This article develops a novel data-driven safe Q-learning method to design the safe optimal controller which can guarantee constrained states of nonlinear systems always stay in the safe region while providing an opti...This article develops a novel data-driven safe Q-learning method to design the safe optimal controller which can guarantee constrained states of nonlinear systems always stay in the safe region while providing an optimal performance.First,we design an augmented utility function consisting of an adjustable positive definite control obstacle function and a quadratic form of the next state to ensure the safety and optimality.Second,by exploiting a pre-designed admissible policy for initialization,an off-policy stabilizing value iteration Q-learning(SVIQL)algorithm is presented to seek the safe optimal policy by using offline data within the safe region rather than the mathematical model.Third,the monotonicity,safety,and optimality of the SVIQL algorithm are theoretically proven.To obtain the initial admissible policy for SVIQL,an offline VIQL algorithm with zero initialization is constructed and a new admissibility criterion is established for immature iterative policies.Moreover,the critic and action networks with precise approximation ability are established to promote the operation of VIQL and SVIQL algorithms.Finally,three simulation experiments are conducted to demonstrate the virtue and superiority of the developed safe Q-learning method.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 stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（ga...The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（gain-scheduled） state feedback control scheme is built to stabilize the constrained timevarying system. The design problem is transformed to a series of convex feasibility problems which can be solved efficiently. A design example is given to illustrate the effect of the proposed algorithm.展开更多
In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertaintie...In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.展开更多
A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solvin...A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.展开更多
The attitude synchronization problem for multiple spacecraft with input constraints is investigated in this paper. Two distributed control laws are presented and analyzed. First, by intro- ducing bounded function, a d...The attitude synchronization problem for multiple spacecraft with input constraints is investigated in this paper. Two distributed control laws are presented and analyzed. First, by intro- ducing bounded function, a distributed asymptotically stable control law is proposed. Such a con- trol scheme can guarantee attitude synchronization and the control inputs of each spacecraft can be a priori bounded regardless of the number of its neighbors. Then, based on graph theory, homoge- neous method, and Lyapunov stability theory, a distributed finite-time control law is designed. Rig- orous proof shows that attitude synchronization of multiple spacecraft can be achieved in finite time, and the control scheme satisfies input saturation requirement. Finally, numerical simulations are presented to demonstrate the effectiveness and feasibility of the oroDosed schemes.展开更多
In recent years,with the continuous development of multi-agent technology represented by unmanned aerial vehicle(UAV)swarm,consensus control has become a hot spot in academic research.In this paper,we put forward a di...In recent years,with the continuous development of multi-agent technology represented by unmanned aerial vehicle(UAV)swarm,consensus control has become a hot spot in academic research.In this paper,we put forward a discrete-time consensus protocol and obtain the necessary and sufficient conditions for the second-order consensus of the second-order multi-agent system with a fixed structure under the condition of no saturation input.The theoretical derivation verifies that the two eigenvalues of the Laplacian of the communication network matrix and the sampling period have an important effect on achieving consensus.Then we construct and verify sufficient conditions to achieve consensus under the condition of input saturation constraints.The results show that consensus can be achieved if velocity,position gain,and sampling period satisfy a set of inequalities related to the eigenvalues of the Laplacian matrix.Finally,the accuracy and validity of the theoretical results are proved by numerical simulations.展开更多
In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model sw...In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.展开更多
In this article,we develop an online robust actor-critic-disturbance guidance law for a missile-target interception system with limited normal acceleration capability.Firstly,the missiletarget engagement is formulated...In this article,we develop an online robust actor-critic-disturbance guidance law for a missile-target interception system with limited normal acceleration capability.Firstly,the missiletarget engagement is formulated as a zero-sum pursuit-evasion game problem.The key is to seek the saddle point solution of the Hamilton Jacobi Isaacs(HJI)equation,which is generally intractable due to the nonlinearity of the problem.Then,based on the universal approximation capability of Neural Networks(NNs),we construct the critic NN,the actor NN and the disturbance NN,respectively.The Bellman error is adjusted by the normalized-least square method.The proposed scheme is proved to be Uniformly Ultimately Bounded(UUB)stable by Lyapunov method.Finally,the effectiveness and robustness of the developed method are illustrated through numerical simulations against different types of non-stationary targets and initial conditions.展开更多
In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in ord...In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints.Then,we develop a Hamilton-Jacobi-Bellman equation(HJBE),which arises in the discounted cost optimal control problem.To obtain the optimal neurocontroller,we utilize a critic neural network(CNN)to solve the HJBE under the framework of reinforcement learning.The CNN's weight vector is tuned via the gradient descent approach.Based on the Lyapunov method,we prove that uniform ultimate boundedness of the CNN's weight vector and the closed-loop system is guaranteed.Finally,we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.展开更多
This paper studies an online iterative algorithm for solving discrete-time multi-agent dynamic graphical games with input constraints.In order to obtain the optimal strategy of each agent,it is necessary to solve a se...This paper studies an online iterative algorithm for solving discrete-time multi-agent dynamic graphical games with input constraints.In order to obtain the optimal strategy of each agent,it is necessary to solve a set of coupled Hamilton-Jacobi-Bellman(HJB)equations.It is very difficult to solve HJB equations by the traditional method.The relevant game problem will become more complex if the control input of each agent in the dynamic graphical game is constrained.In this paper,an online iterative algorithm is proposed to find the online solution to dynamic graphical game without the need for drift dynamics of agents.Actually,this algorithm is to find the optimal solution of Bellman equations online.This solution employs a distributed policy iteration process,using only the local information available to each agent.It can be proved that under certain conditions,when each agent updates its own strategy simultaneously,the whole multi-agent system will reach Nash equilibrium.In the process of algorithm implementation,for each agent,two layers of neural networks are used to fit the value function and control strategy,respectively.Finally,a simulation example is given to show the effectiveness of our method.展开更多
In recent years, cyber attacks have posed great challenges to the development of cyber-physical systems. It is of great significance to study secure state estimation methods to ensure the safe and stable operation of ...In recent years, cyber attacks have posed great challenges to the development of cyber-physical systems. It is of great significance to study secure state estimation methods to ensure the safe and stable operation of the system. This paper proposes a secure state estimation for multi-input and multi-output continuous-time linear cyber-physical systems with sparse actuator and sensor attacks. First, for sparse sensor attacks, we propose an adaptive switching mechanism to mitigate the impact of sparse sensor attacks by filtering out their attack modes. Second, an unknown input sliding mode observer is designed to not only observe the system states, sensor attack signals, and measurement noise present in the system but also counteract the effects of sparse actuator attacks through an unknown input matrix. Finally, for the design of an unknown input sliding mode state observer, the feasibility of the observing system is demonstrated by means of Lyapunov functions. Additionally, simulation experiments are conducted to show the effectiveness of this method.展开更多
This paper investigates the overload stabilization problem of the rolling-missile subject to parameters uncertainty and actuator saturation. In order to solve this problem, a sliding-mode control(SMC) scheme is techni...This paper investigates the overload stabilization problem of the rolling-missile subject to parameters uncertainty and actuator saturation. In order to solve this problem, a sliding-mode control(SMC) scheme is technically employed by using the backstepping approach to make the dynamic system stable. In addition,SMC with the tanh-type switching function plays an important role in reducing intrinsic vibration. Furthermore, an auxiliary system(AS) is developed to compensate for nonlinear terms arising from input saturation. Finally, the simulation results provide a solution to demonstrate that the suggested SMC and the AS methodology have advantages of strong tracking capability, anti-interference ability and anti-saturation performance.展开更多
Rice yield is still low in Nigeria despite the ecological advantages. Several challenges has been traced it production. The study intend to investigate if other rice producing nations are faced with similar challenges...Rice yield is still low in Nigeria despite the ecological advantages. Several challenges has been traced it production. The study intend to investigate if other rice producing nations are faced with similar challenges and at what magnitude and more importantly, what can be learn to improve the rice yield in Nigeria. Based on 2013/2014 survey, a total sample of 400 famers were randomly interviewed;164 from Niger State of Nigeria and 236 from Hainan province of China. The study collate the perception of farmers to rice production constraints categorized into biotic, abiotic and socioeconomics. Biplot analysis was employed to examine multivariate pattern of their perceptions towards production constraints. The multivariate technique simultaneously displaying different yield levels and factor constraints in data matrix providing the inter-unit distances, variance and correlations of variables. According to the study, Niger state farmers identified socioeconomic constraint as the major factors to production and attributed it to lack of or insufficient investment while the Hainan farmers majorly identified abiotic constraints. The study also indicated that great potential remain to further improve rice yield in both regions especially in Nigeria given the appropriate investment on essential inputs. This study is of great use to extension officers more so, given the investment in Africa, policy makers take advantage of the bilateral and multilateral relationship to invest ease transfer of agricultural information and technologies between or among partners.展开更多
The optimal control problems of hyperbolic H-hemivariational inequalities with the state constraints and nonnomotone multivalued mapping term are considered.The optimal solutions are obtained.In addition,their approxi...The optimal control problems of hyperbolic H-hemivariational inequalities with the state constraints and nonnomotone multivalued mapping term are considered.The optimal solutions are obtained.In addition,their approximating problems are also studied.展开更多
A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which i...A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.展开更多
In this paper,the neural network-based adaptive decentralized learning control is investigated for nonlinear interconnected systems with input constraints.Because the decentralized control of interconnected systems is...In this paper,the neural network-based adaptive decentralized learning control is investigated for nonlinear interconnected systems with input constraints.Because the decentralized control of interconnected systems is related to the optimal control of each isolated subsystem,the decentralized control strategy can be established by a series of optimal control policies.A novel policy iteration algorithm is presented to solve the Hamilton–Jacobi–Bellman equation related to the optimal control problem.This algorithm is implemented under the actor-critic structure where both neural networks are simultaneously updated to approximate the optimal control policy and the optimal cost function,respectively.The additional stabilizing term is introduced and an improved weight updating law is derived,which relaxes the requirement of initial admissible control policy.Besides,the input constraints of interconnected systems are taken into account and the Hamilton–Jacobi–Bellman equation is solved in the presence of input constraints.The interconnected system states and the weight approximation errors of two neural networks are proven to be uniformly ultimately bounded by utilizing Lyapunov theory.Finally,the effectiveness of the proposed decentralized learning control method is verified by simulation results.展开更多
This paper addresses the distributed optimization problem of discrete-time multiagent systems with nonconvex control input constraints and switching topologies.We introduce a novel distributed optimization algorithm w...This paper addresses the distributed optimization problem of discrete-time multiagent systems with nonconvex control input constraints and switching topologies.We introduce a novel distributed optimization algorithm with a switching mechanism to guarantee that all agents eventually converge to an optimal solution point,while their control inputs are constrained in their own nonconvex region.It is worth noting that the mechanism is performed to tackle the coexistence of the nonconvex constraint operator and the optimization gradient term.Based on the dynamic transformation technique,the original nonlinear dynamic system is transformed into an equivalent one with a nonlinear error term.By utilizing the nonnegative matrix theory,it is shown that the optimization problem can be solved when the union of switching communication graphs is jointly strongly connected.Finally,a numerical simulation example is used to demonstrate the acquired theoretical results.展开更多
This paper proposes a backstepping technique and Multi-dimensional Taylor Polynomial Networks(MTPN)based adaptive attitude tracking control strategy for Near Space Vehicles(NSVs)subjected to input constraints and stoc...This paper proposes a backstepping technique and Multi-dimensional Taylor Polynomial Networks(MTPN)based adaptive attitude tracking control strategy for Near Space Vehicles(NSVs)subjected to input constraints and stochastic input noises.Firstly,considering the control input has stochastic noises,and the attitude motion dynamical model of the NSVs is actually modeled as the Multi-Input Multi-Output(MIMO)stochastic nonlinear system form.Furthermore,the MTPN is used to estimate the unknown system uncertainties,and an auxiliary system is designed to compensate the influence of the saturation control input.Then,by using backstepping method and the output of the auxiliary system,a MTPN-based robust adaptive attitude control approach is proposed for the NSVs with saturation input nonlinearity,stochastic input noises,and system uncertainties.Stochastic Lyapunov stability theory is utilized to analysis the stability in the sense of probability of the entire closed-loop system.Additionally,by selecting appropriate parameters,the tracking errors will converge to a small neighborhood with a tunable radius.Finally,the numerical simulation results of the NSVs attitude motion show the satisfactory flight control performance under the proposed tracking control strategy.展开更多
基金supported in part by the National Science and Technology Major Project(2021ZD0112302)the National Natural Science Foundation of China(62222301,61890930-5,62021003)。
文摘This article develops a novel data-driven safe Q-learning method to design the safe optimal controller which can guarantee constrained states of nonlinear systems always stay in the safe region while providing an optimal performance.First,we design an augmented utility function consisting of an adjustable positive definite control obstacle function and a quadratic form of the next state to ensure the safety and optimality.Second,by exploiting a pre-designed admissible policy for initialization,an off-policy stabilizing value iteration Q-learning(SVIQL)algorithm is presented to seek the safe optimal policy by using offline data within the safe region rather than the mathematical model.Third,the monotonicity,safety,and optimality of the SVIQL algorithm are theoretically proven.To obtain the initial admissible policy for SVIQL,an offline VIQL algorithm with zero initialization is constructed and a new admissibility criterion is established for immature iterative policies.Moreover,the critic and action networks with precise approximation ability are established to promote the operation of VIQL and SVIQL algorithms.Finally,three simulation experiments are conducted to demonstrate the virtue and superiority of the developed safe Q-learning method.
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(6132106261503100)the China Postdoctoral Science Foundation(2014M550189)
文摘The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（gain-scheduled） state feedback control scheme is built to stabilize the constrained timevarying system. The design problem is transformed to a series of convex feasibility problems which can be solved efficiently. A design example is given to illustrate the effect of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(61803085,61806052,U1713209)the Natural Science Foundation of Jiangsu Province of China(BK20180361)
文摘In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.
基金Supported by the National Natural Science Foundation of China (No.60374037, No.60574036), the Program for New Century Excellent Talents in University of China (NCET), and the Specialized Research Fund for the Doctoral Program of Higher Edu-cation of China (No.20050055013).
文摘A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.
基金supported by the Natural Science Foundation of Heilongjiang Province (No. F201326)
文摘The attitude synchronization problem for multiple spacecraft with input constraints is investigated in this paper. Two distributed control laws are presented and analyzed. First, by intro- ducing bounded function, a distributed asymptotically stable control law is proposed. Such a con- trol scheme can guarantee attitude synchronization and the control inputs of each spacecraft can be a priori bounded regardless of the number of its neighbors. Then, based on graph theory, homoge- neous method, and Lyapunov stability theory, a distributed finite-time control law is designed. Rig- orous proof shows that attitude synchronization of multiple spacecraft can be achieved in finite time, and the control scheme satisfies input saturation requirement. Finally, numerical simulations are presented to demonstrate the effectiveness and feasibility of the oroDosed schemes.
基金supported by the National Natural Science Foundation of China(61703427).
文摘In recent years,with the continuous development of multi-agent technology represented by unmanned aerial vehicle(UAV)swarm,consensus control has become a hot spot in academic research.In this paper,we put forward a discrete-time consensus protocol and obtain the necessary and sufficient conditions for the second-order consensus of the second-order multi-agent system with a fixed structure under the condition of no saturation input.The theoretical derivation verifies that the two eigenvalues of the Laplacian of the communication network matrix and the sampling period have an important effect on achieving consensus.Then we construct and verify sufficient conditions to achieve consensus under the condition of input saturation constraints.The results show that consensus can be achieved if velocity,position gain,and sampling period satisfy a set of inequalities related to the eigenvalues of the Laplacian matrix.Finally,the accuracy and validity of the theoretical results are proved by numerical simulations.
基金supported by the Aeronautics Science Foundation of China(No.2007ZC52039)the National Natural Science Foundation of China(No.90816023)
文摘In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.
基金partially supported by the National Natural Science Foundation of China(Nos.61203095,61403407)。
文摘In this article,we develop an online robust actor-critic-disturbance guidance law for a missile-target interception system with limited normal acceleration capability.Firstly,the missiletarget engagement is formulated as a zero-sum pursuit-evasion game problem.The key is to seek the saddle point solution of the Hamilton Jacobi Isaacs(HJI)equation,which is generally intractable due to the nonlinearity of the problem.Then,based on the universal approximation capability of Neural Networks(NNs),we construct the critic NN,the actor NN and the disturbance NN,respectively.The Bellman error is adjusted by the normalized-least square method.The proposed scheme is proved to be Uniformly Ultimately Bounded(UUB)stable by Lyapunov method.Finally,the effectiveness and robustness of the developed method are illustrated through numerical simulations against different types of non-stationary targets and initial conditions.
基金supported by the National Natural Science Foundation of China(61973228,61973330)
文摘In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints.Then,we develop a Hamilton-Jacobi-Bellman equation(HJBE),which arises in the discounted cost optimal control problem.To obtain the optimal neurocontroller,we utilize a critic neural network(CNN)to solve the HJBE under the framework of reinforcement learning.The CNN's weight vector is tuned via the gradient descent approach.Based on the Lyapunov method,we prove that uniform ultimate boundedness of the CNN's weight vector and the closed-loop system is guaranteed.Finally,we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.
基金supported by the National Natural Science Foundation of China(Nos.61773241,61973183)the Shandong Provincial Natural Science Foundation(No.ZR2019MF041).
文摘This paper studies an online iterative algorithm for solving discrete-time multi-agent dynamic graphical games with input constraints.In order to obtain the optimal strategy of each agent,it is necessary to solve a set of coupled Hamilton-Jacobi-Bellman(HJB)equations.It is very difficult to solve HJB equations by the traditional method.The relevant game problem will become more complex if the control input of each agent in the dynamic graphical game is constrained.In this paper,an online iterative algorithm is proposed to find the online solution to dynamic graphical game without the need for drift dynamics of agents.Actually,this algorithm is to find the optimal solution of Bellman equations online.This solution employs a distributed policy iteration process,using only the local information available to each agent.It can be proved that under certain conditions,when each agent updates its own strategy simultaneously,the whole multi-agent system will reach Nash equilibrium.In the process of algorithm implementation,for each agent,two layers of neural networks are used to fit the value function and control strategy,respectively.Finally,a simulation example is given to show the effectiveness of our method.
基金supported by the National Science Foundation of China(Nos.62271293,61903238)the Natural Science Foundation of Shandong Province,China(No.ZR2021MF035)the Social Science Planning Project of Shandong Province,China(No.22CYYJ13).
文摘In recent years, cyber attacks have posed great challenges to the development of cyber-physical systems. It is of great significance to study secure state estimation methods to ensure the safe and stable operation of the system. This paper proposes a secure state estimation for multi-input and multi-output continuous-time linear cyber-physical systems with sparse actuator and sensor attacks. First, for sparse sensor attacks, we propose an adaptive switching mechanism to mitigate the impact of sparse sensor attacks by filtering out their attack modes. Second, an unknown input sliding mode observer is designed to not only observe the system states, sensor attack signals, and measurement noise present in the system but also counteract the effects of sparse actuator attacks through an unknown input matrix. Finally, for the design of an unknown input sliding mode state observer, the feasibility of the observing system is demonstrated by means of Lyapunov functions. Additionally, simulation experiments are conducted to show the effectiveness of this method.
基金supported by the Fundamental Research Funds for the Central Universities (30919011401)。
文摘This paper investigates the overload stabilization problem of the rolling-missile subject to parameters uncertainty and actuator saturation. In order to solve this problem, a sliding-mode control(SMC) scheme is technically employed by using the backstepping approach to make the dynamic system stable. In addition,SMC with the tanh-type switching function plays an important role in reducing intrinsic vibration. Furthermore, an auxiliary system(AS) is developed to compensate for nonlinear terms arising from input saturation. Finally, the simulation results provide a solution to demonstrate that the suggested SMC and the AS methodology have advantages of strong tracking capability, anti-interference ability and anti-saturation performance.
文摘Rice yield is still low in Nigeria despite the ecological advantages. Several challenges has been traced it production. The study intend to investigate if other rice producing nations are faced with similar challenges and at what magnitude and more importantly, what can be learn to improve the rice yield in Nigeria. Based on 2013/2014 survey, a total sample of 400 famers were randomly interviewed;164 from Niger State of Nigeria and 236 from Hainan province of China. The study collate the perception of farmers to rice production constraints categorized into biotic, abiotic and socioeconomics. Biplot analysis was employed to examine multivariate pattern of their perceptions towards production constraints. The multivariate technique simultaneously displaying different yield levels and factor constraints in data matrix providing the inter-unit distances, variance and correlations of variables. According to the study, Niger state farmers identified socioeconomic constraint as the major factors to production and attributed it to lack of or insufficient investment while the Hainan farmers majorly identified abiotic constraints. The study also indicated that great potential remain to further improve rice yield in both regions especially in Nigeria given the appropriate investment on essential inputs. This study is of great use to extension officers more so, given the investment in Africa, policy makers take advantage of the bilateral and multilateral relationship to invest ease transfer of agricultural information and technologies between or among partners.
文摘The optimal control problems of hyperbolic H-hemivariational inequalities with the state constraints and nonnomotone multivalued mapping term are considered.The optimal solutions are obtained.In addition,their approximating problems are also studied.
文摘A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.
基金This work was supported by the National Key R&D Program of China(No.2018AAA0101400)the National Natural Science Foundation of China(Nos.62022061,61921004).
文摘In this paper,the neural network-based adaptive decentralized learning control is investigated for nonlinear interconnected systems with input constraints.Because the decentralized control of interconnected systems is related to the optimal control of each isolated subsystem,the decentralized control strategy can be established by a series of optimal control policies.A novel policy iteration algorithm is presented to solve the Hamilton–Jacobi–Bellman equation related to the optimal control problem.This algorithm is implemented under the actor-critic structure where both neural networks are simultaneously updated to approximate the optimal control policy and the optimal cost function,respectively.The additional stabilizing term is introduced and an improved weight updating law is derived,which relaxes the requirement of initial admissible control policy.Besides,the input constraints of interconnected systems are taken into account and the Hamilton–Jacobi–Bellman equation is solved in the presence of input constraints.The interconnected system states and the weight approximation errors of two neural networks are proven to be uniformly ultimately bounded by utilizing Lyapunov theory.Finally,the effectiveness of the proposed decentralized learning control method is verified by simulation results.
基金Project supported by the National Engineering Research Center of Rail Transportation Operation and Control System,Beijing Jiaotong University(Grant No.NERC2019K002)。
文摘This paper addresses the distributed optimization problem of discrete-time multiagent systems with nonconvex control input constraints and switching topologies.We introduce a novel distributed optimization algorithm with a switching mechanism to guarantee that all agents eventually converge to an optimal solution point,while their control inputs are constrained in their own nonconvex region.It is worth noting that the mechanism is performed to tackle the coexistence of the nonconvex constraint operator and the optimization gradient term.Based on the dynamic transformation technique,the original nonlinear dynamic system is transformed into an equivalent one with a nonlinear error term.By utilizing the nonnegative matrix theory,it is shown that the optimization problem can be solved when the union of switching communication graphs is jointly strongly connected.Finally,a numerical simulation example is used to demonstrate the acquired theoretical results.
文摘This paper proposes a backstepping technique and Multi-dimensional Taylor Polynomial Networks(MTPN)based adaptive attitude tracking control strategy for Near Space Vehicles(NSVs)subjected to input constraints and stochastic input noises.Firstly,considering the control input has stochastic noises,and the attitude motion dynamical model of the NSVs is actually modeled as the Multi-Input Multi-Output(MIMO)stochastic nonlinear system form.Furthermore,the MTPN is used to estimate the unknown system uncertainties,and an auxiliary system is designed to compensate the influence of the saturation control input.Then,by using backstepping method and the output of the auxiliary system,a MTPN-based robust adaptive attitude control approach is proposed for the NSVs with saturation input nonlinearity,stochastic input noises,and system uncertainties.Stochastic Lyapunov stability theory is utilized to analysis the stability in the sense of probability of the entire closed-loop system.Additionally,by selecting appropriate parameters,the tracking errors will converge to a small neighborhood with a tunable radius.Finally,the numerical simulation results of the NSVs attitude motion show the satisfactory flight control performance under the proposed tracking control strategy.
