Implementation of Model Predictive Control (MPC) on hardware with limited computational resources remains a challenge. Especially for long-distance maneuvers that require small sampling times, the necessary horizon lengths prevent its application on onboard computers. In this paper, we propose a computationally efficient tube-based shrinking horizon MPC that is scalable to long prediction horizons. Using move blocking, we ensure that a given number of decision inputs is efficiently used throughout the maneuver. Next, a method to substantially reduce the number of constraints is introduced. The approach is demonstrated with a helicopter landing on an inclined platform using a prediction horizon of 300 steps. The constraint reduction decreases the computation time by an order of magnitude with a slight increase in trajectory cost.

This paper considers robust disturbance observer design for an autonomous helicopter. The disturbance observer is a well-established method originally developed for the mitigation of disturbance effects in minimum-phase single-input single-output systems. Recently, the disturbance observer design has been extended to non-minimum-phase multiple-input multiple-output systems using an $${\mathcal {H}}_{\infty }$$ H ∞ method. Due to the applicability to non-minimum-phase systems, the new disturbance observer design becomes also attractive for aerospace applications. However, there are no guaranteed stability margins that can be achieved with the $${\mathcal {H}}_{\infty }$$ H ∞ -based design approach. In fact, stability margins may easily be degraded when the disturbance observer is integrated into an existing controller-plant closed-loop. In this paper, a robust disturbance observer design is proposed. Therefore, complex uncertainties are introduced to model gain and phase variations. Robustification of the observer is achieved using design principals of $$\mu$$ μ -synthesis. As consequence, the proposed method enhances the robust performance of the observer and leads to less degradation of stability margins in comparison to the original design procedure. An application example is provided which considers mitigation of wind effects for the inner-loop control of an unmanned helicopter.

Maintenance of offshore wind farms requires the transportation of tools and spare parts in close coordination with the deployment of technicians and the cost-intensive shutdown of the wind turbines. In addition to ships and helicopters, drones are envisioned to support the offshore transportation system in the future. For cost-efficient and scalable offshore drone operations, autonomy is key to minimize the required infrastructure and personnel. In this work, we present a system architecture that integrates the key onboard capabilities for autonomous offshore drone operations: onboard mission and contingency management, en-route trajectory planning, robust flight control, safe landing, communication management, and runtime monitoring. We also present technical solutions for each of these capabilities and discuss their integration and interaction within the autonomy architecture. Furthermore, remaining challenges and the feasibility of autonomous drone operations for offshore wind farm cargo delivery are addressed, contributing to the realization of this vision in the near future. The work presented here summarizes the results of autonomous cargo drone operations within the UDW research project, a joint project between the German Aerospace Center (DLR) and the energy supplier EnBW.

Enforcing multiple constraints based on the concept of control barrier functions (CBFs) is a remaining challenge because each of the CBFs requires a condition on the control inputs to be satisfied which may easily lead to infeasibility problems. The problem becomes even more challenging with input constraints and disturbances. In this paper, we consider enforcement of bounding box constraints for a second order system under limited control authority and input disturbances. To solve the constrained control problem, we apply multiple robust control barrier functions (RCBFs) which, in general, do not provide a feasible solution to the problem. However, we derive conditions on how to select the RCBF parameters to guarantee that a feasible solution always exists.