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The rotary steerable drilling tool is a high-end equipment in the field of oil extraction, and the reliable operation of its core component, dynamic point-the-bit rotary steerable drilling tool, is an important prerequisite to ensure the normal operation of drilling systems. This article explores the issue of intermittent fault (IF) detection for dynamic point-the-bit rotary steerable drilling tool systems (DPRSDTSs). Firstly, considering the complex external working environment and design flaws of the tool itself, the DPRSDTSs are modeled as a class of linear stochastic dynamic systems with bounded disturbances (BDs). Subsequently, by presenting an unknown input observer and a suitable sliding-time window (StW), the truncated residuals are proposed to detect all the appearing time (AT) and disappearing time (DT) of the IF. Furthermore, considering the effect of BDs on IF detection, the IF detectability is discussed in a probabilistic sense. Finally, the experiments are presented to identify the feasibility of the proposed methods.

This paper addresses the trajectory tracking control problem for a quadrotor suspended load system without load's linear velocity measurement and cable length information. Firstly, the cable length is estimated by designing an immersion and invariance ($\mathbf{I \& I}$)-based adaptive law. Secondly, a virtual thrust force vector is designed for the load position subsystem with the denoted auxiliary variables in the lack of load's linear velocity measurement. Then, through the cable direction subsystem and quadrotor attitude subsystem, the angular velocity input is presented to drive the real thrust input to virtual thrust input via the backstepping method. Thereafter, the uniformly boundedness of the whole closed-loop system is obtained by means of building suitable Lyapunov functions. Ultimately, numerical simulations attest to the performance of the proposed trajectory tracking control law.

A cart-double pendulum (CDP) system is a nonlinear underactuated mechanical system with three degrees of freedom (DOF) and two control inputs. This brief studies two motion control problems for this 3-DOF underactuated system. The first one is to construct a motion trajectory for the system from the lowest energy point to the highest energy point. The design of the trajectory is based on the ideas of energy absorption and inverse motion. The second one is to design tracking controllers that make the CDP system accurately track the planned trajectory. As a result, the CDP system can be stabilized at the highest energy point along a planned trajectory. And the motion process and settling time of the entire control system can be accurately known. Finally, numerical simulation results verify the effectiveness of our developed control strategy.

This paper investigates the design problem of a set of distributed filters. These adaptive event-triggered (AET) filters have the l2−l∞property and are suitable for sensor networks characterized by random measurement data loss and dynamic topology switching. In a distributed filtering network, each local filter estimates the system’s state not only relying on the node’s individual information but also using information from neighboring nodes in the network topology. The adaptive event triggering condition is determined by the combination of the latest release data of the filter itself, the estimated value of the current moment, and the latest release data of the neighboring nodes. The event triggering mechanism adopts a threshold adaptive adjustment scheme, which dynamically changes the threshold parameter according to the filtering error under the premise of guaranteeing the filter performance, thus maximally saving the network communication resources. Initially, we used a binary sequence to describe the random loss of sensor measurements, and a Markov chain is used to describe the switching of the filtering network topology. Secondly, a Lyapunov function is created to examine the l2−l∞performance index of the filtering error system and investigate its mean square exponential stability. Subsequently, the distributed filter is formulated, and its parameters are determined using the linear matrix inequality approach. Ultimately, the validity of the proposed method is substantiated through numerical illustrations.