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Summary: In this paper, the adaptive control is investigated by switching linear controllers for a class of nonlinear parameterized systems with multiple unknown control directions. Parametric uncertainties entering the state equations nonlinearly can be fast time‐varying or jumping at unknown time instants, the bounds of the parametric uncertainties are not required to know a priori and the multiple control directions are unknown. Our proposed adaptive controller is a switching type controller. First, sufficient conditions for designing an adaptive stabilizer are derived. Then, a switching‐type adaptive controller is designed, in which a linear controller with two undetermined design parameters to be tuned is recursively designed by adding a power integrator, and a switching mechanism is proposed to tune these parameters online for compensating the multiple unknown control directions and the unknown bounds of the parametric uncertainties. The undetermined design parameters are based on the upper bound estimation of the unknown parameters existing not only in the nonlinear functions but also in the control directions functions. The proposed adaptive controller globally asymptotically stabilizes the system in the sense that, for any initial conditions, the state converges to the origin while all the signals of the closed‐loop system are bounded. Finally, an example is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article studies the adaptive state‐feedback control problem of output‐constrained stochastic high‐order nonlinear systems with stochastic integral input‐to‐state stability (SiISS) inverse dynamics. A key nonlinear transformation function is constructed to convert the original output‐constrained stochastic nonlinear system into an equivalent form without any output constraint. By subtly using the SiISS small‐gain condition and fully extracting the characteristics of system nonlinearities, two new control design and analysis methods are developed to guarantee that the closed‐loop system has an almost surely unique solution, all the closed‐loop signals are bounded almost surely, and the equilibrium point is stable in probability without the violation of output constraint. A simulation result is provided to show the effectiveness of this control method. [ABSTRACT FROM AUTHOR]

This article investigates the adaptive fault‐tolerant tracking control problem for nonlinear systems with prescribed performance and input dead‐zone. First, a new composite variable is constructed by using the characteristics of actuator fault and input dead‐zone for the modeling purpose. Second, an adaptive neural network observer is designed to estimate the system states in the presence of inaccurate feedback information. Third, the proposed control strategy effectively counteracts the effects of sensor failure and unknown nonlinear functions, which makes the tracking error confined within the performance boundary and all the signals of the closed‐loop system semi‐globally uniformly ultimately bounded. Finally, an application oriented example is provided to demonstrate the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]