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
In this paper,an adaptive neural-network(NN)output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics,input saturation and state constraints.Neu...In this paper,an adaptive neural-network(NN)output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics,input saturation and state constraints.Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states.Under the framework of the backstepping design,by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function(BLF),the virtual and actual optimal controllers are developed.In order to accomplish optimal control effectively,a simplified reinforcement learning(RL)algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function,instead of employing existing optimal control methods.In addition,to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error,all state variables are confined within their compact sets all times.Finally,a simulation example is given to illustrate the effectiveness of the proposed control strategy.展开更多
The vessel heave motion caused by wave action increases the difficulty of installing offshore wind equipment.On-board wave heave compensation devices have therefore become increasingly critical in ensuring the stabili...The vessel heave motion caused by wave action increases the difficulty of installing offshore wind equipment.On-board wave heave compensation devices have therefore become increasingly critical in ensuring the stability and safety of the gangway and working platform.This study accordingly improves the compensation effect of such devices by developing a wave heave compensation model and designing an optimized backstepping control method.First,a model of the compensation system including the servo motor and electric cylinder is established by using the mechanism method.Second,a backstepping control method is designed to track the vessel heave motion,and particle swarm optimization is applied to optimize the control parameters.Finally,MATLAB/Simulink is used to simulate the application of the optimized backstepping controller,then regular and irregular heave motions are applied as input to a Stewart platform to evaluate the effectiveness of the control method.The experimental results show that the compensation efficiency provided by the proposed optimized backstepping control method is larger than 75.0%.展开更多
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.
基金This work was supported by National Natural Science Foundation of China(61822307,61773188).
文摘In this paper,an adaptive neural-network(NN)output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics,input saturation and state constraints.Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states.Under the framework of the backstepping design,by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function(BLF),the virtual and actual optimal controllers are developed.In order to accomplish optimal control effectively,a simplified reinforcement learning(RL)algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function,instead of employing existing optimal control methods.In addition,to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error,all state variables are confined within their compact sets all times.Finally,a simulation example is given to illustrate the effectiveness of the proposed control strategy.
基金supported by the National Natural Science Foundation of China(Grant No.62073213).
文摘The vessel heave motion caused by wave action increases the difficulty of installing offshore wind equipment.On-board wave heave compensation devices have therefore become increasingly critical in ensuring the stability and safety of the gangway and working platform.This study accordingly improves the compensation effect of such devices by developing a wave heave compensation model and designing an optimized backstepping control method.First,a model of the compensation system including the servo motor and electric cylinder is established by using the mechanism method.Second,a backstepping control method is designed to track the vessel heave motion,and particle swarm optimization is applied to optimize the control parameters.Finally,MATLAB/Simulink is used to simulate the application of the optimized backstepping controller,then regular and irregular heave motions are applied as input to a Stewart platform to evaluate the effectiveness of the control method.The experimental results show that the compensation efficiency provided by the proposed optimized backstepping control method is larger than 75.0%.
