Alessio Iovine

Yoshinari Takayama, Alessio Iovine, Bart Besselink et al.

Real-world control systems must achieve long-horizon objectives (liveness) while respecting continuous-time safety constraints, a combination that motivates hierarchical layered control architectures (LCAs). Existing LCA research, however, lacks (i) a uniform specification language across discrete planning and continuous execution, (ii) formal guarantees that specifications are preserved when interconnecting subsystems at heterogeneous time scales, and (iii) compositional separation between layers, owing to reliance on naive input-filtering laws. This paper addresses all three gaps by importing the safety--liveness decomposition into a heterogeneous assume--guarantee framework: \emph{safety is enforced by invariance} at the continuous-time layer, while \emph{liveness is achieved through refinement} at the discrete-time layer, with inter-layer coordination formalized via vertical refinement and timing-compatibility conditions. We instantiate this contract with a novel LCA combining an MPC planner, an input-to-state stabilizing (ISS) low-level controller, and a reference-governor bridge, and validate it on a Hybrid Energy Storage System (HESS) comprising a battery and a supercapacitor.

Andrei Sperilă, Alessio Iovine, Sorin Olaru et al.

A two-layer control architecture is proposed, which promotes scalable implementations for model predictive controllers. The top layer acts as both a reference governor for the bottom layer and as a feedback controller for the regulated network. By employing set-based methods, global theoretical guarantees are obtained by enforcing local constraints upon the network's variables and upon those of the first layer's implementation. The proposed technique offers recursive feasibility guarantees as one of its central features, and the expressions of the resulting predictive strategies bear a striking resemblance to classical formulations from model predictive control literature, allowing for flexible and easily customisable implementations.

Andrei Sperilă, Alessio Iovine, Sorin Olaru et al.

A two-layer control architecture is proposed to enable scalable implementations for constraint-based decision strategies, such as model predictive controllers. The bottom layer is based upon a distributed feedback-feedforward scheme that directs the controlled network's information flow according to a pre-specified communication infrastructure. Explicit expressions for the resulting closed-loop maps are obtained, and an offline model-matching procedure is proposed for designing the first layer. The obtained control laws are deployed via distributed state-space-based implementations, and the resulting closed-loop models enable predictive control design for the constraint management procedure described in our companion paper.

Alessio Iovine, Francesco Valentini, Elena De Santis et al.

In this paper a mesoscopic hybrid model, i.e. a microscopic hybrid model that takes into account macroscopic parameters, is introduced for designing a human-inspired Adaptive Cruise Control. A control law is proposed with the design goal of replacing and imitating the behaviour of a human driver in a car-following situation where lane changes are possible. First, a microscopic hybrid automaton model is presented, based on human psycho-physical behavior, for both longitudinal and lateral vehicle control. Then a rule for changing time headway on the basis of macroscopic quantities is used to describe the interaction among next vehicles and their impact on driver performance. Simulation results show the advantages of the mesoscopic model. A feasibility analysis of the needed communication network is also presented.