Aiming at the group of autonomous agents consisting of multiple leader agents and multiple follower ones,a flocking behavior method with multiple leaders and a global trajectory was proposed.In this flocking method,th...Aiming at the group of autonomous agents consisting of multiple leader agents and multiple follower ones,a flocking behavior method with multiple leaders and a global trajectory was proposed.In this flocking method,the group leaders can attain the information of the global trajectory,while each follower can communicate with its neighbors and corresponding leader but does not have global knowledge.Being to a distributed control method,the proposed method firstly sets a movable imaginary point on the global trajectory to ensure that the center and average velocity of the leader agents satisfy the constraints of the global trajectory.Secondly,a two-stage strategy was proposed to make the whole group satisfy the constraints of the global trajectory.Moreover,the distance between the center of the group and the desired trajectory was analyzed in detail according to the number ratio of the followers to the leaders.In this way,on one hand,the agents of the group emerge a basic flocking behavior; on the other hand,the center of the group satisfies the constraints of global trajectory.Simulation results demonstrate the effectiveness of the proposed method.展开更多
A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles(UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. T...A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles(UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. The proposed control system combined great advantages of generalized indirect adaptive sliding mode control(IASMC) and fuzzy control for the UFVs. An on-line adaptive tuning algorithm based on Lyapunov function and Barbalat lemma was designed, thus the stability of the system can be guaranteed. The chattering phenomenon in the sliding mode control was reduced and the steady error was also alleviated. The numerical results, for an underactuated quadcopter and a high speed underwater vehicle as case studies, indicate that the presented adaptive design of fuzzy sliding mode controller performs robustly in the presence of sensor noise and external disturbances. In addition, online unknown parameter estimation of the UFVs, such as ground effect and planing force especially in the cases with the Gaussian sensor noise with zero mean and standard deviation of 0.5 m and 0.1 rad and external disturbances with amplitude of 0.1 m/s2 and frequency of 0.2 Hz, is one of the advantages of this method. These estimated parameters are then used in the controller to improve the trajectory tracking performance.展开更多
基金Projects(61170160,61202338)supported by the National Natural Science Foundation of China
文摘Aiming at the group of autonomous agents consisting of multiple leader agents and multiple follower ones,a flocking behavior method with multiple leaders and a global trajectory was proposed.In this flocking method,the group leaders can attain the information of the global trajectory,while each follower can communicate with its neighbors and corresponding leader but does not have global knowledge.Being to a distributed control method,the proposed method firstly sets a movable imaginary point on the global trajectory to ensure that the center and average velocity of the leader agents satisfy the constraints of the global trajectory.Secondly,a two-stage strategy was proposed to make the whole group satisfy the constraints of the global trajectory.Moreover,the distance between the center of the group and the desired trajectory was analyzed in detail according to the number ratio of the followers to the leaders.In this way,on one hand,the agents of the group emerge a basic flocking behavior; on the other hand,the center of the group satisfies the constraints of global trajectory.Simulation results demonstrate the effectiveness of the proposed method.
文摘A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles(UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. The proposed control system combined great advantages of generalized indirect adaptive sliding mode control(IASMC) and fuzzy control for the UFVs. An on-line adaptive tuning algorithm based on Lyapunov function and Barbalat lemma was designed, thus the stability of the system can be guaranteed. The chattering phenomenon in the sliding mode control was reduced and the steady error was also alleviated. The numerical results, for an underactuated quadcopter and a high speed underwater vehicle as case studies, indicate that the presented adaptive design of fuzzy sliding mode controller performs robustly in the presence of sensor noise and external disturbances. In addition, online unknown parameter estimation of the UFVs, such as ground effect and planing force especially in the cases with the Gaussian sensor noise with zero mean and standard deviation of 0.5 m and 0.1 rad and external disturbances with amplitude of 0.1 m/s2 and frequency of 0.2 Hz, is one of the advantages of this method. These estimated parameters are then used in the controller to improve the trajectory tracking performance.
