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A design method of neural network adaptive interval observer for the continuous‐time unknown nonlinear system is proposed. The bounds of unknown nonlinear functions are described by the state and the input, so that the bounds of the nonlinear function vector are unmeasurable. Therefore, this method can be used to observe systems with higher degree of nonlinearity and any priori knowledge about systems. In this work, the hyper basis function neural network (HBFNN) is presented to estimate the boundary (including lower and upper bounds) of the unknown nonlinear function vector. Furthermore, the non‐negativity and stability of the error dynamic system are given, and the convergence and non‐negativity are also guaranteed. Compared with the traditional interval observer, the efficiency of the proposed approach is verified through a numerical simulation example. [ABSTRACT FROM AUTHOR]

In order to save the usage of system resources and adapt the variation of plant state, this study first proposes a novel stochastic‐sampling‐based adaptive event‐triggered scheme (AETS). Second, in the framework of time‐delay systems, the closed‐loop control system is modelled as a class of delayed stochastic systems where time‐delay is distributed in some intervals with probability. Then, by employing stochastic analysis tool and Lyapunov stability theory, a stability criterion for this class of delayed stochastic systems is established to ensure that the system possesses stochastically asymptotic stability with an H∞ disturbance attenuation performance. Also, a co‐design of parameter matrices of the state‐feedback controller and the stochastic‐sampling‐based AETS is implemented. Third, based on the obtained co‐design condition, a convex optimisation algorithm for the tradeoffs between disturbance attenuation performance and resource utilisation of the closed‐loop control system is further developed. Finally, the effectiveness and feasibility of the proposed control strategy are illustrated by two numerical examples of adaptive event‐triggered control for networked control systems under stochastic sampling. [ABSTRACT FROM AUTHOR]

The additional threshold of a mixed event‐triggering mechanism (ETM) makes it impossible to guarantee the requirement of asymptotic stability. As a consequence, it is necessary to investigate input‐to‐state practically stable (ISpS) which is a mild stability property. In this study, the authors firstly model the closed‐loop event‐triggered control system as a delayed linear parameter‐varying (LPV) systems with a mixed ETM and parameter uncertainty. Then, a co‐design condition in a sense of ISpS of controller gain and trigger parameters for the delayed LPV systems is obtained by employing the parameter‐dependent Lyapunov–Krasovskii functionals and congruent transformation method. Moreover, based on the obtained co‐design condition in a sense of ISpS, an optimisation algorithm for the tradeoffs between resource utilisation and robustness of the closed‐loop system is further proposed. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The problem of robust stochastic stability and delayed‐state‐feedback stabilisation of uncertain Markovian jump linear systems with random Markov delays is investigated. Based on the Lyapunov stability theory and robust analysis techniques, some robust stochastic stability criteria are derived in terms of linear matrix inequalities. Robust delayed‐state‐feedback controllers that stochastically stabilise the uncertain Markovian jump linear systems is also proposed. The state variable on the controller is assumed to be dependent on the Markov delay that has uncertain transition probabilities. Finally, numerical examples are provided to illustrate the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This study studies coordinated control of a platoon of vehicles consisting of a leader and multiple followers when multiple vehicles suffer from disturbances. Small disturbances acting on one vehicle may cause large spacing errors between intervehicles. In addition, the spacing errors propagate up or down in a vehicle platoon. To mitigate the adverse effect of the disturbances, a control law is proposed based on only the position information of the vehicle itself and its preceding vehicle. To analyse the disturbances propagation among the platoon, the upper bounds of two transfer functions are presented including the transfer function matrix 𝒢de from external disturbances to spacing errors as well as the transfer function matrix 𝒢ue from a leader's input control to spacing errors. Furthermore, an alternative control law is proposed when the velocity of the leader is known by the followers. In this case, two tighter upper bounds of 𝒢lde and 𝒢lue are presented. The derived upper bounds of the transfer functions are independent of the number of vehicles in the platoon. Simulations verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]