This research focuses on the basic concepts of continuum robot manipulator and control methodology. OCTARM Continuum robot manipulator is a 6 DOF serial robot manipulator. From the control point of view, robot manipulator divides into two main parts i.e. kinematics and dynamic parts. The dynamic parameters of this system are highly nonlinear. To control of this system nonlinear control methodology (computed torque controller and sliding mode controller) is introduced. Computed torque controller (CTC) is an influential nonlinear controller to certain systems which it is based on feedback linearization and computes the required arm torques using the nonlinear feedback control law. When all dynamic and physical parameters are known computed torque controller works superbly; practically a large amount of systems have uncertainties and sliding mode controller reduce this kind of limitation. Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Comparative study between computed torque controller and sliding mode controller is introduced in this research.

The increasing demand for multi-degree-of-freedom (DOF) continuum robot in presence of highly nonlinear dynamic parameters in a number of industries has motivated a flurry of research in the development of soft computing nonlinear methodology. The robust backstopping controller proposed in this research is used to further demonstrate the appealing features exhibited by the continuum robot. Robust feedback controller is used to position control of continuum robot in presence of uncertainties. Using Lyapunov type stability arguments, a robust backstopping controller is designed to achieve this objective. The controller developed in this research is designed into two steps. Firstly, a robust stabilizing torque is designed for the nominal continuum robot dynamics derived using the constrained Lagrangian formulation based on modified PD backstopping controller. Next, the fuzzy logic methodology applied to it to solution uncertainty problem. The fuzzy model free problem is formulated to estimate the nonlinear formulation of continuum robot. The eventual stability of the controller depends on the torque generating capabilities of the continuum robots.

Refer to this research, a position parallel error-based fuzzy inverse dynamic plus gravity controller is proposed for continuum robot manipulator. The main problem of the pure inverse dynamic controller was equivalent dynamic formulation in certain and uncertain systems. The nonlinear equivalent dynamic problem in uncertain system is solved by using fuzzy logic theory. To estimate the continuum robot manipulator system’s dynamic, 49 rules Mamdani inference system is design and applied to inverse dynamic plus gravity methodology. This methodology is based on applied fuzzy logic in equivalent nonlinear dynamic part to estimate unknown parameters. The results demonstrate that the error-based parallel fuzzy inverse dynamic plus gravity controller is a partly model-free controllers which works well in certain and partly uncertain system.

This paper studies the use of Modified Proportional-Integral-Derivative Sliding Mode Controller (MPIDSMC) control used to control a flexible manipulator. The control gain in the MPIDSMC controller has been determined in an empirical way so far. It is a considerable time-consuming process because the control performance depends not only on the control gain but also on the other parameters such as the payload, references and PID joint servo gains. Hence, the control gain must be tuned considering the other parameters. In order to find the optimal control gain for the MPIDSMC controller, a fuzzy logic approach is proposed in this paper. The proposed fuzzy logic scheme finds an optimum control gain that minimizes the tip vibration for the end effector of the flexible manipulator. Tuned gain response results are compared to results for other types of gains. The effectiveness of using the fuzzy logic appears in the reduction of the computational time and the ability to tune the gain with different loading condition and input parameters.