This paper mainly addresses control problems of strict-feedback systems(SFSs)with increasing dimensions.Compared with the commonly-considered SFSs where the subsystems have the same dimension,we aim to handle more com...This paper mainly addresses control problems of strict-feedback systems(SFSs)with increasing dimensions.Compared with the commonly-considered SFSs where the subsystems have the same dimension,we aim to handle more complex cases,i.e.,the subsystems in the considered SFSs are assumed to have increasing dimensions.By transforming the systems into highorder fully-actuated system(HOFAS)models,the stabilizing controllers can be directly given.Besides first-order SFSs,secondorder and high-order SFSs are also considered.展开更多
Abstract--In this paper, an adaptive neural network （NN） control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assum...Abstract--In this paper, an adaptive neural network （NN） control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions （BLFs） with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closed- loop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. Index TermsmAdaptive control, neural networks （NNs）, non- linear pure-feedback systems, time-varying constraints.展开更多
In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output （MIMO） nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The ...In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output （MIMO） nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The unknown time-varying delay uncer- tainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The approach removes the assumption of linear function outside the deadband without necessarily constructing a dead-zone inverse as an added contribution. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal approximation error as well as the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. In addition, a modified adaptive control algorithm is given in order to avoid the high-frequency chattering phenomenon. Simulation results demonstrate the effectiveness of the approach.展开更多
An uncertain nonlinear discrete-time system model with time-varying input delays for networked control systems (NCSs) is presented. The problem of exponential stability for the system is considered and some new criter...An uncertain nonlinear discrete-time system model with time-varying input delays for networked control systems (NCSs) is presented. The problem of exponential stability for the system is considered and some new criteria of exponential stability are obtained based on norm inequality methods. A numerical example is given todemonstrate that those criteria are useful to analyzing the stability of nonlinear NCSs.展开更多
This paper investigates the problem of delay-dependent robust stability analysis for a class of neutral systems with interval time-varying delays and nonlinear perturbations. Such nonlinear perturbations are with time...This paper investigates the problem of delay-dependent robust stability analysis for a class of neutral systems with interval time-varying delays and nonlinear perturbations. Such nonlinear perturbations are with time-varying but norm-bounded characteristics. Based on a new Lyapunov-Krasovskii functional, together ,sith a free-weighting matrices technique, improved delay-dependent stability criteria are established. It is shown that less conservative results can be obtained in terms of linear matrix inequalities （LMIs）. Numerical examples are provided to demonstrate the effectiveness and less conservatism of the proposed approach.展开更多
In this paper, a fuzzy adaptive tracking control for uncertain strict-feedback nonlinear systems with unknown bounded disturbances is proposed. The generalized fuzzy hyperbolic model (GFHM) with better approximation p...In this paper, a fuzzy adaptive tracking control for uncertain strict-feedback nonlinear systems with unknown bounded disturbances is proposed. The generalized fuzzy hyperbolic model (GFHM) with better approximation performance is used to approximate the unknown nonlinear function in the system. The dynamic surface control (DSC) is used to design the controller, which not only avoids the “explosion of complexity” problem in the process of repeated derivation, but also makes the control system simpler in structure and lower in computational cost because only one adaptive law is designed in the controller design process. Through the Lyapunov stability analysis, all signals in the closed loop system designed in this paper are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the method is verified by a simulation example.展开更多
Safety critical control is often trained in a simulated environment to mitigate risk.Subsequent migration of the biased controller requires further adjustments.In this paper,an experience inference human-behavior lear...Safety critical control is often trained in a simulated environment to mitigate risk.Subsequent migration of the biased controller requires further adjustments.In this paper,an experience inference human-behavior learning is proposed to solve the migration problem of optimal controllers applied to real-world nonlinear systems.The approach is inspired in the complementary properties that exhibits the hippocampus,the neocortex,and the striatum learning systems located in the brain.The hippocampus defines a physics informed reference model of the realworld nonlinear system for experience inference and the neocortex is the adaptive dynamic programming(ADP)or reinforcement learning(RL)algorithm that ensures optimal performance of the reference model.This optimal performance is inferred to the real-world nonlinear system by means of an adaptive neocortex/striatum control policy that forces the nonlinear system to behave as the reference model.Stability and convergence of the proposed approach is analyzed using Lyapunov stability theory.Simulation studies are carried out to verify the approach.展开更多
This paper focuses on the distributed cooperative learning(DCL)problem for a class of discrete-time strict-feedback multi-agent systems under directed graphs.Compared with the previous DCL works based on undirected gr...This paper focuses on the distributed cooperative learning(DCL)problem for a class of discrete-time strict-feedback multi-agent systems under directed graphs.Compared with the previous DCL works based on undirected graphs,two main challenges lie in that the Laplacian matrix of directed graphs is nonsymmetric,and the derived weight error systems exist n-step delays.Two novel lemmas are developed in this paper to show the exponential convergence for two kinds of linear time-varying(LTV)systems with different phenomena including the nonsymmetric Laplacian matrix and time delays.Subsequently,an adaptive neural network(NN)control scheme is proposed by establishing a directed communication graph along with n-step delays weight updating law.Then,by using two novel lemmas on the extended exponential convergence of LTV systems,estimated NN weights of all agents are verified to exponentially converge to small neighbourhoods of their common optimal values if directed communication graphs are strongly connected and balanced.The stored NN weights are reused to structure learning controllers for the improved control performance of similar control tasks by the“mod”function and proper time series.A simulation comparison is shown to demonstrate the validity of the proposed DCL method.展开更多
This paper investigates the issue of adaptive optimal tracking control for nonlinear systems with dynamic state constraints.An asymmetric time-varying integral barrier Lyapunov function(ATIBLF)based integral reinforce...This paper investigates the issue of adaptive optimal tracking control for nonlinear systems with dynamic state constraints.An asymmetric time-varying integral barrier Lyapunov function(ATIBLF)based integral reinforcement learning(IRL)control algorithm with an actor–critic structure is first proposed.The ATIBLF items are appropriately arranged in every step of the optimized backstepping control design to ensure that the dynamic full-state constraints are never violated.Thus,optimal virtual/actual control in every backstepping subsystem is decomposed with ATIBLF items and also with an adaptive optimized item.Meanwhile,neural networks are used to approximate the gradient value functions.According to the Lyapunov stability theorem,the boundedness of all signals of the closed-loop system is proved,and the proposed control scheme ensures that the system states are within predefined compact sets.Finally,the effectiveness of the proposed control approach is validated by simulations.展开更多
This paper proposes the design and a comparative study of two nonlinear systems modeling techniques. These two approaches are developed to address a class of nonlinear systems with time-varying parameter. The first is...This paper proposes the design and a comparative study of two nonlinear systems modeling techniques. These two approaches are developed to address a class of nonlinear systems with time-varying parameter. The first is a Radial Basis Function (RBF) neural networks and the second is a Multi Layer Perceptron (MLP). The MLP model consists of an input layer, an output layer and usually one or more hidden layers. However, training MLP network based on back propagation learning is computationally expensive. In this paper, an RBF network is called. The parameters of the RBF model are optimized by two methods: the Gradient Descent (GD) method and Genetic Algorithms (GA). However, the MLP model is optimized by the Gradient Descent method. The performance of both models are evaluated first by using a numerical simulation and second by handling a chemical process known as the Continuous Stirred Tank Reactor CSTR. It has been shown that in both validation operations the results were successful. The optimized RBF model by Genetic Algorithms gave the best results.展开更多
In this paper, a command filter-based adaptive fuzzy predefined-time event-triggered tracking control problem is investigated for uncertain nonlinear systems with time-varying full-state constraints. By designing a sl...In this paper, a command filter-based adaptive fuzzy predefined-time event-triggered tracking control problem is investigated for uncertain nonlinear systems with time-varying full-state constraints. By designing a sliding mode differentiator, the inherent computational complexity problem within the predefined-time backstepping framework is solved. Different from the existing command filter-based finite-time and fixed-time control strategies that the convergence time of the filtering error is adjusted through the system initial value or numerous parameters, a novel command filtering error compensation method is presented,which tunes one control parameter to make the filtering error converge in the predefined time, thereby reducing the complexity of design and analysis of processing the filtering error. Then, an improved event-triggered mechanism(ETM) that builds upon the switching threshold strategy, in which an inverse cotangent function is designed to replace the residual term of the ETM,is proposed to gradually release the controller's dependence on the residual term with increasing time. Furthermore, a tan-type nonlinear mapping technique is applied to tackle the time-varying full-state constraints problem. By the predefined-time stability theory, all signals in the uncertain nonlinear systems exhibit predefined-time stability. Finally, the feasibility of the proposed algorithm is substantiated through two simulation results.展开更多
Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve t...Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.展开更多
In this paper,the leader–follower consensus of feedforward nonlinear multi-agent systems is achieved by designing the distributed output feedback controllers with a time-varying gain.The agents dynamics are assumed t...In this paper,the leader–follower consensus of feedforward nonlinear multi-agent systems is achieved by designing the distributed output feedback controllers with a time-varying gain.The agents dynamics are assumed to be in upper triangular structure and satisfy Lipschitz conditions with an unknown constant multiplied by a time-varying function.A time-varying gain,which increases monotonously and tends to infinity,is proposed to construct a compensator for each follower agent.Based on a directed communication topology,the distributed output feedback controller with a time-varying gain is designed for each follower agent by only using the output information of the follower and its neighbors.It is proved by the Lyapunov theorem that the leader–follower consensus of the multi-agent system is achieved by the proposed consensus protocol.The effectiveness of the proposed time-varying gain method is demonstrated by a circuit system.展开更多
This paper presents a control Lyapunov function approach to the global stabilizationproblem for general nonlinear and time-varying systems. Explicit stabilizing feedback control laws areproposed based on the method of...This paper presents a control Lyapunov function approach to the global stabilizationproblem for general nonlinear and time-varying systems. Explicit stabilizing feedback control laws areproposed based on the method of control Lyapunov functions and Sontag's universal formula.展开更多
The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are requ...The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are required as these parameters change abruptly in the parameter space. In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems. We demonstrate that the latter approach is better than the former.展开更多
This paper studies the problem of tracking control for a class of switched nonlinear systems with time-varying delay. Based on the average dwell-time and piecewise Lyapunov functional methods, a new exponential stabil...This paper studies the problem of tracking control for a class of switched nonlinear systems with time-varying delay. Based on the average dwell-time and piecewise Lyapunov functional methods, a new exponential stability criterion is obtained for the switched nonlinear systems. The designed output feedback H∞controller can be obtained by solving a set of linear matrix inequalities(LMIs).Moreover, the proposed method does not need that a common Lyapunov function exists for the switched systems, and the switching signal just depends on time. A simulation example is provided to demonstrate the effectiveness of the proposed design scheme.展开更多
In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-doma...In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-domain representation is constructed to derive an error model with relative degree one for our purpose. And time-varying radial basis function neural network is employed to deal with system uncertainty. A new signal is constructed by using a first-order filter, which removes the requirement of strict positive real(SPR) condition and identical initial condition of iterative learning control. Based on property of hyperbolic tangent function,the system tracing error is proved to converge to the origin as the iteration tends to infinity by constructing Lyapunov-like composite energy function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.展开更多
This paper is concerned with the adaptive tracking control problem of nonlinear time-varyingsystems. Based on the backstepping technology, an event-based prescribed performance controlscheme is developed. And the time...This paper is concerned with the adaptive tracking control problem of nonlinear time-varyingsystems. Based on the backstepping technology, an event-based prescribed performance controlscheme is developed. And the time-varying uncertainties of the system are handled byutilising bound estimation method. The proposed controller not only ensures the prescribedtracking performance, but also reduces the communication burden. By using Lyapunov stabilityanalysis, it is proven that all of the closed-loop signals are bounded, and the tracking errorcan converge to zero. Simultaneously, Zeno behaviour is excluded. Finally, the simulation resultsare utilised to illustrate the effectiveness of the proposed adaptive control scheme.展开更多
A new prescribed-time state-feedback design is presented for stochastic nonlinear strictfeedback systems.Different from the existing stochastic prescribed-time design where scaling-free quartic Lyapunov functions or s...A new prescribed-time state-feedback design is presented for stochastic nonlinear strictfeedback systems.Different from the existing stochastic prescribed-time design where scaling-free quartic Lyapunov functions or scaled quadratic Lyapunov functions are used,the design is based on new scaled quartic Lyapunov functions.The designed controller can ensure that the plant has an almost surely unique strong solution and the equilibrium at the origin of the plant is prescribed-time mean-square stable.After that,the authors redesign the controller to solve the prescribed-time inverse optimal mean-square stabilization problem.The merit of the design is that the order of the scaling function in the controller is reduced dramatically,which effectively reduces the control effort.Two simulation examples are given to illustrate the designs.展开更多
This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RS...This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RSDs is modeled by a set of random variables with certain statistical features.In addition,the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering problem.The aim of the addressed issue is to propose a DRFF scheme for MNSSs such that,for both RSDs and estimator gain perturbations,certain upper bounds of estimation error covariance(EEC)are given and locally minimized at every sample time.In the light of the obtained local filters,a new DRFF algorithm is developed via the matrix-weighted fusion method.Furthermore,a sufficient condition is presented,which can guarantee that the local upper bound of the EEC is bounded.Finally,a numerical example is provided,which can show the usefulness of the developed DRFF approach.展开更多
基金partly supported by Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)the National Natural Science Foundation of China(NSFC)(62350055)。
文摘This paper mainly addresses control problems of strict-feedback systems(SFSs)with increasing dimensions.Compared with the commonly-considered SFSs where the subsystems have the same dimension,we aim to handle more complex cases,i.e.,the subsystems in the considered SFSs are assumed to have increasing dimensions.By transforming the systems into highorder fully-actuated system(HOFAS)models,the stabilizing controllers can be directly given.Besides first-order SFSs,secondorder and high-order SFSs are also considered.
基金supported in part by the National Natural Science Foundation of China(61622303,61603164,61773188)the Program for Liaoning Innovative Research Team in University(LT2016006)+1 种基金the Fundamental Research Funds for the Universities of Liaoning Province(JZL201715402)the Program for Distinguished Professor of Liaoning Province
文摘Abstract--In this paper, an adaptive neural network （NN） control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions （BLFs） with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closed- loop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. Index TermsmAdaptive control, neural networks （NNs）, non- linear pure-feedback systems, time-varying constraints.
基金supported by National Natural Science Foundationof China (No. 60774017 and No. 60874045)
文摘In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output （MIMO） nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The unknown time-varying delay uncer- tainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The approach removes the assumption of linear function outside the deadband without necessarily constructing a dead-zone inverse as an added contribution. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal approximation error as well as the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. In addition, a modified adaptive control algorithm is given in order to avoid the high-frequency chattering phenomenon. Simulation results demonstrate the effectiveness of the approach.
文摘An uncertain nonlinear discrete-time system model with time-varying input delays for networked control systems (NCSs) is presented. The problem of exponential stability for the system is considered and some new criteria of exponential stability are obtained based on norm inequality methods. A numerical example is given todemonstrate that those criteria are useful to analyzing the stability of nonlinear NCSs.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61004038)
文摘This paper investigates the problem of delay-dependent robust stability analysis for a class of neutral systems with interval time-varying delays and nonlinear perturbations. Such nonlinear perturbations are with time-varying but norm-bounded characteristics. Based on a new Lyapunov-Krasovskii functional, together ,sith a free-weighting matrices technique, improved delay-dependent stability criteria are established. It is shown that less conservative results can be obtained in terms of linear matrix inequalities （LMIs）. Numerical examples are provided to demonstrate the effectiveness and less conservatism of the proposed approach.
文摘In this paper, a fuzzy adaptive tracking control for uncertain strict-feedback nonlinear systems with unknown bounded disturbances is proposed. The generalized fuzzy hyperbolic model (GFHM) with better approximation performance is used to approximate the unknown nonlinear function in the system. The dynamic surface control (DSC) is used to design the controller, which not only avoids the “explosion of complexity” problem in the process of repeated derivation, but also makes the control system simpler in structure and lower in computational cost because only one adaptive law is designed in the controller design process. Through the Lyapunov stability analysis, all signals in the closed loop system designed in this paper are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the method is verified by a simulation example.
基金supported by the Royal Academy of Engineering and the Office of the Chie Science Adviser for National Security under the UK Intelligence Community Postdoctoral Research Fellowship programme。
文摘Safety critical control is often trained in a simulated environment to mitigate risk.Subsequent migration of the biased controller requires further adjustments.In this paper,an experience inference human-behavior learning is proposed to solve the migration problem of optimal controllers applied to real-world nonlinear systems.The approach is inspired in the complementary properties that exhibits the hippocampus,the neocortex,and the striatum learning systems located in the brain.The hippocampus defines a physics informed reference model of the realworld nonlinear system for experience inference and the neocortex is the adaptive dynamic programming(ADP)or reinforcement learning(RL)algorithm that ensures optimal performance of the reference model.This optimal performance is inferred to the real-world nonlinear system by means of an adaptive neocortex/striatum control policy that forces the nonlinear system to behave as the reference model.Stability and convergence of the proposed approach is analyzed using Lyapunov stability theory.Simulation studies are carried out to verify the approach.
基金supported in part by the Guangdong Natural Science Foundation(2019B151502058)in part by the National Natural Science Foundation of China(61890922,61973129)+1 种基金in part by the Major Key Project of PCL(PCL2021A09)in part by the Guangdong Basic and Applied Basic Research Foundation(2021A1515012004)。
文摘This paper focuses on the distributed cooperative learning(DCL)problem for a class of discrete-time strict-feedback multi-agent systems under directed graphs.Compared with the previous DCL works based on undirected graphs,two main challenges lie in that the Laplacian matrix of directed graphs is nonsymmetric,and the derived weight error systems exist n-step delays.Two novel lemmas are developed in this paper to show the exponential convergence for two kinds of linear time-varying(LTV)systems with different phenomena including the nonsymmetric Laplacian matrix and time delays.Subsequently,an adaptive neural network(NN)control scheme is proposed by establishing a directed communication graph along with n-step delays weight updating law.Then,by using two novel lemmas on the extended exponential convergence of LTV systems,estimated NN weights of all agents are verified to exponentially converge to small neighbourhoods of their common optimal values if directed communication graphs are strongly connected and balanced.The stored NN weights are reused to structure learning controllers for the improved control performance of similar control tasks by the“mod”function and proper time series.A simulation comparison is shown to demonstrate the validity of the proposed DCL method.
基金Project supported by the National Natural Science Foundation of China(Nos.62203392 and 62373329)the Natural Science Foundation of Zhejiang Province,China(No.LY23F030009)the Baima Lake Laboratory Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LBMHD24F030002)。
文摘This paper investigates the issue of adaptive optimal tracking control for nonlinear systems with dynamic state constraints.An asymmetric time-varying integral barrier Lyapunov function(ATIBLF)based integral reinforcement learning(IRL)control algorithm with an actor–critic structure is first proposed.The ATIBLF items are appropriately arranged in every step of the optimized backstepping control design to ensure that the dynamic full-state constraints are never violated.Thus,optimal virtual/actual control in every backstepping subsystem is decomposed with ATIBLF items and also with an adaptive optimized item.Meanwhile,neural networks are used to approximate the gradient value functions.According to the Lyapunov stability theorem,the boundedness of all signals of the closed-loop system is proved,and the proposed control scheme ensures that the system states are within predefined compact sets.Finally,the effectiveness of the proposed control approach is validated by simulations.
文摘This paper proposes the design and a comparative study of two nonlinear systems modeling techniques. These two approaches are developed to address a class of nonlinear systems with time-varying parameter. The first is a Radial Basis Function (RBF) neural networks and the second is a Multi Layer Perceptron (MLP). The MLP model consists of an input layer, an output layer and usually one or more hidden layers. However, training MLP network based on back propagation learning is computationally expensive. In this paper, an RBF network is called. The parameters of the RBF model are optimized by two methods: the Gradient Descent (GD) method and Genetic Algorithms (GA). However, the MLP model is optimized by the Gradient Descent method. The performance of both models are evaluated first by using a numerical simulation and second by handling a chemical process known as the Continuous Stirred Tank Reactor CSTR. It has been shown that in both validation operations the results were successful. The optimized RBF model by Genetic Algorithms gave the best results.
基金supported by the Revitalization of Liaoning Talents Program(Grant No.XLYC2203201)。
文摘In this paper, a command filter-based adaptive fuzzy predefined-time event-triggered tracking control problem is investigated for uncertain nonlinear systems with time-varying full-state constraints. By designing a sliding mode differentiator, the inherent computational complexity problem within the predefined-time backstepping framework is solved. Different from the existing command filter-based finite-time and fixed-time control strategies that the convergence time of the filtering error is adjusted through the system initial value or numerous parameters, a novel command filtering error compensation method is presented,which tunes one control parameter to make the filtering error converge in the predefined time, thereby reducing the complexity of design and analysis of processing the filtering error. Then, an improved event-triggered mechanism(ETM) that builds upon the switching threshold strategy, in which an inverse cotangent function is designed to replace the residual term of the ETM,is proposed to gradually release the controller's dependence on the residual term with increasing time. Furthermore, a tan-type nonlinear mapping technique is applied to tackle the time-varying full-state constraints problem. By the predefined-time stability theory, all signals in the uncertain nonlinear systems exhibit predefined-time stability. Finally, the feasibility of the proposed algorithm is substantiated through two simulation results.
基金Aeronautical Science Foundation of China (20100751010, 2010ZD11007)
文摘Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.
基金the National Natural Science Foundation of China(Nos.61973189,62073190)the Research Fund for the Taishan Scholar Project of Shandong Province of China(No.ts20190905)the Natural Science Foundation of Shandong Province of China(No.ZR2020ZD25).
文摘In this paper,the leader–follower consensus of feedforward nonlinear multi-agent systems is achieved by designing the distributed output feedback controllers with a time-varying gain.The agents dynamics are assumed to be in upper triangular structure and satisfy Lipschitz conditions with an unknown constant multiplied by a time-varying function.A time-varying gain,which increases monotonously and tends to infinity,is proposed to construct a compensator for each follower agent.Based on a directed communication topology,the distributed output feedback controller with a time-varying gain is designed for each follower agent by only using the output information of the follower and its neighbors.It is proved by the Lyapunov theorem that the leader–follower consensus of the multi-agent system is achieved by the proposed consensus protocol.The effectiveness of the proposed time-varying gain method is demonstrated by a circuit system.
基金supported in part by National Science Foundation under Grants Nos. ECS-0093176, DMS- 0906659, and DMS-0504296in part by National Natural Science Foundation of China under Grant Nos 60228003 and 60628302
文摘This paper presents a control Lyapunov function approach to the global stabilizationproblem for general nonlinear and time-varying systems. Explicit stabilizing feedback control laws areproposed based on the method of control Lyapunov functions and Sontag's universal formula.
文摘The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are required as these parameters change abruptly in the parameter space. In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems. We demonstrate that the latter approach is better than the former.
基金supported by National Natural Science Foundation of China(Nos.61473082,61273119,and 61104068)Six Talents Peaks Program of Jiangsu Province(No.2014-DZXX-003)the Fundamental Research Funds for the Central Universities(No.2242013R30006)
文摘This paper studies the problem of tracking control for a class of switched nonlinear systems with time-varying delay. Based on the average dwell-time and piecewise Lyapunov functional methods, a new exponential stability criterion is obtained for the switched nonlinear systems. The designed output feedback H∞controller can be obtained by solving a set of linear matrix inequalities(LMIs).Moreover, the proposed method does not need that a common Lyapunov function exists for the switched systems, and the switching signal just depends on time. A simulation example is provided to demonstrate the effectiveness of the proposed design scheme.
基金the National Natural Science Foundation of China(No.61273058)
文摘In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-domain representation is constructed to derive an error model with relative degree one for our purpose. And time-varying radial basis function neural network is employed to deal with system uncertainty. A new signal is constructed by using a first-order filter, which removes the requirement of strict positive real(SPR) condition and identical initial condition of iterative learning control. Based on property of hyperbolic tangent function,the system tracing error is proved to converge to the origin as the iteration tends to infinity by constructing Lyapunov-like composite energy function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.
基金the Funds of National Science of China[grant number 61973146]in part by the Distinguished Young Scientifific Research Talents Plan in Liaoning Province[grant number XLYC1907077]in part by the Taishan Scholar Project of Shandong Province ofChina[grant number tsqn201909097].
文摘This paper is concerned with the adaptive tracking control problem of nonlinear time-varyingsystems. Based on the backstepping technology, an event-based prescribed performance controlscheme is developed. And the time-varying uncertainties of the system are handled byutilising bound estimation method. The proposed controller not only ensures the prescribedtracking performance, but also reduces the communication burden. By using Lyapunov stabilityanalysis, it is proven that all of the closed-loop signals are bounded, and the tracking errorcan converge to zero. Simultaneously, Zeno behaviour is excluded. Finally, the simulation resultsare utilised to illustrate the effectiveness of the proposed adaptive control scheme.
基金the National Natural Science Foundation of China under Grant No.61973150the Young Taishan Scholars Program of Shandong Province of China under Grant No.tsqn20161043+1 种基金Shandong Provincial Natural Science Foundation for Distinguished Young Scholars under Grant No.ZR2019JQ22Shandong Province Higher Educational Excellent Youth Innovation team under Grant No.2019KJN017。
文摘A new prescribed-time state-feedback design is presented for stochastic nonlinear strictfeedback systems.Different from the existing stochastic prescribed-time design where scaling-free quartic Lyapunov functions or scaled quadratic Lyapunov functions are used,the design is based on new scaled quartic Lyapunov functions.The designed controller can ensure that the plant has an almost surely unique strong solution and the equilibrium at the origin of the plant is prescribed-time mean-square stable.After that,the authors redesign the controller to solve the prescribed-time inverse optimal mean-square stabilization problem.The merit of the design is that the order of the scaling function in the controller is reduced dramatically,which effectively reduces the control effort.Two simulation examples are given to illustrate the designs.
基金This work was supported in part by the National Natural Science Foundation of China under Grant Nos.12171124,61873058,and 61673141the Natural Science Foundation of Heilongjiang Province of China under Grant No.ZD2022F003+1 种基金the Key Foundation of Educational Science Planning in Heilongjiang Province of China under Grant No.GJB1422069the Alexander von Humboldt Foundation of Germany。
文摘This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RSDs is modeled by a set of random variables with certain statistical features.In addition,the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering problem.The aim of the addressed issue is to propose a DRFF scheme for MNSSs such that,for both RSDs and estimator gain perturbations,certain upper bounds of estimation error covariance(EEC)are given and locally minimized at every sample time.In the light of the obtained local filters,a new DRFF algorithm is developed via the matrix-weighted fusion method.Furthermore,a sufficient condition is presented,which can guarantee that the local upper bound of the EEC is bounded.Finally,a numerical example is provided,which can show the usefulness of the developed DRFF approach.
