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
The authors investigate the trajectory tracking control problem of an upper limb reha-bilitation robot system with unknown dynamics.To address the system's uncertainties and improve the tracking accuracy of the re...The authors investigate the trajectory tracking control problem of an upper limb reha-bilitation robot system with unknown dynamics.To address the system's uncertainties and improve the tracking accuracy of the rehabilitation robot,an adaptive neural full-state feedback control is proposed.The neural network is utilised to approximate the dy-namics that are not fully modelled and adapt to the interaction between the upper limb rehabilitation robot and the patient.By incorporating a high-gain observer,unmeasurable state information is integrated into the output feedback control.Taking into consider-ation the issue of joint position constraints during the actual rehabilitation training process,an adaptive neural full-state and output feedback control scheme with output constraint is further designed.From the perspective of safety in human–robot interaction during rehabilitation training,log-type barrier Lyapunov function is introduced in the output constraint controller to ensure that the output remains within the predefined constraint region.The stability of the closed-loop system is proved by Lyapunov stability theory.The effectiveness of the proposed control scheme is validated by applying it to an upper limb rehabilitation robot through simulations.展开更多
Satellite swarm coordinated flight(SSCF)technology has promising applications,but its complex nature poses significant challenges for control implementation.In response,this paper proposes an easily solvable adaptive ...Satellite swarm coordinated flight(SSCF)technology has promising applications,but its complex nature poses significant challenges for control implementation.In response,this paper proposes an easily solvable adaptive control scheme to achieve high-performance trajectory tracking of the SSCF system subject to actuator efficiency losses and external disturbances.Most existing adaptive controllers based on the certaintyequivalent(CE)principle show unpredictability and nonconvergence in their online parameter estimations.To overcome the above vulnerabilities and the difficulties caused by input failures of SSCF,this paper proposes an adaptive estimator based on scaling immersion and invariance(I&I),which reduces the computational complexity while improving the performance of the parameter estimator.Besides,a barrier Lyapunov function(BLF)is applied to satisfy both the boundedness of the system states and the singularity avoidance of the computation.It is proved that the estimator error becomes sufficiently small to converge to a specified attractive invariant manifold and the closed-loop SSCF system can obtain asymptotic stability under full-state constraints.Finally,numerical simulations are performed for comparison and analysis to verify the effectiveness and superiority of the proposed method.展开更多
Taking a single magnet levitation system as theobject, a nonlinear numerical model of the vehicle–guidewaycoupling system was established to study the levitationcontrol strategies. According to the similarity in dyna...Taking a single magnet levitation system as theobject, a nonlinear numerical model of the vehicle–guidewaycoupling system was established to study the levitationcontrol strategies. According to the similarity in dynamics,the single magnet-guideway coupling system was simplifiedinto a magnet-suspended track system, and the correspondinghardware-in-loop test rig was set up usingdSPACE. A full-state-feedback controller was developedusing the levitation gap signal and the current signal, andcontroller parameters were optimized by particle swarmalgorithm. The results from the simulation and the test rigshow that, the proposed control method can keep the systemstable by calculating the controller output with the fullstateinformation of the coupling system, Step responsesfrom the test rig show that the controller can stabilize thesystem within 0.15 s with a 2 % overshot, and performswell even in the condition of violent external disturbances.Unlike the linear quadratic optimal method, the particleswarm algorithm carries out the optimization with thenonlinear controlled object included, and its optimizedresults make the system responses much better.展开更多
This paper studies the problem of the formation control for the multiple nonholonomic agents on a plane. A dynamic feedback linearization method is used to transform the dynamical model of each agent into two third-or...This paper studies the problem of the formation control for the multiple nonholonomic agents on a plane. A dynamic feedback linearization method is used to transform the dynamical model of each agent into two third-order integrator chains. A decentralized formation control law with the inter-agent damping .injection is derived. The asymptotical stability of the overall system is proven using the Lyapunov method. The simulation of the formation maneuver of a planar vehicle shows the effectiveness of the proposed method.展开更多
In this paper we present a new projective synchronization scheme, where two chaotic （hyperchaotic） discrete-time systems synchronize for any arbitrary scaling matrix. Specifically, each drive system state synchroniz...In this paper we present a new projective synchronization scheme, where two chaotic （hyperchaotic） discrete-time systems synchronize for any arbitrary scaling matrix. Specifically, each drive system state synchronizes with a linear combination of response system states. The proposed observer-based approach presents some useful features： i） it enables exact synchronization to be achieved in finite time （i.e., dead-beat synchronization）; ii） it exploits a scalar synchronizing signal; iii） it can be applied to a wide class of discrete-time chaotic （hyperchaotic） systems; iv） it includes, as a particular case, most of the synchronization types defined so far. An example is reported, which shows in detail that exact synchronization is effectively achieved in finite time, using a scalar synchronizing signal only, for any arbitrary scaling matrix.展开更多
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National Natural Science Foundation of China,Grant/Award Numbers:61563032,61963025Science and Technology Program of Gansu Province,Grant/Award Numbers:22CX8GA131,22YF7GA164。
文摘The authors investigate the trajectory tracking control problem of an upper limb reha-bilitation robot system with unknown dynamics.To address the system's uncertainties and improve the tracking accuracy of the rehabilitation robot,an adaptive neural full-state feedback control is proposed.The neural network is utilised to approximate the dy-namics that are not fully modelled and adapt to the interaction between the upper limb rehabilitation robot and the patient.By incorporating a high-gain observer,unmeasurable state information is integrated into the output feedback control.Taking into consider-ation the issue of joint position constraints during the actual rehabilitation training process,an adaptive neural full-state and output feedback control scheme with output constraint is further designed.From the perspective of safety in human–robot interaction during rehabilitation training,log-type barrier Lyapunov function is introduced in the output constraint controller to ensure that the output remains within the predefined constraint region.The stability of the closed-loop system is proved by Lyapunov stability theory.The effectiveness of the proposed control scheme is validated by applying it to an upper limb rehabilitation robot through simulations.
基金supported by the Natural Science Foundation of Shaanxi Province(2020JQ-132)China Postdoctoral Science Foundation(2020M683571)+1 种基金National Natural Science Foundation of China(62103336,11972026,U2013206)Funds for the Central Universities(3102019HTQD007)。
文摘Satellite swarm coordinated flight(SSCF)technology has promising applications,but its complex nature poses significant challenges for control implementation.In response,this paper proposes an easily solvable adaptive control scheme to achieve high-performance trajectory tracking of the SSCF system subject to actuator efficiency losses and external disturbances.Most existing adaptive controllers based on the certaintyequivalent(CE)principle show unpredictability and nonconvergence in their online parameter estimations.To overcome the above vulnerabilities and the difficulties caused by input failures of SSCF,this paper proposes an adaptive estimator based on scaling immersion and invariance(I&I),which reduces the computational complexity while improving the performance of the parameter estimator.Besides,a barrier Lyapunov function(BLF)is applied to satisfy both the boundedness of the system states and the singularity avoidance of the computation.It is proved that the estimator error becomes sufficiently small to converge to a specified attractive invariant manifold and the closed-loop SSCF system can obtain asymptotic stability under full-state constraints.Finally,numerical simulations are performed for comparison and analysis to verify the effectiveness and superiority of the proposed method.
文摘Taking a single magnet levitation system as theobject, a nonlinear numerical model of the vehicle–guidewaycoupling system was established to study the levitationcontrol strategies. According to the similarity in dynamics,the single magnet-guideway coupling system was simplifiedinto a magnet-suspended track system, and the correspondinghardware-in-loop test rig was set up usingdSPACE. A full-state-feedback controller was developedusing the levitation gap signal and the current signal, andcontroller parameters were optimized by particle swarmalgorithm. The results from the simulation and the test rigshow that, the proposed control method can keep the systemstable by calculating the controller output with the fullstateinformation of the coupling system, Step responsesfrom the test rig show that the controller can stabilize thesystem within 0.15 s with a 2 % overshot, and performswell even in the condition of violent external disturbances.Unlike the linear quadratic optimal method, the particleswarm algorithm carries out the optimization with thenonlinear controlled object included, and its optimizedresults make the system responses much better.
基金supported by the National Natural Science Foundation of China (No. 10832006)
文摘This paper studies the problem of the formation control for the multiple nonholonomic agents on a plane. A dynamic feedback linearization method is used to transform the dynamical model of each agent into two third-order integrator chains. A decentralized formation control law with the inter-agent damping .injection is derived. The asymptotical stability of the overall system is proven using the Lyapunov method. The simulation of the formation maneuver of a planar vehicle shows the effectiveness of the proposed method.
文摘In this paper we present a new projective synchronization scheme, where two chaotic （hyperchaotic） discrete-time systems synchronize for any arbitrary scaling matrix. Specifically, each drive system state synchronizes with a linear combination of response system states. The proposed observer-based approach presents some useful features： i） it enables exact synchronization to be achieved in finite time （i.e., dead-beat synchronization）; ii） it exploits a scalar synchronizing signal; iii） it can be applied to a wide class of discrete-time chaotic （hyperchaotic） systems; iv） it includes, as a particular case, most of the synchronization types defined so far. An example is reported, which shows in detail that exact synchronization is effectively achieved in finite time, using a scalar synchronizing signal only, for any arbitrary scaling matrix.
