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A decentralized adaptive neural output feedback control scheme is presented for a class of large-scale time-delay systems with saturating inputs. The observer is constructed to estimate the immeasurable states of the system and the auxiliary system is designed to compensate the nonsmooth nonlinearities of input saturation constraints. Also, the control strategy is developed by the backstepping recursive method combining with neural networks (NNs) for the approximation of the unknown functions and dynamic surface control (DSC) technique for the well known ‘explosion of complexity’ problem. The advantage of this scheme is that it only relies on the output information of the system and there is no requirement for exact priori knowledge about the system parameters. It is proved that the control approach guarantees all signals in the closed-loop system uniformly ultimately bounded. Simulation results are provided to demonstrate the effectiveness and usefulness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, we focus on an output secure consensus control issue for nonlinear multi-agent systems (MASs) under sensor and actuator attacks. Followers in an MAS are in strict-feedback form with unknown control directions and unknown dead-zone input, where both sensors and nonlinear characteristics of dead-zone in actuators are paralyzed by malicious attacks. To deal with sensor attacks, uncertain dynamics in individual follower are separated by a separation theorem, and estimation parameters are introduced for compensating and mitigating the influence from adversaries. The influence from actuator attacks are treated as a total displacement in a dead-zone nonlinearity, and an upper bound, as well as its estimation, is introduced for this displacement. The dead-zone nonlinearity, sensor attacks and unknown control gains are gathered together regarded as composite unknown control directions, and Nussbaum functions are utilized to address the issue of unknown control directions. A distributed secure consensus control strategy is thus developed recursively for each follower in the framework of surface control method. Theoretically, the stability of the closed-loop MAS is analyzed, and it is proved that the MAS achieves output consensus in spite of nonlinear dynamics and malicious attacks. Finally, theoretical results are verified via a numerical example and a group of electromechanical systems. [ABSTRACT FROM AUTHOR]

An event-trigger-based adaptive output feedback approximately optimal tracking control strategy is presented for a class of multiple-input multiple-output (MIMO) non-affine nonlinear systems. A fuzzy state observer is designed for estimation of internal states, where fuzzy logic systems (FLSs) are employed to approximate unknown nonlinear dynamics. In the event-triggered and backstepping control framework, an output feedback control strategy, consisting of the adaptive backstepping control and the approximately optimal control via the adaptive dynamic programming (ADP) method, is presented. This strategy not only reduces the number of executions from the actuator but also guarantees that the performance index of the system is approximately minimized. Finally, two simulation examples are provided to verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]