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In this study, by means of the techniques of adaptive multi‐dimensional Taylor network (MTN) control, the tracking problem of a class of stochastic non‐linear systems with input delay is studied. Firstly, based on the Padé approximation method, a new variable is employed to overcome the problem of input delay, and MTNs are applied to estimate the non‐linear functions in the design of the controller. Secondly, an adaptive MTN controller design scheme is developed in the framework of backstepping. Thirdly, the stability of the closed‐loop system is analysed using Lyapunov's stability theory. Finally, the effectiveness of the proposed design approach is demonstrated by two examples. [ABSTRACT FROM AUTHOR]

This study is concerned with a joint localisation and tracking problem for autonomous underwater vehicle (AUV), subject to asynchronous clock and stratification effect in cyber channels as well as the model disturbances in physical channels. The authors first construct an integrated state and clock model, which allows the co‐design of communication and control strategies. Then, an asynchronous localisation algorithm is developed to estimate the position of AUV, where the asynchronous clock and the stratification effect are both considered. With the estimated position information, a reinforcement learning based tracking controller is developed for the AUV to track the reference point. Particularly, the multivariate probabilistic collocation method is adopted to evaluate the model uncertainty. Moreover, the convergence analyses for the localisation algorithm and tracking controller are also given. Finally, simulation results are presented to show the effectiveness of the proposed method. It is demonstrated that the communication energy consumption and tracking error can be significantly reduced, through the co‐design of localisation and tracking strategies. [ABSTRACT FROM AUTHOR]

In this study, the zero‐error convergence of the iterative learning control for a tracking problem is realised by incorporating a uniform quantiser with an encoding and decoding mechanism. Under this scheme, the system output is first transformed and encoded. Then, the encoded information is transmitted back for updating the input. The results are extended to a finite quantisation level situation under the same framework and a simulation using a permanent magnet linear motor is performed to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]