This letter develops an information-geometric framework for robust sensor geometry design in multi-sensor localization with heterogeneous measurements. Bearing, range, and range-difference measurements are represented in a unified Fisher information matrix (FIM) form as tangential, radial, and differential-radial information components, respectively. Based on this directional decomposition, the sensing geometry is mapped to a D-optimality surface, and an error-weighted sensitivity measure is introduced to quantify the local variation of information under deployment perturbations. A hierarchical selection criterion is then proposed to choose a low-sensitivity geometry within a prescribed information-retention region, instead of directly pursuing the classical D-optimal peak. Numerical results in a three-sensor scenario show that the selected geometry preserves about$90\%$of the maximum information while reducing sensitivity and information variance under perturbations. These results demonstrate that sensitivity-aware geometry selection can improve deployment robustness with only a small loss of nominal localization performance.

In this paper, an adaptive event-triggered output feedback FTC scheme is proposed for a class of uncertain nonlinear systems with unknown actuator faults. Multiple channels, including the state estimation filter-to-controller channel, the parameter estimator-to-controller channel and the controller-to-actuator channel, are all event-triggered. Meanwhile, by designing a series of uncorrelated triggering conditions, the triggering of different channels is independent and asynchronous. Therefore, the communication and computation burden of the system can be released. Meanwhile, the signal transmission frequency of each channel can be adjusted independently. It is proved that, with our proposed control scheme, all the closed-loop signals are bounded and the output tracking error converges into an adjustable residual set. Also, the Zeno behavior can be excluded. Finally, the scheme is applied to a robot manipulator and the simulation results illustrate the effectiveness of the proposed control scheme.

This article proposes an adaptive fuzzy decentralized output-feedback control scheme for large-scale systems with multichannel asynchronous event triggering. Uncertain system dynamics, unknown interconnections, and time-varying sensor faults are handled simultaneously, even though only the corrupted measured local output is available for the design procedure. First, local state estimation filters are designed to estimate the unmeasured states. Then, considering that the resource-limited communication and computation may exist in all three types of channels, including state estimation filter-to-controller channel, parameter estimator-to-controller channel, and controller-to-actuator channel, an asynchronous event-triggering framework with time-varying triggering threshold is developed for multiple channels to release the communication and computation burden. Compared with the existing synchronous triggering mechanism, the proposed asynchronous one has several advantages: 1) the redundant signal transmission is reduced; 2) the triggering conditions for different channels are uncorrelated, which enables the designer to adjust the signal transmission frequency of each channel independently; and 3) the robustness could be improved in the face of noise as ideal synchronous triggering is not easy to be achieved in practice. In addition, both gain and bias faults, which are allowed to be unknown and time varying, are covered in a unified sensor fault model. It is proved that, with the proposed control scheme, all the closed-loop signals are semiglobally uniformly ultimately bounded, and the output tracking error converges into an adjustable residual set without Zeno behavior. Finally, simulation and comparison results illustrate the effectiveness and advantages of the proposed control scheme.

In this article, an adaptive finite-time fault-tolerant control (FTC) strategy is proposed for state tracking of uncertain systems subject to actuator faults and input saturation. The actuator faults, which include multiplicative and time-varying additive modes, are allowed to be unknown. To deal with the coupling of the unknown model uncertainties and the actuator faults, an adaptive mechanism is designed based on a wisely chosen Lyapunov function. The proposed finite-time FTC scheme, with adaptive laws of uncertain parameters, guarantees the stability of the closed-loop system, and the practical finite-time state tracking property. Then, the algorithm is applied to a flight control system, simulation and comparison results demonstrate the effectiveness and advantages of the designed control scheme.

A distributed algorithm is presented in this article under directed communication networks, which is used to solve the economic dispatch problem in fixed time in smart grid systems. A new globally fixed-time stability theory (Lemma 3) is first given in this article, which contains a new upper bound for the estimation of the settling time. Moreover, the fixed-time convergence for the proposed algorithm is rigorously proved with the aid of convex optimization theory, the new lemma, and Lyapunov stability theory under the strongly connected and weight-balanced network topology. Finally, numerical simulations show the effectiveness and advantages of the distributed fixed-time optimization algorithm.

The sensor-target placement plays a pivotal role in improving the localization accuracy; the commonly used evaluation function for this problem is built by Cramer-Rao lower bound (CRLB) or Fisher information matrix (FIM). Frame theory, which was developed recently, can provide a more easily tractable evaluation function, especially for some complex cases. In this letter, we derive an evaluation function by frame theory and prove its equivalence with that by CRLB and FIM. For angle-of-arrival based localization system, we present the optimal sensor augmentation problem with several fixed sensors and an arbitrary number of new sensors, and for arbitrary but fixed distances between new sensors and target. The conclusions are verified by several simulations.

Source localization is a fundamental problem in underwater wireless sensor networks. From the observability analysis, we know that the sensor placement can significantly affect the localization performance. This letter is concerned with the optimal sensor placement for underwater source localization, and a parameter is introduced into the measurement model to represent the distance-dependent noise. The evaluation criterion used to solve the optimal placement is built by the Cramer-Rao lower bound theory. Subsequently, we mainly discuss the case when the distances between the sensors and the source are fixed, and the optimal sensor placement is affected by the relative magnitude of the distances.

The relative sensor-target formation configuration can significantly affect the potential performance of any particular localization algorithm. An evaluation measure based on the one-step prediction error covariance in extended Kalman filter process is derived in this letter. We determine the optimal sensor placement for the range-only localization considering the prior target location information. The conclusions are different from that in the static estimation problem.