The purpose of this paper is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum, which is a two degree-of-freedom underactuated system. Adaptation laws for the input and output weights are also provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position. The key aspect of the derivation of the controller is the definition of an output function that depends on the position and velocity errors. The internal and external dynamics are rigorously analyzed, thereby proving the uniform ultimate boundedness of the error trajectories. By using real-time experiments, the new scheme is compared with other control methodologies, therein demonstrating the improved performance of the proposed adaptive algorithm.
This document proposes a parameter identification procedure, which overcomes drawbacks due to disturbances in an experimental platform. The main purpose of this work is to describe and formalize a MATLAB-based identification procedure that can be used by undergraduate and graduate students. The procedure can be easily extended to many types of system. As an application example, this work considers a two-degrees-offreedom rigid link robot manipulator. The program code for MATLAB is provided, only requiring the joint position and applied torque measurements. Finally, the estimated parameters of the identified system are validated, showing that simulations and experiments are consistent. Assessment of the identification method by engineering students is described. Specifically, learning of parameter identification was observed since students were able to perform the proposed methodology and to apply it to other systems.
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