Abstract-Classical sliding mode controller is robust to model uncertainties and external d isturbances. A sliding mode control method with a switching control low guarantees asymptotic stability of the system, but the addition of the switching control law introduces chattering in to the system. One way of attenuating chattering is to insert a saturation function inside of a boundary layer around the sliding surface. Unfortunately, this addition disrupts Lyapunov stability of the closed-loop system. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combin ing a sliding mode controller and fuzzy system together. Fuzzy rules allow fuzzy systems to approximate arbitrary continuous functions. To approximate a time-varying nonlinear system, a fu zzy system requires a large amount of fuzzy rules. This large number of fuzzy ru les will cause a high computation load. The addition of an adaptive law to a fuzzy slid ing mode controller to online tune th e parameters of the fuzzy ru les in use will ensure a moderate co mputational load. Refer to this research; tuning methodology can online adjust both the premise and the consequence parts of the fuzzy rules. Since this algorith m for is specifically applied to a robot manipulator.

Continuum robot manipulators are optimized to meet best trajectory requirements. Closed loop control is a key technology that is used to optimize the system output process to achieve this goal. In order to conduct research in the area of closed loop control, a control oriented cycle-to-cycle continuum robot model, containing dynamic model information for each individual continuum robot manipulator, is a necessity. In this research, the continuum robot man ipulator is modeled accord ing to information between joint variable and torque, which is represented by the nonlinear dynamic equation. After that, a mult i-input-mu lti-output baseline computed torque control scheme is used to simu ltaneously control the torque load of system to regulate the joint variables to desired levels. One of the most impo rtant challenge in control theory is on -line tuning therefore fuzzy supervised optimization is used to tune the modified baseline and co mputed torque control coefficient. The performance of the modified baseline computed torque controller is compared with that of a baseline proportional, integral, and derivative (PID) controller.

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