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This paper designs a resilient stubborn observer for switched autonomous systems (SASs) with an energy harvesting sensor (EHS). In order to extend the lifespan of the sensor, a sensor with the ability of harvesting energy is used. For restraining the effect of measurement outliers, a stubborn observer is designed which includes a saturation function with the varying saturation levels. The observer gain fluctuation is considered which is dealt with by the resilience filtering scheme. The aim of this paper is, considering observer gain fluctuation, the measurement outliers and an EHS for SASs, to design a resilient stubborn observer which can guarantee the error dynamics to meet the given performance requirement. Specifically, the existence conditions are derived firstly and then the resilient stubborn observer is designed by linear matrix inequality (LMI) technology. At the end, the performance of the designed observer is evaluated through a numerical simulation and a set of comparative experiments are implemented to further signify the merits of the designed observer. [ABSTRACT FROM AUTHOR]

In this paper, the localization problem of the mobile robot (MR) with an energy harvesting sensor (EHS) is studied. A specific situation is regarded, which is that the MR equips with an EHS who owns the capacity for absorbing the energy from the surroundings. When the sensor energy is sufficient, the sensor can send its measurement to the filter. Otherwise, the sensor energy‐induced measurement missing phenomenon will occur. For the sake of acquiring the measurement missing rate, the relationship between the amount of the sensor energy and its probability distribution is derived recursively at each time instant. The purpose of this paper is to seek out a feasible solution to the addressed MR localization (MRL) problem (MRLP) by devising the desired time‐varying filter. Firstly, in the presence of the sensor energy‐induced measurement missing phenomenon, an upper bound (UB) for the filtering error (FE) covariance (FEC) is acquired recursively. Then, the acquired UB is minimized by characterizing the desired filter gain adequately. Finally, a simulation is conducted to testify the availability of the proposed MRL algorithm (MRLA). [ABSTRACT FROM AUTHOR]

This paper is concerned with the recursive fusion estimation‐based mobile robot localization (RL) problem by employing multiple energy harvesting sensors (EHSs). In the addressed RL problem, multiple sensors with energy harvesting capacity are deployed to produce measurements used for RL. When the sensors own sufficient energy, the sensors can output measurements and then send them to the corresponding local filter. Otherwise, the sensor energy‐induced missing measurement phenomenon will occur. In order to obtain the missing measurement rate, at each time instant, the relationship between the totality of the sensor energy and its probability distribution is derived recursively. This paper aims at seeking out a practicable solution to the addressed mobile RL problem. First, in the presence of the sensor energy‐induced measurement missing phenomenon, an upper bound (UB) of the local localization error covariance is recursively acquired. Then, such a derived UB is minimized by suitably devising the desired local filter parameter. Subsequently, the covariance intersection fusion method is adopted to achieve the addressed RL problem. In the end, a simulation is conducted to verify the practicability of the developed RL scheme. [ABSTRACT FROM AUTHOR]