In this paper,we present a novel adaptive critic design with a finite excitation(FE)condition for adaptive optimal control of continuous-time nonlinear systems.The online recorded data is combined with instantaneous d...In this paper,we present a novel adaptive critic design with a finite excitation(FE)condition for adaptive optimal control of continuous-time nonlinear systems.The online recorded data is combined with instantaneous data via a hierarchical design scheme to replace the persistence of excitation condition with a FE condition.The online data is recorded in the preprocessing step and verified online,whereas the novel critic is implemented in the assembling step through appropriate algebraic calculations.On this basis,the composite adaptive critic design is developed with satisfactory convergence.The adaptive critic design can be implemented in an online fashion with the hierarchical design scheme,and the FE condition can also be online verifiable.It is shown that the composite adaptive critic design guarantees the closed-loop stability of the equilibrium point and convergence to the optimal solution.Simulations are conducted to show the efficacy of the composite adaptive critic design.展开更多
In this paper,a composite adaptive fault-tolerant control strategy is proposed for a quadrotor unmanned aerial vehicle(UAV)to simultaneously compensate actuator faults,model uncertainties and external disturbances.By ...In this paper,a composite adaptive fault-tolerant control strategy is proposed for a quadrotor unmanned aerial vehicle(UAV)to simultaneously compensate actuator faults,model uncertainties and external disturbances.By assuming knowledge of the bounds on external disturbances,a baseline sliding mode control is first designed to achieve the desired system tracking performance and retain insensitive to disturbances.Then,regarding actuator faults and model uncertainties of the quadrotor UAV,neural adaptive control schemes are constructed and incorporated into the baseline sliding mode control to deal with them.Moreover,in terms of unknown external disturbances,a disturbance observer is designed and synthesized with the control law to further improve the robustness of the proposed control strategy.Finally,a series of comparative simulation tests are conducted to validate the effectiveness of the proposed control strategy where a quadrotor UAV is subject to inertial moment variations and different level of actuator faults.The capabilities and advantages of the proposed control strategy are confirmed and verified by simulation results.展开更多
The wide applications of Generative adversarial networks benefit from the successful training methods,guaranteeing that an object function converges to the local minimum.Nevertheless,designing an efficient and competi...The wide applications of Generative adversarial networks benefit from the successful training methods,guaranteeing that an object function converges to the local minimum.Nevertheless,designing an efficient and competitive training method is still a challenging task due to the cyclic behaviors of some gradient-based ways and the expensive computational cost of acquiring the Hessian matrix.To address this problem,we proposed the Adaptive Composite Gradients(ACG)method,linearly convergent in bilinear games under suitable settings.Theory analysis and toy-function experiments both suggest that our approach alleviates the cyclic behaviors and converges faster than recently proposed SOTA algorithms.The convergence speed of the ACG is improved by 33%than other methods.Our ACG method is a novel Semi-Gradient-Free algorithm that can reduce the computational cost of gradient and Hessian by utilizing the predictive information in future iterations.The mixture of Gaussians experiments and real-world digital image generative experiments show that our ACG method outperforms several existing technologies,illustrating the superiority and efficacy of our method.展开更多
This paper investigates the disturbance observer based actor-critic learning control for a class of uncertain nonlinear systems in the presence of unmodeled dynamics and time-varying disturbances.The proposed control ...This paper investigates the disturbance observer based actor-critic learning control for a class of uncertain nonlinear systems in the presence of unmodeled dynamics and time-varying disturbances.The proposed control algorithm integrates a filter-based design method with actor-critic learning architecture and disturbance observer to circumvent the unmodeled dynamic and the timevarying disturbance.To be specific,the actor network is employed to estimate the unknown system dynamic,the critic network is developed to evaluate the control performance,and the disturbance observer is leveraged to provide efficient estimation of the compounded disturbance which includes the time-varying disturbance and the actor-critic network approximation error.Consequently,highgain feedback is avoided and the improved tracking performance can be expected.Moreover,a composite weight adaptation law for actor network is constructed by utilizing two types of signals,the cost function and the modeling error.Eventually,theoretical analysis demonstrates that the developed controller can guarantee bounded stability.Extensive simulations and experiments on a robot manipulator are implemented to validate the performance of the resulted control strategy.展开更多
Composite lattice anisogrid adapters are highly favored in space rocketry design,serving as crucial interface structures between rocket stages or between the payload and its supporting structure.Their unique structura...Composite lattice anisogrid adapters are highly favored in space rocketry design,serving as crucial interface structures between rocket stages or between the payload and its supporting structure.Their unique structural configuration allows them to withstand significant weight loads without succumbing to buckling.However,optimizing their design parameters could further enhance their strength and efficiency.Particularly,reinforcing the lower hoop ribs in a conical lattice adapter can substantially enhance its strength under axial compressive loads,thus preventing buckling.In this study,we begin by presenting a finite-element model of a lattice adapter featuring helical ribs that follow geodesic paths.To validate the model’s accuracy,experimental prototypes and finite-element models from previous research are utilized.Subsequently,a neural network model is trained using the dataset generated from finite-element analysis results.This neural network model aims to predict,explore,and optimize the impact of lower hoop ribs’thicknesses on the critical axial buckling load of the adapter.The analysis ultimately confirms that an adapter designed with optimized ribs demonstrates a remarkable 51% increase in load capacity before buckling compared to an adapter designed with uniform ribs.This underscores the significance of optimizing design parameters for enhancing structural performance in space rocketry applications.展开更多
基金supported in part by the National Natural Science Foundation of China(62373039)the National Science Foundation(CPS2227185,CPS2038589)
文摘In this paper,we present a novel adaptive critic design with a finite excitation(FE)condition for adaptive optimal control of continuous-time nonlinear systems.The online recorded data is combined with instantaneous data via a hierarchical design scheme to replace the persistence of excitation condition with a FE condition.The online data is recorded in the preprocessing step and verified online,whereas the novel critic is implemented in the assembling step through appropriate algebraic calculations.On this basis,the composite adaptive critic design is developed with satisfactory convergence.The adaptive critic design can be implemented in an online fashion with the hierarchical design scheme,and the FE condition can also be online verifiable.It is shown that the composite adaptive critic design guarantees the closed-loop stability of the equilibrium point and convergence to the optimal solution.Simulations are conducted to show the efficacy of the composite adaptive critic design.
基金partially supported by the National Natural Science Foundation of China under Grant Nos.62003266 and 61833013the Fundamental Research Funds for the Central Universities under Grant No.G2019KY05103the Natural Sciences and Engineering Research Council of Canada。
文摘In this paper,a composite adaptive fault-tolerant control strategy is proposed for a quadrotor unmanned aerial vehicle(UAV)to simultaneously compensate actuator faults,model uncertainties and external disturbances.By assuming knowledge of the bounds on external disturbances,a baseline sliding mode control is first designed to achieve the desired system tracking performance and retain insensitive to disturbances.Then,regarding actuator faults and model uncertainties of the quadrotor UAV,neural adaptive control schemes are constructed and incorporated into the baseline sliding mode control to deal with them.Moreover,in terms of unknown external disturbances,a disturbance observer is designed and synthesized with the control law to further improve the robustness of the proposed control strategy.Finally,a series of comparative simulation tests are conducted to validate the effectiveness of the proposed control strategy where a quadrotor UAV is subject to inertial moment variations and different level of actuator faults.The capabilities and advantages of the proposed control strategy are confirmed and verified by simulation results.
基金This work is supported by the National Key Research and Development Program of China(No.2018AAA0101001)Science and Technology Commission of Shanghai Municipality(No.20511100200)supported in part by the Science and Technology Commission of Shanghai Municipality(No.18dz2271000).
文摘The wide applications of Generative adversarial networks benefit from the successful training methods,guaranteeing that an object function converges to the local minimum.Nevertheless,designing an efficient and competitive training method is still a challenging task due to the cyclic behaviors of some gradient-based ways and the expensive computational cost of acquiring the Hessian matrix.To address this problem,we proposed the Adaptive Composite Gradients(ACG)method,linearly convergent in bilinear games under suitable settings.Theory analysis and toy-function experiments both suggest that our approach alleviates the cyclic behaviors and converges faster than recently proposed SOTA algorithms.The convergence speed of the ACG is improved by 33%than other methods.Our ACG method is a novel Semi-Gradient-Free algorithm that can reduce the computational cost of gradient and Hessian by utilizing the predictive information in future iterations.The mixture of Gaussians experiments and real-world digital image generative experiments show that our ACG method outperforms several existing technologies,illustrating the superiority and efficacy of our method.
基金supported by the National Key R&D Program of China(No.2021YFB2011300)the National Natural Science Foundation of China(No.52075262).
文摘This paper investigates the disturbance observer based actor-critic learning control for a class of uncertain nonlinear systems in the presence of unmodeled dynamics and time-varying disturbances.The proposed control algorithm integrates a filter-based design method with actor-critic learning architecture and disturbance observer to circumvent the unmodeled dynamic and the timevarying disturbance.To be specific,the actor network is employed to estimate the unknown system dynamic,the critic network is developed to evaluate the control performance,and the disturbance observer is leveraged to provide efficient estimation of the compounded disturbance which includes the time-varying disturbance and the actor-critic network approximation error.Consequently,highgain feedback is avoided and the improved tracking performance can be expected.Moreover,a composite weight adaptation law for actor network is constructed by utilizing two types of signals,the cost function and the modeling error.Eventually,theoretical analysis demonstrates that the developed controller can guarantee bounded stability.Extensive simulations and experiments on a robot manipulator are implemented to validate the performance of the resulted control strategy.
文摘Composite lattice anisogrid adapters are highly favored in space rocketry design,serving as crucial interface structures between rocket stages or between the payload and its supporting structure.Their unique structural configuration allows them to withstand significant weight loads without succumbing to buckling.However,optimizing their design parameters could further enhance their strength and efficiency.Particularly,reinforcing the lower hoop ribs in a conical lattice adapter can substantially enhance its strength under axial compressive loads,thus preventing buckling.In this study,we begin by presenting a finite-element model of a lattice adapter featuring helical ribs that follow geodesic paths.To validate the model’s accuracy,experimental prototypes and finite-element models from previous research are utilized.Subsequently,a neural network model is trained using the dataset generated from finite-element analysis results.This neural network model aims to predict,explore,and optimize the impact of lower hoop ribs’thicknesses on the critical axial buckling load of the adapter.The analysis ultimately confirms that an adapter designed with optimized ribs demonstrates a remarkable 51% increase in load capacity before buckling compared to an adapter designed with uniform ribs.This underscores the significance of optimizing design parameters for enhancing structural performance in space rocketry applications.
