Abstract: Control of robotic systems is vital due to wide range of their applications because this system is multi-input multi-output, nonlinear and uncertainty. Consequently, it is difficult to design accurately mathematical models for multiple degrees of freedoms robot manipulator. Therefore, strong mathematical tools used in new control methodologies to design a controller with acceptable performance. As it is obvious stability is the minimum requirement in any control system, however the proof of stability is not trivial especially in the case of nonlinear systems. One of the best nonlinear robust to control of robot manipulator is sliding mode controller. A review of sliding mode controller for robot manipulator will be investigated in this paper.

Robotic manipulators must operate in complex scenarios, which make the overall operational space quite large, and the system dynamics within that space subject to significant variations and uncertainties. Sliding mode control (SMC) strategies have been successfully applied in this context, yet, if a worst-case approach is taken in the face of large operational variations, the resulting performance may happen to be suboptimal. Moreover, attention must be paid, in an industrial setting, to vibrations that may be induced on the robot joints by the presence of chattering induced by the SMC algorithm. This work tests, in a challenging application context, a recently-proposed r-order SMC strategy that encompasses both continuous and discrete adaptation strategies to adjust its parameters, giving rise to an overall switched approach. In particular, two realistic case studies of robot motion control are discussed, proving its effectiveness in enhancing both performance and robustness in complex operational scenarios.

Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This course focuses on the design, implementation and analysis of a chattering free sliding mode controller for highly nonlinear dynamic PUMA robot manipulator and compare to computed torque controller, in presence of uncertainties. These simulation models are developed as a part of a software laboratory to support and enhance graduate/undergraduate robotics courses, nonlinear control courses and MATLAB/SIMULINK courses at research and development company (SSP Co.) research center, Shiraz, Iran.

Refer to the research, review of sliding mode controller is introduced and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in sliding mode controller, fuzzy logic controller and adaptive method, the output in most of research have improved. Each method by adding to the previous algorithm has covered negative points. Obviously robot manipulator is nonlinear, and a number of parameters are uncertain, this research focuses on comparison between sliding mode algorithm which analyzed by many researcher. Sliding mode controller (SMC) is one of the nonlinear robust controllers which it can be used in uncertainty nonlinear dynamic systems. This nonlinear controller has two challenges namely nonlinear dynamic equivalent part and chattering phenomenon. A review of sliding mode controller for robot manipulator will be investigated in this research

In this paper, a novel model-free control scheme is developed to enhance the tracking performance of robotic systems based on an adaptive dynamic sliding mode control and voltage control strategy. In the voltage control strategy, actuator dynamics have not been excluded. In other words, instead of the applied torques to the robot joints, motor voltages are computed by the control law. First, a dynamic sliding mode control is designed for the robotic system. Then, to enhance the tracking performance of the system, an adaptive mechanism is developed and integrated with the dynamic sliding mode control. Since the lumped uncertainty is unknown in practical applications, the uncertainty upper bound is necessary in the design of the dynamic sliding mode controller. Hence, the lumped uncertainty is estimated by an adaptive law. The stability of the closed-loop system is proved based on the Lyapunov stability theorem. The simulation results demonstrate the superior performance of the proposed adaptive dynamic sliding mode control strategy.