Hong Wang

TL;DR: An Iterative Learning Control scheme is presented for the control of the shape of the output probability density functions (PDF) for a class of stochastic systems in which the relationship between approximation basis functions and the control input is linear, and the Stochastic system is not necessarily Gaussian.

TL;DR: It is shown that the solvability can be cast into a group of matrix inequalities and this leads to a feasible controller design procedure that can guarantee the required tracking convergence with enhanced robustness.

TL;DR: In this article, a new particle filter is developed based on Student-t distributions, which are heavier tailed than Gaussians and hence more robust, called the student-t distribution particle filter (SPF).

TL;DR: This paper presents a novel concept which is firstly established to describe the probabilistic property of the couplings among system states, and a new algorithm is presented to minimize the elements of the states covariance matrix for a class of multivariate dynamic stochastic nonlinear systems.

TL;DR: In this paper, an adaptive observer-based approach is established so as to construct several effective residual signals that can be used to perform the required fault detection and estimation, and a parameter dependent Lyapunov function is used to formulate a set of adaptive tuning rules for the time-varying parameters involved in both the adaptive observer and the fault estimation error.