The purpose of this chapter is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum. Adaptation laws for the input and output weights are provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position. Using real-time experiments, the new scheme is compared with other control methodologies, therein demonstrating the improved performance of the proposed adaptive algorithm.
|Title of host publication||Intelligent Systems, Control and Automation|
|Subtitle of host publication||Science and Engineering|
|Number of pages||26|
|State||Published - 1 Jan 2018|
|Name||Intelligent Systems, Control and Automation: Science and Engineering|