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Summary: In this article, the adaptive tracking control problem is considered for a class of uncertain nonlinear systems with input delay and saturation. To compensate for the effect of the input delay and saturation, a compensation system is designed. Radial basis function neural networks are directly utilized to approximate the unknown nonlinear functions. With the aid of the backstepping method, novel adaptive neural network tracking controllers are developed, which can guarantee all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded, and the system output can track the desired signal with a small tracking error. In the end, a simulation example is given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of static anti-windup design for uncertain continuous-time Markovian jump systems with partially unknown transition rates in the face of actuator saturation. The underlying system is subject to time-varying and norm-bounded parameter uncertainties in both the state and input matrices. It is assumed that a set of stabilizing dynamic output-feedback controllers have been designed for the system in the absence of control saturation. The objective is to design anti-windup compensation gains for the given controllers such that the system can still be stabilized, irrespective of whether actuator saturation appears or not. To obtain a maximum estimation of the domain of attraction of the resulting closed-loop system, a convex optimization problem in the linear matrix inequality framework is formulated. Furthermore, the results are extended to the cases of the systems with completely known transition rates and with completely unknown transition rates. Finally, the usefulness of the developed method is demonstrated through simulation examples. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]