Neural Network Based Model Predictive Control for a Quadrotor UAV

Abstract

A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape and low Reynolds number behavior. This work proposes an approach to model the full translational dynamics of a quadrotor UAV by a feedforward neural network, which is adopted as the prediction model in a model predictive controller (MPC) for precise position control. The raw flight data are collected by tracking various pre-designed trajectories with PX4 autopilot. The neural network model is trained to predict the linear accelerations from the flight log. The neural network-based model predictive controller is then implemented with the automatic control and dynamic optimization toolkit (ACADO) to achieve real-time online optimization. Software in the loop (SITL) simulation and indoor flight experiments are conducted to verify the controller performance. The results indicate that the proposed controller leads to a 40% reduction in the average trajectory tracking error compared to the traditional PID controller.

Trajectory Optimization with Complex Obstacle Avoidance Constraints via Homotopy Network Sequential Convex Programming

“…The traditional method of fitting control variables and trajectories using supervised learning requires many samples [28][29][30][31][32][33], and the only way to obtain samples is to solve complex optimal control problems. Therefore, acquiring a large number of samples has become a challenge in neural network training.…”

“…As an auxiliary tool for trajectory optimization, the neural network can effectively improve the performance of traditional algorithms [28][29][30][31][32][33]. Yin [28] proposed a trajectory planning method based on a neural network, thus improving the indirect method's convergence rate.…”

“…The concept of the HNN is proposed for the first time. Compared with [28][29][30][31][32][33], training the HNN does not require solving optimal control problems. Only the MSD field is needed so that adequate samples can be generated efficiently and stably.…”

Deep Pose Graph-Matching-Based Loop Closure Detection for Semantic Visual SLAM

“…VLSAM is an active research topic in the field of computer vision and robotics, especially for GNSS-denied environments, such as urban areas, indoor rooms, or underground spaces [5]. The most common solution to the visual navigation system is the VSLAM [6][7][8][9][10]. We illustrate a general VSLAM system in Figure 1.…”

A study of the motivation of clerical officers in the Hong Kong Polytechnic University

“…The flight tests were conducted under a VICON motion capture system, which provided real-time ground truth data to the ground station computer to ask the flight controller to track the target item. The proposed SAV uses a nonlinear model predictive controller (NMPC) [22], [23] for minimizing tracking error and conducting aerial grasping. This paper does not include the details of the NMPC of SAV, as we would like to concentrate on the contributions of the modular pneumatic soft gripper in aerial grasping.…”