Design Artificial Nonlinear Robust Controller Based on CTLC and FSMC With Tunable Gain.

One of the most active research areas in the field of robotics is robot manipulators control, because these systems are multi-input multi-output (MIMO), nonlinear, time variant and uncertainty. An artificial non linear robust controller design is major subject in this work. At present, robot manipulators are used in unknown and unstructured situation and caused to provide complicated systems, consequently nonlinear classical controllers are used in artificial intelligence control methodologies to design nonlinear robust controller with satisfactory performance (e.g., minimum error, good trajectory, disturbance rejection). Sliding mode controller (SMC) and computed torque controller (CTC) are the best nonlinear robust controllers which can be used in uncertainty nonlinear. Sliding mode controller has two most important challenges in uncertain systems: chattering phenomenon and nonlinear dynamic equivalent part. Computed torque controller works very well when all nonlinear dynamic parameters are known. This research is focused on the applied non-classical method (e.g., Fuzzy Logic) in robust classical method (e.g., Sliding Mode Controller and computed torque controller) in the presence of uncertainties and external disturbance to reduce the limitations. Applying the Mamdani’s error based fuzzy logic controller with minimum rules is the first goal that causes the elimination of the mathematical nonlinear dynamic in SMC and CTC. Second target focuses on the elimination of chattering phenomenon with regard to the variety of uncertainty and external disturbance in fuzzy sliding mode controller and computed torque like controller by optimization the tunable gain. Therefore fuzzy sliding mode controller with tunable gain (GTFSMC) and computed torque like controller with tunable gain (GTCTLC) will be presented in this paper