Hand Tremors Reduction Based on Integral Intelligent Filter Computed Torque Controller

Abstract

Computed torque controller (CTC) is one of the types of feedback linearization nonlinear controller. This controller works very well in certain positions. However this controller has many advantages in certain conditions but it has challenges in uncertainty. The main challenge in CTC is fluctuations in uncertainties. In this research low pass filter is used to reduce the fluctuations in CTC. To improve the result of this controller intelligent CTC is recommended based on fuzzy logic engineering. In this research fuzzy logic theory is used to tune the new low pass filter CTC coefficients. The process of setting of integral intelligent Computed Torque Controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. This research is used to reduce or eliminate the computed torque controller problem based on low pass filter and fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.

Design Sensitive Model Reference Controller with Application to Medical Technology

“…A controller (control system) is a device, which cans sense information from system to improvement the dynamic behavior of first order delay system based on actuation and computation. From scientific perspectives, control theory is divided into two parts; linear control theory and nonlinear control theory [6,[1][2][3]. Linear control theory is divided into following groups:…”

“…Computed Torque Control (CTC) and Sliding Mode Control (SMC) are two nonlinear conventional controller which introduced by many researchers to control of types of robot manipulator [1][2][3][4][5].…”

Design Active Robot Controller for Dental Automation

“…Uncertainty in system can causes some problems about dentistry. To solve this problem, neural network, fuzzy logic, and neurofuzzy are synergically combined with classical controller and used in nonlinear, time variant, and uncertainty plant (e.g., robot) [14][15][16][17][18][19][20].…”

“…The current number of researchers in this field and research societies mushrooming around the world show the attractiveness of this theory for both theoretical and practical researchers. One of the newest and most challenging technological applications is of the fuzzy logic in the control system of a dental robot, which is fundamentally unstable and with highly nonlinear dynamics [16][17][18][19][20]. In this research the main target is design PID intelligent controller to improve the robust, stability and reliability in dental robot manipulator.…”

“…The second step is implementing the Coriolis and Centrifugal matrix which they can calculate by partial derivatives of kinetic energy. The last step to implement the dynamic equation of robot manipulator is to find the gravity vector by performing the summation of Lagrange's formulation [19][20].…”