Joint state and fault estimation for nonlinear systems with missing measurements and random component faults under Round-Robin Protocol - Song, Xiaoke; Rong, Lihong; Li, Beiyuan; Wang, Ziyang; Li, Juan (International journal of electrical power & energy systems, 2023)
This work studies the fault estimation problem for a class of nonlinear systems subjected to missing measurements and random component faults under the Round-Robin protocol. Considering that the communication bandwidth is not infinite in the actual projects, the Round-Robin protocol is used for data scheduling, in which sensor nodes cyclically transmit data to the controller/filter. Meanwhile, component faults are also considered which usually caused by actuator faults. It should be emphasized that the introduction of RR protocol will make the system output equation more complex, the MMs and component faults will generate more unknown parameters, which will bring new challenges during the design of the filter. In respect of the problems above, a recursive joint state and fault estimation (JSFE) algorithm is proposed to solve the difficulties caused by missing measurements, component faults, and nonlinearity. The performance of the designed JSFE filter is analyzed by using the boundedness theorem and the reasonable parameter condition is derived for this filter. Finally, an engineering example is given to verify the effectiveness and feasibility of the proposed joint state and fault estimation scheme. This study provides a new theoretical basis and reference for the JSFE of nonlinear systems with mixed faults under RR protocol. •A system which subjected to missing measurements, component faults and nonlinearity under Round-robin protocol is considered, and a joint state and fault estimation (JSFE) algorithm is designed.•A new JSFE filter is proposed by giving a prediction error covariance upper bound, and the JSFE filter gain is given by minimizing the error covariance.•The performance of the proposed JSFE algorithm is analyzed and the parameters condition is given which guarantees boundedness in the mean-square sense of the error covariance.