Trajectory tracking and vibration control of redundant cable-driven parallel robots (CDPR) attracted the attention of different researchers; however, the effects of the CDPRs end-effectors disturbing force/moment on the undesired vibrations and minimization of such effects has received few attention. A new general dynamic modeling and robust control structure for redundant CDPR is proposed in this paper, which evaluates such factors and tries to minimize their effects on the performance of CDPRs. Considering the effects of redundant cables on the CDPRs' stiffness improvement, we neglect the second and higher order terms of motion errors that helps to separate the moving platform's undesired vibration equation from its desired (nominal) equation of motion. The obtained vibration model forms a linear parametric variable (LPV) dynamic system that lets us investigate the effects of external disturbances on the undesired vibration in different directions, and use a wide class of well-established robust and optimal vibration control techniques. Consequently, in order to minimize the effect of disturbances on the moving platform trajectory tracking performance, a robust and optimal vibration compensator is designed using the LPV - H _∞ control design techniques. Based on the platform's desired trajectory, the proposed control scheme calculates the cables' nominal tension required for trajectory tracking and further generates the auxiliary signals to augment these tensions to counteract the undesirable vibrations. Based on the obtained dynamic model, the developed approach is simplified for kinematically-constrained redundant CDPRs as a new actuation method with restricted rotational degrees of freedom. Via numerical analysis and experimental tests of a planar kinematically-constrained CDPR, the proposed modeling and robust control design approach are examined and compared with other common control design methodologies. Moreover, the effectiveness of kinematically-constrained actuation method in vibration elimination and control design simplification of CDPRs is demonstrated.

Vehicle-trailer systems have various unstable modes including trailer snaking, jack-knifing, and roll-over, which should be considered in their stability control. For stability control design purposes, various techniques have been proposed to estimate vehicle-trailer system states and parameters. Some of these techniques rely on vehicle kinematic/dynamic models while others are data-driven and do not require a model. This review paper provides a comprehensive overview of different model-based and non-model-based techniques/algorithms developed for estimating vehicle-trailer system states and parameters. The main features, limitations, and assumptions for each estimation method are discussed. The trailer parameter estimation feasibility is also investigated for different possible vehicle-trailer on-board sensor settings. This paper can be used as a review and reference resource for engineers working in vehicle with semi-trailer state estimation and safety systems.

We present a graph-theoretic approach to analyze the robustness of leader-follower consensus dynamics to disturbances and time delays. Robustness to disturbances is captured via the system H ₂ and H _∞ norms, and robustness to time delay is defined as the maximum-allowable delay for the system to remain asymptotically stable. Our analysis is built on understanding certain spectral properties of the grounded Laplacian matrix that play a key role in such dynamics. Specifically, we give graph-theoretic bounds on the extreme eigenvalues of the grounded Laplacian matrix that quantify the impact of disturbances and time delays on the leader-follower dynamics. We then provide tight characterizations of these robustness metrics in Erdös-Rényi random graphs and random d-regular graphs. Finally, we view robustness to disturbances and time delay as network centrality metrics, and provide conditions under which a leader in a network optimizes each robustness objective. Furthermore, we propose a sufficient condition under which a single leader optimizes both robustness objectives simultaneously.

Implantable sensor systems are effective tools for biomedical diagnosis, visualization and treatment of various health conditions, attracting the interest of researchers, as well as healthcare practitioners. These systems efficiently and conveniently provide essential data of the body part being diagnosed, such as gastrointestinal (temperature, pH, pressure) parameter values, blood glucose and pressure levels and electrocardiogram data. Such data are first transmitted from the implantable sensor units to an external receiver node or network and then to a central monitoring and control (computer) unit for analysis, diagnosis and/or treatment. Implantable sensor units are typically in the form of mobile microrobotic capsules or implanted stationary (body-fixed) units. In particular, capsule-based systems have attracted significant research interest recently, with a variety of applications, including endoscopy, microsurgery, drug delivery and biopsy. In such implantable sensor systems, one of the most challenging problems is the accurate localization and tracking of the microrobotic sensor unit (e.g., robotic capsule) inside the human body. This article presents a literature review of the existing localization and tracking techniques for robotic implantable sensor systems with their merits and limitations and possible solutions of the proposed localization methods. The article also provides a brief discussion on the connection and cooperation of such techniques with wearable biomedical sensor systems.

Trailer mass is one of the important trailer parameters that affects the stability of the tractor-trailer systems. In this paper, two different approaches are proposed to estimate trailer mass for arbitrary tractor-trailer configurations; dynamic system model-based and Machine Learning (ML) approaches. The stability of the dynamic system model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a Deep Neural Network (DNN) is designed to estimate trailer mass. The inputs of the ML-based method have been selected based on the tractor-trailer dynamic model, and are considered to be normalized by the tractor mass, tire sizes, and geometry so that re-training of the network is not needed for different towing vehicles. The simulation and experimental results justify the accuracy of the trailer mass estimation in various cases and demonstrate that the trailer mass can be estimated with less than 10% error.