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Refer to this research, an intelligent robust fuzzy parallel feedback linearization estimator for ProportionalIntegral-Derivative (PID) controller is proposed for highly nonlinear continuum robot manipulator. In the absence of robot knowledge, PID may be the best controller, because it is model-free, and its parameters can be adjusted easily and separately. And it is the most used in robot manipulators. In order to remove steadystate error caused by uncertainties and noise, the integrator gain has to be increased. This leads to worse transient performance, even destroys the stability. The integrator in a PID controller also reduces the bandwidth of the closed-loop system. Model-based compensation for PD control is an alternative method to substitute PID control. Feedback linearization compensation is one of the nonlinear compensator. The first problem of the pure feedback linearization compensator (FLC) was equivalent problem in certain and uncertain systems. The nonlinear equivalent dynamic problem in uncertain system is solved by using parallel fuzzy logic theory. To eliminate the continuum robot manipulator system's dynamic; Mamdani fuzzy inference system is design and applied to FLC. This methodology is based on design parallel fuzzy inference system and applied to equivalent nonlinear dynamic part of FLC. The results demonstrate that the model free fuzzy FLC estimator works well to compensate linear PID controller in presence of partly uncertainty system (e.g., continuum robot).

This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm to control of continuum robot manipulator. Variable structure methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable error result and adjust the trajectory following. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and modified Proportional plus Derivative (P+D) fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the modified rate of error. The outputs represent joint torque, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC based on-line tuned by fuzzy backstepping algorithm (FBSAVSC) is validated through comparison with VSC. Simulation results signify good performance of trajectory in presence of uncertainty joint torque load

Outcome prediction using baseline characteristics (post-traumatic headache and seizure), CT and laboratory findings (6-hour S100B) were valuable factors for identification of the individual MTBI patient at risk for developing PCS 1 month after the injury.