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Dynamical flow networks with heterogeneous routing are analyzed in terms of stability and resilience to perturbations. Particles flow through the network and, at each junction, decide which downstream link to take on the basis of the local state of the network. Differently from single-commodity scenarios, particles belong to different classes, or commodities, with different origins and destination
The position and orientation estimation problem for mobile robots is approached by fusing measurements from inertial sensors, wheel encoders, and a camera. The sensor fusion approach is based on the standard extended Kalman filter, which is modified to handle measurements from the camera with unknown prior delay. A real-time implementation is done on a four-wheeled omni-directional mobile robot, u
Use of feedforward can alleviate feedback and adaptive actions. Feedforward signals can be generated from reference models and the same models can also be used as reference models in adaptive control. A method for designing the reference models is presented in the paper. By exploiting the structure of the equations describing air vehicles it is possible to find reference models that scale to the p
The development of a predictive control algorithm for glycaemia regulation in diabetic subjects requires patient-specific models of the glucose metabolism which are physiologically relevant, parsimonious, yet able to accurately forecast blood glucose. Given the measured data: total plasma insulin [mIU/L]; plasma glucose [mg/dL]; plasma glucose rate of appearance after intestinal absorption [mg/kg
We propose here a new procedure for output feedback design for systems with nonlinearities satisfying quadratic constraints. It provides an alternative for the classical observer-based design and relies on transformation of the closed-loop system with a dynamic controller of particular structure into a special block form. We present two sets of sufficient conditions for stability of the transforme
We consider model predictive control (MPC) problems with linear dynamics, polytopic constraints, and quadratic objective. The resulting optimization problem is solved by applying an accelerated gradient method to the dual problem. The focus of this paper is to provide bounds on the number of iterations needed in the algorithm to guarantee a prespecified accuracy of the dual function value and the
This manuscript proposes a novel viewpoint on computing systems' modelling. The classical approach is to consider fully functional systems and model them, aiming at closing some external loops to optimise their behaviour. On the contrary, we only model strictly physical phenomena, and realise the rest of the system as a set of controllers. Such an approach permits rigorous assessment of the obtain
Adaptive reservation is a real-time scheduling technique in which each application is associated a fraction of the computational resource (a reservation) that can be dynamically adapted to the varying requirements of the application by using appropriate feedback control algorithms. An adaptive reservation is typically implemented by using an aperiodic server (e.g. sporadic server) algorithm with f
A parametrized model in addition to the control and state-space variables depends on time-independent design parameters, which essentially define a family of models. The goal of parametrized model reduction is to approximate this family of models. In this paper, a reduction method for linear time-invariant (LTI) parametrized models is presented, which constitutes the development of a recently prop
Nowadays, we live in a society with billions of devices that are interconnected and interact together to improve the quality of our lives. The management and processing of information and knowledge have by now become our main resources, and the fundamental factors of economic and social development, and it is achieved through Big Data Frameworks (BDFs). The amount of such data is becoming larger e
We consider a networked control system where a linear time-invariant (LTI) plant, subject to a stochastic disturbance, is controlled over a communication channel with colored noise and a signal-to-noise ratio (SNR) constraint. The controller is based on output feedback and consists of an encoder that measures the plant output and transmits over the channel, and a decoder that receives the channel
In some situations the closed-loop system obtained by L1 adaptive control is equivalent to linear systems. The architectures of these systems are investigated and compared with internal model control and the input observer architecture. The analysis is focused on aerospace application. An effort has been made to understand and describe what fundamental control characteristic of flying applications
In this paper, optimization problems arising in model predictive control (MPC) and in distributed MPC aresolved by applying a fast gradient method to the dual of the MPC optimization problem. Although the development of fast gradient methods has improved the convergence rate of gradient-based methods considerably, they are still sensitive to ill-conditioning of the problem data. Since similar opti
In manipulation tasks, motion trajectories are characterized by a set of key phases (i.e., motion primitives). It is therefore important to learn the motion primitives embedded in such tasks from a complete demonstration. In this paper, we propose a core framework that autonomously segments motion trajectories to support the learning of motion primitives. For this purpose, a set of segmentation po
We propose a distributed optimization algorithm for mixed L_1/L_2-norm optimization based on accelerated gradient methods using dual decomposition. The algorithm achieves convergence rate O(1/k^2), where k is the iteration number, which significantly improves the convergence rates of existing duality-based distributed optimization algorithms that achieve O(1/k). The performance of the developed al
In embedded systems, the computing resources are often scarce and several control tasks may have to share the same computer. In this brief, we assume that a set of feedback controllers should be implemented on a single-CPU platform. We study the problem of optimal sampling period assignment, where the goal is to assign sampling rates to the controllers so that the overall control performance is ma
Recent papers have proposed to design scheduling algorithms entirely as discrete-time controllers, i.e., to refrain from preserving the already installed scheduler, and replace it completely. At the cost of some system re-design impact, this new approach has been proved to yield significant advantages in terms of code size and simplicity, and above all to open the way to a system-theoretical analy
