Adaptive Fuzzy Inverse Compensation For Actuator Deadzone With Piecewise Timevarying Parameters Existing adaptive control methods for canceling actuator deadzone nonlinearity are restricted to an assumption that the deadzone parameters are constants. But in realistic circumstances, such assumption cannot be always satisfied because the deadzone parameters may vary with time and even experience abrupt jumps. To address this issue, a new adaptive fuzzy control strategy is proposed to handle such dynamic deadzone characteristics. Specifically, our scheme is developed with a modified tuning functions method and a novel piecewise Lyapunov analysis. Moreover, to improve the system transient performance in the presence of the dynamic deadzone nonlinearities, prescribed performance bounds based method is incorporated with the backstepping design. It is proved that besides the system stability, the tracking error is always preserved in a predefined compact set irrespective of the sudden parameter jumps. Simulation example demonstrates this conclusion.

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.