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We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control. Our approach also quantifies sub-optimality for the computed solutions. Inspired by Branch-and-Bound techniques, the key idea is to train a Neural Network/Random Forest, which for a given parameter, predicts a strategy consisting of (1) a set of Linear Programs (LPs) such that their feasible sets form a partition of the feasible set of the MILP and (2) a candidate integer solution. For control computation and sub-optimality quantification, we solve a set of LPs online in parallel. We demonstrate our approach for a motion planning example and compare against various commercial and open-source mixed-integer programming solvers., Comment: First two authors contributed equally

A novel prize-winner algorithm designed for a path following problem within the Unmanned Aerial Vehicle (UAV) field is presented in this paper. The proposed approach exploits the advantages offered by the pure pursuing algorithm to set up an intuitive and simple control framework. A path fora quad-rotor UAV is obtained by using downward facing camera images implementing an Image-Based Visual Servoing (IBVS) approach. Numerical simulations in MATLAB together with the MathWorks Virtual Reality (VR) toolbox demonstrate the validity and the effectiveness of the proposed solution. The code is released as open-source making it possible to go through any part of the system and to replicate the obtained results., Comment: 6 pages, 11 figures, conference

A Nonlinear Model Predictive Control (NMPC) strategy aimed at controlling a small-scale car model for autonomous racing competitions is presented in this paper. The proposed control strategy is concerned with minimizing the lap time while keeping the vehicle within track boundaries. The optimization problem considers both the vehicle's actuation limits and the lateral and longitudinal forces acting on the car modeled through the Pacejka's magic formula and a simple drivetrain model. Furthermore, the approach allows to safely race on a track populated by static obstacles generating collision-free trajectories and tracking them while enhancing the lap timing performance. Gazebo simulations using the F1/10 simulator showcase the feasibility and validity of the proposed control strategy. The code is released as open-source making it possible to replicate the obtained results., Comment: 6 pages, 8 figures, conference

Backward reachability (also termed controllability) has been extensively studied in control theory, and tools for a wide class of systems have been developed. Nevertheless, assessing a backward reachability analysis or synthesis remains challenging as the system dimension grows. In this paper we study the backward reachability problem for large scale networked nonlinear systems with coupled dynamics and subject to states and inputs nonlinear constraints. We propose a theory for completely general nonlinear constrained large scale controllability problems. We demonstrate that it is always possible to recast such problems for the overall large scale system into an equivalent distributed form where, without introducing any conservativeness, each node of the network iteratively solves a local reachability subproblem by exchanging information with the adjacent nodes. Although the proposed algorithm is completely decentralized, the solution of the backward reachability problem for the overall system is equivalently determined by the local ones and satisfies all the given constraints. Not being linked to any specific assumption on the system dynamics nor static constraints, the proposed results hold irrespectively of any possible analytical/numerical solver to be adopted for backward reachability computation.

In this letter, we model the day-ahead price-based demand response of a residential household with battery energy storage and other controllable loads, as a convex optimization problem. Further using duality theory and Karush-Kuhn-Tucker optimality conditions, we derive a sufficient criterion which guarantees non-simultaneous charging and discharging of the battery energy storage, without explicitly modelling it as a constraint, Comment: 3 pages and 1 figure

This paper presents a novel solution technique for scheduling multi-energy system (MES) in a commercial urban building to perform price-based demand response and reduce energy costs. The MES scheduling problem is formulated as a mixed integer nonlinear program (MINLP), a non-convex NPhard problem with uncertainties due to renewable generation and demand. A model predictive control approach is used to handle the uncertainties and price variations. This in-turn requires solving a time-coupled multi-time step MINLP during each time-epoch which is computationally intensive. This investigation proposes an approach called the Scenario-Based Branch-and-Bound (SB3), a light-weight solver to reduce the computational complexity. It combines the simplicity of convex programs with the ability of meta-heuristic techniques to handle complex nonlinear problems. The performance of the SB3 solver is validated in the Cleantech building, Singapore and the results demonstrate that the proposed algorithm reduces energy cost by about 17.26% and 22.46% as against solving a multi-time step heuristic optimization model., Comment: 10 pages, 9 figures, Accepted for publication in IEEE Transactions on Industrial Informatics