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
This paper introduces an optimized backstepping control method for Flexible Airbreathing Hypersonic Vehicles(FAHVs).The approach incorporates nonlinear disturbance observation and reinforcement learning to address com...This paper introduces an optimized backstepping control method for Flexible Airbreathing Hypersonic Vehicles(FAHVs).The approach incorporates nonlinear disturbance observation and reinforcement learning to address complex control challenges.The Minimal Learning Parameter(MLP)technique is applied to manage unknown nonlinear dynamics,significantly reducing the computational load usually associated with Neural Network(NN)weight updates.To improve the control system robustness,an MLP-based nonlinear disturbance observer is designed,which estimates lumped disturbances,including flexibility effects,model uncertainties,and external disruptions within the FAHVs.In parallel,the control strategy integrates reinforcement learning using an MLP-based actor-critic framework within the backstepping design to achieve both optimality and robustness.The actor performs control actions,while the critic assesses the optimal performance index function.To minimize this index function,an adaptive gradient descent method constructs both the actor and critic.Lyapunov analysis is employed to demonstrate that all signals in the closed-loop system are semiglobally uniformly ultimately bounded.Simulation results confirm that the proposed control strategy delivers high control performance,marked by improved accuracy and reduced energy consumption.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co-supported by the National Natural Science Foundation of China(Nos.62303380,62176214,62101590,62003268)。
文摘This paper introduces an optimized backstepping control method for Flexible Airbreathing Hypersonic Vehicles(FAHVs).The approach incorporates nonlinear disturbance observation and reinforcement learning to address complex control challenges.The Minimal Learning Parameter(MLP)technique is applied to manage unknown nonlinear dynamics,significantly reducing the computational load usually associated with Neural Network(NN)weight updates.To improve the control system robustness,an MLP-based nonlinear disturbance observer is designed,which estimates lumped disturbances,including flexibility effects,model uncertainties,and external disruptions within the FAHVs.In parallel,the control strategy integrates reinforcement learning using an MLP-based actor-critic framework within the backstepping design to achieve both optimality and robustness.The actor performs control actions,while the critic assesses the optimal performance index function.To minimize this index function,an adaptive gradient descent method constructs both the actor and critic.Lyapunov analysis is employed to demonstrate that all signals in the closed-loop system are semiglobally uniformly ultimately bounded.Simulation results confirm that the proposed control strategy delivers high control performance,marked by improved accuracy and reduced energy consumption.
