A linear quadratic optimal direct track-keeping control law was proposed based on first-order Nomoto nominal model. Furthermore, based on Lyapunov stabilized theory, considering parametric uncertainty from variations ...A linear quadratic optimal direct track-keeping control law was proposed based on first-order Nomoto nominal model. Furthermore, based on Lyapunov stabilized theory, considering parametric uncertainty from variations of ship speed and disturbances uncertain from wind, wave and sea current, a direct compensative robust optimal control (DCROC) law was developed. It can guarantee closed-loop system globally and uniformly converge to a remained set. High accuracy and robustness were achieved. By introducing some nonlinear blocks, closed-loop system achieves global and uniform asymptotical stableness. Numerical simulations on a Mariner Class ship are presented to validate the control law.展开更多
The study investigates the necessary maximum principle for robust optimal control problems associated with quadratic backward stochastic differential equations(BSDEs).The system coefficients depend on parameter θ,whi...The study investigates the necessary maximum principle for robust optimal control problems associated with quadratic backward stochastic differential equations(BSDEs).The system coefficients depend on parameter θ,while the generator of BSDEs exhibits quadratic growth with respect to z.To address the uncertainty present in the model,the variational inequality is derived using weak convergence techniques.Additionally,due to the generator being quadratic with respect to z,the forward adjoint equations are stochastic differential equations with unbounded coefficients,involving mean oscillation martingales.By using the reverse Holder inequality and John-Nirenberg inequality,we demonstrate that the solutions are continuous with respect to parameter θ.Moreover,the necessary and sufficient conditions for robust optimal control are established using the linearization method.展开更多
We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of ...We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of the ions to create phonon-mediated entangling gates and,unlike the state of the art,requires neither weakcoupling Lamb-Dicke approximation nor perturbation treatment.With the application of gradient-based optimal control,it enables finding amplitude-and phase-modulated laser control protocols that work without the Lamb-Dicke approximation,promising gate speeds on the order of microseconds comparable to the characteristic trap frequencies.Also,robustness requirements on the temperature of the ions and initial optical phase can be conveniently included to pursue high-quality fast gates against experimental imperfections.Our approach represents a step in speeding up quantum gates to achieve larger quantum circuits for quantum computation and simulation,and thus can find applications in near-future experiments.展开更多
Anaerobic-anoxic-oxic(A^(2)O)reactors,as the core parts of wastewater treatment process(WWTP),have attracted considerable attention to achieve the reliability of denitrification and dephosphorization.However,it is dif...Anaerobic-anoxic-oxic(A^(2)O)reactors,as the core parts of wastewater treatment process(WWTP),have attracted considerable attention to achieve the reliability of denitrification and dephosphorization.However,it is difficult to realize the optimal operation of A^(2)O reactors due to the existence of nonlinear dynamics and large uncertainties.To solve this problem,a robust optimal control(ROC)strategy is developed to improve the operation performance of A^(2)O reactors.First,data-driven systematic evaluation criteria are developed to describe the operational indicators of changeable conditions.Second,a robust optimization algorithm is designed to select the optimal solution.Third,a fuzzy neural network(FNN)is used to track the optimal solution in the control process.Finally,this proposed ROC strategy is applied to the phosphorus removal benchmark simulation model(BSM1-P)and the real A^(2)O reactors.The results demonstrate that the strategy developed in this paper has great potential for application in real A^(2)O reactors.展开更多
A robust optimal output tracking control method for a midwater trawl system is investigated based on T-S fuzzy nonlinear model. A simplified nonlinear mathematical model is first employed to represent a midwater trawl...A robust optimal output tracking control method for a midwater trawl system is investigated based on T-S fuzzy nonlinear model. A simplified nonlinear mathematical model is first employed to represent a midwater trawl system, and then a T-S fuzzy model is adopted to approximate the nonlinear system. Since the strong nonlinearities and the external disturbance of the trawling system, a mixed H2/H∞ fuzzy output tracking control strategy via T-S fuzzy system is proposed to regulate the trawl depth to follow a desired trajectory. The trawl depth can be regulated by adjusting the winch velocity automatically and the tracking error can be minimized according to the robust optimal criterion. In order to validate the proposed control method, a computer simulation is conducted. The simulation results indicate that the proposed fuzzy robust optimal controller make the trawl net rapidly follow the desired trajectory under the model uncertainties and the extemal disturbance caused by wave and current.展开更多
Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach wa...Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.展开更多
This paper has investigated how the optimization methods can be used to deal with plant uncertainty in linear feedback control design. Firstly, we define a weighted sensitivity error function based on robust redesign...This paper has investigated how the optimization methods can be used to deal with plant uncertainty in linear feedback control design. Firstly, we define a weighted sensitivity error function based on robust redesign. Then, by modifying the nominal controller to minimize the variance of the actual system performanee from the desired performance over the whole frequency range, we obtain an optimal robust design method for a class of stochastic model errors. Moreover, the result can be used to give a good prediction to the achievable average tracking performance and control energy for practical system designs. The validity of obtained results can be illustrated by the simulation research.展开更多
Because of the tire nonlinearity and vehicle＇s parameters＇uncertainties,robust control methods based on the worst cases,such as H_∞,μsynthesis,have been widely used in active front steering control,however,in orde...Because of the tire nonlinearity and vehicle＇s parameters＇uncertainties,robust control methods based on the worst cases,such as H_∞,μsynthesis,have been widely used in active front steering control,however,in order to guarantee the stability of active front steering system（AFS）controller,the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control.In this paper,a generalized internal model robust control（GIMC）that can overcome the contradiction between performance and stability is used in the AFS control.In GIMC,the Youla parameterization is used in an improved way.And GIMC controller includes two sections：a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters＇uncertainties and some external disturbances.Simulations of double lane change（DLC）maneuver and that of braking on split-μroad are conducted to compare the performance and stability of the GIMC control,the nominal performance PID controller and the H_∞controller.Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle＇s parameters variations,H_∞controller is conservative so that the performance is a little low,and only the GIMC controller overcomes the contradiction between performance and robustness,which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller.Therefore,the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system,that is,can solve the instability of PID or LQP control methods and the low performance of the standard H_∞controller.展开更多
This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide...This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.展开更多
To realize the stabilization and the tracking of flight control for an air-breathing hypersonic cruise vehicle, the linearization of the longitudinal model under trimmed cruise condition is processed firstly. Furtherm...To realize the stabilization and the tracking of flight control for an air-breathing hypersonic cruise vehicle, the linearization of the longitudinal model under trimmed cruise condition is processed firstly. Furthermore, the flight control problem is formulated as a robust model tracking control problem. And then, based on the robust parametric approach, eigenstructure assignment and reference model tracking theory, a parametric optimization method for robust controller design is presented. The simulation results show the effectiveness of the proposed approach.展开更多
Path tracking performed by multiple chassis actuators remains a significant yet challenging issue in intelligent vehicles.This paper presents a robust optimal path tracking control for intelligent vehicles equipped wi...Path tracking performed by multiple chassis actuators remains a significant yet challenging issue in intelligent vehicles.This paper presents a robust optimal path tracking control for intelligent vehicles equipped with a novel wheel module.A wheel module,named corner drive system(CDS),is proposed by integrating drive,brake,steering,and suspension subsystems.In light of the multi-actuator-integrate characteristic of the CDS,a reconfigurable concept is adopted to carry out the vehicle dynamics modeling.To realize an invariance system for both parametric and nonparametric uncertainties,an integral sliding mode control(ISMC)scheme is devised for desired path tracking by combining the advantages of linear quadratic regulator(LQR)in optimization and SMC in robustness.The chattering problem of the ISMC is analyzed and two continuous controllers for chattering elimination are proposed.A control allocation strategy is proposed to dynamically assign the virtual inputs generated by ISMC among four wheels to optimize vehicle stability and minimize energy dissipation.The superiority of the proposed control scheme is demonstrated under different chassis layouts,road friction conditions,and benchmark controllers on a CarSim-based high-fidelity vehicle model with simulation comparison.Subsequently,the Hardware-in-the-Loop(HiL)test is carried out to further evaluate the feasibility and real-time control performance.The quantitative results demonstrate the path tracking performance of the proposed robust optimal controller under both parametric and nonparametric uncertainties.展开更多
A component synthesis vibration suppression (CSVS) method for flexible structures is put forward. It can eliminate any unwanted orders of flexible vibration modes while achieves desired rigid motion. This method has ...A component synthesis vibration suppression (CSVS) method for flexible structures is put forward. It can eliminate any unwanted orders of flexible vibration modes while achieves desired rigid motion. This method has robustness to uncertainty of frequency, which makes it practical in engineering. Several time optimal and time-fuel optimal control strategies are designed for a kind of single flexible link. Simulation results validate the feasibility of our method.展开更多
As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimiz...As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily movements.When these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the uncertainties.In order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory data.An online learning algorithm is provided in this paper,with rigorous convergence and stability analysis.Then,it is applied to simulate several experiments reported from the past literature.By comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental observations.In addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.展开更多
This paper investigates the optimal reinsurance-investment strategy for an insurer whose premium is subject to extrapolative bias.In other words,the insurance premium is dynamically updated by a weighted average of pr...This paper investigates the optimal reinsurance-investment strategy for an insurer whose premium is subject to extrapolative bias.In other words,the insurance premium is dynamically updated by a weighted average of prior claims and the initial estimation of claims.The insurer’s surplus follows a diffusion approximation process.He purchases proportional reinsurance or acquires new business to manage insurance risk,and invests his surplus in the financial market,containing a risk-free asset and a risky asset(stock).The price of the risky asset is described by a constant elasticity of variance(CEV)model.The insurer is uncertain about the models of claims and risky asset.In order to derive robust optimal reinsurance-investment strategies,we establish an optimal control problem by maximizing the insurer’s expected exponential utility of terminal wealth and solve the optimization problem explicitly.Finally,we present several numerical examples to illustrate our theoretical results.展开更多
基金Navy Engineering University Natural Science Foundation (NoHGDJJ05013)
文摘A linear quadratic optimal direct track-keeping control law was proposed based on first-order Nomoto nominal model. Furthermore, based on Lyapunov stabilized theory, considering parametric uncertainty from variations of ship speed and disturbances uncertain from wind, wave and sea current, a direct compensative robust optimal control (DCROC) law was developed. It can guarantee closed-loop system globally and uniformly converge to a remained set. High accuracy and robustness were achieved. By introducing some nonlinear blocks, closed-loop system achieves global and uniform asymptotical stableness. Numerical simulations on a Mariner Class ship are presented to validate the control law.
基金supported by the National Key R&D Program of China(Grant Nos.2022YFA1006101 and 2022YFA1006102)National Natural Science Foundation of China(Grant Nos.72171133,12101291,and 12371445)+3 种基金Natural Science Foundation of Shandong Province(Grant Nos.ZR2024MA039 and ZR2022MA029)Guangdong Basic and Applied Basic Research Foundation(Grant No.2025B1515020091)Shenzhen Fundamental Research General Program(Grant No.JCYJ20230807093309021)Science and Technology Commission of Shanghai Municipality(Grant No.22ZR1407600).
文摘The study investigates the necessary maximum principle for robust optimal control problems associated with quadratic backward stochastic differential equations(BSDEs).The system coefficients depend on parameter θ,while the generator of BSDEs exhibits quadratic growth with respect to z.To address the uncertainty present in the model,the variational inequality is derived using weak convergence techniques.Additionally,due to the generator being quadratic with respect to z,the forward adjoint equations are stochastic differential equations with unbounded coefficients,involving mean oscillation martingales.By using the reverse Holder inequality and John-Nirenberg inequality,we demonstrate that the solutions are continuous with respect to parameter θ.Moreover,the necessary and sufficient conditions for robust optimal control are established using the linearization method.
基金supported by the National Natural Science Foundation of China(Grant Nos.12441502,12122506,12204230,and 12404554)the National Science and Technology Major Project of the Ministry of Science and Technology of China(2024ZD0300404)+6 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.2021B1515020070)Shenzhen Science and Technology Program(Grant No.RCYX20200714114522109)China Postdoctoral Science Foundation(CPSF)(2024M762114)Postdoctoral Fellowship Program of CPSF(GZC20231727)supported by the National Natural Science Foundation of China(Grant Nos.92165206 and 11974330)Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301603)the Fundamental Research Funds for the Central Universities。
文摘We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of the ions to create phonon-mediated entangling gates and,unlike the state of the art,requires neither weakcoupling Lamb-Dicke approximation nor perturbation treatment.With the application of gradient-based optimal control,it enables finding amplitude-and phase-modulated laser control protocols that work without the Lamb-Dicke approximation,promising gate speeds on the order of microseconds comparable to the characteristic trap frequencies.Also,robustness requirements on the temperature of the ions and initial optical phase can be conveniently included to pursue high-quality fast gates against experimental imperfections.Our approach represents a step in speeding up quantum gates to achieve larger quantum circuits for quantum computation and simulation,and thus can find applications in near-future experiments.
基金supported by the National Key Research and Development Project(Grant No.2018YFC1900800-5)the National Natural Science Foundation of China(Grant Nos.61890930-5 and 61622301)+1 种基金Beijing Natural Science Foundation(Grant No.4172005)Beijing Outstanding Young Scientist Program(Grant No.BJJWZYJH01201910005020)。
文摘Anaerobic-anoxic-oxic(A^(2)O)reactors,as the core parts of wastewater treatment process(WWTP),have attracted considerable attention to achieve the reliability of denitrification and dephosphorization.However,it is difficult to realize the optimal operation of A^(2)O reactors due to the existence of nonlinear dynamics and large uncertainties.To solve this problem,a robust optimal control(ROC)strategy is developed to improve the operation performance of A^(2)O reactors.First,data-driven systematic evaluation criteria are developed to describe the operational indicators of changeable conditions.Second,a robust optimization algorithm is designed to select the optimal solution.Third,a fuzzy neural network(FNN)is used to track the optimal solution in the control process.Finally,this proposed ROC strategy is applied to the phosphorus removal benchmark simulation model(BSM1-P)and the real A^(2)O reactors.The results demonstrate that the strategy developed in this paper has great potential for application in real A^(2)O reactors.
基金supported by the National High-Technology Research and Development Program of China (863 Program,Grant No. 2008AA042703)
文摘A robust optimal output tracking control method for a midwater trawl system is investigated based on T-S fuzzy nonlinear model. A simplified nonlinear mathematical model is first employed to represent a midwater trawl system, and then a T-S fuzzy model is adopted to approximate the nonlinear system. Since the strong nonlinearities and the external disturbance of the trawling system, a mixed H2/H∞ fuzzy output tracking control strategy via T-S fuzzy system is proposed to regulate the trawl depth to follow a desired trajectory. The trawl depth can be regulated by adjusting the winch velocity automatically and the tracking error can be minimized according to the robust optimal criterion. In order to validate the proposed control method, a computer simulation is conducted. The simulation results indicate that the proposed fuzzy robust optimal controller make the trawl net rapidly follow the desired trajectory under the model uncertainties and the extemal disturbance caused by wave and current.
文摘Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.
文摘This paper has investigated how the optimization methods can be used to deal with plant uncertainty in linear feedback control design. Firstly, we define a weighted sensitivity error function based on robust redesign. Then, by modifying the nominal controller to minimize the variance of the actual system performanee from the desired performance over the whole frequency range, we obtain an optimal robust design method for a class of stochastic model errors. Moreover, the result can be used to give a good prediction to the achievable average tracking performance and control energy for practical system designs. The validity of obtained results can be illustrated by the simulation research.
基金Supported by National Natural Science Foundation of China(Grant Nos.11072106,51375009)
文摘Because of the tire nonlinearity and vehicle＇s parameters＇uncertainties,robust control methods based on the worst cases,such as H_∞,μsynthesis,have been widely used in active front steering control,however,in order to guarantee the stability of active front steering system（AFS）controller,the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control.In this paper,a generalized internal model robust control（GIMC）that can overcome the contradiction between performance and stability is used in the AFS control.In GIMC,the Youla parameterization is used in an improved way.And GIMC controller includes two sections：a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters＇uncertainties and some external disturbances.Simulations of double lane change（DLC）maneuver and that of braking on split-μroad are conducted to compare the performance and stability of the GIMC control,the nominal performance PID controller and the H_∞controller.Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle＇s parameters variations,H_∞controller is conservative so that the performance is a little low,and only the GIMC controller overcomes the contradiction between performance and robustness,which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller.Therefore,the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system,that is,can solve the instability of PID or LQP control methods and the low performance of the standard H_∞controller.
基金supported by the National Natural Science Foundation of China(61973105,62373137)。
文摘This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.
基金Sponsored by the Major Program of National Natural Science Foundation of China (Grant No.60710002)the Program for Changjiang Scholars and Innovative Research Team in University
文摘To realize the stabilization and the tracking of flight control for an air-breathing hypersonic cruise vehicle, the linearization of the longitudinal model under trimmed cruise condition is processed firstly. Furthermore, the flight control problem is formulated as a robust model tracking control problem. And then, based on the robust parametric approach, eigenstructure assignment and reference model tracking theory, a parametric optimization method for robust controller design is presented. The simulation results show the effectiveness of the proposed approach.
基金supported by the“Beijing Natural Science Foundation”under Grant L233039“Pioneer and Leading Goose R&D Program of Zhejiang”under Grant 2023C01133“Key R&D Program of Ningbo”under Grant 2023Z014.
文摘Path tracking performed by multiple chassis actuators remains a significant yet challenging issue in intelligent vehicles.This paper presents a robust optimal path tracking control for intelligent vehicles equipped with a novel wheel module.A wheel module,named corner drive system(CDS),is proposed by integrating drive,brake,steering,and suspension subsystems.In light of the multi-actuator-integrate characteristic of the CDS,a reconfigurable concept is adopted to carry out the vehicle dynamics modeling.To realize an invariance system for both parametric and nonparametric uncertainties,an integral sliding mode control(ISMC)scheme is devised for desired path tracking by combining the advantages of linear quadratic regulator(LQR)in optimization and SMC in robustness.The chattering problem of the ISMC is analyzed and two continuous controllers for chattering elimination are proposed.A control allocation strategy is proposed to dynamically assign the virtual inputs generated by ISMC among four wheels to optimize vehicle stability and minimize energy dissipation.The superiority of the proposed control scheme is demonstrated under different chassis layouts,road friction conditions,and benchmark controllers on a CarSim-based high-fidelity vehicle model with simulation comparison.Subsequently,the Hardware-in-the-Loop(HiL)test is carried out to further evaluate the feasibility and real-time control performance.The quantitative results demonstrate the path tracking performance of the proposed robust optimal controller under both parametric and nonparametric uncertainties.
基金This project is supported by National 211 Project.
文摘A component synthesis vibration suppression (CSVS) method for flexible structures is put forward. It can eliminate any unwanted orders of flexible vibration modes while achieves desired rigid motion. This method has robustness to uncertainty of frequency, which makes it practical in engineering. Several time optimal and time-fuel optimal control strategies are designed for a kind of single flexible link. Simulation results validate the feasibility of our method.
基金supported in part by the US National Science Foundation Grant Nos.ECCS-1101401 and ECCS-1230040
文摘As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily movements.When these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the uncertainties.In order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory data.An online learning algorithm is provided in this paper,with rigorous convergence and stability analysis.Then,it is applied to simulate several experiments reported from the past literature.By comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental observations.In addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.
基金supported by National Natural Science Foundation of China[72171056].
文摘This paper investigates the optimal reinsurance-investment strategy for an insurer whose premium is subject to extrapolative bias.In other words,the insurance premium is dynamically updated by a weighted average of prior claims and the initial estimation of claims.The insurer’s surplus follows a diffusion approximation process.He purchases proportional reinsurance or acquires new business to manage insurance risk,and invests his surplus in the financial market,containing a risk-free asset and a risky asset(stock).The price of the risky asset is described by a constant elasticity of variance(CEV)model.The insurer is uncertain about the models of claims and risky asset.In order to derive robust optimal reinsurance-investment strategies,we establish an optimal control problem by maximizing the insurer’s expected exponential utility of terminal wealth and solve the optimization problem explicitly.Finally,we present several numerical examples to illustrate our theoretical results.
