Adaptive sliding mode control with radial basis function neural network for time dependent disturbances and uncertainties

A radial basis function neural network (RBFNN) based adaptive sliding mode controller is presented in this paper to cater for a 3-DOF robot manipulator with time-dependent uncertainties and disturbance. RBF is one of the most popular intelligent methods to approximate uncertainties due to its simple structure and fast learning capacity. By choosing a proper Lyapunov function, the stability of the controller can be proven and the update laws of the RBFN can be derived easily. Simulation test has been conducted to verify the effectiveness of the controller. The result shows that the controller has successfully compensate the time-varying uncertainties and disturbances with error less than 0.001 rad

Experimental investigation on Magnetorheological Damper’s characterization

Magnetorheological (MR) damper is one of the most advanced application of semi active devices. Its use is increasing day by day due to its huge advantages and wide range of application. The force delivered by MR damper can be varied by changing the viscosity of its internal MR fluids. Till now no details experimental analysis has been accomplished by considering various parameters. In this paper a brief experimental analysis has been investigated with the help of Universal Testing Machine to characterize MR damper. To characterize accurately MR damper has been analyzed experimentally for different stroke length, stroke rate, stroke mode. From the experimental results it is seen that the force delivered by MR damper has a proportional relation with input excitation current, stroke length and stroke rate