Neural Control and Online Learning for Speed Adaptation of Unmanned Aerial Vehicles.

Unmanned aerial vehicles (UAVs) are involved in critical tasks such as inspection and exploration. Thus, they have to perform several intelligent functions. Various control approaches have been proposed to implement these functions. Most classical UAV control approaches, such as model predictive control, require a dynamic model to determine the optimal control parameters. Other control approaches use machine learning techniques that require multiple learning trials to obtain the proper control parameters. All these approaches are computationally expensive. Our goal is to develop an efficient control system for UAVs that does not require a dynamic model and allows them to learn control parameters online with only a few trials and inexpensive computations. To achieve this, we developed a neural control method with fast online learning. Neural control is based on a three-neuron network, whereas the online learning algorithm is derived from a neural correlation-based learning principle with predictive and reflexive sensory information. This neural control technique is used here for the speed adaptation of the UAV. The control technique relies on a simple input signal from a compact optical distance measurement sensor that can be converted into predictive and reflexive sensory information for the learning algorithm. Such speed adaptation is a fundamental function that can be used as part of other complex control functions, such as obstacle avoidance. The proposed technique was implemented on a real UAV system. Consequently, the UAV can quickly learn within 3-4 trials to proactively adapt its flying speed to brake at a safe distance from the obstacle or target in the horizontal and vertical planes. This speed adaptation is also robust against wind perturbation. We also demonstrated a combination of speed adaptation and obstacle avoidance for UAV navigations, which is an important intelligent function toward inspection and exploration.