Mattia Bruschetta

University of Padua | UNIPD · Department of Information Engineering

In recent years, several competitions have highlighted the need to investigate vision-based solutions to address scenarios with functional insufficiencies in perception, world modeling and localization. This article presents the Vision-based Lane Keeping System (VbLKS) developed by the DEI-Unipd Team within the context of the Bosch Future Mobility...

Lately, nonlinear model predictive control (NMPC) has been successfully applied to (semi-) autonomous driving problems and has proven to be a very promising technique. However, accurate control models for real vehicles could require costly and time-demanding specific measurements. To address this problem, the exploitation of system data to compleme...

Learning-based Nonlinear Model Predictive Control (LbNMPC) is a promising framework for applying NMPC using dynamics models obtained directly from riding data. We developed a LbNMPC controller based on a black-box dynamics model and compare it with a previously published NMPC controller based on a physics-based description. The controllers have bee...

Lately, Nonlinear Model Predictive Control (NMPC)has been successfully applied to (semi-) autonomous driving problems and has proven to be a very promising technique. However, accurate control models for real vehicles could require costly and time-demanding specific measurements. To address this problem, the exploitation of system data to complemen...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective: Effective dosing of anticoagulants aims to prevent blood clot formation while avoiding hemorrhages. This complex task is challenged by several disturbing factors and drug-effect uncertainties, requesting frequent monitoring and adjustment. B...

This paper proposes a fast and accurate solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related control problem requires an iterative solver to find the optimal solution. The real-time certificatio...

An increasing number of vehicles today are equipped with advanced driver-assistance systems that provide humans involved in the driving tasks with continuous and active support. State-of-the-art implementations of these systems frequently rely on an underlying vehicle controller based on the model-predictive control strategy. In this article, we pr...

In dynamic driving simulators, the experience of operating a vehicle is reproduced by combining visual stimuli generated by graphical rendering with inertial stimuli generated by platform motion. Due to inherent limitations of the platform workspace, inertial stimulation is subject to shortcomings in the form of missing cues, false cues, and/or sca...

div>This paper proposes an efficient QP solver for electric motors.</div

div>This paper proposes a fast and accurate solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related control problem requires an iterative solver to find the optimal solution. The real-time certific...

Efficient management of energy resources is crucial in smart buildings. In this work, model predictive control (MPC) is used to minimize the economic costs of prosumers equipped with production units, energy storage systems, and electric vehicles. To this purpose, the predictive control manages the available energy resources by exploiting future in...

In recent years the increasing needs of reducing the costs of car development expressed by the automotive market have determined a rapid development of virtual driver prototyping tools that aims at reproducing vehicle behaviors. Nevertheless, these advanced tools are still not designed to exploit the entire vehicle dynamics potential, preferring to...

div>This paper proposes an effective solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related quadratic programming problem requires an iterative solver to find the optimal solution. The real-time c...

div>This paper proposes an effective solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related quadratic programming problem requires an iterative solver to find the optimal solution. The real-time c...

Vehicle attitude estimation is nowadays essential for a wide range of applications, e.g. guidance of unmanned vehicles, robotics and automotive controls. In this paper, the attitude estimation problem is solved by means of a velocity-aided, Extended Kalman Filter with correlated noise (CEKF), exploiting the intrinsic correlation between sensor nois...

We describe Urban Driving Games (UDGs) as a particular class of differential games that model the interactions and incentives of the urban driving task. The drivers possess a “communal” interest, such as not colliding with each other, but are also self-interested in fulfilling traffic rules and personal objectives. Subject to their physical dynamic...

Virtual prototyping tools are widely used in the development of new motorcycles, but the instability of two-wheel vehicles leads to the necessity of implementing a vehicle driver. In this brief, the development of an effective virtual rider (VR) is described that is capable of controlling the motorcycle during complex, high performance maneuvers. T...

Virtual prototyping tools are nowadays widespread among automotive manufacturers and a successful usage of these tools requires a reliable human-like driver/rider. Specifically addressing motorcycles, the design of such controllers, called Virtual Riders (VR), is still a challenging task. In this paper, we first analyze the state of the art of the av...

Move blocking (MB) is a widely used strategy to reduce the degrees of freedom of the Optimal Control Problem (OCP) arising in receding horizon control. The size of the OCP is reduced by forcing the input variables to be constant over multiple discretization steps. In this paper, we focus on developing computationally efficient MB schemes for multip...

Various rider models have been proposed that provide control inputs for the simulation of motorcycle dynamics. However, those models are mostly used to simulate production motorcycles, so they assume that all motions are in the linear region such as those in a constant radius turn. As such, their performance is insufficient for simulating racing mo...

This work presents the application of model predic-tive control (MPC) for the energy management of smart buildings in microgrids. It is shown that by means of the described MPC formulation the power exchange at the point of connection of the building can be made close to a given power reference, typically available in microgrid contexts, and, there...

Move blocking (MB) is a widely used strategy to reduce the degrees of freedom of the Optimal Control Problem (OCP) arising in receding horizon control. The size of the OCP is reduced by forcing the input variables to be constant over multiple discretization steps. In this paper, we focus on developing computationally efficient MB schemes for multip...

Virtual prototyping is currently a widely used tool for the development of new cars. In this paper, the development of an effective virtual driver (VD) is described, that aims at reproducing real-time driver's behaviour, also at the limit of performance. The proposed VD model, a four-wheel vehicle with longitudinal load transfer and Pacejka's later...

We propose a non-linear model predictive scheme for planning fuel efficient maneuvers of small spacecrafts that shall rendezvous space debris. The paper addresses the specific issues of potential limited on-board computational capabilities and low-thrust actuators in the chasing spacecraft, and solves them by using a novel MatLab-based toolbox for...

In this paper we introduce MATMPC, an open source software built in MATLAB for nonlinear model predictive control (NMPC). It is designed to facilitate modelling, controller design and simulation for a wide class of NMPC applications. MATMPC has a number of algorithmic modules, including automatic differentiation, direct multiple shooting, condensin...

In recent years, efficient optimization algorithms for Nonlinear Model Predictive Control (NMPC) have been proposed, that significantly reduce the on-line computational time. In particular, direct multiple shooting and Sequential Quadratic Programming (SQP) are used to efficiently solve Nonlinear Programming (NLP) problems arising from continuous-t...

In recent years, efficient optimization algorithms for Nonlinear Model Predictive Control (NMPC) have been proposed, that significantly reduce the on-line computational time. In particular, direct multiple shooting and Sequential Quadratic Programming (SQP) are used to efficiently solve Nonlinear Programming (NLP) problems arising from continuous-t...

In Nonlinear Model Predictive Control(NMPC), an optimal control problem (OCP) is solved repeatedly at every sampling instant. To satisfy the real-time restriction, modern methods tend to convert the OCP into structured Nonlinear Programming problems (NLP), which are approximately solved on-line. Real-Time Iteration is one of the promising NMPC algo...

Dynamic driving simulators are nowadays a common tool in the automotive industry. Issues related to automated driving topic are moving the target user of these tools from professional drivers to non-professional ones. Unfortunately, these latter typically suffer more the virtual environment, mainly due to the unavoidable lack of low frequency susta...

In Nonlinear Model Predictive Control(NMPC), an optimal control problem (OCP) is solved repeatedly at every sampling instant. To satisfy the real-time restriction, modern methods tend to convert the OCP into structured Nonlinear Programming problems (NLP), which are approximately solved on-line. Real-Time Iteration is one of the promising NMPC algo...

In recent years several fast Nonlinear Model Predictive Control (NMPC) strategies have been proposed, aiming at reducing computational burden and widening the scope of NMPC techniques. A promising approach is the Real-Time Iteration (RTI) scheme, where Nonlinear Programming (NLP) problems are parametrized by multiple shooting and only one Sequentia...

Driving simulators are nowadays a widely used tool in the automotive industry. In particular, the need for safe and repeatable conditions in automated driving testing is now defining a new challenge: to extend the use of the tool to nonprofessional drivers. Quality of the motion control strategies in generating both realistic and feasible inputs to...

Widespread application of real-time, Nonlinear Model Predictive Control (NMPC) algorithms to systems of large scale or with fast dynamics is challenged by the high associated computational cost, in particular in presence of long prediction horizons. In this paper, a fast NMPC strategy to reduce the on-line computational cost is proposed. A Curvatur...

The use of dynamic driving simulators is nowadays common practice in the automotive industry. The effectiveness of such devices is strongly related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are...

The use of dynamic driving simulators is constantly increasing in the automotive community, with applications ranging from vehicle development to rehab and driver training. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate bo...

In this paper, we consider the problem of assessing the performance of different power management strategies for a hybrid, 125cc sport motorbike. The electrical machine is used to obtain a torque boost during accelerations, with reduced emissions. Being a sport motorcycle, impact of the hybridisation on performance in terms of handling has to be ca...

The use of dynamical driving simulators is nowadays common in many different application fields, such as driver training, vehicle development, and medical studies. Platforms with different mechanical structures have been designed, depending on the particular application and the corresponding targeted market. The effectiveness of such devices is rel...

The use of dynamical driving simulators is nowadays common practice in many different application fields, such as driver training, vehicle development, and medical studies. Platforms with different mechanical structure are designed, depending on the particular application and the corresponding targeted market. The effectiveness of such devices is r...

Dynamic driving simulators are seeing an increasing interest in the automotive community, both in the research and industrial fields. Different aspects are involved, from virtual prototyping to rehab: racing applications are of particular interest, where simulators are exploited to improve the driver's capabilities and test different vehicle set-up...

The theory of variational integration provides a systematic procedure to discretize the equations of motion of a mechanical system, preserving key properties of the continuous time flow. The discrete-time model obtained by variational integration theory inherits structural conditions which in general are not guaranteed under general discretization...

Driving simulators are widely used in many different applications, such as driver training, vehicle development, and medical studies. To fully exploit the potential of such devices, it is crucial to develop platform motion control strategies that generate realistic driving feelings. This has to be achieved while keeping the platform within its limi...

Driving simulators play an important role in the development of new vehicles and advanced driver assistance devices. In fact, on the one hand, having a human driver on a driving simulator allows automotive OEMs to bridge the gap between virtual prototyping and on-road testing during the vehicle development phase. On the other hand, novel driver ass...

Variational integrators is a a new discretization technique of the equations of motion of a mechanical system introduced by Veselov and further developed by J. Marsden an co-workers, which is now widely used by numerical analysts working in various applied fields. This discretization technique, unlike the usual discretization procedures familiar in...

We exploit a new discretization technique of the Lagrange equations of motion of a mechanical system, derived from the concept of {em variational integrators} introduced by Veselov and further developed by J.Marsden an co-workers, for the identification of linear mechanical systems. This discretization technique preserves the physical meaning of th...

We exploit a new discretization technique of the Lagrange equations of motion of a mechanical system, derived from the concept of variational integrators introduced by Veselov and further developed by J.Marsden an co-workers, for the identification of linear mechanical systems. This discretization technique preserves the physical meaning of the mod...