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The problem of adaptive output feedback control for a class of nonlinear Cyber-Physical Systems(CPSs) with unknown control direction under sensor and actuator attacks is studied. By implementing a suitable linear state transformation, the original system is transformed into a new system for facilitating the design of output feedback controller. Nussbaum gain function method is adopted to overcome the design difficulty resulted from unknown control coefficients and network attacks. Based on the state estimate and the attacked measurement output signal, a new event-triggered adaptive output-feedback control strategy is proposed by means of neural network approximation method. The proposed controller can not only achieve the semi-globally uniformly ultimately boundedness of the closed-loop system, but also greatly reduce the number of transmitted data. In addition, the "complexity explosion" problem in backstepping design can be overcome by means of the dynamic surface control (DSC) method based on a nonlinear filter. Finally, a robotic arm system is utilized to verify the effectiveness and practicality of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper addresses the output feedback adaptive resilient control problem for a class of stochastic nonlinear systems under sensor and actuator attacks. By applying a linear state transformation, the original system is transformed into a new one for which output feedback control becomes possible. The Nussbaum function is exploited to overcome the unknown control direction problem generated by uncertain control coefficients and adversary attacks. Based on the available compromised output signal and the states reconstructed by a new extended state observer, an output feedback adaptive resilient control scheme is proposed for such systems by combining the neural-networks-based approximation approach, backstepping technique and some special methods. The proposed controller can guarantee that all signals in the closed-loop system are semiglobally bounded in probability and the stabilisation error variables remain in an adjustable vicinity of origin. Finally, the effectiveness and applicability of the proposed control methodology is verified by two illustrative examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of output feedback adaptive tracking control for a class of uncertain nonlinear time-delay systems (NTDS) with both uncertain output function and unknown control directions. By introducing a linear state transformation, the original system is transformed into a new system for which output feedback control design becomes feasible. Without the precise information on output function, an input-driven high-gain state observer is presented to estimate the unavailable states of the new system. Moreover, some radial basis function neural networks (RBFNNs) are employed to approximate the lumped unknown nonlinear functions during the procedure of control design. Then, by combining the Lyapunov–Krasovskii functional method, the Nussbaum gain function approach and the backstepping technique, a novel adaptive output feedback control scheme including only one parameter to be updated is developed for such systems. As a result, all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) while the tracking error can remain in a small neighbourhood of zero. Finally, two simulation examples are given to validate the effectiveness and applicability of the proposed design method. [ABSTRACT FROM AUTHOR]