Ahmed S. Al-Araji1; Attarid K. Ahmed
Volume 18, Issue 2 , September 2018, , Page 1-16
Abstract
This paper presents a cognitive system based on a nonlinear Multi-Input Multi-Output (MIMO) Proportion Integral Derivative (PID) Modified Elman Neural Network(MENN) controller and the Square Road Map (SRM) method to guide the mobile robot duringthe continuous path-tracking with collision-free navigation ...
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This paper presents a cognitive system based on a nonlinear Multi-Input Multi-Output (MIMO) Proportion Integral Derivative (PID) Modified Elman Neural Network(MENN) controller and the Square Road Map (SRM) method to guide the mobile robot duringthe continuous path-tracking with collision-free navigation through static obstacles. Theproposed cognitive system consists of two parts: the first part is to plan the desired path for themobile robot with the static obstacle environment in order to determine the target point and toavoid the obstacles based on the proposed square road map algorithm. The second part is toguide and track the wheeled mobile robot on the desired path equation based on the proposednonlinear MIMO-PID-MENN controller with the intelligent algorithm. The Particle SwarmOptimization (PSO) is used to on-line tune the variable control parameters of the proposedcontroller to get the optimal torques actions for the mobile robot platform. Based on using theMATLAB package (2017), the numerical simulation results show that the proposed cognitivesystem has high accuracy for planning the desired path equation in terms of avoiding the staticobstacles with smooth and short distance and generating a perfect torque action of (0.7 N.m)without a saturation state of (3.07 N.m), which leads to minimize the tracking pose error forthe mobile robot to the zero value approximation. These results were confirmed by acomparative study with different nonlinear PID controller types in terms of number ofiterations and the performance index.
Ivan I. Gorial; Dr. Firas A. Raheem
Volume 13, Issue 2 , August 2013, , Page 1-10
Abstract
Abstract –This paper focuses on the comparison of two proposed fuzzy logic-based
path planning systems for a 2-DOF robot manipulator. The first system is joint space
path planning and the second system is Cartesian space path planning. The proposed
planning systems were composed of several separate ...
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Abstract –This paper focuses on the comparison of two proposed fuzzy logic-based
path planning systems for a 2-DOF robot manipulator. The first system is joint space
path planning and the second system is Cartesian space path planning. The proposed
planning systems were composed of several separate fuzzy units which individually
control each manipulator joint. For the 1st system, the main inputs of the two fuzzy
blocks were the current joint position and the difference in joint angle between the goal
and the current positions. For the 2ndsystem the main inputs were the new x-axis error
and the current x-axis value of the robot end-effector for the first fuzzy bock, and the
new y-axis error and the current y-axis value of the robot end-effector for the second
fuzzy block.The objectives were to move the arm from the start configuration to the
goal configuration. The comparison of the simulation results shows clearly that the
results of the second system is better and the robot reached the goal configuration in the
two cases successfully with relatively small error in the order of (0.00041775 m in xaxis;
and
