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Considering interconnections among subsystems, we propose an adaptive neural tracking control scheme for a class of multiple-input-multiple-output (MIMO) non-affine pure-feedback time-delay nonlinear systems with input saturation. Neural networks (NNs) are employed to approximate unknown functions in the design procedure, and the separation technology is introduced here to tackle the problem induced from unknown time-delay items. The adaptive neural tracking control scheme is constructed by combining Lyapunov–Krasovskii functionals, NNs, the auxiliary system, the implicit function theory and the mean value theorem along with the dynamic surface control technique. Also, it is proven that the strategy guarantees tracking errors converge to a small neighbourhood around the origin by appropriate choice of design parameters and all signals in the closed-loop system uniformly ultimately bounded. Numerical simulation results are presented to demonstrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Rendezvous problem of nonholonomic unicycles has been considered in this paper and nonlinear distributed controllers based on backstepping are constructed to guarantee the consensus of both position and direction simultaneously. Cascaded theory of nonlinear system is utilised to reduce the rendezvous problem of nonholonomic unicycles to consensus problem of moving direction and consensus problem of position, respectively. The uncontrollable challenge of position system after consensus of moving direction has been successfully removed through dilated coordinate transformation. Then, a modified backstepping methodology based on the coupling structure of position system is derived to guarantee the consensus of each unicycle's position. In the end, not only consensus of position but also consensus of direction has been realised for multiple nonholonomic unicycles using the controllers proposed in this paper. Simulation results using Matlab illustrate the effectiveness of the results of this paper. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered adaptive approximately optimal tracking control approach is proposed for a class of non-affine nonlinear single-input single-output (SISO) systems via output feedback. With the help of fuzzy logic systems (FLSs), a fuzzy state observer is designed for the estimation of internal states by approximating unknown nonlinear functions, where a low-pass filter (LPF) is added for the algebraic loop problem. Then, the output feedback control approach, in the backstepping framework and the event-triggered mechanism, is presented. It contains the adaptive backstepping control and the approximately optimal control via adaptive dynamic programming (ADP) technology, and the computation cost and transmission load are reduced while guaranteeing that the performance index of the system is approximately minimised. Finally, two simulation examples are provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]