This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Model Predictive Control in Aerospace Systems: Current State and Opportunities

This paper implements robust decentralized model predictive control (DMPC) for a team of cooperating uninhabited aerial vehicles (UAVs). The problem involves vehicles with independent dynamics but with coupled constraints to capture required cooperative behavior. Using a recently-developed form of DMPC, each vehicle plans only for its own actions, but feasibility of the sub-problems and satisfaction of the coupling constraints are guaranteed throughout, despite the action of unknown but bounded disturbances. UAVs communicate relevant plan data to ensure that decisions are consistent across the team. Collision avoidance is used as an example of coupled constraints and the paper shows how the speed, turn rate and avoidance distance limits in the optimization should be modified in order to guarantee robust constraint satisfaction. Integer programming is used to solve the non-convex problem of path-planning subject to avoidance constraints. Numerical simulations compare computation time and target arrival time under decentralized and centralized control and investigate the impact of decentralization on team performance. The results show that the computation required for DMPC is significantly lower than for its centralized counterpart and scales better with the size of the team, at the expense of only a small increase in UAV flight times.

Decentralized model predictive control of co-operating UAVs

Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles

https://upcommons.upc.edu/bitstream/handle/2117/13664/1225-Partitioning-Approach-oriented-to-the-Decentralised-Predictive-Control-of-Large-Scale-Systems.pdf;sequence=1

Partitioning Approach oriented to the Decentralised Predictive Control of Large-Scale Systems

Hierarchical and Hybrid Model Predictive Control of Quadcopter Air Vehicles

Future safety critical space missions call for increasing levels of embedded spacecraft autonomy. Spacecraft and mission responsiveness will be highly improved via onboard automation and autonomy functions, simplifying operations and ground control capabilities. Recently developed real-time embedded MPC guidance and control strategies have a great potential for the next generation of high performance reusable ESA launch vehicles and GNC systems. This paper addresses these technologies for real-time embedded MPC covering thrust vectored control that could be proficiently used during the ascent phase as well as on powered descent enabling accurate pin-point landing features.

Model predictive control for powered descent guidance and control

This paper investigates the dynamics of networks of systems achieving rendezvous under linear quadratic optimal control While the dynamics of rendezvous were studied extensively for the symmetric case, where all systems have exactly the same dynamics (such as simple integrators), this paper investigates the rendezvous dynamics for the general case when the dynamics of the systems may be different We show that the rendezvous is stable and that the post-rendezvous dynamics of the network of systems is entirely defined by the common eigenvalues with common eigenvectors output image The approach is also extended to the case of constraints on systems states, inputs, and outputs

The rendezvous dynamics under linear quadratic optimal control

This paper is focused on the theoretical development and the hardware implementation of low-complexity piecewise-affine direct virtual sensors for the estimation of unmeasured variables of interest of nonlinear systems. The direct virtual sensor is designed directly from measured inputs and outputs of the system and does not require a dynamical model. The proposed approach allows one to design estimators which mitigate the effect of the so-called ‘curse of dimensionality’ of simplicial piecewise-affine functions, and can be therefore applied to relatively high-order systems, enjoying convergence and optimality properties. An automatic toolchain is also presented to generate the VHDL code describing the digital circuit implementing the virtual sensor, starting from the set of measured input and output data. The proposed methodology is applied to generate an FPGA implementation of the virtual sensor for the estimation of vehicle lateral velocity, using a hardware-in-the-loop setting.

Carlo Alberto Pascucci

Papers

Hierarchical and Hybrid Model Predictive Control of Quadcopter Air Vehicles

Model predictive control for powered descent guidance and control

The rendezvous dynamics under linear quadratic optimal control

Low-complexity piecewise-affine virtual sensors: theory and design