Search

This paper focuses on the control of a cooperative system composed of an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) manipulating an object. The two units are subject to input saturations and collaborate to move the object to a desired pose characterized by its position and inclination. The dynamics are derived using the Euler–Lagrange method. A stabilizing control law is proposed where the UGV is tasked to deploy the object to a certain position whereas the UAV adjusts its inclination. In particular, a proportional–derivative control law is proposed for the UGV, and a cascade control is used for the UAV, where the inner loop controls the attitude of the UAV and the outer loop stabilizes the inclination of the object. The stability of the points of equilibrium is proven using small gain arguments. To ensure constraints satisfaction at all times, a reference governor unit is added to this stabilizing control law. Simulations and experimental results are provided to validate the effectiveness of the proposed control scheme.

With the emergence of vehicle-communication technologies, many researchers have strongly focused their interest in vehicle energy-efficiency control using this connectivity. For instance, the exploitation of preview traffic enables the vehicle to plan its speed and position trajectories given a prediction horizon so that energy consumption is minimized. To handle the strong uncertainties in the traffic model in the future, a constrained controller is generally employed in the existing researches. However, its expensive computational feature largely prevents its commercialization. This paper addresses computational burden of the constrained controller by proposing a computationally tractable model prediction control (MPC) for real-time implementation in autonomous electric vehicles. We present several remedies to achieve a computationally manageable constrained control, and analyze its real-time computation feasibility and effectiveness in various driving conditions. In particular, both warmstarting and move-blocking methods could relax the computations significantly. Through the validations, we confirm the effectiveness of the proposed approach while maintaining good performance compared to other alternative schemes.

This paper presents recursive-least-squares-based model predictive control (RLSMPC). RLSMPC uses only output feedback, and thus does not require full-state measurements. Online learning is performed through concurrent system identification, and thus no a priori model is needed. RLSMPC employs separate RLS algorithms for identification, offset determination, and control. Variable-rate forgetting is used to facilitate system identification and offset estimation.

This paper focuses on the constrained control of UAVs in geofencing applications. Although geofence systems are becoming more attractive as a research topic, most works are focusing on defining the boundaries of the admissible geographical region without addressing the control issues and boundary-handling problems. In this paper, we propose a constrained control scheme to steer an UAV to the desired position while ensuring constraints satisfaction at all times. To do so, we make use of the recently introduced Explicit Reference Governor framework. The proposed scheme is validated through extensive experimental studies carried out in a laboratory environment.