This paper addresses the tracking control problem of a class of multiple-input–multiple-output nonlinear systems subject to actuator faults.Achieving a balance between input saturation and performance constraints,rat...This paper addresses the tracking control problem of a class of multiple-input–multiple-output nonlinear systems subject to actuator faults.Achieving a balance between input saturation and performance constraints,rather than conducting isolated analyses,especially in the presence of frequently encountered unknown actuator faults,becomes an interesting yet challenging problem.First,to enhance the tracking performance,Tunnel Prescribed Performance(TPP)is proposed to provide narrow tunnel-shape constraints instead of the common over-relaxed trumpet-shape performance constraints.A pair of non-negative signals produced by an auxiliary system is then integrated into TPP,resulting in Saturation-tolerant Prescribed Performance(SPP)with flexible performance boundaries that account for input saturation situations.Namely,SPP can appropriately relax TPP when needed and decrease the conservatism of control design.With the help of SPP,our developed Saturation-tolerant Prescribed Control(SPC)guarantees finite-time convergence while satisfying both input saturation and performance constraints,even under serious actuator faults.Simulations are conducted to illustrate the effectiveness of the proposed SPC.展开更多
In the field of fault diagnosis, the state equation of nonlinear system, including the actuator and the component, has been established. When the faults in the system appear, it is difficult to observe the fault isola...In the field of fault diagnosis, the state equation of nonlinear system, including the actuator and the component, has been established. When the faults in the system appear, it is difficult to observe the fault isolation between the actuator and the component. In order to diagnose the component fault in the nonlinear systems, a novel strategy is proposed. The nonlinear state equation with only the component system is built on mathematical equations. The nonlinearity of the component equation is expanded and estimated with Taylor series. If the actuator is perfect, the anomaly of the state equations reflects the component fault. The fault feature index is defined to detect the component fault and the initial fault. The numerical examples of the component faults are simulated for multiple-input multiple-output(MIMO)nonlinear systems. The results show that the component faults,as well as the incipient faults, can be detected. Furthermore, the effectiveness of the proposed strategy is verified. This method can also provide a foundation for the component fault reconfiguration control.展开更多
The learning control law for the general MIMO nonlinear systems with white noise distrubance is presented in the paper,it has extremely simple, recursive, incremental form,and strong robustness,it can also deal with t...The learning control law for the general MIMO nonlinear systems with white noise distrubance is presented in the paper,it has extremely simple, recursive, incremental form,and strong robustness,it can also deal with the ill-conditioned systems.The new adaptive control scheme is presented when the parameters of the MIMO nonlinear systems are unknown.The convergence,BIBO stability,and robustness of learning adaptive control scheme are also discussed in this paper.展开更多
In this paper,an adaptive neural tracking control scheme for a class of uncertain switched multi-input multi-output(MIMO)pure-feedback nonlinear systems is proposed.The considered MIMO pure-feedback nonlinear system c...In this paper,an adaptive neural tracking control scheme for a class of uncertain switched multi-input multi-output(MIMO)pure-feedback nonlinear systems is proposed.The considered MIMO pure-feedback nonlinear system contains input and output constraints,completely unknown nonlinear functions and time-varying external disturbances.The unknown nonlinear functions representing system uncertainties are identified via radial basis function neural networks(RBFNNs).Then,the Nussbaum function is utilized to deal with the nonlinearity issue caused by the input saturation.To prevent system outputs from violating prescribed constraints,the barrier Lyapunov functions(BLFs)are introduced.Also,a switched disturbance observer is designed to make the time-varying external disturbances estimable.Based on the backstepping recursive design technique and the Lyapunov stability theory,the developed control method is verified applicable to ensure the boundedness of all signals in the closed-loop system and make the system output track given reference signals well.Finally,a numerical simulation is given to demonstrate the effectiveness of the proposed control method.展开更多
基金supported by the National Research Foundation Singapore under its AI Singapore Programme(Award Number:[AISG2-GC-2023-007]).
文摘This paper addresses the tracking control problem of a class of multiple-input–multiple-output nonlinear systems subject to actuator faults.Achieving a balance between input saturation and performance constraints,rather than conducting isolated analyses,especially in the presence of frequently encountered unknown actuator faults,becomes an interesting yet challenging problem.First,to enhance the tracking performance,Tunnel Prescribed Performance(TPP)is proposed to provide narrow tunnel-shape constraints instead of the common over-relaxed trumpet-shape performance constraints.A pair of non-negative signals produced by an auxiliary system is then integrated into TPP,resulting in Saturation-tolerant Prescribed Performance(SPP)with flexible performance boundaries that account for input saturation situations.Namely,SPP can appropriately relax TPP when needed and decrease the conservatism of control design.With the help of SPP,our developed Saturation-tolerant Prescribed Control(SPC)guarantees finite-time convergence while satisfying both input saturation and performance constraints,even under serious actuator faults.Simulations are conducted to illustrate the effectiveness of the proposed SPC.
基金supported by the National Natural Science Foundation of China(6117509261433016)
文摘In the field of fault diagnosis, the state equation of nonlinear system, including the actuator and the component, has been established. When the faults in the system appear, it is difficult to observe the fault isolation between the actuator and the component. In order to diagnose the component fault in the nonlinear systems, a novel strategy is proposed. The nonlinear state equation with only the component system is built on mathematical equations. The nonlinearity of the component equation is expanded and estimated with Taylor series. If the actuator is perfect, the anomaly of the state equations reflects the component fault. The fault feature index is defined to detect the component fault and the initial fault. The numerical examples of the component faults are simulated for multiple-input multiple-output(MIMO)nonlinear systems. The results show that the component faults,as well as the incipient faults, can be detected. Furthermore, the effectiveness of the proposed strategy is verified. This method can also provide a foundation for the component fault reconfiguration control.
文摘The learning control law for the general MIMO nonlinear systems with white noise distrubance is presented in the paper,it has extremely simple, recursive, incremental form,and strong robustness,it can also deal with the ill-conditioned systems.The new adaptive control scheme is presented when the parameters of the MIMO nonlinear systems are unknown.The convergence,BIBO stability,and robustness of learning adaptive control scheme are also discussed in this paper.
基金partially supported by the National Natural Science Foundation of China under Grant No.62203064the Eduction Committee Liaoning Province,China under Grant No. LJ2019002
文摘In this paper,an adaptive neural tracking control scheme for a class of uncertain switched multi-input multi-output(MIMO)pure-feedback nonlinear systems is proposed.The considered MIMO pure-feedback nonlinear system contains input and output constraints,completely unknown nonlinear functions and time-varying external disturbances.The unknown nonlinear functions representing system uncertainties are identified via radial basis function neural networks(RBFNNs).Then,the Nussbaum function is utilized to deal with the nonlinearity issue caused by the input saturation.To prevent system outputs from violating prescribed constraints,the barrier Lyapunov functions(BLFs)are introduced.Also,a switched disturbance observer is designed to make the time-varying external disturbances estimable.Based on the backstepping recursive design technique and the Lyapunov stability theory,the developed control method is verified applicable to ensure the boundedness of all signals in the closed-loop system and make the system output track given reference signals well.Finally,a numerical simulation is given to demonstrate the effectiveness of the proposed control method.
