CN110632940B - Active anti-interference time-varying track tracking control method for multiple unmanned aerial vehicles with hybrid quantizers - Google Patents
Active anti-interference time-varying track tracking control method for multiple unmanned aerial vehicles with hybrid quantizers Download PDFInfo
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Abstract
The invention discloses a multi-unmanned aerial vehicle active anti-interference time-varying track tracking control method with a hybrid quantizer, which comprises the steps of firstly, establishing an unmanned aerial vehicle kinematic model with non-integrity constraint, designing a hybrid quantizer integrating average quantization and hysteresis quantization characteristics, and establishing an automatic pilot dynamic state containing environmental interference and quantized input; secondly, designing a virtual control quantity of an unmanned aerial vehicle displacement loop by combining a given time-varying formation style instruction, and generating an expected linear speed and course angle instruction; then, constructing a model auxiliary extended state observer to estimate lumped interference in the autopilot and counteract the influence of the interference on the control performance; and finally, designing a robust dynamic feedback linearization control law capable of inhibiting the quantization error effect so as to update the dynamic output of the automatic pilot. The invention can effectively overcome the adverse effect of unknown environmental interference on the formation system and ensure the realization of strong robust formation tracking performance.
Description
Technical Field
The invention relates to the technical field of automatic control of multiple unmanned aerial vehicles, in particular to an active anti-interference time-varying track tracking control method for multiple unmanned aerial vehicles with hybrid quantizers, which is mainly applied to time-varying track tracking control of multiple unmanned aerial vehicles in environments with strong interference and limited communication bandwidth.
Background
Unmanned aerial vehicle is as the result under the modern science and technology, and many characteristics such as low cost, no casualties, equipment are simple, convenient operation, action are nimble reliable can effectual realization closely to ground target selective and pertinence observation to can get into some special areas and monitor and patrol and examine, the information that its acquireed has very high reliability and ageing. The unmanned aerial vehicle for formation is used for military reconnaissance and civil measurement, and the reconnaissance and detection range can be enlarged. At present, although a single unmanned aerial vehicle can adopt an advanced control strategy to realize real-time high-precision attitude control and complete trajectory tracking, the success rate of the formation flight execution task of multiple unmanned aerial vehicles and the capability of resisting emergency events are higher than those of the single unmanned aerial vehicle. For example, in the process of executing military reconnaissance missions, one unmanned aerial vehicle fails and cannot continue to work, so that the unmanned aerial vehicle can return to the air for maintenance, and the rest unmanned aerial vehicles can still keep formation flying according to the original plan, so that the missions can be completed satisfactorily.
However, the existing technology level can not support the autonomous decision-making function of unmanned aerial vehicle formation in the complete sense, and the high-integration intelligent cluster type large-scale formation flying is difficult to realize. Therefore, aiming at a multi-unmanned aerial vehicle cluster system, multiple targets of expanding the flight task range of unmanned aerial vehicle collaborative formation, improving the task execution efficiency and the completion quality, improving the effectiveness of combat in high-risk environments, enhancing the environment self-adaptive capacity of the system and the like are achieved, and researches in various aspects such as an information perception technology, a multiple data fusion technology, a task allocation principle, a flight trajectory planning technology, a formation control strategy, a communication transmission network technology, a virtual/physical comprehensive verification platform technology and the like and researches on effective collaborative work of multiple technologies are necessary.
Compared with a distributed formation control system, the centralized formation control framework utilizes ground station software to calculate the control law of the unmanned aerial vehicles in real time based on the position information telemetered by the unmanned aerial vehicles, and distributes the control law to the corresponding unmanned aerial vehicles to be executed through uplink communication links, so that the global formation control effect under the optimal meaning is achieved. Therefore, the existing centralized multi-unmanned aerial vehicle formation control strategy puts extremely high requirements on the communication bandwidth between the ground station and the unmanned aerial vehicle, and the formation effect of the asymptotic tracking is usually to use a high-bandwidth communication channel and a high-sampling-rate precise execution mechanism as a support. However, in an actual dynamic environment, problems such as signal interruption and signal disturbance caused by multipath effect and obstacle shielding inevitably exist, and packet loss of communication data causes a series of unpredictable consequences such as non-negligible tracking performance deterioration, stability margin reduction and even control instability to a formation control system due to time delay. In addition, the design of the flight formation controller in a practical environment must explicitly take into account the adverse effects of environmental disturbances to achieve robust flight control in a dynamic environment. Therefore, the requirement on the communication bandwidth of the measurement and control link of the ground station is reduced as much as possible on the premise of not sacrificing the formation control performance, and the active anti-interference cooperative tracking control method for the multiple unmanned aerial vehicles, which considers the quantitative input, has important engineering application value.
Disclosure of Invention
The invention provides an active anti-interference time-varying track tracking control method for multiple unmanned aerial vehicles with hybrid quantizers, which aims to solve the control problem of centralized time-varying formation of multiple unmanned aerial vehicles under the conditions of strong interference and limited communication bandwidth.
The invention is realized by the following technical scheme: a multi-unmanned aerial vehicle active anti-interference time-varying track tracking control method with a hybrid quantizer comprises the following steps:
(1) establishing an unmanned aerial vehicle kinematics model with non-integrity constraint, designing a hybrid quantizer integrating average quantization and hysteresis quantization characteristics, and constructing an automatic pilot dynamic state containing environmental interference and quantitative input, wherein the method specifically comprises the following steps:
firstly, the kinematics model of the ith unmanned aerial vehicle is established as follows:
wherein (x)i,yi) Represents the position of the ith drone in a Cartesian coordinate system, phiiIs the course angle, v, of the unmanned aerial vehiclei,ωiRespectively representing the linear velocity and the angular velocity of the unmanned aerial vehicle;
secondly, establishing the dynamic state of the automatic pilot which comprises environmental interference and quantitative input:
wherein, thetaivAnd thetaiωIs the time constant of the autopilot,
and
respectively actual control quantity
And
discrete control signal after the hybrid quantizer, divAnd diωThe interference signal is acted on the autopilot by the external environment; here, the hybrid quantizer design that integrates the average quantization and hysteresis quantization characteristics is as follows:
where u represents the input to the hybrid quantizer and q (u) represents the quantized output; hybrid quantizer in the hysteretic quantization state uj＝ρ1-juminThe integer j is 1, 2.; u. ofminThe quantization dead zone is represented by more than 0, the hysteresis interval parameter is represented by delta and used for adjusting the hysteresis interval of the hybrid quantizer, delta belongs to (0,1), and the smaller delta is, the more sensitive the hybrid quantizer is to interference signals;
representing the quantization density of the hybrid quantizer, the larger the ρ, the finer the hybrid quantizer quantizes the signal;
represents the switching threshold of the hybrid quantizer, in the average quantization state, ΔqRepresents the minimum resolution of the hybrid quantizer quantization; n is a radical of an alkyl radicalqRepresenting the quantization magnitude of the hybrid quantizer; sign (u) denotes
(2) The method comprises the following steps of designing a virtual control quantity of an unmanned aerial vehicle displacement loop by combining a given time-varying formation style instruction, and generating an expected linear speed and course angle instruction so as to solve the problem of incomplete constraint of an unmanned aerial vehicle model, wherein the method specifically comprises the following steps: .
And designing a virtual control quantity of the displacement loop of the unmanned aerial vehicle by combining a given time-varying formation style instruction:
wherein alpha isixAnd alphaiyRespectively representing the virtual speed control quantity of the ith unmanned aerial vehicle in the directions of an x axis and a y axis; k is a radical ofixAnd kiyRespectively representing the control gain of the displacement loop of the ith unmanned aerial vehicle; x is the number ofrAnd yrRespectively representing the reference tracks of the unmanned aerial vehicle in the directions of an x axis and a y axis;
and
respectively representing the reference speeds of the unmanned aerial vehicle in the directions of an x axis and a y axis; sigmaixAnd σiyRespectively representing the offset of the ith unmanned aerial vehicle in the x-axis direction and the y-axis direction relative to the reference track;
and
respectively representing the offset speeds of the ith unmanned aerial vehicle in the directions of the x axis and the y axis relative to the reference speed;
generating expected linear velocity command v by utilizing triangular geometric relationiαAnd a course angle command phiiα：
Further, the following virtual angular velocity control amount α is designediω：
Wherein k isiφAnd the control gain of the ith unmanned aerial vehicle course angle loop is shown.
(3) Aiming at the autopilot quantitative model given in the step (1), a model auxiliary extended state observer is constructed to estimate lumped interference in the autopilot and counteract the influence of the interference on the control performance, and the method specifically comprises the following steps:
the model-assisted extended state observer for dealing with external disturbances is designed as follows:
wherein λ isivRepresents the bandwidth, lambda, of the auxiliary extended state observer of the linear velocity model of the ith unmanned aerial vehicleiωThe bandwidth of the ith unmanned aerial vehicle angular velocity model auxiliary extended state observer is represented;
represents the linear velocity viIs determined by the estimated value of (c),
representing the angular velocity omegaiAn estimated value of (d);
indicating disturbance d in autopilotivIs determined by the estimated value of (c),
indicating disturbance d in autopilotiωAn estimate of (d).
(4) Based on the expected linear velocity and course angle instructions provided in the step (2) and the interference estimation provided in the step (3), a robust dynamic feedback linearization control law capable of suppressing the quantization error effect is designed to update the dynamic output of the automatic pilot, so that the multi-unmanned aerial vehicle time-varying trajectory tracking control under the conditions of a given formation pattern, external interference and quantization input is realized, and the method specifically comprises the following steps:
the control updating rule of the ith unmanned aerial vehicle is designed as follows:
wherein ξa(a ═ iv1, i ω 1) is an arbitrarily small positive constant used to limit the quantization effect; robust dynamic feedback linearized control law kivAnd kappaiωThe following were chosen:
wherein k isivAnd kiωAnd the control gain of the i-th unmanned aerial vehicle autopilot is shown.
The invention designs a multi-unmanned aerial vehicle active anti-interference time-varying track tracking control method with a hybrid quantizer, which comprises the steps of firstly, establishing an unmanned aerial vehicle kinematic model with non-integrity constraint, designing a hybrid quantizer integrating average quantization and hysteresis quantization characteristics, and establishing an automatic pilot dynamic state containing environmental interference and quantized input; secondly, designing a virtual control quantity of an unmanned aerial vehicle displacement loop by combining a given time-varying formation style instruction, and generating an expected linear speed and course angle instruction; secondly, constructing a model auxiliary extended state observer to estimate lumped interference in the autopilot and counteract the influence of the interference on the control performance; and finally, designing a robust dynamic feedback linearization control law capable of inhibiting the quantization error effect so as to update the dynamic output of the automatic pilot.
Compared with the prior art, the invention has the following beneficial effects: the active anti-interference time-varying track tracking control method for the multiple unmanned aerial vehicles with the hybrid quantizers, provided by the invention, definitely considers discontinuous control input so as to reduce the harsh requirement on channel transmission bandwidth in the formation process. The problem that the existing quantizer is easy to cause remarkable quantization error when the variation range of the control quantity is large can be solved by adopting the hybrid quantizer. The model auxiliary extended state observer technology is introduced, the adverse effect of unknown environmental interference on a formation system can be effectively overcome, and the realization of strong robust formation tracking performance is ensured.
Drawings
Fig. 1 is a schematic diagram of a centralized multi-drone formation structure.
Fig. 2 is a diagram of a multi-drone time-varying formation trajectory tracking control structure.
Fig. 3 is a simulation result of time-varying formation of four drones.
FIG. 4 shows the simulation effect of the displacement tracking error and the heading angle control error of four unmanned aerial vehicles.
Fig. 5 is a comparison of four drone control signals before and after quantization using a hybrid quantizer.
FIG. 6 is a disturbance estimation effect on the linear velocity loop in the autopilot using an extended state observer.
Fig. 7 is a disturbance estimation effect on an angular velocity loop in a self-driving machine using an extended state observer.
Detailed Description
The present invention is further illustrated by the following specific examples.
A multi-unmanned aerial vehicle active anti-interference time-varying track tracking control method with a hybrid quantizer comprises the following steps:
(1) establishing an unmanned aerial vehicle kinematics model with non-integrity constraint, designing a hybrid quantizer integrating average quantization and hysteresis quantization characteristics, and constructing an automatic pilot dynamic state containing environmental interference and quantitative input, wherein the method specifically comprises the following steps:
firstly, the kinematics model of the ith unmanned aerial vehicle is established as follows:
wherein (x)i,yi) Represent that the ith unmanned plane is in the cartesianPosition in the coordinate system, phiiIs the course angle, v, of the unmanned aerial vehiclei,ωiRespectively representing the linear velocity and the angular velocity of the unmanned aerial vehicle;
secondly, establishing the automatic pilot dynamics containing environmental interference and quantitative input:
wherein, thetaivAnd thetaiωIs the time constant of the autopilot,
and
respectively actual control quantity
And
discrete control signal after the hybrid quantizer, divAnd diωThe interference signal is acted on the autopilot by the external environment; here, the hybrid quantizer design that integrates the average quantization and hysteresis quantization characteristics is as follows:
where u represents the input to the hybrid quantizer and q (u) represents the quantized output; hybrid quantizer in the hysteretic quantization state, uj＝ρ1-juminThe integer j is 1, 2.; u. ofminThe quantization dead zone is represented by more than 0, the hysteresis interval parameter is represented by delta and used for adjusting the hysteresis interval of the hybrid quantizer, delta belongs to (0,1), and the smaller delta is, the more sensitive the hybrid quantizer is to interference signals;
representing the quantization density of the hybrid quantizer, the larger the ρ, the finer the hybrid quantizer quantizes the signal;
represents the switching threshold of the hybrid quantizer, in the average quantization state, ΔqRepresents the minimum resolution of the hybrid quantizer quantization; n isqRepresenting the quantization magnitude of the hybrid quantizer; sign (u) denotes
(2) The method comprises the following steps of designing a virtual control quantity of an unmanned aerial vehicle displacement loop by combining a given time-varying formation style instruction, and generating an expected linear speed and course angle instruction so as to solve the problem of incomplete constraint of an unmanned aerial vehicle model, wherein the method specifically comprises the following steps: .
And designing a virtual control quantity of the displacement loop of the unmanned aerial vehicle by combining a given time-varying formation style instruction:
wherein alpha isixAnd alphaiyRespectively representing the virtual speed control quantity of the ith unmanned aerial vehicle in the x-axis direction and the y-axis direction; k is a radical of formulaixAnd kiyRespectively representing the control gain of the displacement loop of the ith unmanned aerial vehicle; x is the number ofrAnd yrRespectively representing the reference tracks of the unmanned aerial vehicle in the directions of an x axis and a y axis;
and
respectively representing the reference speeds of the unmanned aerial vehicle in the directions of an x axis and a y axis; sigmaixAnd σiyRespectively representing the offset of the ith unmanned aerial vehicle relative to the reference track in the directions of an x axis and a y axis;
and
respectively representing the offset speeds of the ith unmanned aerial vehicle in the directions of the x axis and the y axis relative to the reference speed;
generating expected linear velocity command v by utilizing triangular geometric relationiαAnd a course angle command phiiα：
Further, the following virtual angular velocity control amount α is designediω：
Wherein k isiφAnd the control gain of the ith unmanned aerial vehicle course angle loop is shown.
(3) Aiming at the autopilot quantitative model given in the step (1), a model auxiliary extended state observer is constructed to estimate lumped interference in the autopilot and counteract the influence of the interference on the control performance, and the method specifically comprises the following steps:
the model-assisted extended state observer coping with external disturbances is designed as follows:
wherein λ isivRepresents the bandwidth, lambda, of the auxiliary extended state observer of the linear velocity model of the ith unmanned aerial vehicleiωThe bandwidth of an ith unmanned aerial vehicle angular velocity model auxiliary extended state observer is represented;
represents the linear velocity viIs determined by the estimated value of (c),
representing the angular velocity omegaiAn estimated value of (d);
indicating disturbance d in autopilotivIs determined by the estimated value of (c),
indicating disturbance d in autopilotiωAn estimate of (d).
(4) Based on the expected linear velocity and course angle instructions provided in the step (2) and the interference estimation provided in the step (3), a robust dynamic feedback linearization control law capable of suppressing the quantization error effect is designed to update the dynamic output of the automatic pilot, so that the multi-unmanned aerial vehicle time-varying trajectory tracking control under the conditions of a given formation pattern, external interference and quantization input is realized, and the method specifically comprises the following steps:
the control updating rule of the ith unmanned aerial vehicle is designed as follows:
wherein ξa(a ═ iv1, i ω 1) is an arbitrarily small positive constant used to limit the quantization effect; robust dynamic feedback linearized control law kivAnd kappaiωThe following were chosen:
wherein k isivAnd kiωAnd the control gain of the i-th unmanned aerial vehicle autopilot is shown.
The model initialization parameters, controller and hybrid quantizer parameters, extended state observer parameters, and simulated disturbance parameters are detailed in tables 1,2, 3, and 4.
TABLE 1 initial position of four unmanned aerial vehicles, formation instruction and autopilot time constant
TABLE 2 four unmanned aerial vehicle model auxiliary extended state observer parameters
TABLE 3 four unmanned aerial vehicle formation trajectory tracking quantizer and controller parameters
TABLE 4 simulation interference parameters for four unmanned aerial vehicles
The centralized multi-unmanned aerial vehicle formation structure principle is shown in fig. 1, unmanned aerial vehicle formation instructions and reference tracks are manually input and transmitted to a console, the console receives instruction signals and then communicates with each unmanned aerial vehicle to calculate the actual control quantity of each unmanned aerial vehicle, and then the actual control quantity is quantized by a hybrid quantizer and then transmitted to the unmanned aerial vehicles to reduce communication burden. And finally, the unmanned aerial vehicle completes the formation track tracking task by combining a flight control system and hardware of the unmanned aerial vehicle. The time-varying formation trajectory tracking control structure of the multiple unmanned aerial vehicles is shown in fig. 2, and a controller completes the formation trajectory tracking task of the unmanned aerial vehicles in four steps.
The simulation results of the time-varying formation of the four unmanned aerial vehicles are shown in fig. 3, and the four unmanned aerial vehicles complete the expected time-varying formation and the stable tracking task of the target track in the simulation.
As shown in fig. 4, the displacement tracking error and the heading angle control error of each drone can be converged in a short time and stabilized in a small zero-field range.
As shown in fig. 5, after the control signals of the four drones are quantized by the hybrid quantizer, the requirement on the communication bandwidth of the measurement and control link of the ground station is reduced to a certain extent on the premise of not sacrificing the formation control performance.
As shown in fig. 6 and 7, by estimating the interference by using the active interference rejection strategy, the stability of the task executed by the unmanned aerial vehicle can be improved well. From the result graph, the active anti-interference strategy can estimate large high-frequency and low-frequency sinusoidal interference and free attenuation interference, effectively inhibits interference in an automatic pilot, and enhances robustness and stability of the multi-unmanned-aerial-vehicle time-varying trajectory tracking controller in an uncertain environment.
The scope of the invention is not limited to the above embodiments, and various modifications and changes may be made by those skilled in the art, and any modifications, improvements and equivalents within the spirit and principle of the invention should be included in the scope of the invention.
Claims (1)
1. The active anti-interference time-varying track tracking control method for the multiple unmanned aerial vehicles with the hybrid quantizers is characterized by comprising the following steps of: the method comprises the following steps:
(1) establishing an unmanned aerial vehicle kinematic model with non-integrity constraint, designing a hybrid quantizer integrating average quantization and hysteresis quantization characteristics, and constructing an automatic pilot dynamic state containing environmental interference and quantitative input:
firstly, the kinematics model of the ith unmanned aerial vehicle is established as follows:
wherein (x)i,yi) Represents the position of the ith drone in a Cartesian coordinate system, phiiIs the course angle, v, of the unmanned aerial vehiclei,ωiRespectively representing the linear velocity and the angular velocity of the unmanned aerial vehicle;
secondly, establishing the automatic pilot dynamics containing environmental interference and quantitative input:
wherein, thetaivAnd thetaiωIs the time constant of the autopilot,
and
respectively actual control quantity
And
discrete control signal after the hybrid quantizer, divAnd diωThe interference signal is acted on the autopilot by the external environment; here, the hybrid quantizer design that integrates the average quantization and hysteresis quantization characteristics is as follows:
where u represents the input to the hybrid quantizer and q (u) represents the quantized output; hybrid quantizer in the hysteretic quantization state, uj＝ρ1-juminThe integer j is 1, 2.; u. ofminThe quantization dead zone is represented by more than 0, the hysteresis interval parameter is represented by delta and used for adjusting the hysteresis interval of the hybrid quantizer, delta belongs to (0,1), and the smaller delta is, the more sensitive the hybrid quantizer is to interference signals;
representing the quantization density of the hybrid quantizer, the larger the ρ, the finer the hybrid quantizer quantizes the signal;
representing the switching threshold of the hybrid quantizer in the average quantization stateqRepresents the minimum resolution of the hybrid quantizer quantization; n is a radical of an alkyl radicalqRepresenting the quantization magnitude of the hybrid quantizer; sign (u) denotes
(2) Designing a virtual control quantity of an unmanned aerial vehicle displacement loop by combining a given time-varying formation style instruction, and generating an expected linear speed and course angle instruction:
and designing a virtual control quantity of the displacement loop of the unmanned aerial vehicle by combining a given time-varying formation style instruction:
wherein alpha isixAnd alphaiyRespectively representing the virtual speed control quantity of the ith unmanned aerial vehicle in the x-axis direction and the y-axis direction; k is a radical ofixAnd kiyRespectively representing the control gain of the displacement loop of the ith unmanned aerial vehicle; x is the number ofrAnd yrRespectively representing the reference trajectories of the unmanned aerial vehicle in the directions of an x axis and a y axis;
and
respectively representing the reference speeds of the unmanned aerial vehicle in the directions of an x axis and a y axis; sigmaixAnd σiyRespectively representing the offset of the ith unmanned aerial vehicle in the x-axis direction and the y-axis direction relative to the reference track;
and
respectively indicate that the i-th unmanned aerial vehicle is atOffset velocities in the x-axis and y-axis directions relative to a reference velocity;
generating expected linear velocity command v by utilizing triangular geometric relationiαAnd a course angle command phiiα：
Further, the following virtual angular velocity control amount α is designediω：
Wherein k isiφRepresenting the control gain of the i-th unmanned aerial vehicle course angle loop;
(3) aiming at the autopilot quantitative model given in the step (1), a model auxiliary extended state observer is constructed to estimate lumped interference in the autopilot:
the model-assisted extended state observer for dealing with external disturbances is designed as follows:
wherein λ isivRepresents the bandwidth, lambda, of the auxiliary extended state observer of the linear velocity model of the ith unmanned aerial vehicleiωThe bandwidth of the ith unmanned aerial vehicle angular velocity model auxiliary extended state observer is represented;
represents the linear velocity viIs determined by the estimated value of (c),
representing the angular velocity omegaiAn estimated value of (d);
indicating disturbance d in autopilotivIs determined by the estimated value of (c),
indicating disturbance d in autopilotiωAn estimated value of (d);
(4) designing a robust dynamic feedback linearization control law capable of inhibiting quantization error effects based on the expected linear speed and course angle instructions provided in the step (2) and the interference estimation provided in the step (3) to update the dynamic output of the automatic pilot, and realizing multi-unmanned aerial vehicle time-varying trajectory tracking control under the conditions of a given formation pattern, external interference and quantization input:
the control updating rule of the ith unmanned aerial vehicle is designed as follows:
wherein ξa(a ═ iv1, i ω 1) is an arbitrarily small positive constant used to limit the quantization effect; robust dynamic feedback linearized control law kivAnd kappaiωThe following were chosen:
wherein k isivAnd kiωAnd the control gain of the i-th unmanned aerial vehicle autopilot is shown.
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| CN110007682A (en) * | 2019-02-02 | 2019-07-12 | 曲阜师范大学 | Attitude of flight vehicle output feedback ontrol method and system, controller and control method based on input and output quantization |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN110007682A (en) * | 2019-02-02 | 2019-07-12 | 曲阜师范大学 | Attitude of flight vehicle output feedback ontrol method and system, controller and control method based on input and output quantization |
Non-Patent Citations (3)
| Title |
|---|
| Adaptive Tracking Control of Uncertain Nonlinear Systems With Saturated Input Quantization;Guanyu Lai等;《2019 12th Asian Control Conference (ASCC)》;20190612;全文 * |
| Distributed Adaptive Finite-Time Approach for Formation–Containment Control of Networked Nonlinear Systems Under Directed Topology;Yujuan Wang等;《TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS》;20180731;全文 * |
| 量化和事件触发控制若干问题研究;邢兰涛;《中国优秀博硕士学位论文全文数据库(博士)信息科技辑》;20180815;全文 * |
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