1,721,020 research outputs found
Consistent Sobolev regression via fuzzy systems with overlapping concepts
No full textIn this paper we propose a new nonparametric regression algorithm based on Fuzzy systems with overlapping concepts. We analyze its consistency properties, showing that it is capable to reconstruct an infinite-dimensional class of function when the size of the noisy dataset grows to infinity. Moreover, convergence to the target function is guaranteed in Sobolev norms so ensuring uniform convergence also for a certain number of derivatives. The
connection with Regularization Networks, Bayesian estimation and
Tychonov regularization is highlighted
Design of plug-and-play model predictive control: an approach based on linear programming
No full textIn this paper we consider a linear system represented by subsystems coupled through states and propose a distributed control scheme for guaranteeing asymptotic stability and satisfaction of constraints on system inputs and states. Our design procedure enables Plug-and-Play (PnP) operations, meaning that (i) the addition or removal of subsystems triggers the synthesis of local controllers associated to successors to the subsystem only and (ii) the synthesis of a local controller for a subsystem requires information only from predecessors of the subsystem and it can be performed using only local computational resources. Our method, that is based on Model Predictive Control (MPC) advances the PnP design procedure proposed in [1] in several directions. Notably, we show how critical steps in the design of a local controller can be solved through linear programming
A hybrid model predictive control scheme for containment and distributed sensing in multi-agent systems
Full text linkThis paper proposes a control scheme for distributed sensing using a leader/follower multi-agent architecture. The control objective is to make a group of mobile agents cover and sense a sequence of regions of interest. More specifically, when the leaders reach a new target region, they stop until the followers have performed a sensing task. Furthermore, the followers must be contained inside the convex-hull of the leaders’ positions during the motion. Key features of our method, that combines hybrid control with Model Predictive Control (MPC) techniques, are the possibility to take into account input constraints in order to plan the sensing maneuver and the ability of the followers to detect containment violations by simple computation based on the available information about the leaders’ positions
Zeros of continuous-time linear periodic systems
No full textZeros of continuous-time linear periodic systems are defined and their properties investigated. Under the assumption that the system has uniform relative degree, the zero-dynamics of the system is characterized and a closed-form expression of the blocking inputs is derived. This leads to the definition of zeros as unobservable characteristic exponents of a suitably defined periodic pair. The zeros of periodic linear systems satisfy blocking properties that generalize the well-known time-invariant case. Finally, an efficient computational scheme is provided that essentially amounts to solving an eigenvalue problem
Moving-horizon partition-based state estimation of large-scale systems
No full textThis paper presents three novel moving-horizon estimation (MHE) methods for discrete-time partitioned
linear systems, i.e., systems decomposed into coupled subsystems with non-overlapping states. The
MHE approach is used due to its capability of exploiting physical constraints on states and noise in the
estimation process. In the proposed algorithms, each subsystem solves reduced-order MHE problems to
estimate its own state and different estimators have different computational complexity, accuracy and
transmission requirements among subsystems. In all cases, proper tuning of the design parameters, i.e.,
the penalties on the states at the beginning of the estimation horizon, guarantees convergence of the
estimation error to zero. Numerical simulations demonstrate the viability of the approach
Tools for modeling, simulation, control, and verification of piecewise affine systems
No full textTools for mixed logical dynamical and piecewise affine systems based on the model description language HYSDEL are described. They concern various modeling, identification, analysis, control design, and verification tasks. The data exchange format explained in this chapter facilitates the combination of these tools
Moving horizon estimation for distributed nonlinear systems with application to cascade river reaches
No full textThis paper presents a Moving Horizon Estimation (MHE) method for discrete-time nonlinear systems decomposed into coupled subsystems with non-overlapping states. In the proposed algorithm, each subsystem solves a reduced-order MHE problem to estimate its own state based on the estimates computed by its neighbors. Conditions for the convergence of the estimates are investigated. The algorithm is applied to a model of three river reaches
Model Predictive Control Schemes for Consensus in Multi-agent Systems with Integrator Dynamics and Time-varying Communication
No full textIn this paper we address the problem of driving a group of agents towards
a consensus point when agents have a discrete-time integrator dynamics and
the communication graph is time-varying. We propose two decentralized Model
Predictive Control (MPC) schemes that take into account constraints on the
agents’ inputs and show that they guarantee consensus under mild assumptions.
Since the global cost does not decrease monotonically, it cannot be used as
a Lyapunov function for proving convergence to consensus. Rather, our proofs
exploit geometric properties of the optimal path followed by individual agents
Distributed moving horizon estimation for sensor Networks
No full textThis paper focuses on distributed state estimation using a sensor network for monitoring a linear system. In order to account for physical constraints on process states and inputs, we propose a moving horizon approach where each sensor has to solve a quadratic programming problem at each time instant. We discuss conditions guaranteeing convergence of all estimates to a common value by characterizing the dynamics of the unobservable component of the state. Furthermore, we highlight how the performance of the state estimation scheme depends upon various observability properties of the system and discuss how different communication protocols impact on the quality of the estimates
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