Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,co...Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.展开更多
Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of i...Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.展开更多
In this paper,a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper.The existence of nonlinear terms in the studied sy...In this paper,a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper.The existence of nonlinear terms in the studied system makes it very difficult to design the optimal controller using traditional methods.To achieve optimal control,RL algorithm based on critic–actor architecture is considered for the nonlinear system.Due to the significant security risks of network transmission,the system is vulnerable to deception attacks,which can make all the system state unavailable.By using the attacked states to design coordinate transformation,the harm brought by unknown deception attacks has been overcome.The presented control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded.Finally,the simulation experiment is shown to prove the effectiveness of the strategy.展开更多
The system performance of grid-connected photovoltaic(PV)has a serious impact on the grid stability.To improve the control performance and shorten the convergence time,a predefined-time controller based on backsteppin...The system performance of grid-connected photovoltaic(PV)has a serious impact on the grid stability.To improve the control performance and shorten the convergence time,a predefined-time controller based on backstepping technology and dynamic surface control is formulated for the inverter in the grid-connected photovoltaic.The time-varying tuning functions are introduced into state-tracking errors to realize the predefined-time control effect.To address the“computational explosion problem”in the design process of backstepping control,dynamic surface control is adopted to avoid the analytical calculations of virtual control.The disturbances of the PV system are estimated and compensated by adaptive laws.The control parameters are chosen and the global stability of the closed-loop is ensured by Lyapunov conditions.Simulation results confirm the effectiveness of the proposed controller and ensure the predefined time control in the photovoltaic inverter.展开更多
磁悬浮系统能使铁磁性物体悬浮在空中,可以应用于科研、医疗、娱乐、交通等多个领域。为了解决非线性、不稳定磁悬浮系统的控制问题,建立了磁悬浮时滞系统的数学模型,采用了Backstepping控制以及RBF神经网络和Backstepping控制结合的方...磁悬浮系统能使铁磁性物体悬浮在空中,可以应用于科研、医疗、娱乐、交通等多个领域。为了解决非线性、不稳定磁悬浮系统的控制问题,建立了磁悬浮时滞系统的数学模型,采用了Backstepping控制以及RBF神经网络和Backstepping控制结合的方法进行研究,采用李雅普诺夫稳定性理论分别设计非线性控制器,保证闭环系统的理论稳定。在此基础上,利用Radial basis function (RBF)神经网络的逼近特性,设计了自适应律,研究了对系统中未知函数的拟合。最后,通过MATLAB对两种控制方法的效果进行对比,仿真结果表明,两种方法均能使系统稳定,RBF神经网络与Backstepping控制结合的方法能较快实现稳定,效果更好,且RBF神经网络对未知函数的拟合效果也良好。展开更多
在某些特殊条件下,电力系统会受影响产生混沌振荡现象,影响电网的安全稳定运行。比如,电力系统在运行过程中产生过多的无功功率将会导致电压相角偏移,从而进一步引发电力系统混沌振荡现象。通过对电力系统相对电角度和相对角速度的调控...在某些特殊条件下,电力系统会受影响产生混沌振荡现象,影响电网的安全稳定运行。比如,电力系统在运行过程中产生过多的无功功率将会导致电压相角偏移,从而进一步引发电力系统混沌振荡现象。通过对电力系统相对电角度和相对角速度的调控和观测,可以实现电力系统稳定性的控制。文中首先对二阶电力系统模型(model of second order power system, MSOPS)进行混沌振荡的动力学分析,综合利用反步法和固定时间控制器等方法,设计了一种改进有限时间(finite-time, FT)的控制器,既实现了对电力系统混沌振荡的抑制又减轻了计算负担、提升系统收敛速度。最后采用MATLAB软件进行数值仿真表明:文中提出的控制策略能够快速抑制电力系统的混沌状态,验证了控制方法的鲁棒性和快速性。展开更多
文摘Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFF0708903)Ningbo Municipal Key Technology Research and Development Program of China(Grant No.2022Z006)Youth Fund of National Natural Science Foundation of China(Grant No.52205043)。
文摘Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.
基金supported in part by the National Key R&D Program of China under Grants 2021YFE0206100in part by the National Natural Science Foundation of China under Grant 62073321+2 种基金in part by National Defense Basic Scientific Research Program JCKY2019203C029in part by the Science and Technology Development Fund,Macao SAR under Grants FDCT-22-009-MISE,0060/2021/A2 and 0015/2020/AMJin part by the financial support from the National Defense Basic Scientific Research Project(JCKY2020130C025).
文摘In this paper,a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper.The existence of nonlinear terms in the studied system makes it very difficult to design the optimal controller using traditional methods.To achieve optimal control,RL algorithm based on critic–actor architecture is considered for the nonlinear system.Due to the significant security risks of network transmission,the system is vulnerable to deception attacks,which can make all the system state unavailable.By using the attacked states to design coordinate transformation,the harm brought by unknown deception attacks has been overcome.The presented control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded.Finally,the simulation experiment is shown to prove the effectiveness of the strategy.
基金supported by the State Grid Corporation of China Headquarters Science and Technology Project under Grant No.5400-202122573A-0-5-SF。
文摘The system performance of grid-connected photovoltaic(PV)has a serious impact on the grid stability.To improve the control performance and shorten the convergence time,a predefined-time controller based on backstepping technology and dynamic surface control is formulated for the inverter in the grid-connected photovoltaic.The time-varying tuning functions are introduced into state-tracking errors to realize the predefined-time control effect.To address the“computational explosion problem”in the design process of backstepping control,dynamic surface control is adopted to avoid the analytical calculations of virtual control.The disturbances of the PV system are estimated and compensated by adaptive laws.The control parameters are chosen and the global stability of the closed-loop is ensured by Lyapunov conditions.Simulation results confirm the effectiveness of the proposed controller and ensure the predefined time control in the photovoltaic inverter.
文摘磁悬浮系统能使铁磁性物体悬浮在空中,可以应用于科研、医疗、娱乐、交通等多个领域。为了解决非线性、不稳定磁悬浮系统的控制问题,建立了磁悬浮时滞系统的数学模型,采用了Backstepping控制以及RBF神经网络和Backstepping控制结合的方法进行研究,采用李雅普诺夫稳定性理论分别设计非线性控制器,保证闭环系统的理论稳定。在此基础上,利用Radial basis function (RBF)神经网络的逼近特性,设计了自适应律,研究了对系统中未知函数的拟合。最后,通过MATLAB对两种控制方法的效果进行对比,仿真结果表明,两种方法均能使系统稳定,RBF神经网络与Backstepping控制结合的方法能较快实现稳定,效果更好,且RBF神经网络对未知函数的拟合效果也良好。
文摘在某些特殊条件下,电力系统会受影响产生混沌振荡现象,影响电网的安全稳定运行。比如,电力系统在运行过程中产生过多的无功功率将会导致电压相角偏移,从而进一步引发电力系统混沌振荡现象。通过对电力系统相对电角度和相对角速度的调控和观测,可以实现电力系统稳定性的控制。文中首先对二阶电力系统模型(model of second order power system, MSOPS)进行混沌振荡的动力学分析,综合利用反步法和固定时间控制器等方法,设计了一种改进有限时间(finite-time, FT)的控制器,既实现了对电力系统混沌振荡的抑制又减轻了计算负担、提升系统收敛速度。最后采用MATLAB软件进行数值仿真表明:文中提出的控制策略能够快速抑制电力系统的混沌状态,验证了控制方法的鲁棒性和快速性。
