A Hybrid Systems Model Predictive Control Framework for AUV Motion Control

A Robust Reach Set MPC Scheme for Control of AUVs

“…The power and computational requirements for running most of these MPC schemes in a are weakly suitable for AUVs: (i) intense on-line computational burden, and (ii) design issues to ensure the required performances do note consider onboard resource constraints. This work considers some of these issues, and, in line of the one proposed in 3 and is an extension of 4 ,concerns a substantially different approach to AUV formation control.…”

“…By considering, for each vehicle (we drop the index i), the state and the controls given by η = [x, y, ψ] T and ν = [u, v, r] T and τ = [τ u , τ r ] T , respectively, the dynamics are given in 6 . For details, 4 . Other types of constraints that might usually be present in AUV formations are: endpoint state constraints, η…”

“…Moreover, we note that for T = T L , by a standard change of state variable that eliminates the running cost, and by using the principle of optimality (we are considering only positional functionals, 25 ), we conclude that (without relabelling) (P T ) and (P A T L ) are equivalent. See 4 for details. The need to consider approximations are due to the fact that, in general, the computational complexity of the AS and VF are huge even for relatively small systems.…”

“…These features are critical for AUV systems as they operate in contexts of scarce resources and high uncertainty or environmental variability. The proposed control structure, and the various underlying results, enabling the articulation of resources optimization with state feedback control while keeping the onboard computational burden very low, were developed in the context of 2 , and were partially published in a number of papers, 3,4 . The key idea to tackle this challenge consists in, by taking advantage of time-invariant data underlying the formulation of the involved optimization problems, computing a priori (i.e., off-line) a number of, as comprehensive as possible, simple building blocks required for optimal or sub-optimal control synthesis as a function of a set of parameters associated with a satisfactory number of more likely typified on-line situations.…”

Attainable-Set Model Predictive Control for AUV Formation Control

“…Here, we present further refinements of the control architecture presented in [4] for the management and control of the formation of AUVs. This development is in the sequel of [5] in which an MPC scheme with very low on-line computational budget obtained by taking advantage of time invariance of the dynamics and some environmental features.…”