A. Sangiovanni Vincentelli

Mixed signal design space exploration through analog platforms

We propose a hierarchical mixed signal design methodology based on the principles of platform-based design (PBD). The methodology is a meet-in-the-middle approach where design components are modeled bottom-up at various abstraction levels and performance constraints are mapped top-down to select among the available components the ones that best meet the constraints. The design methodology can seamlessly operate on both analog and digital designs, thus dealing with mixed signal designs in a consistent way. We demonstrate the effectiveness of the approach optimizing an 80 MS/s 14 bit pipelined analog-to-digital converter (ADC) including digital calibration, yielding 64% power reduction compared to the original hand optimized design.

Hybrid modelling and control of the common rail injection system

We present an industrial case study in automotive control of significant complexity: the new common-rail fuel-injection system for Diesel engines under development at 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 desired torque). The standard approach in automotive control based on a mean-value model for the plant does not provide a satisfactory solution as the discrete-continuous interactions in the fuel injection system, due to the slow time-varying frequency of the HP pump cycles and the fast sampling frequency of sensing and actuation, play a fundamental role. We present a design approach based on a hybrid model of the Magneti Marelli Powertrain common-rail fuel-injection system for four-cylinder multi-jet engines and a hybrid approach to the design of a rail pressure controller. The hybrid controller performs significantly better when compared with the classical mean-value based approach.

Design space exploration for a UMTS front-end exploiting analog platforms

universal mobile telecommunication system (UMTS) front end design is challenging because of the need to optimize power while satisfying a very high dynamic range requirement. Dealing with this design problem at the transistor level does not allow exploring efficiently the design space, while using behavioral models does not allow taking into consideration important second-order effects. We present an extension of the platform-based design methodology originally developed for digital systems to the analog domain to conjugate the need of higher levels of abstraction to deal with complexity as well as the one of capturing enough of the actual circuit-level characteristics to deal with second order effects. We show how this methodology applied to the UMTS front-end design yields power savings as large as 47% versus an original hand optimized design.

Robust system level design with analog platforms

An approach to robust system level mixed signal design is presented based on analog platforms. The bottom-up characterization phase of platform components provides accurate performance models that export architectural constraints to the system level. From the one side, performance models can be affected by residual errors and usually do not consider process variations and modeling uncertainties. Conversely, behavioral models cannot match accurate circuit level simulations, so that during the mapping (exploration) process circuit configurations difficult to be realized may be obtained. We propose a methodology that extends techniques from optimization and design centering to system level analog design exploiting general, implicit architectural constraints to control the robustness of the solution. The approach allows quantitative extension of robust techniques to hierarchical designs. Its effectiveness is illustrated with the design of a pipeline A/D converter and a UMTS receiver front-end

Modeling and estimating yield and efficiency of photovoltaic solar parks

Subsidy reduction and the increased importance of photovoltaic energy generation have triggered the need to develop accurate approaches to measure, estimate and possibly forecast energy production with the goal of enabling early detection and diagnosis of parametric faults before they become catastrophic and lead to partial or full shut down of the plant. Moreover the ability of accurately predicting the energy output of a photovoltaic plant is becoming increasingly important to operate efficiently the existing power grid infrastructure that has difficulties with intermittent, unpredictable power sources. In this paper a new model of photovoltaic solar parks designed to estimate efficiency, predict energy production and enable early fault detection is presented. The proposed model is based on an equivalent electro-thermal model of the PhotoVoltaic (PV) cell, which takes as input the irradiance, ambient temperature and wind speed measured by existing sensors and data-logging equipment used in most solar PV parks. The proposed model is demonstrated to accurately predict the performance of real-life MW scale plants. By directly comparing predicted vs. actual energy production, it also allows easy and early identification and diagnose of malfunctions and efficiency drops of PV systems.

Complexity reduction for the design of interacting controllers

The complexity of embedded controllers in important industrial domains such as automotive and avionics is growing constantly making error-free system integration almost impossible. We address the complexity issues posed by the analysis and design of interacting controllers introducing approximation techniques that are shown to be effective on a substantial industrial test case: the control system for common-rail fuel-injection developed by Magneti Marelli Powertrain.

Enriching an analog platform for analog-to-digital converter design

Platform-based analog design is used to design an analog-to-digital converter reducing power consumption by 26%. This result was achieved by enriching the library of analog components used as the basis of the design with a telescopic operational transconductance amplifier (OTA). We present the way in which the models for this new component necessary to use platform based design can be added quickly and efficiently with the use of analog constraint graphs.

Analysis of the quantization noise effects on the SQNR behaviour in analog to digital conversion

The characterization of the quantization effects due to the conversion of infinite precision quantities to fixed-point arithmetic constitutes a relevant research area. It is of fundamental importance for the development of automatic tools for the transformation of DSP algorithms from floating point to fixed point arithmetic. In this paper a novel approach for the direct computation of the probability density function (PDF) at the output of generic memory-less nonlinear processing blocks is derived. Our analysis has been applied to the modeling of the THA-ADC chain. By using this approach, we are able to determine the exact behavior of the Signal to Noise Quantization Ratio (SQNR) for cases where the well-known Pseudo Quantization Noise (PQN) model does not give accurate results.

Special Issue on Advanced design methodologies in automotive control

This Special Issue addresses the use of hybrid systems in an important industrial domain of embedded systems: automotive control. Hybrid systems are a fascinating topic of research that has captured the attention of the research community in the past few years. Important theory results have been obtained but applying them to industrial strength control problems has been challenging. Automotive control has been the first field where hybrid systems have revealed their potential. The contributions published in the Special Issue span a number of applications in automotive control: from design flows to traffic control, from engine to suspension control. In Hybrid systems in automotive electronics design, A. Balluchi, L. Benvenuti, A. Ferrari and A.L. Sangiovanni-Vincentelli present a broad view of the development process for embedded control systems in the automotive industry with the purpose of identifying challenges and opportunities for hybrid systems. Critical steps in the design flow are identified and a number of open problems where hybrid system technology might play an important role are pointed out. In Verification of cooperating traffic agents, W. Damm, H. Hungar and E.-R. Olderog exploit design patterns used in coordinating autonomous transport vehicles to ease the burden of verifying cooperating hybrid systems. The authors present a verification rule explicating the essence of employed design patterns, guaranteeing global safety properties of the kind ‘‘a collision will never occur’’, and whose premises can either be established by off-line analysis of the worstcase behavior of the involved traffic agents, or by purely local proofs, involving only a single traffic agent. In Hybrid control of homogeneous charge compression ignition (HCCI) engine dynamics, J. Bengtsson, P. Strandh, R. Johansson, P. Tunestal and B. Johansson consider real-time control of a six-cylinder heavy-duty HCCI engine on a cycle-to-cycle basis. The authors compare different combustion controllers obtained by considering possible choices for sensors (ion current and cylinder pressure), actuators (dual-fuel and variable valve actuations) and control structures (MPC, PID and LQG). While satisfying the constraints on cylinder pressure, both control of the combustion phasing and control of load torque were achieved with simultaneous minimization of the fuel consumption and emissions. In Automotive engine hybrid modelling and control for reduction of hydrocarbon emissions, P.R. Sanketi, J.C. Zavala and K. Hedrick develop hybrid models for engine control that incorporate time and events in their formulation. The resulting hybrid controllers have the capability of switching between two alternative control modes. The first mode is designed to reduce the raw HC (hydrocarbon) emissions while the second mode tries to increase the temperature of the catalytic converter as rapidly as possible during the initial transient or ‘‘cold start’’ period. Reachability, as a tool for system analysis, is used to verify the properties of the closed loop system. In Constrained optimal control of an electronic throttle, M. Vašak, M. Baotić, M. Morari, I. Petrović and N. Perić propose a constrained optimal control problem formulation for a discrete-time piecewise affine model of the throttle valve. The optimal control is represented by look-up tables for on-line implementation. The reference tracking controller significantly outperforms a tuned PID controller with feed-forward compensation of non-linearities in terms of the response speed while preserving the response quality regarding the absence of an overshoot and the static accuracy within the measurement resolution. In Modelling and control of auxiliary loads in heavy vehicles, N. Pettersson and K. Johansson evaluate the benefits of driving auxiliary units with electricity instead of mechanically are evaluated in terms of fuel saving. A Modelica library of the energy consumption of the auxiliaries is presented. A case study on optimal control of the cooling system is illustrated. Control actuators are the electrical generator, the cooling fan and the

A Mobile Health System for Neurocognitive Impairment Evaluation based on P300 Detection

A new mobile healthcare system for neuro-cognitive function monitoring and treatment is presented. The architecture of the system features sensors to measure the brain potential, localized data analysis and filtering, and in-cloud distribution to specialized medical personnel. As such, it presents tradeoffs typical of other cyber-physical systems, where hardware, algorithms, and software implementations have to come together in a coherent fashion. The system is based on spatio-temporal detection and characterization of a specific brain potential called P300. The diagnosis of cognitive deficit is achieved by analyzing the data collected by the system with a new algorithm called tuned-Residue Iteration Decomposition (t-RIDE). The system has been tested on 17 subjects (n = 12 healthy, n = 3 mildly cognitive impaired, and n = 2 with Alzheimers disease involved in three different cognitive tasks with increasing difficulty. The system allows fast diagnosis of cognitive deficit, including mild and heavy cognitive impairment: t-RIDE convergence is achieved in 79 iterations (i.e., 1.95s), yielding an 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel.