Manas Mejari

Bio: Manas Mejari is an academic researcher from Dalle Molle Institute for Artificial Intelligence Research. The author has contributed to research in topics: Affine transformation & System identification. The author has an hindex of 5, co-authored 26 publications receiving 110 citations. Previous affiliations of Manas Mejari include IMT Institute for Advanced Studies Lucca & Veermata Jijabai Technological Institute.

Improving Lifetime of Fuel Cell in Hybrid Energy Management System by Lure–Lyapunov-Based Control Formulation

Abstract: Fuel cell (FC) is emerging as a clean and nonpollutant energy source and is being used widely in most of the transport and microgrid applications. However, certain applications demanding fast dynamic response are restricting usage of FC on account of its lifetime issues. Slow dynamic response of FC is not suitable to provide transient load demands, typically arising in electrical vehicles during acceleration and in braking. This paper presents a novel control strategy and associated power electronics for such applications. We propose a Lure–Lyapunov-based controller executed in cascade mode for the control of hybrid energy management system of FC and ultracapacitor (UC). In the proposed scheme, UC responds to transients in load demand whereas FC takes care of the nominal load bus requirements. This combination helps in improving lifetime of FC as auxiliary storage device such as UC provides a cushion for sudden transients. The proposed control strategy also helps in maintaining state of charge of UC for better performance. The inherent nonlinearities in DC–DC converter are efficiently handled by the outer-loop Lure–Lyapunov framework. Mathematical modeling and a proof for absolute stability of the system is presented. The proposed controller strategy is robust by virtue of being passivity based control methodology. The paper also presents analysis of the required power electronics involving DC–DC converters and inner-loop current control action. The synthesized controller is experimentally validated and the results are presented with their analysis.

Regularized moving-horizon piecewise affine regression using mixed-integer quadratic programming

Abstract: This paper presents a novel two-stage regularized moving-horizon algorithm for PieceWise Affine (PWA) regression. At the first stage, the training samples are processed iteratively, and a Mixed-Integer Quadratic-Programming (MIQP) problem is solved to find the sequence of active modes and the model parameters which best match the training data, within a relatively short time window in the past. According to a moving-horizon strategy, only the last element of the optimal sequence of active modes is kept, and the next sample is processed by shifting forward the estimation horizon. A regularization term on the model parameters is included in the cost of the formulated MIQP problem, to partly take into account also the past training data outside the considered time horizon. At the second stage, linear multi-category discrimination techniques are used to compute a polyhedral partition of the regressor space based on the estimated sequence of active modes.

Identification of hybrid and linear parameter‐varying models via piecewise affine regression using mixed integer programming

Abstract: Correspondence Manas Mejari, IDSIA, Dalle Molle Institute for Artificial Intelligence SUPSI-USI, 6928 Manno, Switzerland. Email: manas.mejari@idsia.ch Summary This article presents a two-stage algorithm for piecewise affine (PWA) regression. In the first stage, a moving horizon strategy is employed to simultaneously estimate the model parameters and to classify the training data by solving a small-size mixed-integer quadratic programming problem. In the second stage, linear multicategory separation methods are used to partition the regressor space. The framework of PWA regression is adapted to the identification of PWA AutoRegressive with eXogenous input (PWARX) models as well as linear parameter-varying (LPV) models. The performance of the proposed algorithm is demonstrated on an academic example and on two benchmark experimental case studies. The first experimental example concerns modeling the placement process in a pick-and-place machine, while the second one consists in the identification of an LPV model describing the input-output relationship of an electronic bandpass filter with time-varying resonant frequency.

Robust tube based MPC for PVTOL trajectory tracking using systems flatness property

Abstract: The paper presents a tube based Model Predictive Control (MPC) strategy for robust tracking control of PVTOL aircraft. PVTOL system is considered as a benchmark for investigating dynamics and control related issues for unmanned aerial vehicles (UAVs). Due to non-linear dynamics and under actuated nature of this system, the task of building control algorithm for such systems becomes challenging. Here, the systems flatness property is used to obtain the dynamics in terms of flat outputs. The linear form of the obtained dynamics facilitates the computation of error invariant set using LMI's. Also, by an LMI based optimisation approach the problem of maximisation of terminal region of MPC is solved. This Quasiinfinite horizon strategy for MPC guarantees the stability of system. The complete process reduces the computation cost for MPC and makes the system robust against large additive disturbances. The effectiveness of the proposed control strategy is shown via simulations by tracking a given trajectory while rejecting the effects of disturbances.

Hybrid energy storage system for microgrids applications: A review

Abstract: Energy storages introduce many advantages such as balancing generation and demand, power quality improvement, smoothing the renewable resource’s intermittency, and enabling ancillary services like frequency and voltage regulation in microgrid (MG) operation. Hybrid energy storage systems (HESSs) characterized by coupling of two or more energy storage technologies are emerged as a solution to achieve the desired performance by combining the appropriate features of different technologies. A single ESS technology cannot fulfill the desired operation due to its limited capability and potency in terms of lifespan, cost, energy and power density, and dynamic response. Hence, different configurations of HESSs considering storage type, interface, control method, and the provided service have been proposed in the literature. This paper comprehensively reviews the state of the art of HESSs system for MG applications and presents a general outlook of developing HESS industry. Important aspects of HESS utilization in MGs including capacity sizing methods, power converter topologies for HESS interface, architecture, controlling, and energy management of HESS in MGs are reviewed and classified. An economic analysis along with design methodology is also included to point out the HESS from investor and distribution systems engineers view. Regarding literature review and available shortcomings, future trends of HESS in MGs are proposed.

An Adaptive State Machine Based Energy Management Strategy for a Multi-Stack Fuel Cell Hybrid Electric Vehicle

Abstract: This paper aims at designing an online energy management strategy (EMS) for a multi-stack fuel cell hybrid electric vehicle (FCHEV) to enhance the fuel economy as well as the fuel cell stacks (FCSs) lifetime. In this respect, a two-layer strategy is proposed to share the power among four FCSs and a battery pack. The first layer (local to each FCS) is held solely responsible for constantly determining the real maximum power and efficiency of each stack since the operating conditions variation and ageing noticeably influence stacks’ performance. This layer is composed of a FCS semi-empirical model and a Kalman filter. The utilized filter updates the FCS model parameters to compensate for the FCSs’ performance drifts. The second layer (global management) is held accountable for splitting the power among components. This layer uses two inputs per each FCS, updated maximum power and efficiency, as well as the battery state of charge (SOC) and powertrain demanded power to perform the power sharing. The proposed EMS, called adaptive state machine strategy, employs the first two inputs to sort the FCSs out and the other inputs to do the power allocation. The ultimate results of the suggested strategy are compared with two commonly used power sharing methods, namely Daisy Chain and Equal Distribution. The results of the suggested EMS indicate promising improvement in the overall performance of the system. The performance validation is conducted on a developed test bench by means of hardware-in-the-loop (HIL) technique.

Absolute stability of nonlinear control systems

Abstract: The Popov or frequency method for the problem of absolute stability of nonlinear systems is discussed, using the direct method of Liapunov. The work is tutorial in nature and attempts to provide and explain the most recent results obtained by several authors. The relation between the problem of absolute stability and the problem of Aizerman is also discussed .