Alessandro D’Innocenzo

Critical Observability of a Class of Hybrid Systems and Application to Air Traffic Management

We present a novel observability notion for switching systems that model safety–critical systems, where a set of states – called critical states – must be detected within a prescribed delay since they correspond to hazards that may yield catastrophic events. Some sufficient and some necessary conditions for critical observability are derived. An observer is proposed for reconstructing the hybrid state evolution of the switching system whenever a critical state is reached. We apply our results to the runway crossing control problem, i.e., the control of aircraft that cross landing or take–off runways. In the hybrid model of the system, five agents are present; four are humans, each modeled as hybrid systems, subject to situation awareness errors.

Hybrid Systems and Verification by Abstraction

The increase of functionality offered by today’s control systems based on embedded systems requires more effort to verify the controlled system, as a malfunction can yield catastrophic results. These systems are usually hybrid systems, mixing continuous and discrete dynamics. When analyzing a hybrid system, the dimension of the state space is often so large that formal verification is out of the question. Its analysis can be carried out using abstraction, namely constructing a system with a smaller state space, preserving the properties to verify in the original system. Making use of a notion of diagnosability for hybrid systems, generalizing the notion of observability, in this paper it is shown an abstraction procedure translating a hybrid system into a timed automaton, in order to verify observability and diagnosability properties. The subclass of hybrid systems here considered is that of the durational graphs. We propose a procedure to check diagnosability, and show that the verification problem belongs to the complexity class P. This procedure is applied to an electromagnetic valve system for camless engines.

Approximate Abstractions of Markov Chains with Interval Decision Processes

Abstract This work introduces a new abstraction technique for reducing the state space of large, discrete-time labelled Markov chains. The abstraction leverages the semantics of interval Markov decision processes and the existing notion of approximate probabilistic bisimulation. Whilst standard abstractions make use of abstract points that are taken from the state space of the concrete model and which serve as representatives for sets of concrete states, in this work the abstract structure is constructed considering abstract points that are not necessarily selected from the states of the concrete model, rather they are a function of these states. The resulting model presents a smaller one-step bisimulation error, when compared to a like-sized, standard Markov chain abstraction. We outline a method to perform probabilistic model checking, and show that the computational complexity of the new method is comparable to that of standard abstractions based on approximate probabilistic bisimulations.

Data-driven Switched Affine Modeling for Model Predictive Control

Abstract Model Predictive Control (MPC) is a well consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict the system’s behavior over a predictive horizon. However, building physics-based models for large scale systems, such as buildings and process control, can be cost and time prohibitive. To overcome this problem we propose in this paper a methodology to exploit machine learning techniques (i.e. regression trees and random forests) in order to build a state-space switched affine dynamical model of a large scale system only using historical data. Finite Receding Horizon Control (RHC) setup using control-oriented data-driven models based on regression trees and random forests is presented as well. A comparison with an optimal MPC benchmark and a related methodology is provided on an energy management system to show the performance of the proposed modeling framework. Simulation results show that the proposed approach is very close to the optimum and provides better performance with respect to the related methodology in terms of cost function optimization.