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 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 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
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
This study concentrates on solving the output consensus problem for a class of heterogeneous uncertain nonstrict-feedback nonlinear multi-agent systems under switching-directed communication topologies,in which all fo...This study concentrates on solving the output consensus problem for a class of heterogeneous uncertain nonstrict-feedback nonlinear multi-agent systems under switching-directed communication topologies,in which all followers are subjected to multi-type input constraints such as unknown asymmetric saturation,unknown dead-zone and their integration.A unified representation is presented to overcome the difficulties originating from multi-agent input constraints.Moreover,the uncertain system functions in a non-lower triangular form and the interaction terms among agents are dealt with by exploiting the fuzzy logic systems and their special property.Furthermore,by introducing a nonlinear filter to alleviate the problem of“explosion of complexity”during the backstepping design,a distributed common adaptive control protocol is proposed to ensure that the synchronization errors converge to a small neighborhood of the origin despite the existence of multiple input constraints and arbitrary switching communication topologies.Both stability analysis and simulation results are conducted to show the effectiveness and performance of the proposed control methodology.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
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
Consensus control of multi-agent systems has attracted compelling attentions from various scientific communities for its promising applications.This paper presents a discrete-time consensus protocol for a class of mul...Consensus control of multi-agent systems has attracted compelling attentions from various scientific communities for its promising applications.This paper presents a discrete-time consensus protocol for a class of multi-agent systems with switching topologies and input constraints based on distributed predictive control scheme.The consensus protocol is not only distributed but also depends on the errors of states between agent and its neighbors.We focus mainly on dealing with the input constraints and a distributed model predictive control scheme is developed to achieve stable consensus under the condition that both velocity and acceleration constraints are included simultaneously.The acceleration constraint is regarded as the changing rate of velocity based on some reasonable assumptions so as to simplify the analysis.Theoretical analysis shows that the constrained system steered by the proposed protocol achieves consensus asymptotically if the switching interaction graphs always have a spanning tree.Numerical examples are also provided to illustrate the validity of the algorithm.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
This paper presents a two-stage robust model predictive control (RMPC) algorithm named as IRMPC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence o...This paper presents a two-stage robust model predictive control (RMPC) algorithm named as IRMPC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies.展开更多
A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and c...A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.展开更多
In this paper, a finite-time neural funnel control(FTNFC) scheme is proposed for motor servo systems with unknown input constraint. To deal with the non-smooth input saturation constraint problem, a smooth non-affine ...In this paper, a finite-time neural funnel control(FTNFC) scheme is proposed for motor servo systems with unknown input constraint. To deal with the non-smooth input saturation constraint problem, a smooth non-affine function of the control input signal is employed to approximate the saturation constraint, which is further transformed into an affine form according to the mean-value theorem. A fast terminal sliding mode manifold is constructed by using a novel funnel error variable to force the tracking error falling into a prescribe boundary within a finite time. Then, a simple sigmoid neural network is utilized to approximate the unknown system nonlinearity including the saturation.Different from the prescribed performance control(PPC), the proposed finite-time neural funnel control avoids using the inverse transformed function in the controller design, and could guarantee the prescribed tracking performance without knowing the saturation bounds in prior. The effectiveness and superior performance of the proposed method are verified by comparative simulation results.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
An application-oriented homogeneous control protocol is proposed to solve the consensus problem of multi-agent system(MAS)modeled by higher-order integrator.This nonlinear control protocol is able to homogenize the li...An application-oriented homogeneous control protocol is proposed to solve the consensus problem of multi-agent system(MAS)modeled by higher-order integrator.This nonlinear control protocol is able to homogenize the linear system with a special degree called homogeneity degree.This homogeneous control protocol ensures asymptotically/finite-time stable multi-agent systems(MASs)(or fixed-time attractive to compact sets containing the origin)by selecting di®erent homogeneity degrees.If the linear control protocol for each agent is provided,the proposed nonlinear homogeneous control protocol in this paper can be easily implemented without requiring any tuning of the control parameters.A bounded homogeneous control protocol,which is a special form of the controller proposed,is also introduced to address the same problem with input constraints.Finally,numerical simulations are conducted to demonstrate the e®ectiveness of the proposed approach.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
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 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.
基金supported by the Chinese National Natural Science Foundation(No.71871135)the Fundamental Research Funds for the Central Universities(Nos.222201714055,222201717006).
文摘This study concentrates on solving the output consensus problem for a class of heterogeneous uncertain nonstrict-feedback nonlinear multi-agent systems under switching-directed communication topologies,in which all followers are subjected to multi-type input constraints such as unknown asymmetric saturation,unknown dead-zone and their integration.A unified representation is presented to overcome the difficulties originating from multi-agent input constraints.Moreover,the uncertain system functions in a non-lower triangular form and the interaction terms among agents are dealt with by exploiting the fuzzy logic systems and their special property.Furthermore,by introducing a nonlinear filter to alleviate the problem of“explosion of complexity”during the backstepping design,a distributed common adaptive control protocol is proposed to ensure that the synchronization errors converge to a small neighborhood of the origin despite the existence of multiple input constraints and arbitrary switching communication topologies.Both stability analysis and simulation results are conducted to show the effectiveness and performance of the proposed control methodology.
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.
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This work was financially supported by the Major Program of National Natural Science Foundation of China[grant numbers is not public]the National Natural Science Foundation of China[Grant No.61703427].
文摘Consensus control of multi-agent systems has attracted compelling attentions from various scientific communities for its promising applications.This paper presents a discrete-time consensus protocol for a class of multi-agent systems with switching topologies and input constraints based on distributed predictive control scheme.The consensus protocol is not only distributed but also depends on the errors of states between agent and its neighbors.We focus mainly on dealing with the input constraints and a distributed model predictive control scheme is developed to achieve stable consensus under the condition that both velocity and acceleration constraints are included simultaneously.The acceleration constraint is regarded as the changing rate of velocity based on some reasonable assumptions so as to simplify the analysis.Theoretical analysis shows that the constrained system steered by the proposed protocol achieves consensus asymptotically if the switching interaction graphs always have a spanning tree.Numerical examples are also provided to illustrate the validity of the algorithm.
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.
文摘This paper presents a two-stage robust model predictive control (RMPC) algorithm named as IRMPC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies.
基金Supported by Deep Exploration Technology and Experimentation Project(201311194-04)
文摘A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.
基金supported by the National Natural Science Foundation of China under Grant Nos.61403343 and 61433003Zhejiang Provincial Natural Science Foundation of China under Grant No.Y17F030063the China Postdoctoral Science Foundation Funded Project under Grant No.2015M580521
文摘In this paper, a finite-time neural funnel control(FTNFC) scheme is proposed for motor servo systems with unknown input constraint. To deal with the non-smooth input saturation constraint problem, a smooth non-affine function of the control input signal is employed to approximate the saturation constraint, which is further transformed into an affine form according to the mean-value theorem. A fast terminal sliding mode manifold is constructed by using a novel funnel error variable to force the tracking error falling into a prescribe boundary within a finite time. Then, a simple sigmoid neural network is utilized to approximate the unknown system nonlinearity including the saturation.Different from the prescribed performance control(PPC), the proposed finite-time neural funnel control avoids using the inverse transformed function in the controller design, and could guarantee the prescribed tracking performance without knowing the saturation bounds in prior. The effectiveness and superior performance of the proposed method are verified by comparative simulation results.
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.
文摘An application-oriented homogeneous control protocol is proposed to solve the consensus problem of multi-agent system(MAS)modeled by higher-order integrator.This nonlinear control protocol is able to homogenize the linear system with a special degree called homogeneity degree.This homogeneous control protocol ensures asymptotically/finite-time stable multi-agent systems(MASs)(or fixed-time attractive to compact sets containing the origin)by selecting di®erent homogeneity degrees.If the linear control protocol for each agent is provided,the proposed nonlinear homogeneous control protocol in this paper can be easily implemented without requiring any tuning of the control parameters.A bounded homogeneous control protocol,which is a special form of the controller proposed,is also introduced to address the same problem with input constraints.Finally,numerical simulations are conducted to demonstrate the e®ectiveness of the proposed approach.
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.
