Direct Adaptive Data Cloud based Fuzzy Control for NARMAX System This work presents a novel nonlinear direct adaptive data cloud based fuzzy controller design method for a general class of NARMAX systems employing the evolving fuzzy approximation. In the proposed fuzzy controller, dynamic approximation capability is performed by the combined action of structure configuration and parameter adjustment using multiple strategies of recruiting, pruning and updating the data clouds, and selftuning the consequent parameters. The normalization distance between two local densities is employed to measure the familiarity of each two data clouds, which is the criteria to trigger the pruning of redundant data cloud to further avoid the overlap or conflict. The simulation results demonstrate low computation cost and favorable tracking performance.

Local stabilization of nonlinear discretetime systems with timevarying delay in the states and saturating actuators Abstract   Although most of the control design methods assume unbounded control signals, real systems do have saturating actuators, which may degenerate closedloop performance or even lead to unstable behavior. Additionally, the delay is generally almost ubiquitous in processes, that is also imposing performance and stability constraints. Our main contribution is to provide a controller design methodology for the stabilization of delayed systems under saturating actuators. Specifically, we address the design of nonParallel Distributed Compensation (nonPDC) state feedback fuzzy control laws that locally stabilize a class of nonlinear discretetime systems with state timevarying delay and saturating actuators. The proposed nonPDC control law depends on the current state xk and the state delayed by     d  xc2xaf     samples. Based on the LyapunovKrasovskii approach, we characterize the safe region of initial conditions through two sets: an ellipsoidal one for the current state vector, and another set for the delayed state vectors. Trough two convex optimization procedures, we can maximize the estimate of the region of attraction of the closedloop control system. Additionally, a relaxation method inspired by the FrankWolfe algorithm is introduced, yielding better estimates of the region of attraction. The achievements are compared with other finds in the literature, illustrating the efficiency of this proposal.