Dynamical Modelling and a Decentralized Adaptive Controller for a 12-Tetrahedral Rolling Robot

The 12-tetrahedral robot is an addressable reconfigurable technology (ART)-based variable geometry truss mechanism with twenty-six extensible struts and nine nodes arranged in a tetrahedral mesh. The robot has the capability of reconfiguring shape and dimension for environment sensing requirements, which makes it suitable for space exploration and environmental perception. In this paper, we have derived a dynamics model and presented a decentralized adaptive controller for a 12-tetrahedral robot. First, the robot is divided into the node and the strut subsystems, and the kinetic and the potential energy are calculated for the two subsystems. Then, the dynamics model is achieved by applying the Lagrangian formalism on the total energy of the robot. Since the dynamics is too complicated for implementing model-based controllers, a two-layer controller is presented to control the robot, in which the planning layer determines gait and trajectory of the robot, and the executive layer adopts the decentralized adaptive control strategy and consists of twenty-six strut controllers. Each strut controller regulates the movement of the corresponding strut without information exchange with other struts. Co-simulations based on ADAMS and Matlab have been conducted to verify the feasibility and effectiveness of the proposed controller

NUMERICAL INVESTIGATION OF GAS SAMPLING FROM FLUIDIZED BEDS

Gas mixing in a tall narrow fluidized bed operated in the slugging fluidization regime is studied with the aid of computational fluid dynamics. Three-dimensional numerical simulations are performed with an Eulerian-Eulerian model. Predicted axial and radial tracer concentration profiles for various operating conditions are generally in good agreement with experimental data from the literature. Different field variables including voidage, tracer concentration, and gas velocity at upstream and downstream levels are analysed to study gas mixing. Mean tracer concentrations in the dense phase and the bubble phase are evaluated and significant differences between them are found. The time-mean concentration is weighted heavily towards the dense phase concentration which may lead to misinterpretation of sampling data in dispersion models. Caution is needed when interpreting time-mean tracer concentration data. A flux-based mean tracer concentration is introduced to characterize the gas mixing in numerical simulations of two-phase fluidized beds