Luigi Glielmo

Switchings, bifurcations, and chaos in DC/DC converters

Nonlinear phenomena in closed-loop pulsewidth modulated (PWM) DC/DC converters are analyzed. We introduce a new discrete time nonlinear map-the A-switching map-which is related to the asynchronous switchings, i.e., the changes of converter configuration occurring within the modulating period. This map is compared with the stroboscopic map, which is typically used in the study of DC/DC converters. Analytical conditions for the occurrence of periodic orbits and flip bifurcations are obtained. Moreover, necessary conditions for infinite local stretching on the phase space are derived. Finally, a possible explanation of the sudden jump to chaos exhibited by DC/DC converters is proposed. Analytical and numerical results can be applied to all fundamental DC/DC converter topologies. The case of the voltage-controlled buck converter is treated in detail.

A Model Predictive Control Approach to Microgrid Operation Optimization

Microgrids are subsystems of the distribution grid, which comprises generation capacities, storage devices, and controllable loads, operating as a single controllable system either connected or isolated from the utility grid. In this paper, we present a study on applying a model predictive control approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using mixed-integer linear programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then, the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a microgrid is employed to assess the performance of the online optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid located in Athens, Greece. The experimental results show the feasibility and the effectiveness of the proposed approach.

Gearshift control for automated manual transmissions

A gearshift control strategy for modern automated manual transmissions (AMTs) with dry clutches is proposed. The controller is designed through a hierarchical approach by discriminating among five different AMT operating phases: engaged, slipping-opening, synchronization, go-to-slipping, and slipping-closing. The control schemes consist of decoupled and cascaded feedback loops based on measurements of engine speed, clutch speed, and throwout bearing position, and on estimation of the transmitted torque. Models of driveline, dry clutch, and controlled actuator are estimated on experimental data of a medium size gasoline car and used to check through simulations the effectiveness of the proposed controller.

Closed-Loop Control of Deep Brain Stimulation: A Simulation Study

Deep brain stimulation (DBS) is an effective therapy to treat movement disorders including essential tremor, dystonia, and Parkinsons disease. Despite over a decade of clinical experience the mechanisms of DBS are still unclear, and this lack of understanding makes the selection of stimulation parameters quite challenging. The objective of this work was to develop a closed-loop control system that automatically adjusted the stimulation amplitude to reduce oscillatory neuronal activity, based on feedback of electrical signals recorded from the brain using the same electrode as implanted for stimulation. We simulated a population of 100 intrinsically active model neurons in the Vim thalamus, and the local field potentials (LFPs) generated by the population were used as the feedback (control) variable for closed loop control of DBS amplitude. Based on the correlation between the spectral content of the thalamic activity and tremor (Hua , 1998), (Lenz , 1988), we implemented an adaptive minimum variance controller to regulate the power spectrum of the simulated LFPs and restore the LFP power spectrum present under tremor conditions to a reference profile derived under tremor free conditions. The controller was based on a recursively identified autoregressive model (ARX) of the relationship between stimulation input and LFP output, and showed excellent performances in tracking the reference spectral features through selective changes in the theta (2-7 Hz), alpha (7-13 Hz), and beta (13-35 Hz) frequency ranges. Such changes reflected modifications in the firing patterns of the model neuronal population, and, differently from open-loop DBS, replaced the tremor-related pathological patterns with patterns similar to those simulated in tremor-free conditions. The closed-loop controller generated a LFP spectrum that approximated more closely the spectrum present in the tremor-free condition than did open loop fixed intensity stimulation and adapted to match the spectrum after a change in the neuronal oscillation frequency. This computational study suggests the feasibility of closed-loop control of DBS amplitude to regulate the spectrum of the local field potentials and thereby normalize the aberrant pattern of neuronal activity present in tremor.

Stability robustness of interval matrices via Lyapunov quadratic forms

A sufficient condition for the robust stability of a class of interval matrices is derived using the Lyapunov approach. The matrices considered have elements which are nonlinear functions of a vector of independent and bounded parameters. The robust stability condition requires that a quadratic form be positive definite in a finite number of conspicuous points of an enlarged parameter space. >

Optimal tracking for automotive dry clutch engagement

Abstract Based on a state space dynamic model of a typical automotive driveline, a new control technique for the dry clutch engagement process is proposed. The feedback controller is designed following an optimal control approach by using the crankshaft speed and the clutch disk speed as state variables: a tracking problem is formulated and solved by using the engine torque and the clutch torque as control variables. The controller guarantees fast engagement, minimum slipping losses and comfortable lock-up. The critical standing start operating conditions are considered. Numerical results show the good performance obtained with the proposed controller.

Energy efficient microgrid management using Model Predictive Control

Microgrids are subsystems of the distribution grid which comprises small generation capacities, storage devices and controllable loads, operating as a single controllable system that can operate either connected or isolated from the utility grid. In this paper we present a preliminary study on applying a Model Predictive Control (MPC) approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using Mixed-Integer Linear Programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a typical microgrid is employed to assess the performance of the on-line optimization-based control strategy: simulation results show the feasibility and the effectiveness of the proposed approach.

New results on composite control of singularly perturbed uncertain linear systems

Abstract We consider the problem of “stabilizing” uncertain, singularly perturbed, linear, time-varying, systems whose fast dynamics can be unstable. We propose a class of nonlinear composite controllers which assure global uniform ultimate boundedness of the trajectories of the closed loop system, provided the singular perturbation parameter is sufficiently small. These controllers consist of a fast controller which stabilizes the fast dynamics and a slow controller which yields the desired stability properties for the slow dynamics. We consider the structure of the fast controller to be simpler than structures previously proposed in the literature. To obtain these controllers, we first develop some new results for singularly perturbed systems under output feedback. In particular, it is shown that the matching assumption , which deals with the manner in which the uncertainties enter the system, and is made on the reduced-order system, is invariant under linear feedback of the fast variable.

Smooth engagement for automotive dry clutch

Two piecewise linear time-invariant models of the automotive driveline are presented and their hybrid structure due to the presence of the dry clutch is highlighted. Based on a second order model, a slip control technique for the dry clutch engagement process is proposed. The feedback controller is designed by using the crankshaft speed and the clutch disk speed as measured variables. The controller guarantees comfortable lock-up and avoids the engine stall by decoupling the control of engine speed and slip speed. The regulation of the slip acceleration at the lock-up is shown to reduce the undesired driveline oscillations. The critical standing start operating conditions are considered and numerical results show the good performance of the proposed controller.

On the exponential stability of singularly perturbed systems

This paper establishes some results and properties related to the exponential stability of general dynamical systems and, in particular, singularly perturbed systems. For singularly perturbed systems it is shown that if both the reduced-order system and the boundary-layer system are exponentially stable, then, provided that some further regularity conditions are satisfied, the full-order system is exponentially stable for sufficiently small values of the perturbation parameter