Fuzzy position-velocity control of underactuated finger of FTN robot hand

Abstract

The significant progress in robotics worldwide, brings further advancements in the design of the mechanical components, miniaturization of sensors and control hardware and more sophisticated control algorithms that come together with more available processing power. The state of the art humanoid robots are usually equipped with dexterous hands. This paper presents the design of the FTN robot hand for humanoid robot MARKO, with the emphasis on the fuzzy logic controller to control the Brushed DC motors used to actuate the underactuated fingers of the hand. The design of the robotic hand is highly anthropomorphic and biologically inspired by the human hands. The hand is passively adaptive to the shape of an object, due to a tendon-driven mechanism and torsional spring in each finger joint. Each of the five fingers has three DOFs (Degrees Of Freedom), except the thumb which has an additional DOF, for the rotation in its base. The fingers are tendon-driven, actuated with five DC motors, embedded in the palm. The proposed fuzzy controller is used to control the position of each finger. The results of the controller are compared with traditional PID control algorithms tuned with Ziegler-Nichols tuning method. The algorithms are first developed in a simulation environment and later are implemented on a real-time ARM Cortex M4 controller.

Smooth velocity tracking and kinematic-based fuzzy control of carrying mobile robots

“…The velocity graph computed using the method in [2] has 453.16% maximum overshoot from the reference velocity, while this overshoot is 3.69% using the presented method. On the other hand, if the errors derivatives are entered to the fuzzy logic [7][8][9][10][11], velocity is tracked at the price of a larger number of enforced fuzzy rules. Compared to 2*20 rules employed in Table 5, 3*49 rules are designed in [8] and [11].…”

“…Few previous works are dedicated to designing the proper fuzzy logics that guarantee velocity tracking. In this regard, the velocity tracking feature has been investigated in specific applications like food delivery robots [7], robots carrying rehabilitation devices [8], and the under actuated finger of a robotic hand [9]. Typically, posture errors are entered into a fuzzy logic for path tracking purposes.…”

“…Typically, posture errors are entered into a fuzzy logic for path tracking purposes. However, for a velocity tracking observation, the rate changes of posture errors are also entered to the fuzzy logic [7][8][9]. The same combination of errors and errors rate are exploited as fuzzy inputs in [10], where a fuzzy ring network method is used to control a multi-motor coordinated system and in [11], in which the trajectory tracking of a pneumatic driven linear delta robot is studied.…”

Optimal Position Fuzzy Control of an Underactuated Robotic Finger

“…Generally, the control systems have a closed-loop structure, since they seek to reduce errors so that the hand nger can maintain a speci c desired position [1,2], by using pneumatic [3][4][5] and touch [6][7][8] sensors. In the literature, the most common control schemes are the proportional-integral-derivative (PID) control [9][10][11] and fuzzy control [12][13][14]. Controllers based on fuzzy logic are an alternative solution that does not require a mathematical model such as the PID [15].…”

“…FLC must have a exible behavior to adapt to various situations, as well as being robust to maintain the state of the desired output. e implementation of FLC is fairly common for solving problems, where (a) the systems are partially de ned, (b) systems with variables that cannot be measured, and (c) system with large disturbances [12,14,16]. e principal fuzzy systems are Mandani and Takagi-Sugeno.…”