estimate of unknown function of model uncertainty and time-varying ocean current disturbanceAnd the information input by the interactive information network topology module calculates a longitudinal speed control input tau iu And reference yaw sequence->And inputting a longitudinal speed control input tau iu Input to said controlled unmanned ship, will reference the sequence of yaw angles +.>Inputting the predicted bow swing angle into the bow swing angle prediction control module;
the bow and roll angle prediction control module is used for acquiring the reference bow and roll angle sequenceControlled unmanned ship bow swing angular velocity r under ship body coordinate system i And an estimated value of a yaw direction unknown function +.>To calculate the yaw rate control input tau ir And controlling the bow swing angular velocity to input value tau ir Inputting to the controlled unmanned ship;
the uncertainty and disturbance estimation module is used for acquiring the position information p of the controlled unmanned ship under the earth coordinate system i Velocity information q in the earth coordinate system i Yaw angle information in the earth coordinate systemYaw rate r in hull coordinate system i Longitudinal speed control input value τ iu Input value τ for controlling yaw rate ir To calculate the estimated value of the unknown function of the yaw direction +.>And model uncertainty and estimate of unknown function of time-varying current disturbance +.>And the estimated value of the unknown function of the yaw direction is +.>Inputting the model uncertainty and the estimated value of the unknown function of the time-varying ocean current disturbance into the bow and roll angle prediction control module>And inputting the position prediction control module.
Further, the kinematic and kinetic model of the controlled unmanned ship is expressed as:
wherein: x is x i 、y i 、The position information of the unmanned ship in the X-axis direction, the position information in the Y-axis direction and the bow and roll angle information under the earth coordinate system are obtained; u (u) i 、v i And r i The longitudinal speed, the transverse drift speed and the bow swing angular speed of the unmanned ship under a ship body coordinate system; f (f) iu 、f iv And f ir The method comprises the steps of obtaining a longitudinal unknown function, a transverse unknown function and a bow swing angle direction unknown function which are uncertainty and time-varying ocean current disturbance; τ iu And τ ir Controlling input values for longitudinal speed and yaw rate; m is m iu And m ir Inertia coefficients in the longitudinal direction and the bow direction of the ship body respectively; t is time.
Further, the process of reconstructing the kinematic and dynamic model comprises the following steps: the unmanned ship is transformed and decoupled into a position ring model (2) and a bow rocking angle ring model (3) by a kinematic and dynamic model (1), and the method comprises the following steps of:
wherein: p is p i ＝[x i ,y i ]、q i ＝[q ix ,q iy ]For controlling the position information and the speed information of the unmanned ship in the earth coordinate system,speed information of the controlled unmanned ship in the X-axis and Y-axis directions under the earth coordinate system; />P is respectively i 、q i Derivative; f (f) iq ＝[f ix ,f iy ]Is an unknown function of model uncertainty and time-varying ocean current disturbance of the unmanned ship in the earth coordinate system, f ix 、f iy The specific transformation mode of the model uncertainty and the time-varying ocean current disturbance unknown function of the unmanned ship in the X-axis and Y-axis directions under the earth coordinate system is as follows:
further, calculating an estimated value of unknown function model uncertainty of the yaw angle and time-varying ocean current disturbanceAnd model uncertainty and estimate of unknown function of time-varying current disturbance +.>The process of (1) comprises:
wherein the method comprises the steps ofRespectively f iq ,f ir ,q i ,r i Estimate of k iq ,k ir Gain coefficients, respectively.
Further, the calculated longitudinal speed control input τ iu And a reference bow tie sequenceThe process is as follows:
d1, rewriting the position ring model (2) as follows:
wherein: τ iq ＝[τ ix ,τ iy ] T Is the control input of unmanned ship in the earth coordinate system, whereinFor the control input of unmanned ship in X-axis direction under the earth coordinate system, +.>The control input of the unmanned ship in the Y-axis direction under the earth coordinate system is provided;
d2, including the unknown function estimated value of model uncertainty and time-varying ocean current disturbance of the unmanned ship under the earth coordinate systemSubstituting into the model (6) and discretizing, wherein the specific formula is as follows:
X iq (k+Ts)＝A i X iq (k)+B i τ iq (k)+C i (7)
wherein: x is X iq (k)＝[p i (k),q i (k)] T An unmanned ship state vector representing time k; ts is sampling intervalTime; vector quantityVector->Vector->
Wherein:0 2 ＝[0,0] T ；
d3, performing state prediction at time k by using the formula (7) as follows:
wherein: x is X iq (k|k) is the value of the state sample at time k; x is X iq (k+lTs|k),l＝1,...,N p For state prediction at time k versus time k+ lTs, N p To predict the time domain; τ iq (k-1) is a control input applied at time k-1 to the controlled unmanned ship in the earth coordinate system; Δτ iq (k+mts|k), m=1,..nc is the control increment at time k+mts, N c Representing the control time domain;
d4, expressing the formula (8) by using a recursive relation as follows:
wherein the method comprises the steps ofOutputting a sequence for the predicted state at the moment k; />Controlling an increment sequence for the moment k;
corresponding orderFormula (9) is written as follows:
d5, constructing the following optimization problem model:
formulas (11 a), (11 b), (11 c) and (11 d) are respectively control increment constraint, control input constraint, state constraint and collision prevention constraint;and->And->And +.>And->The upper and lower bounds of control increment, control input and speed state under the earth coordinate system are respectively; z is Z ij Is a collision avoidance coefficient matrix; q (Q) 1 、Q 2 And Q 3 Respectively maintaining a weight matrix and a formation tracking error weight matrix for the energy index weight matrix and the formation;
d6, converting the optimization problem model (11) into the following form:
wherein:
obtaining an optimal control input delta sequence by solving an optimization problem model (12)Thereby obtaining an optimal control input sequence +.>
D7 calculating the optimal longitudinal speed control input τ by equation (13) iu ：
Wherein τ 1i (k) And τ 2i (k) Optimally controlling the first two elements of the input sequence;
d8, calculating a reference bow swing angle through a formula (14):
wherein τ 2i (l) And τ 1i (l) 2l and 2l-1 elements in the optimal control input sequence, respectively.
Further, the calculated yaw rate control input value τ ir The process comprises the following steps:
e1, discretizing the model (3) as follows:
X ir (k+Ts)＝A ir X ir (k)+B ir τ ir (k)+C ir (15)
wherein:an unmanned ship bow swing angle state vector at the moment k is represented; ts is the sampling interval time; vector->Vector->Vector->
E2, the prediction using formula (15) is as follows:
wherein: x is X ir (k|k) is the value of the state sample at time k; x is X ir (k+lTs|k),l＝1,...,N p For predicting the bow and roll angle state of the moment k to the moment k+ lTs, N rp Predicting a time domain for a bow swing angle; τ ir (k-1) a yaw rate control input applied at a previous sampling instant; Δτ ir (k+mts|k), m=1,.. rc Representing the control time domain of the yaw angle；
E3, using a recursive relation, expression (16) is expressed as follows:
wherein:outputting a sequence for the predicted state of the bow rocking angle at the moment k; />Controlling an increment sequence for the bow swing angular speed at the moment k;
corresponding orderFormula (17) is written as follows:
e4, constructing the following optimization problem model:
formulas (19 a), (19 b) and (19 c) are respectively a yaw rate control increment constraint, a yaw rate control input constraint and a yaw rate state constraint;and->The upper and lower bounds of the yaw rate control increment are respectively. />And->Respectively an upper boundary and a lower boundary of the control input of the bow swing angular speed; />And->Respectively an upper boundary and a lower boundary of a bow swing angular speed state;is a reference bow crank sequence;
e5, converting the optimization problem model (11) into the following form:
wherein:
solving an optimization problem model (20) to obtain an optimal yaw rate control input increment sequenceObtain the optimal control input sequence->The first element in the sequence is applied to the controlled unmanned ship.
Compared with the prior art, the anti-collision anti-interference control system for the multi-unmanned ship formation disclosed by the invention has the following beneficial effects: 1. according to the invention, the unmanned ship model is reconstructed into the position ring model and the bow-and-roll angle ring model through the model reconstruction module, and the uncertainty and disturbance estimation module is utilized to collect the input and output information of the unmanned ship so as to estimate the model uncertainty and the time-varying disturbance unknown function in the unmanned ship position ring model and the bow-and-roll angle ring model in real time in the actual marine environment. Therefore, an accurate unmanned ship mathematical model is not required to be fixed, and real-time estimation and dynamic updating of model information are only carried out through unmanned ship control input information and output state information, so that the disturbance resistance and control accuracy of the unmanned ship can be improved in a complex marine environment.
2. According to the invention, the position prediction control module and the bow and roll angle prediction control module utilize model information obtained by real-time estimation and reconstruction to perform state prediction, and consider the actual state constraint, the execution mechanism constraint and the formation collision prevention constraint of the unmanned ship, control targets, energy optimization and control input smoothness are used as optimization performance indexes of an optimization problem, a distributed optimization problem is designed to perform rolling optimization control on formation movement, and the optimality and engineering applicability of a control method are improved.
Drawings
Fig. 1 is a schematic structural diagram of a multi-unmanned ship formation anti-collision anti-interference control system disclosed by the invention.
Fig. 2 is a schematic diagram of an interactive information network topology according to the present invention.
Fig. 3 is a schematic diagram of an unmanned ship formation collision-free tracking trajectory.
Fig. 4a, 4b, 4c and 4d are respectively diagrams of unmanned ship formation trajectories at different moments in time.
Fig. 5a and 5b are schematic diagrams of control inputs, respectively.
Fig. 6a and 6b are schematic diagrams of unmanned ship formation tracking errors, respectively.
Fig. 7a, 7b and 7c are schematic diagrams of model uncertainty and time-varying disturbance unknowns estimation for the ith unmanned ship, respectively.
Fig. 8a, 8b and 8c are schematic views of the speed state of the unmanned ship, respectively.
Detailed Description
FIG. 1 shows a multi-unmanned ship formation anti-collision anti-interference control system disclosed by the invention, which comprises a model reconstruction module, an interactive information network topology module, a position prediction control module, a bow and roll angle prediction control module and an uncertainty and disturbance estimation module;
the model reconstruction module is used for acquiring the kinematics and dynamics model of the controlled unmanned ship established by the controlled unmanned ship module, and reconstructing the kinematics and dynamics model to acquire the position information p of the controlled unmanned ship under the earth coordinate system i Velocity information q in the earth coordinate system i Yaw angle information in the earth coordinate systemYaw rate r in hull coordinate system i ；
Specifically, in the presence of model uncertainty and time-varying ocean current disturbances, the kinematic and kinetic model of the ith underactuated unmanned ship in the controlled unmanned ship formation is represented as follows:
wherein: x is x i Sitting on earth for unmanned shipPosition information in the X-axis direction under the standard system; y is i The position information of the unmanned ship in the Y-axis direction under the earth coordinate system is obtained;the information of the bow and the roll angle of the unmanned ship under the earth coordinate system is obtained; u (u) i Is the longitudinal speed of the unmanned ship under the ship body coordinate system; vi is the transverse floating speed of the unmanned ship under a ship body coordinate system; r is (r) i The bow swing angular speed of the unmanned ship under a ship body coordinate system is obtained; f (f) iu 、f iv And f ir Respectively a longitudinal unknown function, a transverse unknown function and a bow swing angle direction unknown function which comprise model uncertainty and time-varying ocean current disturbance under a ship body coordinate system; τ iu Controlling an input value for the longitudinal speed; τ ir The input value is controlled for the yaw rate; m is m iu And m ir Inertia coefficients in the longitudinal direction and the bow direction of the ship body respectively; t is time; />Respectively x i 、y i 、/>u i 、v i 、r i Is a derivative of (a).
The process for reconstructing the controlled unmanned ship kinematics and dynamics model comprises the following steps:
the unmanned ship is transformed and decoupled into a position ring model (2) and a bow rocking angle ring model (3) by a kinematic and dynamic model (1), and the method comprises the following steps of:
wherein: p is p i ＝[x i ,y i ]The position information of the unmanned ship in the earth coordinate system is obtained;q i ＝[q ix ,q iy ]is the speed information of the unmanned ship in the earth coordinate system,is the speed information of unmanned ship in X-axis direction under the earth coordinate system>The speed information of the unmanned ship in the Y-axis direction under the earth coordinate system is obtained; />P is respectively i 、q i Derivative; f (f) iq ＝[f ix ,f iy ]Is an unknown function of model uncertainty and time-varying ocean current disturbance of the unmanned ship in the earth coordinate system, f ix Is an unknown function of model uncertainty and time-varying ocean current disturbance of unmanned ship in X-axis direction under the earth coordinate system, f iy The model uncertainty and time-varying sea current disturbance unknown function of the unmanned ship in the Y-axis direction under the earth coordinate system is specifically converted as follows:
the interactive information network topology module is used for acquiring the position information p of the controlled unmanned ship in the earth coordinate system, which is interacted with the existence information of the controlled unmanned ship in the unmanned ship formation i Velocity information q in the earth coordinate system i Yaw angle information in the earth coordinate systemYaw rate r in hull coordinate system i And the position information p of the controlled unmanned ship with information interaction with the controlled unmanned ship in the earth coordinate system i And velocity information q in the earth coordinate system i Sending to a position prediction control module;
in particular, as shown in FIG. 2, the interactive information networkTopology diagram for network topology moduleRepresentation of->A node set formed by N unmanned vessels in formation; i.e. < ->Epsilon represents the edge set between the ith unmanned ship and the jth unmanned ship in the formation, and +.>If (i, j) epsilon represents that the information interaction relationship exists between the ith unmanned ship and the jth unmanned ship, and the communication variable a ij =1, otherwise the communication variable a ij ＝0。d i D, if the ith unmanned ship can access the reference track information as the reference track access authority variable i =1, otherwise d i ＝0。
The position prediction control module is used for acquiring the position information p of the controlled unmanned ship under the earth coordinate system i Velocity information q in the earth coordinate system i Estimate of unknown function of model uncertainty and time-varying ocean current disturbanceAnd the information input by the interactive information network topology module calculates a longitudinal speed control input tau iu And reference yaw sequence->And inputting a longitudinal speed control input tau iu Input to said controlled unmanned ship, will reference the sequence of yaw angles +.>Inputting the predicted bow swing angle into the bow swing angle prediction control module;
the position prediction control module is used for interactionThe information network topology module receives neighbor ship information with communication relation in formation and is obtained by a position loop model (2) and a model uncertainty and disturbance estimation module in the model reconstruction moduleAnd carrying out prediction and optimization problem construction.
In particular, the calculated longitudinal speed control input τ iu And a reference bow tie sequenceThe process is as follows:
d1, rewriting the position ring model (2) as follows:
wherein: τ iq ＝[τ ix ,τ iy ] T Is the control input of unmanned ship in the earth coordinate system, whereinFor the control input of unmanned ship in X-axis direction under the earth coordinate system, +.>The control input of the unmanned ship in the Y-axis direction under the earth coordinate system is provided;
d2, including the unknown function estimated value of model uncertainty and time-varying ocean current disturbance of the unmanned ship under the earth coordinate systemSubstituting into the model (6) and discretizing, wherein the specific formula is as follows:
X iq (k+Ts)＝A i X iq (k)+B i τ iq (k)+C i (7)
wherein: x is X iq (k)＝[p i (k),q i (k)] T Representing unmanned ship state at time kVector; ts is the sampling interval time; vector quantityVector->Vector->
Wherein:0 2 ＝[0,0] T ；
d3, performing state prediction at time k by using the formula (7) as follows:
wherein: x is X iq (k|k) is the value of the state sample at time k; x is X iq (k+lTs|k),l＝1,...,N p For state prediction at time k versus time k+ lTs, N p To predict the time domain; τ iq (k-1) is a control input applied at time k-1 to the controlled unmanned ship in the earth coordinate system; Δτ iq (k+mts|k), m=1,..nc is the control increment at time k+mts, N c Representing the control time domain;
d4, expressing the formula (8) by using a recursive relation as follows:
wherein the method comprises the steps ofOutputting a sequence for the predicted state at the moment k; />Controlling an increment sequence for the moment k;
/>
corresponding orderFormula (9) is written as follows:
d5, constructing the following optimization problem model:
formulas (11 a), (11 b), (11 c) and (11 d) are respectively control increment constraint, control input constraint, state constraint and collision prevention constraint;and->The upper and lower bounds of the control increment under the earth coordinate system are respectively; />And->The upper and lower bounds of the control input under the earth coordinate system are respectively; />And->Upper and lower bounds of the velocity state in the earth coordinate system, respectively; z is Z ij Is a collision avoidance coefficient matrix->And r is ij The collision prevention safety distance between the ships in unmanned ship formation is set; q (Q) 1 、Q 2 And Q 3 Respectively maintaining a weight matrix and a formation tracking error weight matrix for the energy index weight matrix and the formation; />Wherein->X j ＝[p j ,q j ]Position state information and speed state information of neighbor unmanned ship j at k moment received for ith unmanned ship,/->D ij Forming a deviation vector for a formation in the formation mode;
d6, converting the optimization problem model (11) into the following form:
wherein:
/>
obtaining an optimal control input delta sequence by solving an optimization problem model (12)Thereby obtaining an optimal control input sequence +.>
D7 calculating the optimal longitudinal speed control input τ by equation (13) iu ：
Wherein τ 1i (k) And τ 2i (k) Optimally controlling the first two elements of the input sequence;
d8, calculating a reference bow swing angle through a formula (14):
wherein τ 2i (l) And τ 1i (l) 2l and 2l-1 elements in the optimal control input sequence, respectively.
The bow and roll angle prediction control module is used for acquiring the reference bow and roll angle sequenceControlled unmanned ship bow swing angular velocity r under ship body coordinate system i And an estimated value of a yaw direction unknown function +.>To calculate the yaw rate control input tau ir And controlling the bow swing angular velocity to input value tau ir Inputting to the controlled unmanned ship;
concrete embodimentsThe calculated yaw rate control input value tau ir The process comprises the following steps:
e1, discretizing the model (3) as follows:
X ir (k+Ts)＝A ir X ir (k)+B ir τ ir (k)+C ir (15)
wherein:an unmanned ship bow swing angle state vector at the moment k is represented; ts is the sampling interval time; vector->Vector->Vector->
E2, the prediction using formula (15) is as follows:
wherein: x is X ir (k|k) is the value of the state sample at time k; x is X ir (k+lTs|k),l＝1,...,N p For predicting the bow and roll angle state of the moment k to the moment k+ lTs, N rp Predicting a time domain for a bow swing angle; τ ir (k-1) a yaw rate control input applied at a previous sampling instant; Δτ ir (k+mts|k), m=1,.. rc Representing a bow swing angle control time domain;
e3, using a recursive relation, expression (16) is expressed as follows:
wherein:outputting a sequence for the predicted state of the bow rocking angle at the moment k; />Controlling an increment sequence for the bow swing angular speed at the moment k; />
Corresponding orderFormula (17) is written as follows:
e4, constructing the following optimization problem model:
formulas (19 a), (19 b) and (19 c) are respectively a yaw rate control increment constraint, a yaw rate control input constraint and a yaw rate state constraint;and->The upper and lower bounds of the increment are controlled for the yaw rate. />And->Upper and lower bounds for the yaw rate control input; />And->Is the upper and lower bounds of the bow swing angular velocity state; />As a reference yaw sequence, calculated in D8 by formula (14);
e5, converting the optimization problem model (11) into the following form:
wherein:
/>
obtaining the optimal yaw rate control input increment by solving an optimization problem model (20)Sequence(s)Thereby obtaining an optimal control input sequence +.>The first element in the sequence is applied to the controlled unmanned ship.
The uncertainty and disturbance estimation module is used for acquiring the position information p of the controlled unmanned ship under the earth coordinate system i Velocity information q in the earth coordinate system i Yaw angle information in the earth coordinate systemYaw rate r in hull coordinate system i Longitudinal speed control input value τ iu Input value τ for controlling yaw rate ir To calculate the estimated value of the unknown function of the yaw direction +.>And model uncertainty and estimate of unknown function of time-varying current disturbance +.>And the estimated value of the unknown function of the yaw direction is +.>Inputting the model uncertainty and the estimated value of the unknown function of the time-varying ocean current disturbance into the bow and roll angle prediction control module>And inputting the position prediction control module.
Specifically, further, the calculation of the estimated value of the unknown function of the yaw directionModel uncertainty and time-varying current disturbance uncertaintyKnowing the estimated value of the function +.>The process of (1) comprises:
wherein the method comprises the steps ofRespectively f iq ,f ir ,q i ,r i Estimate of k iq ,k ir Gain coefficients, respectively.
Example 1
The present invention is further described below with respect to a specific unmanned ship formation for example in terms of trajectory tracking simulation. As shown in FIG. 2, the Interactive information network topology is that ship 1 can access the reference track information generated by the virtual leader, i.e. d 1 =1; the No. 2 ship and the No. 3 ship can receive the information of the No. 1 ship, namely a 21 ＝1，d 2 ＝0，a 31 ＝1，d 3 =0; the ship No. 4 can receive the information of the ship No. 2, namely a 42 ＝1，d 4 =0; the ship No. 5 can receive the information of the ship No. 3, namely a 52 ＝1，d 5 =0. Tracking a reference track generated by the virtual leader:
in this example, the unmanned vessels are under-actuated unmanned vessels, i.e. only longitudinal speed control input τ iu And a yaw rate control input τ ir . Because of the thrust and torsional torque limitations of an actual unmanned ship, there are constraints on the longitudinal speed control input and the yaw rate control input, i.e., τ iumax ＝3，τ iumax ＝0，τ irmax ＝-τ irmin =1; because the actuating mechanism of the actual unmanned ship is limited, the longitudinal speed and the bow-and-roll angular speed are restrained, namelyu imax ＝0.8，u imin ＝0r imax ＝-r imin ＝0.6；
The starting states of the unmanned ships in the formation are respectively as follows: x is X 1q (0)＝[-8,-8,0,0] T ，X 1r (0)＝[0,0] T ，X 2q (0)＝[-25,0,0,0] T ，X 2r (0)＝[0,0] T ，X 3q (0)＝[1,-24,0,0] T ，X 3r (0)＝[4π/7,0] T ，X 4q (0)＝[-16,-25,0,0] T ，X 4r (0)＝[π/5,0] T ，X 4q (0)＝[-26,-13,0,0] T ，X 5r (0)＝[π/4,0] T . Formation mode: d (D) 10 ＝[0,0,0,0] T ，D 21 ＝[0,-8,0,0] T ，D 31 ＝[-8,0,0,0] T ，D 42 ＝[0,-8,0,0] T ，D 53 ＝[-8,,0,0,0] T 。
Sampling interval time ts=0.1 s, prediction time domain N p Control time domain n=5 c =4. Foreshadowing time domain N of foreshadowing time domain of bow swing angle rp =4, bow-roll angle control time domain N rc ＝3。
The simulation results are shown in fig. 3-8. Fig. 3 is a schematic diagram of collision-free tracking tracks of unmanned ship formation, from which it can be seen that five unmanned ships in the formation gradually enter a formation mode and track a reference straight-line track in a fixed formation. Fig. 4 a-4 b are schematic diagrams of unmanned ship formation tracks at different time points, and fig. 4 a-4 d are a 46 th second, a 54 th second, a 64 th second and a 90 th second formation unmanned ship track map and a schematic diagram of the positions of the unmanned ships respectively, so that five unmanned ships can be seen from the map to realize collision avoidance among the unmanned ships in the process from formation aggregation to formation holding.
Fig. 5a and 5b are control input diagrams of five unmanned vessels in a formation, longitudinal speed control input diagram and yaw rate control input diagram, respectively. It can be seen from the figure that the longitudinal speed control input amount satisfies the set constraint upper and lower limits. The control input quantity of the bow swing angular speed direction meets the upper limit and the lower limit of the set constraint.
Fig. 6a and 6b are diagrams of tracking errors of unmanned ship formation, respectively representing the diagrams of tracking errors in the X direction and the Y direction, and it can be seen from the diagrams that the position error between each unmanned ship and each target position in the unmanned ship formation is reduced to about 0 in about 100 seconds, which indicates that the unmanned ship formation can realize track tracking in the form of formation.
Fig. 7 a-7 c are schematic diagrams of model uncertainty and time-varying disturbance unknown function estimation of the ith unmanned ship, wherein a dotted line is a model uncertainty and time-varying disturbance estimated value, a solid line is a model uncertainty and time-varying disturbance actual value, and the degree of coincidence of the solid line and the dotted line is very high in the diagrams, so that the method designed by the invention can accurately estimate the model uncertainty and the time-varying disturbance in real time.
Fig. 8 a-8 c are schematic diagrams of the speed states of five unmanned vessels in a formation, from which it can be seen that the longitudinal speed, the yaw speed and the yaw speed of the unmanned vessels are all within the set ranges.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (6)
1. The utility model provides a many unmanned ship formation anticollision anti-interference control system which characterized in that: the system comprises a model reconstruction module, an interaction information network topology module, a position prediction control module, a bow swing angle prediction control module and an uncertainty and disturbance estimation module;
the model reconstruction module is used for acquiring the kinematics and dynamics model of the controlled unmanned ship established by the controlled unmanned ship module, and reconstructing the kinematics and dynamics model to acquire the position information p of the controlled unmanned ship under the earth coordinate system i Velocity information q in the earth coordinate system i Yaw angle information in the earth coordinate systemYaw rate r in hull coordinate system i ；
The interactive information network topology module is used for acquiring the position information p of the controlled unmanned ship in the earth coordinate system, which is interacted with the existence information of the controlled unmanned ship in the unmanned ship formation i Velocity information q in the earth coordinate system i Yaw angle information in the earth coordinate systemYaw rate r in hull coordinate system i And the position information p of the controlled unmanned ship with information interaction with the controlled unmanned ship in the earth coordinate system i And velocity information q in the earth coordinate system i Sending to a position prediction control module;
the position prediction control module is used for acquiring the position information p of the controlled unmanned ship under the earth coordinate system i Velocity information q in the earth coordinate system i Estimate of unknown function of model uncertainty and time-varying ocean current disturbanceAnd the information input by the interactive information network topology module calculates a longitudinal speed control input tau iu And reference yaw sequence->And inputting a longitudinal speed control input tau iu Input to said controlled unmanned ship, will reference the sequence of yaw angles +.>Inputting the predicted bow swing angle into the bow swing angle prediction control module;
the bow and roll angle prediction control module is used for acquiring the reference bow and roll angle sequenceControlled unmanned ship bow swing angular velocity r under ship body coordinate system i And an estimated value of a yaw direction unknown function +.>To calculate the yaw rate control input tau ir And controlling the input value tau of the yaw rate ir Inputting to the controlled unmanned ship;
the uncertainty and disturbance estimation module is used for acquiring the position information p of the controlled unmanned ship under the earth coordinate system i Velocity information q in the earth coordinate system i Yaw angle information in the earth coordinate systemYaw rate r in hull coordinate system i Longitudinal speed control input value τ iu Input value τ for controlling yaw rate ir To calculate the estimated value of the unknown function of the yaw direction +.>And model uncertainty and estimate of unknown function of time-varying current disturbance +.>And the estimated value of the unknown function of the yaw direction is +.>Inputting the model uncertainty and the estimated value of the unknown function of the time-varying ocean current disturbance into the bow and roll angle prediction control module>And inputting the position prediction control module.
2. The multi-unmanned ship formation anti-collision and anti-interference control system according to claim 1, wherein: the kinematic and kinetic model of the controlled unmanned ship is expressed as:
wherein: x is x i The position information of the unmanned ship in the X-axis direction under the earth coordinate system is obtained; y is i The position information of the unmanned ship in the Y-axis direction under the earth coordinate system is obtained;the information of the bow and the roll angle of the unmanned ship under the earth coordinate system is obtained; u (u) i Is the longitudinal speed of the unmanned ship under the ship body coordinate system; v i The transverse drifting speed of the unmanned ship under a ship body coordinate system is obtained; r is (r) i The bow swing angular speed of the unmanned ship under a ship body coordinate system is obtained; f (f) iu 、f iv And f ir Respectively a longitudinal unknown function, a transverse unknown function and a bow swing angle direction unknown function which comprise model uncertainty and time-varying ocean current disturbance under a ship body coordinate system; τ iu Controlling an input value for the longitudinal speed; τ ir The input value is controlled for the yaw rate; m is m iu And m ir Inertia coefficients in the longitudinal direction and the bow direction of the ship body respectively; t is time; />Respectively x i 、y i 、/>u i 、v i 、r i Is a derivative of (a).
3. The multi-unmanned ship formation anti-collision and anti-interference control system according to claim 2, wherein: the process for reconstructing the controlled unmanned ship kinematics and dynamics model comprises the following steps:
the unmanned ship is transformed and decoupled into a position ring model (2) and a bow rocking angle ring model (3) by a kinematic and dynamic model (1), and the method comprises the following steps of:
wherein: p is p i ＝[x i ,y i ]The position information of the unmanned ship in the earth coordinate system is obtained; q i ＝[q ix ,q iy ]Is the speed information of the unmanned ship in the earth coordinate system,is velocity information of the unmanned ship in the X-axis direction under the earth coordinate system,the speed information of the unmanned ship in the Y-axis direction under the earth coordinate system is obtained; />P is respectively i 、q i Derivative; f (f) iq ＝[f ix ,f iy ]Is the unknown function of model uncertainty and time-varying ocean current disturbance of the unmanned ship in the earth coordinate system, fix is the unknown function of model uncertainty and time-varying ocean current disturbance of the unmanned ship in the X-axis direction in the earth coordinate system, f iy The model uncertainty and time-varying sea current disturbance unknown function of the unmanned ship in the Y-axis direction under the earth coordinate system is specifically converted as follows:
4. a multi-unmanned ship formation collision-resistant and interference-resistant control system according to claim 3, wherein:
the estimated value of the unknown function of the bow swing angle direction is calculatedAnd model uncertainty and estimate of unknown function of time-varying current disturbance +.>The process of (1) comprises:
wherein the method comprises the steps ofRespectively f iq ,f ir ,q i ,r i Estimate of k iq ,k ir Gain coefficients, respectively.
5. The multi-unmanned ship formation anti-collision and anti-interference control system according to claim 4, wherein:
said calculated longitudinal speed control input τ iu And a reference bow tie sequenceThe process is as follows:
d1, rewriting the position ring model (2) as follows:
wherein: τ iq ＝[τ ix ,τ iy ] T Is the control input of unmanned ship in the earth coordinate system, whereinFor the control input of unmanned ship in X-axis direction under the earth coordinate system, +.>The control input of the unmanned ship in the Y-axis direction under the earth coordinate system is provided;
d2, including the unknown function estimated value of model uncertainty and time-varying ocean current disturbance of the unmanned ship under the earth coordinate systemSubstituting into the model (6) and discretizing, wherein the specific formula is as follows:
X iq (k+Ts)＝A i X iq (k)+B i τ iq (k)+C i (7)
wherein: x is X iq (k)＝[p i (k),q i (k)] T An unmanned ship state vector representing time k; ts is the sampling interval time; vector quantityVector->Vector->
Wherein:
0 2 ＝[0,0] T ；
d3, performing state prediction at time k by using the formula (7) as follows:
wherein: x is X iq (k|k) is the value of the state sample at time k; x is X iq (k+lTs|k),l＝1,...,N p For state prediction at time k versus time k+ lTs, N p To predict the time domain; τ iq (k-1) is a control input applied at time k-1 to the controlled unmanned ship in the earth coordinate system; Δτ iq (k+mts|k), m=1,..nc is the control increment at time k+mts, N c Representing the control time domain;
d4, expressing the formula (8) by using a recursive relation as follows:
wherein the method comprises the steps ofOutputting a sequence for the predicted state at the moment k; />A control increment sequence at the moment k;
corresponding orderFormula (9) is written as follows:
d5, constructing the following optimization problem model:
formulas (11 a), (11 b), (11 c) and (11 d) are respectively control increment constraint, control input constraint, state constraint and collision prevention constraint;and->The upper and lower bounds of the control increment under the earth coordinate system are respectively; />And->The upper and lower bounds of the control input under the earth coordinate system are respectively; />And->Upper and lower bounds of the velocity state in the earth coordinate system, respectively; z is Z ij Is a collision avoidance coefficient matrix->And r is ij The collision prevention safety distance between the ships in unmanned ship formation is set; q (Q) 1 、Q 2 And Q 3 Respectively maintaining a weight matrix and a formation tracking error weight matrix for the energy index weight matrix and the formation; />Wherein->X j ＝[p j ,q j ]Position state information and speed state information of neighbor unmanned ship j at k moment received for ith unmanned ship,/->D ij Forming a deviation vector for a formation in the formation mode;
d6, converting the optimization problem model (11) into the following form:
wherein:
obtaining an optimal control input delta sequence by solving an optimization problem model (12)Thereby obtaining an optimal control input sequence +.>
D7 calculating the optimal longitudinal speed control input τ by equation (13) iu ：
Wherein τ 1i (k) And τ 2i (k) Optimally controlling the first two elements of the input sequence;
d8, calculating a reference bow swing angle through a formula (14):
wherein τ 2i (l) And τ 1i (l) 2l and 2l-1 elements in the optimal control input sequence, respectively.
6. The multi-unmanned ship formation anti-collision and anti-interference control system according to claim 5, wherein:
said calculating a yaw rate control input τ ir The process comprises the following steps:
e1, discretizing the model (3) as follows:
X ir (k+Ts)＝A ir X ir (k)+B ir τ ir (k)+C ir (15)
wherein:an unmanned ship bow swing angle state vector at the moment k is represented; ts is the sampling interval time; vector quantityVector->Vector->
E2, the prediction using formula (15) is as follows:
wherein: x is X ir (k|k) is the value of the state sample at time k; x is X ir (k+lTs|k),l＝1,...,N p For predicting the bow and roll angle state of the moment k to the moment k+ lTs, N rp Predicting a time domain for a bow swing angle; τ ir (k-1) a yaw rate control input applied at a previous sampling instant; Δτ ir (k+mts|k), m=1,.. rc Representing a bow swing angle control time domain;
e3, using a recursive relation, expression (16) is expressed as follows:
wherein:outputting a sequence for the predicted state of the bow rocking angle at the moment k; />Controlling an increment sequence for the bow swing angular speed at the moment k;
corresponding orderFormula (17) is written as follows:
e4, constructing the following optimization problem model:
formulas (19 a), (19 b) and (19 c) are respectively a yaw rate control increment constraint, a yaw rate control input constraint and a yaw rate state constraint;and->Upper and lower bounds of the yaw rate control increment, respectively>And->Respectively an upper boundary and a lower boundary of the control input of the bow swing angular speed; />And->Respectively an upper boundary and a lower boundary of a bow swing angular speed state; />Is a reference bow crank sequence;
e5, converting the optimization problem model (11) into the following form:
wherein:
obtaining an optimal yaw rate control input increment sequence by solving an optimization problem model (20)Thereby obtaining an optimal control input sequence +.>The first element in the sequence is applied to the controlled unmanned ship.
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