We present an industrial case study in automotive control of significant complexity: the new common-rail fuel- injection system for Diesel engines developed by Magneti Marelli Powertrain. In this system, an inlet metering valve, inserted before the High Pressure (HP) pump, regulates the fuel flow that supplies the common rail according to the engine operating point (e.g. engine speed and torque). The standard approach to controller design adopted in the automotive in- dustry is based on mean-value models of the plant. However, the fuel injection system exhibits complex discrete-continuous interactions, due to the slow time-varying frequency of the HP pump cycles and the fast sampling frequency of sensing and actuation. Hence, a hybrid approach to controller design is very promising. In this paper, we present the synthesis of a hybrid multi-rate controller for a Magneti Marelli Powertrain common-rail fuel- injection system for four-cylinder multi-jet engines. Controller design is based on a hybrid model of the injection system. The resulting hybrid controller performs significantly better then previous controllers developed in the company on the basis of mean-value models of the plant.
In this paper we address the problem of detecting faulty behaviors of cooperative mobile agents. A novel decentralized and scalable architecture that can be adopted to realize a monitor of the agents
This paper focuses on the detection of misbehaving agents within a group of mobile robots. A novel approach to automatically synthesize a decentralized Intrusion Detection System (IDS) as well as an efficient implementation of local monitors are presented. In our scenario, agents perform possibly different independent tasks, but cooperate to guarantee the entire system
An antagonistic actuation with variable stiffness is proposed for ensuring safety and performance in human friendly robotic applications. Various arrangements are analysed with respect to performance, safety and dependability. The results are expected to provide useful guidelines for choosing an actuation mechanism and its implementation for human-robot interactive applications.
In this paper we introduce an analysis of dependability of an elementary yet critical component of robotic systems designed to operate in environments shared with humans, i.e. the joint-level actuation system. We consider robot joints that implement the Variable Impedance Actuation (VIA) paradigm. The VIA has been demonstrated to be an effective mean to achieve high performance while constantly keeping injury risks to humans by accidental impacts below a given threshold. The paper describe possible implementations of the VIA concept which use the Antagonistic Actuation (AA) in three different arrangements. This study follows a previously reported paper dealing with safety. Here a detailed comparative dependability and performability analysis in front of possible specific failure modes is conducted, whose results provide additional and useful guidelines for design of safe and dependable actuation systems for physical human-robot interaction.
The problem of accurate localization using only measurements from a LIDAR sensor is analyzed in this paper. The sensor is rigidly fixed on a generic moving platform, which moves on a plane. Practical on–line applications of localization algorithms impose constraints on the execution time, problem that is addressed in this paper and compared with other existing solutions. Due to the nature of the sensor adopted, the localization algorithm is based on a fast and accurate {\em registration} algorithm, which is able to deal with noisy measurements, outliers and dynamic environments. The proposed solution relies on the RANSAC algorithm in combination with a Huber kernel in order to cope with typical nuisances in LIDAR measurements. The robust registration is successively used in combination with an Extended Kalman Filter to track the trajectory of the LIDAR over time, hence to solve the localization problem. Simulations and experimental results are reported to show the feasibility of the proposed approach.
Complex dynamical systems can be steered by using symbolic input plans. These plans must have a finite descriptive length, and can be expressed by means of words chosen in an alphabet of symbols. In this way, such plans can be sent through a limited capacity channel to a remote system, where they are decoded in suitable control actions. The choice of this symbols is essential to efficiently encode steering plans. To this aim, in this paper, we state the problem of finding symbols maximizing the interval of points reachable by the system along paths with constrained length. We focus on the problem with two symbols, and compare the results with those produced by plans not accounting for the length constraint. Moreover, the behavior of a simple helicopter, steered by both kinds of plans, has been simulated, in order to illustrate the power of the overall control system, and to emphasize the improvements introduced by the new plans.
In this paper we consider scheduling the execution of a number of different software tasks implementing a hierarchy of real-time controllers for a given plant. Controllers are considered to be given and ordered according to a measure of the performance they provide. Software tasks implementing higher-performance controllers are assumed to require a larger worst-case execution time. Control tasks are to be scheduled within given availability limits on execution time, which change stochastically in time. The ensuing switching system is prone to instability, unless a very conservative policy is enforced of always using the simplest, least performant controller. The presented method allows to condition the stochastic scheduling of control tasks so as to obtain a better exploitation of computing capabilities, while guaranteeing almost sure stability of the resulting switching system.
A simple approach for mobile robots to exploit multipath fading in order to improve received radio signal strength (RSS), is presented. The strategy is to sample the RSS at discrete points, without deviating too far from the desired position. We first solve the problem of how many samples are needed for given communications performance and how they should be spaced. Second, we propose a circular and a grid trajectory for sampling and give lower bounds on how many samples they will yield. Third, we estimate the parameters of our strategy from measurements. Finally we demonstrate the validity of our analysis through experiments.
In this paper we deal with the optimal feedback synthesis problem for robotic vehicles with trailers which can be modeled by differential equations in chained-form. With respect to classical methods for numerical evolution of optimal feedback synthesis via Dynamic Programming which are based on both input and state discretization, our method exploits the lattice structure naturally imposed on the reachable set by input quantization. A generalized Dijkstra algorithm can be used to obtain optimal feedback control laws, for chained-form vehicles with n-trailers, in an effective way.
Many challenging verification problems arise from complex hybrid automata that model decentralized control systems. As an example, we will consider decentralized policies that steer multiple vehicles in a shared environment: properties of safety and liveness, such as collision avoidance and ultimate convergence of all vehicles to their goals, must be verified. To formally verify the behavior of proposed policies, it is desired to identify the broadest class of start and goal configurations, such that safety and liveness would be guaranteed. Simple conditions are proposed to identify such a class: ideally, a formal proof that such conditions are necessary and sufficient for safety and liveness is requested. Unfortunately, in decentralized control frameworks classical approaches are difficult to apply. Hence, probabilistic verification method can be applied to quantify the accuracy and the confidence of the veridicity of the desired predicate. The probabilistic verification method is applied to a recently proposed cooperative and completely decentralized collision avoidance policy for non-holonomic vehicles.
