Nonlinear control techniques are applied on mechanical systems namely two link robot manipulator to study the effect of the controllers on the tracking performance of the two system. A design of sliding mode control(SMC) for the position tracking of two link robot manipulator based on the sliding mode control technique and the Lyapunov stability theory is carried out to eliminate the perturbation and asymptotical stability can be achieved when the system is subjected to the sliding mode. A sliding mode control method based on RBF(radial basis function) neural network is addressed which has the capability of learning uncertain control actions shown by the several industrial robots. In RBFNN-SMC method the algorithm for tuning the parameters are extracted from the RBF function. The comparative study is done based on the evaluated parameters for the system Keywords Sliding mode control, RBF Neural Network, Two-link robot manipulator.
The present work represents the implementation of the various fuzzy controller with robust sliding mode control (SMC) technique on a nonlinear system considering various external disturbances and model uncertainties. The nonlinear system considered here is a single link inverted pendulum. The proposed work combines the advantages of the sliding mode controlling technique and fuzzy logic controller. A set of linguistic rules are designed in fuzzy logic control, which causes the system to be chattering free. Parameters of the nonlinear system are adjusted according to fuzzy adaptive laws, while the uncertainties of the nonlinear system have been approximated using a fuzzy system. Various types of controller based on fuzzy sliding mode, like approximation based sliding mode control technique; equivalent control based fuzzy sliding mode technique, and switch-gain regulation based sliding mode control methods have been implemented here. A comparative analysis of various methods is also have been discussed.
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