Robotic Manipulator Control Using Neural Networks
A research case study onto a 2-DOF robotic arm
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The learning capabilities of artificial neural networks (ANNs) to identify and emulate the behavior of complicated nonlinear systems have made them effective tools that can be utilized in intelligent adaptive control strategies. The use of ANNs in the design of trajectory tracking controllers for robotic manipulators is dated back to the 1980s. Due to the flexibility of their structure as well as the continuous development and enhancement of their self-training algorithms, the use of ANNs in the field of robotic manipulator trajectory tracking control is being considered an important research ...
The learning capabilities of artificial neural networks (ANNs) to identify and emulate the behavior of complicated nonlinear systems have made them effective tools that can be utilized in intelligent adaptive control strategies. The use of ANNs in the design of trajectory tracking controllers for robotic manipulators is dated back to the 1980s. Due to the flexibility of their structure as well as the continuous development and enhancement of their self-training algorithms, the use of ANNs in the field of robotic manipulator trajectory tracking control is being considered an important research area. This textbook explains in great detail the process of designing an effective controller to enhance the trajectory tracking performance of a two degree of freedom (2-DOF) robotic arm using neural networks. Feed-forward ANNs were used in both model-based and non-model-based control strategies. Since it also includes a deep explanation of the modeling of the 2-DOF robotic arm system including its actuating DC-motors and their control using a PD controller, this textbook can also serve as an effective educational tool for both undergraduate and graduate electrical engineering students.
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