Trusses are suitable load-bearing structural systems for heavy concentrated loads. In this paper, it is shown that it is possible to use active control mechanisms to enhance the load-bearing capacity of the trusses. Under heavy loading, some elernents of a truss might experience high stresses and show non-linear behavior, resulting in large deformations in the truss. Under such a condition, some elernents of the truss might damage which can lead to the collapse of the truss. Application of control forces on some of the degrees of freedom of the truss can render help the truss tolerate larger forces before its collapse. A neural network can then be trained to learn the relationship between the Information about the external loads on the truss, as input, and the required control forces, as output, and act as a neuro-controller for the truss. This method is explained and then tested on a smáli truss to show the capabilities of the method.
Solving inverted pendulum by co-simulation between multi-body solver MotionSolve and signal processing control in solidThinking Activate. The simulation of inverted pendulum uses an innovative model of friction which is physically and mathematically more accurate than usual CAE friction models. This model of friction adds nonlinearity to the system. Two types of controlling mechanism for active balancing of inverted pendulum are used: PID and ANN controller. A non-traditional false angular deviation approach for returning a cart to its initial position was used.