This work is concerned with consensus control for a class of leader-following multi-agentsystems (MASs).The information that each agent received is corrupted by measurement noises.Toreduce the impact of noises on cons...This work is concerned with consensus control for a class of leader-following multi-agentsystems (MASs).The information that each agent received is corrupted by measurement noises.Toreduce the impact of noises on consensus,time-varying consensus gains are adopted,based on whichconsensus protocols are designed.By using the tools of stochastic analysis and algebraic graph theory,asufficient condition is obtained for the protocol to ensure strong mean square consensus under the fixedtopologies.This condition is shown to be necessary and sufficient in the noise-free case.Furthermore,by using a common Lyapunov function,the result is extended to the switching topology case.展开更多
Path-following control is one of the key technologies of autonomous vehicles,but the complex coupling effects and system uncertainties of vehicles can degrade their control performance.Accordingly,this study proposes ...Path-following control is one of the key technologies of autonomous vehicles,but the complex coupling effects and system uncertainties of vehicles can degrade their control performance.Accordingly,this study proposes targeted methods to solve different types of coupling in vehicle dynamics.First,the types of coupling are figured out and different handling strategies are proposed for each type,among which the coupling caused by steering angle,unsaturated tire forces,and load transfer can be treated as uncertainties in a unified form,such that the coupling effects can be treated in a decoupling way.Then,robust control methods for both lateral and longitudinal dynamics are proposed to deal with the uncertainties in dynamic and physical parameters.In lateral control,a robust feedback-feedforward scheme is utilized in lateral control to deal with such uncertainties.In longitudinal control,a radial basis function neural network-based adaptive sliding mode controller is introduced to deal with uncertainties and disturbances.In addition,the tire saturation coupling that cannot be handled by controllers is treated by a proposed speed profile.Simulation results based on the CarSim-Simulink joint platform evaluate the effectiveness and robustness of the proposed control method.The results show that compared with a well-designed robust controller,the velocity tracking performance,lateral tracking performance,and heading tracking performance improve by 55.68%,34.26%,and 52.41%,respectively,in the double-lane change maneuver,and increase by 87.79%,30.18%,and 9.68%,respectively,in the ramp maneuver.展开更多
In this paper,an integrated guidance and control method based on an adaptive path-following controller is proposed to control a spin-stabilized projectile with only translational motion information under the constrain...In this paper,an integrated guidance and control method based on an adaptive path-following controller is proposed to control a spin-stabilized projectile with only translational motion information under the constraint of an actuator,uncertainties in aerodynamic parameters and measurements,and control system complexity.Owing to the fairly high rotation speed,the dynamic model of this missile is strongly nonlinear,uncertain and coupled in pitch,yaw and roll channels.A theoretical equivalent resultant force and uncertainty compensation method are comprehensively used to realize decoupling of pitch and yaw.In response to the strong nonlinear and time-varying characteristics of the dynamic system,the quasi-linear model whose parameters are obtained by interpolation of points selected as the segmentation points in the trajectory envelope,is used for calculation in each step.To cope with the system uncertainty caused by model approximation,parameter uncertainty and ballistic interference,an extended state estimator is used to compensate the output feedback according to the test ballistic angle.In order to improve the tracking efficiency and ensure the tracking error convergence with only translational motion information,the virtual guide point,whose derivative is deduced according to the Lyapunov principle,is calculated in real time according to the projection relationship between the real-time position and the reference trajectory,and a virtual line-of-sight angle and the backstepping method are used for the design of the guidance and control system.In order to avoid the influence of control input saturation on the guidance and control performance due to the actuator limitation and improve the robustness of the system,an anti-saturation compensator is designed according to the two-step method.The feasibility and effectiveness of the path-following controller is verified through closed-loop flight simulations with measurement,control,and condition uncertainties.The results indicate that the designed controller can conv展开更多
Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward contr...Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos. 60821091 and 60934006Part of this work was presented at the 17th IFAC World Congress, Seoul, Korea, July 2008
文摘This work is concerned with consensus control for a class of leader-following multi-agentsystems (MASs).The information that each agent received is corrupted by measurement noises.Toreduce the impact of noises on consensus,time-varying consensus gains are adopted,based on whichconsensus protocols are designed.By using the tools of stochastic analysis and algebraic graph theory,asufficient condition is obtained for the protocol to ensure strong mean square consensus under the fixedtopologies.This condition is shown to be necessary and sufficient in the noise-free case.Furthermore,by using a common Lyapunov function,the result is extended to the switching topology case.
基金This work was supported by the key research program of the Ministry of Science and Technology(2017YFB0102603-3)the National Nature Science Foundation of China(51875061)+2 种基金Chongqing Science and Technology Program Project Basic Science and Frontier Technology(cstc2018jcyjAX0630)China Scholarship Council(201906050066)Graduate Sicentific Research/Innovation Foundation of Chongqing(CYB19063).
文摘Path-following control is one of the key technologies of autonomous vehicles,but the complex coupling effects and system uncertainties of vehicles can degrade their control performance.Accordingly,this study proposes targeted methods to solve different types of coupling in vehicle dynamics.First,the types of coupling are figured out and different handling strategies are proposed for each type,among which the coupling caused by steering angle,unsaturated tire forces,and load transfer can be treated as uncertainties in a unified form,such that the coupling effects can be treated in a decoupling way.Then,robust control methods for both lateral and longitudinal dynamics are proposed to deal with the uncertainties in dynamic and physical parameters.In lateral control,a robust feedback-feedforward scheme is utilized in lateral control to deal with such uncertainties.In longitudinal control,a radial basis function neural network-based adaptive sliding mode controller is introduced to deal with uncertainties and disturbances.In addition,the tire saturation coupling that cannot be handled by controllers is treated by a proposed speed profile.Simulation results based on the CarSim-Simulink joint platform evaluate the effectiveness and robustness of the proposed control method.The results show that compared with a well-designed robust controller,the velocity tracking performance,lateral tracking performance,and heading tracking performance improve by 55.68%,34.26%,and 52.41%,respectively,in the double-lane change maneuver,and increase by 87.79%,30.18%,and 9.68%,respectively,in the ramp maneuver.
文摘In this paper,an integrated guidance and control method based on an adaptive path-following controller is proposed to control a spin-stabilized projectile with only translational motion information under the constraint of an actuator,uncertainties in aerodynamic parameters and measurements,and control system complexity.Owing to the fairly high rotation speed,the dynamic model of this missile is strongly nonlinear,uncertain and coupled in pitch,yaw and roll channels.A theoretical equivalent resultant force and uncertainty compensation method are comprehensively used to realize decoupling of pitch and yaw.In response to the strong nonlinear and time-varying characteristics of the dynamic system,the quasi-linear model whose parameters are obtained by interpolation of points selected as the segmentation points in the trajectory envelope,is used for calculation in each step.To cope with the system uncertainty caused by model approximation,parameter uncertainty and ballistic interference,an extended state estimator is used to compensate the output feedback according to the test ballistic angle.In order to improve the tracking efficiency and ensure the tracking error convergence with only translational motion information,the virtual guide point,whose derivative is deduced according to the Lyapunov principle,is calculated in real time according to the projection relationship between the real-time position and the reference trajectory,and a virtual line-of-sight angle and the backstepping method are used for the design of the guidance and control system.In order to avoid the influence of control input saturation on the guidance and control performance due to the actuator limitation and improve the robustness of the system,an anti-saturation compensator is designed according to the two-step method.The feasibility and effectiveness of the path-following controller is verified through closed-loop flight simulations with measurement,control,and condition uncertainties.The results indicate that the designed controller can conv
基金supported by Grant-in-Aid for Scientific Research(C) (No. 20560248) of Japan
文摘Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.
