This paper concerns the observer-based adaptive control problem of uncertain time-delay switched systems with stuck actuator faults. Under the case where the original controller cannot stabilize the faulty system, mul...This paper concerns the observer-based adaptive control problem of uncertain time-delay switched systems with stuck actuator faults. Under the case where the original controller cannot stabilize the faulty system, multiple adaptive controllers are designed and a suitable switching logic is incorporated to ensure the closed-loop system stability and state tracking. New delay-independent sufficient conditions for asymptotic stability are obtained in terms of linear matrix inequalities based on piecewise Lyapunov stability theory. On the other hand, adaptive laws for on-line updating of some of the controller parameters are also designed to compensate the effect of stuck failures. Finally, simulation results for reference [1] model show that the design is feasible and efficient.展开更多
In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of ...In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of pneumatic systems can significantly impact the force control accuracy of pneumatic polishing system end-effectors.To enhance responsiveness and control precision during the flexible polishing process,this study proposes an observer-based fuzzy adaptive control(OBFAC)scheme.To ensure control accuracy under an uncertain dynamic contact model,a fuzzy state observer is designed to estimate unmeasured states,while fuzzy logic approximates the uncertain nonlinear functions in the model to improve control performance.Additionally,the integral barrier Lyapunov function is employed to ensure that all states remain within predefined constraints.The stability of the proposed control scheme is analyzed using the Lyapunov function,and a pneumatic polishing experimental platform is constructed to conduct polishing contact force control experiments under multiple scenarios.Experimental results demonstrate that the proposed OBFAC scheme achieves superior tracking control performance compared to existing control schemes.展开更多
To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,whic...To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,which can produce near-optimal tracking commands.Unlike the existing designs,the proposed scheme is less conservative and successfully prioritizes the solution optimality.The established RMPC follows a dualloop structure.Specifically,in the outer feedback loop,the reference attitude angle profiles are optimally tracked,while in the inner feedback loop,the control moment commands are produced by optimally tracking the desired angular rate trajectories.Besides,an adaptive disturbance observer(ADO)is designed and embedded in the inner and outer RMPC controllers to alleviate the negative effects caused by unknown external disturbances.The recursive feasibility of the optimization process,together with the input-to-state stability of the proposed RMPC,is theoretically guaranteed by introducing a tightened control constraint and terminal region.The derived property reveals that our proposal can steer the tracking error within a small region of convergence.Finally,the effectiveness of the proposed scheme is demonstrated by performing simulation studies.展开更多
This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temp...This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.展开更多
An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control...An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.展开更多
This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-d...This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.展开更多
An observer-based adaptive iterative learning control(AILC)scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays.The linear matrix inequality(LMI)met...An observer-based adaptive iterative learning control(AILC)scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays.The linear matrix inequality(LMI)method is employed to design the nonlinear observer.The designed controller contains a proportional-integral-derivative(PID)feedback term in time domain.The learning law of unknown constant parameter is differential-difference-type,and the learning law of unknown time-varying parameter is difference-type.It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized.By constructing a Lyapunov-Krasovskii-like composite energy function(CEF),we prove the boundedness of all closed-loop signals and the convergence of tracking error.A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.展开更多
A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with...A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with uncertain and continuous functions in the process of backstepping design.The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint,but also mixes the states and errors to directly constrain the state,reducing the conservativeness of the constraint satisfaction condition.Considering that the states in most nonlinear systems are immeasurable,a fuzzy adaptive states observer is constructed to estimate the unknown states.Combined with adaptive backstepping technique,an adaptive fuzzy output feedback control method is proposed.The proposed control method ensures that all signals in the closed-loop system are bounded,and that the tracking error converges to a bounded tight set without violating the full state constraint.The simulation results prove the effectiveness of the proposed control 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 study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal s...This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal stimuli.The circuit model converts photothermal signals into electrical signals,and its dynamic behavior is described using dimensionless equations derived from Kirchhoff's laws.Based on Helmholtz's theorem,a pseudo-Hamiltonian energy function is introduced to characterize the system's energy metabolism.Furthermore,an adaptive control function is proposed to elucidate temperature-dependent firing mechanisms,in which temperature dynamics are regulated by pseudo-Hamiltonian energy.Numerical simulations using the fourth-order Runge-Kutta method,combined with bifurcation diagrams,Lyapunov exponent spectra,and phase portraits,reveal that parameters such as capacitance ratio,phototube voltage amplitude/frequency,temperature,and thermistor reference resistance significantly modulate neuronal firing patterns,inducing transitions between periodic and chaotic states.Periodic states typically exhibit higher average pseudo-Hamiltonian energy than chaotic states.Two-parameter analysis demonstrates that phototube voltage amplitude and temperature jointly govern firing modes,with chaotic behavior emerging within specific parameter ranges.Adaptive control studies show that gain/attenuation factors,energy thresholds,ceiling temperatures,and initial temperatures regulate the timing and magnitude of system temperature saturation.During both heating and cooling phases,temperature dynamics are tightly coupled with pseudoHamiltonian energy and neuronal firing activity.These findings validate the circuit's ability to simulate photothermal perception and adaptive temperature regulation,contributing to a deeper understanding of neuronal encoding mechanisms and multimodal sensory processing.展开更多
The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches of...The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches often overlook practical input constraints or rely on centralized information. To address these issues, a novel edge-based double-layer adaptive control framework is proposed. Specifically, adaptive scaling parameters are embedded into the edge weights of the communication graph, enabling a fully distributed scheme that avoids dependence on centralized or global knowledge. Every participant modifies its strategy by exclusively utilizing local information and communicating with its neighbors to iteratively approach the NE. By incorporating damping terms into the design of the adaptive parameters, the proposed approach effectively suppresses unbounded parameter growth and consequently guarantees the boundedness of the adaptive gains. In addition, to account for actuator saturation, the proposed distributed NE seeking approach incorporates a saturation function, which ensures that control inputs do not exceed allowable ranges. A rigorous Lyapunov-based analysis guarantees the convergence and boundedness of all system variables. Finally, the presentation of simulation results aims to validate the efficacy and theoretical soundness of the proposed approach.展开更多
This study examines the adaptive boundary control problem of flexible marine riser with internal flow coupling.The dynamic model of the flexible marine riser system with internal flow coupling is derived using the Ham...This study examines the adaptive boundary control problem of flexible marine riser with internal flow coupling.The dynamic model of the flexible marine riser system with internal flow coupling is derived using the Hamiltonian principle.An analysis of internal flow’s influence on the vibration characteristics of flexible marine risers is conducted.Then,for the uncertain environmental disturbance,the adaptive fuzzy logic system is introduced to dynamically approximate the boundary disturbance,and a robust adaptive fuzzy boundary control is proposed.The uniform boundedness of the closed-loop system is proved based on Lyapunov theory.The well-posedness of the closed-loop system is proved by operator semigroup theory.The proposed control’s effectiveness is validated through comparison with existing control methods.展开更多
Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed ...Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)topologies.However,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be guaranteed.This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing.To this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is proposed.Simulation results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.展开更多
The stability of supersonic inlets faces challenges due to various changes in flight conditions,and flow control methods that address shock wave/boundary layer interactions under only one set of conditions cannot meet...The stability of supersonic inlets faces challenges due to various changes in flight conditions,and flow control methods that address shock wave/boundary layer interactions under only one set of conditions cannot meet developmental requirements.This paper proposes an adaptive bump control scheme and employs dynamic mesh technology for numerical simulation to investigate the unsteady control effects of adaptive bumps.The obtained results indicate that the use of moving bumps to control shock wave/boundary layer interactions is feasible.The adaptive control effects of five different bump speeds are evaluated.Within the range of bump speeds studied,the analysis of the flow field structure reveals the patterns of change in the separation zone area during the control process,as well as the relationship between the bump motion speed and the control effect on the separation zone.It is concluded that the moving bump endows the boundary layer with additional energy.展开更多
This paper explores the issue of secure synchronization control in piecewise-homogeneous Markovian jump delay neural networks affected by denial-of-service(DoS)attacks.Initially,a novel memory-based adaptive event-tri...This paper explores the issue of secure synchronization control in piecewise-homogeneous Markovian jump delay neural networks affected by denial-of-service(DoS)attacks.Initially,a novel memory-based adaptive event-triggered mechanism(MBAETM)is designed based on sequential growth rates,focusing on event-triggered conditions and thresholds.Subsequently,from the perspective of defenders,non-periodic DoS attacks are re-characterized,and a model of irregular DoS attacks with cyclic fluctuations within time series is further introduced to enhance the system's defense capabilities more effectively.Additionally,considering the unified demands of network security and communication efficiency,a resilient memory-based adaptive event-triggered mechanism(RMBAETM)is proposed.A unified Lyapunov-Krasovskii functional is then constructed,incorporating a loop functional to thoroughly consider information at trigger moments.The master-slave system achieves synchronization through the application of linear matrix inequality techniques.Finally,the proposed methods'effectiveness and superiority are confirmed through four numerical simulation examples.展开更多
Dear Editor,It is well known that event-triggered control(ETC)is an effective approach in addressing networked control problems for Industry 5.0.Its feasibility,however,is still restricted to canonical nonlinear syste...Dear Editor,It is well known that event-triggered control(ETC)is an effective approach in addressing networked control problems for Industry 5.0.Its feasibility,however,is still restricted to canonical nonlinear systems so far.Considering this,a gradient-based adaptive ETC scheme for noncanonical nonlinear systems is newly developed in this letter,where the hysteresis input constraints are considered also.By proper decomposition,the technical issue of handling ETC-induced measurement errors and hysteresis inputs can be transformed into the robustness problem to bounded disturbance-like terms,which is then addressed by integrating a switching modification strategy in adaptive design and developing a novel augmented error-based analysis framework.Experimental results based on a practical piezoactuator confirm the effectiveness of the proposed scheme.展开更多
The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To addre...The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.展开更多
This paper investigates the problem of fuzzy adaptive finite-time inverse optimal control for active suspension systems(ASSs).The fuzzy logic systems(FLSs)are utilized to learn the unknown non-linear dynamics and an a...This paper investigates the problem of fuzzy adaptive finite-time inverse optimal control for active suspension systems(ASSs).The fuzzy logic systems(FLSs)are utilized to learn the unknown non-linear dynamics and an auxiliary system is established.Based on the finite-time stability theory and inverse optimal theory,a fuzzy adaptive inverse finite-time inverse optimal control method is proposed.It is proven that the formulated control approach guarantees the stability of the controlled systems,while ensuring that errors converge to a small neighborhood of zero within finite time.Moreover,the optimized control performance can be achieved.Eventually,the simulation results demonstrate the effectiveness of the proposed fuzzy adaptive finite-time inverse optimal control scheme.展开更多
基金supported by the National Basic Research Program of China (No.2007CB714006)
文摘This paper concerns the observer-based adaptive control problem of uncertain time-delay switched systems with stuck actuator faults. Under the case where the original controller cannot stabilize the faulty system, multiple adaptive controllers are designed and a suitable switching logic is incorporated to ensure the closed-loop system stability and state tracking. New delay-independent sufficient conditions for asymptotic stability are obtained in terms of linear matrix inequalities based on piecewise Lyapunov stability theory. On the other hand, adaptive laws for on-line updating of some of the controller parameters are also designed to compensate the effect of stuck failures. Finally, simulation results for reference [1] model show that the design is feasible and efficient.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFB3403402)National Natural Science Foundation of China Basic Research Programme for PhD Students(Grant No.524B2049)。
文摘In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of pneumatic systems can significantly impact the force control accuracy of pneumatic polishing system end-effectors.To enhance responsiveness and control precision during the flexible polishing process,this study proposes an observer-based fuzzy adaptive control(OBFAC)scheme.To ensure control accuracy under an uncertain dynamic contact model,a fuzzy state observer is designed to estimate unmeasured states,while fuzzy logic approximates the uncertain nonlinear functions in the model to improve control performance.Additionally,the integral barrier Lyapunov function is employed to ensure that all states remain within predefined constraints.The stability of the proposed control scheme is analyzed using the Lyapunov function,and a pneumatic polishing experimental platform is constructed to conduct polishing contact force control experiments under multiple scenarios.Experimental results demonstrate that the proposed OBFAC scheme achieves superior tracking control performance compared to existing control schemes.
文摘To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,which can produce near-optimal tracking commands.Unlike the existing designs,the proposed scheme is less conservative and successfully prioritizes the solution optimality.The established RMPC follows a dualloop structure.Specifically,in the outer feedback loop,the reference attitude angle profiles are optimally tracked,while in the inner feedback loop,the control moment commands are produced by optimally tracking the desired angular rate trajectories.Besides,an adaptive disturbance observer(ADO)is designed and embedded in the inner and outer RMPC controllers to alleviate the negative effects caused by unknown external disturbances.The recursive feasibility of the optimization process,together with the input-to-state stability of the proposed RMPC,is theoretically guaranteed by introducing a tightened control constraint and terminal region.The derived property reveals that our proposal can steer the tracking error within a small region of convergence.Finally,the effectiveness of the proposed scheme is demonstrated by performing simulation studies.
文摘This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.
文摘An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China(61773056)the Scientific and Technological Innovation Foundation of Shunde Graduate School,University of Science and Technology Beijing(USTB)(BK19AE018)+2 种基金the Fundamental Research Funds for the Central Universities of USTB(FRF-TP-20-09B,230201606500061,FRF-DF-20-35,FRF-BD-19-002A)supported by Zhejiang Natural Science Foundation(LD21F030001)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(Ministry of Science and Information and Communications Technology)(NRF-2020R1A2C1005449)。
文摘This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.
基金supported by National Natural Science Foundation of China(No.60804021,No.60702063)
文摘An observer-based adaptive iterative learning control(AILC)scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays.The linear matrix inequality(LMI)method is employed to design the nonlinear observer.The designed controller contains a proportional-integral-derivative(PID)feedback term in time domain.The learning law of unknown constant parameter is differential-difference-type,and the learning law of unknown time-varying parameter is difference-type.It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized.By constructing a Lyapunov-Krasovskii-like composite energy function(CEF),we prove the boundedness of all closed-loop signals and the convergence of tracking error.A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.
基金supported in part by the National Natural Science Foundation of China(6202530361973147)the LiaoNing Revitalization Talents Program(XLYC1907050)。
文摘A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with uncertain and continuous functions in the process of backstepping design.The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint,but also mixes the states and errors to directly constrain the state,reducing the conservativeness of the constraint satisfaction condition.Considering that the states in most nonlinear systems are immeasurable,a fuzzy adaptive states observer is constructed to estimate the unknown states.Combined with adaptive backstepping technique,an adaptive fuzzy output feedback control method is proposed.The proposed control method ensures that all signals in the closed-loop system are bounded,and that the tracking error converges to a bounded tight set without violating the full state constraint.The simulation results prove the effectiveness of the proposed control scheme.
基金supported by the Natural Sciences and Engineering Research Council of Canada(N00892)in part by National Natural Science Foundation of China(51405436,51375452,61573174)
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supported by the Natural Science Founda tion of Chongqing(Grant No.CSTB2024NSCQ-MSX0944)。
文摘This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal stimuli.The circuit model converts photothermal signals into electrical signals,and its dynamic behavior is described using dimensionless equations derived from Kirchhoff's laws.Based on Helmholtz's theorem,a pseudo-Hamiltonian energy function is introduced to characterize the system's energy metabolism.Furthermore,an adaptive control function is proposed to elucidate temperature-dependent firing mechanisms,in which temperature dynamics are regulated by pseudo-Hamiltonian energy.Numerical simulations using the fourth-order Runge-Kutta method,combined with bifurcation diagrams,Lyapunov exponent spectra,and phase portraits,reveal that parameters such as capacitance ratio,phototube voltage amplitude/frequency,temperature,and thermistor reference resistance significantly modulate neuronal firing patterns,inducing transitions between periodic and chaotic states.Periodic states typically exhibit higher average pseudo-Hamiltonian energy than chaotic states.Two-parameter analysis demonstrates that phototube voltage amplitude and temperature jointly govern firing modes,with chaotic behavior emerging within specific parameter ranges.Adaptive control studies show that gain/attenuation factors,energy thresholds,ceiling temperatures,and initial temperatures regulate the timing and magnitude of system temperature saturation.During both heating and cooling phases,temperature dynamics are tightly coupled with pseudoHamiltonian energy and neuronal firing activity.These findings validate the circuit's ability to simulate photothermal perception and adaptive temperature regulation,contributing to a deeper understanding of neuronal encoding mechanisms and multimodal sensory processing.
基金supported by the National Natural Science Foundation of China (Grant No.62173009)the National Key Research and Development Program of China (Grant No.2021ZD0112302)。
文摘The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches often overlook practical input constraints or rely on centralized information. To address these issues, a novel edge-based double-layer adaptive control framework is proposed. Specifically, adaptive scaling parameters are embedded into the edge weights of the communication graph, enabling a fully distributed scheme that avoids dependence on centralized or global knowledge. Every participant modifies its strategy by exclusively utilizing local information and communicating with its neighbors to iteratively approach the NE. By incorporating damping terms into the design of the adaptive parameters, the proposed approach effectively suppresses unbounded parameter growth and consequently guarantees the boundedness of the adaptive gains. In addition, to account for actuator saturation, the proposed distributed NE seeking approach incorporates a saturation function, which ensures that control inputs do not exceed allowable ranges. A rigorous Lyapunov-based analysis guarantees the convergence and boundedness of all system variables. Finally, the presentation of simulation results aims to validate the efficacy and theoretical soundness of the proposed approach.
基金financially supported by Sichuan Science and Technology Program(Grant No.2023NSFSC1980).
文摘This study examines the adaptive boundary control problem of flexible marine riser with internal flow coupling.The dynamic model of the flexible marine riser system with internal flow coupling is derived using the Hamiltonian principle.An analysis of internal flow’s influence on the vibration characteristics of flexible marine risers is conducted.Then,for the uncertain environmental disturbance,the adaptive fuzzy logic system is introduced to dynamically approximate the boundary disturbance,and a robust adaptive fuzzy boundary control is proposed.The uniform boundedness of the closed-loop system is proved based on Lyapunov theory.The well-posedness of the closed-loop system is proved by operator semigroup theory.The proposed control’s effectiveness is validated through comparison with existing control methods.
基金supported by the National Natural Science Foundation of China under Grant U21A20449in part by Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2。
文摘Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)topologies.However,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be guaranteed.This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing.To this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is proposed.Simulation results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.
基金supported by the National Key R&D Program of China(Grant No.2019YFA0405300)the National Natural Science Foundation of China(Grant No.11972368)the Natural Science Foundation of Hunan Province(Grant No.2021JJ10045).
文摘The stability of supersonic inlets faces challenges due to various changes in flight conditions,and flow control methods that address shock wave/boundary layer interactions under only one set of conditions cannot meet developmental requirements.This paper proposes an adaptive bump control scheme and employs dynamic mesh technology for numerical simulation to investigate the unsteady control effects of adaptive bumps.The obtained results indicate that the use of moving bumps to control shock wave/boundary layer interactions is feasible.The adaptive control effects of five different bump speeds are evaluated.Within the range of bump speeds studied,the analysis of the flow field structure reveals the patterns of change in the separation zone area during the control process,as well as the relationship between the bump motion speed and the control effect on the separation zone.It is concluded that the moving bump endows the boundary layer with additional energy.
文摘This paper explores the issue of secure synchronization control in piecewise-homogeneous Markovian jump delay neural networks affected by denial-of-service(DoS)attacks.Initially,a novel memory-based adaptive event-triggered mechanism(MBAETM)is designed based on sequential growth rates,focusing on event-triggered conditions and thresholds.Subsequently,from the perspective of defenders,non-periodic DoS attacks are re-characterized,and a model of irregular DoS attacks with cyclic fluctuations within time series is further introduced to enhance the system's defense capabilities more effectively.Additionally,considering the unified demands of network security and communication efficiency,a resilient memory-based adaptive event-triggered mechanism(RMBAETM)is proposed.A unified Lyapunov-Krasovskii functional is then constructed,incorporating a loop functional to thoroughly consider information at trigger moments.The master-slave system achieves synchronization through the application of linear matrix inequality techniques.Finally,the proposed methods'effectiveness and superiority are confirmed through four numerical simulation examples.
文摘Dear Editor,It is well known that event-triggered control(ETC)is an effective approach in addressing networked control problems for Industry 5.0.Its feasibility,however,is still restricted to canonical nonlinear systems so far.Considering this,a gradient-based adaptive ETC scheme for noncanonical nonlinear systems is newly developed in this letter,where the hysteresis input constraints are considered also.By proper decomposition,the technical issue of handling ETC-induced measurement errors and hysteresis inputs can be transformed into the robustness problem to bounded disturbance-like terms,which is then addressed by integrating a switching modification strategy in adaptive design and developing a novel augmented error-based analysis framework.Experimental results based on a practical piezoactuator confirm the effectiveness of the proposed scheme.
基金supported by National Natural Science Foundation of China(No.52302472)。
文摘The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.
基金supported by the National Natural Science Foundation of China under 62173172。
文摘This paper investigates the problem of fuzzy adaptive finite-time inverse optimal control for active suspension systems(ASSs).The fuzzy logic systems(FLSs)are utilized to learn the unknown non-linear dynamics and an auxiliary system is established.Based on the finite-time stability theory and inverse optimal theory,a fuzzy adaptive inverse finite-time inverse optimal control method is proposed.It is proven that the formulated control approach guarantees the stability of the controlled systems,while ensuring that errors converge to a small neighborhood of zero within finite time.Moreover,the optimized control performance can be achieved.Eventually,the simulation results demonstrate the effectiveness of the proposed fuzzy adaptive finite-time inverse optimal control scheme.
