This paper considers the adaptive finite-time control and observer design method for a class of non-strict feedback systems with unmeasurable states,unknown nonlinear dynamics and actuator faults.In this paper,an obse...This paper considers the adaptive finite-time control and observer design method for a class of non-strict feedback systems with unmeasurable states,unknown nonlinear dynamics and actuator faults.In this paper,an observer is proposed to estimate the unmeasurable states in finite-time based on adaptive technique and neural networks,while the actuator faults are not included.Command filter is used to solve the computational explosion and singularity problems caused by the traditional backstepping and non-strict feedback structure,respectively.Since the fault efficiency indicators in real systems are not available,two-layer neural networks are adopted,where the first network is to estimate the unknown nonlinearities of systems and the second one is to estimate fault efficiency indicators and unknown nonlinear terms.The proposed scheme guarantees that states are bounded through stability theorem.Finally,two experiments including a numerical example and a spring-mass-damper system are given to verify the effectiveness of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.62003183,62373208,and 62003097the Taishan Scholar program of Shandong Province of China under Grant No.tsqn202306218the Talent Introduction and Cultivation Plan for Youth Innovation of Universities in Shandong Province。
文摘This paper considers the adaptive finite-time control and observer design method for a class of non-strict feedback systems with unmeasurable states,unknown nonlinear dynamics and actuator faults.In this paper,an observer is proposed to estimate the unmeasurable states in finite-time based on adaptive technique and neural networks,while the actuator faults are not included.Command filter is used to solve the computational explosion and singularity problems caused by the traditional backstepping and non-strict feedback structure,respectively.Since the fault efficiency indicators in real systems are not available,two-layer neural networks are adopted,where the first network is to estimate the unknown nonlinearities of systems and the second one is to estimate fault efficiency indicators and unknown nonlinear terms.The proposed scheme guarantees that states are bounded through stability theorem.Finally,two experiments including a numerical example and a spring-mass-damper system are given to verify the effectiveness of the proposed method.
