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
The article is devoted to the almost disturbance decoupling problem for high-order fully actuated(HOFA)nonlinear systems with strict-feedback form.Using the full-actuation feature of high-order fully actuated systems ...The article is devoted to the almost disturbance decoupling problem for high-order fully actuated(HOFA)nonlinear systems with strict-feedback form.Using the full-actuation feature of high-order fully actuated systems and Lyapunov stability theory,a state feedback control law and virtual control laws are designed.The unknown disturbances are handled by almost disturbance decoupling(ADD)method.Finally,the effectiveness of the control strategy is verified by stability analysis and simulation.展开更多
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.
基金This research was supported by the Taishan Scholar Project of Shandong Province of China under Grant Nos.2015162 and tsqn201812093.
文摘The article is devoted to the almost disturbance decoupling problem for high-order fully actuated(HOFA)nonlinear systems with strict-feedback form.Using the full-actuation feature of high-order fully actuated systems and Lyapunov stability theory,a state feedback control law and virtual control laws are designed.The unknown disturbances are handled by almost disturbance decoupling(ADD)method.Finally,the effectiveness of the control strategy is verified by stability analysis and simulation.
