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This paper investigates the problem of robust output feedback control tracking for a class of nonlinear dual-rate sample-data systems subject to time-varying disturbances. These dual-rate systems, known as slow measurement fast control (SMFC) systems, are characterised by a slow sampling rate of the sensor and a fast updating rate of the controller. To facilitate high-frequency signal tracking, this paper introduces an alternating predictive generalised proportional integral observer (APGPIO). The proposed control method not only effectively restores the system state and captures disturbance changes between samples but also enhances the control bandwidth and anti-disturbance ability, enabling effective tracking of high-frequency reference outputs. By combining it with a fast updating sample-data feedback controller, APGPIO utilises predictive methods to increase the sampling rate of the system's sensors, matching the update frequency of the controller. Stability analysis demonstrates that the tracking error of this hybrid nonlinear system converges to a bounded region centred at the origin. Finally, the designed method's high control bandwidth and anti-disturbance ability are demonstrated through multiple sets of simulations using a real system. [ABSTRACT FROM AUTHOR]

In this brief, a fault diagnosis (FD) and fault-tolerant control framework is addressed for a class of typical non-Gaussian processes by using two kinds of different fuzzy modeling methods. In order to describe the gray-box dynamics between probability distribution function (PDF) of system output and controlled input, the well-known fuzzy logic systems and the T–S fuzzy models are imported at the same time. Different from some ecumenical results of FD, the PDF of output is assumed to be available rather than output signal itself. By utilizing adaptive projection algorithm, a measurable distribution-based fuzzy diagnostic filter is raised to successfully calculate the state vector and the size of fault. Meanwhile, the dynamical stability of error system can be ensured when non-Gaussian processes exist unknown fault. Moreover, the fault-tolerant controller is provided where not only the diagnostic error but also the system state is retained in a small region. By considering constant and time-varying faults, respectively, a satisfactory simulation result can be achieved to show the effectiveness of designed algorithm. [ABSTRACT FROM AUTHOR]