Utilizing Model Predictive Control to Haptically Assist Users in Piloting a Quadcopter
I. van Osnabrugge (TU Delft - Mechanical Engineering)
L Marchal – Mentor (TU Delft - Human-Robot Interaction)
Riccardo Ferrari – Mentor (TU Delft - Team Riccardo Ferrari)
L. Peternel – Graduation committee member (TU Delft - Human-Robot Interaction)
Marta Zagorowska – Graduation committee member (TU Delft - Team Jan-Willem van Wingerden)
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Abstract
Haptic technology focuses on the recreation of haptic information, i.e., a type of sensory input that uses tactile cues, forces, vibrations, or pressure to provide users with the sensation of touch, enabling users to interact physically with virtual or remote environments. One promising application of this technology lies in haptic training, where the possibility of using haptic feedback to facilitate or promote motor learning is studied. The focus of this paper lies on performance-enhancing haptic training methods, with a focus on designing a dynamic motor task. The objective of this paper is, therefore, to establish a preliminary framework that can be used to provide minimal haptic feedback while flying a quadcopter through a set of gates.
We focused on creating a preliminary framework that provides haptic feedback on the altitudinal axis of the quadcopter to the pilot using the control method Model Predictive Control (MPC). The haptic feedback is provided on the z-axis of a haptic Sigma.7 robot, which is also used as a remote controller to fly the quadcopter. The MPC implements the dynamical models of the quadcopter, and a haptic Sigma.7 robot, to determine the minimal force required to steer the Sigma.7 robot towards motor task completion. The system should provide minimal haptic force feedback within the proposed design requirements to prevent reliance on the assistance. We evaluated the effectiveness of our framework by evaluating its ability to control the quadcopter to the desired altitude setpoint under autonomous conditions using a haptic Sigma robot. Additionally, the design and performance of each of the individual building blocks of this framework, i.e. the quadcopter model, the haptic interface, and the MPC, were evaluated separately. The quadcopter, with the implementation of the onboard PID controllers, eliminating the steady-state errors and meeting the required settling times. The Sigma.7 model was sufficient within the established time horizon and range of operation, although shows limitations due to unmodelled frictional forces. The completed framework is capable of providing the Sigma.7 with the necessary input command to autonomously guide the quadcopter to its desired references in real-time, therefore completing its primary objective. Future work should explore improving the model components and integrating human elements into the predictive model.