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Mehdi Maasoumy

PhD Candidate
University of California, Berkeley

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My research is on Management (Control and Optimization) of Cyber-Physical Energy Systems. As shown in figures below, I have been working on multiple systems/projects such as Energy Efficient Buildings, Smart Grid and Aircraft Load Management System. In particular, my work involves optimization and control-oriented modeling of cyber-physical systems and platform-based design of their controllers.

For more information on each project please click on the associated figure.

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Energy Efficient Buildings:

Publications:

Mehdi Maasoumy, Meysam Razmara, Mahdi Shabakhti, and Alberto Sangiovanni-Vincentelli, "Handling model uncertainty in model predictive control for energy efficient buildings" Journal of Energy and Buildings 2014. Accepted [ URL ] [ bib ]

Abstract: Model uncertainty is a significant challenge to more widespread use of Model Predictive Controllers (MPC) for optimizing building energy consumption. This paper presents two methodologies to handle model uncertainty for building MPC. First, we propose a modeling framework for online estimation of states and unknown parameters leading to a Parameter-Adaptive Building (PAB) model. Second, we propose a Robust Model Predictive Control (RMPC) formulation to make a building controller robust to model uncertainties. The results from these two approaches are compared with those from a nominal MPC and a common building Rule Based Control (RBC). The results are then used to develop a methodology for selecting a controller type (i.e. RMPC, MPC, or RBC) as a function of building model uncertainty. RMPC is found to be the superior controller for the cases with an intermediate level of model uncertainty (30%-67%), while the nominal MPC is preferred for the cases with a low level of model uncertainty (0-30%). Further, a common RBC outperforms MPC or RMPC if the model uncertainty goes beyond a certain threshold (e.g. 67%).

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Vasumathi Raman, Alexandre Donze, Mehdi Maasoumy, Richard Murray, Alberto Sangiovanni-Vincentelli, and Sanjit A Seshia, "Model Predictive Control with Signal Temporal Logic Specifications", IEEE Conference on Decision and Control (CDC 2014), December 2014, Los Angeles, CA. Submitted [URL] [ bib ]

Abstract: We present a mathematical programming-based method for model predictive control of discrete-time cyberphysical systems subject to signal temporal logic (STL) specifications. We describe the use of STL to specify a wide range of properties of these systems, including safety, response and bounded liveness. For synthesis, we encode STL specifications as mixed integer-linear constraints on the system variables in the optimization problem at each step of model predictive control. We present experimental results for controller synthesis on simplified models of a smart building-level micro-grid and HVAC system.

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Mehdi Maasoumy, Catherine Rosenberg, Alberto Sangiovanni-Vincentelli and Duncan Callaway, "Model Predictive Control Approach to Online Computation of Demand-Side Flexibility of Commercial Buildings HVAC Systems for Supply Following", IEEE American Control Conference, ACC 2014. Accepted (BEST PAPER AWARD Finalist) [ URL ] [ pdf ] [ bib ]

Abstract: Commercial buildings have inherent flexibility in how their HVAC systems consume electricity. We investigate how to take advantage of this flexibility. We first propose a means to define and quantify the flexibility of a commercial building. We then propose a contractual framework that could be used by the building operator and the utility to declare flexibility on the one side and reward structure on the other side. We then design a control mechanism for the building to decide its flexibility for the next contractual period to maximize the reward, given the contractual framework. Finally, we perform at-scale experiments to demonstrate the feasibility of the proposed algorithm.

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Mehdi Maasoumy, Meysam Razmara, Mahdi Shahbakhti, and Alberto Sangiovanni-Vincentelli, "Selecting Building Predictive Control Based on Model Uncertainty", IEEE American Control Conference, ACC 2014. Accepted [ URL ] [ pdf ] [ bib ]

Abstract: Model uncertainty limits the utilization of Model Predictive Controllers (MPC) to minimize building energy consumption. We propose a new Robust Model Predictive Control (RMPC) structure to make a building controller robust to model uncertainty. The results from RMPC are compared with those from a nominal MPC and a common building Rule Based Control (RBC). The results are then used to develop a methodology for selecting a controller type (i.e. RMPC, MPC, and RBC) as a function of building model uncertainty. RMPC is found to be the desirable controller for the cases with an intermediate level (30%-67%) of model uncertainty, while MPC is preferred for the cases with a low level (0-30%) of model uncertainty. A common RBC is found to outperform MPC or RMPC if the model uncertainty goes beyond a certain threshold (e.g. 67%).

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Mehdi Maasoumy and Alberto Sangiovanni-Vincentelli, "Comparison of Control Strategies for Energy Efficient Building HVAC Systems", ACM Symposium on Simulation for Architecture and Urban Design (SimAUD 2014), Tampa, FL, USA. Accepted. [ URL ] [ pdf ] [ bib ]

Abstract: A framework for the design and simulation of a building envelope and an HVAC system is presented. Building models are first captured in Modelica to leverage its rich building component library and then imported into Simulink to exploit its strong control design environment that enables efficient control design and implementation. Four controllers with different computational intensity are considered and compared: a proportional (P) controller with time varying temperature bounds, a tracking LQR controller with time varying tuning parameters, a tracking d-LQR controller with time varying tuning parameters which incorporates the predictive disturbance information in control derivation and a model predictive controller (MPC).We assess the performance of these controllers using two defined criteria, i.e. energy and discomfort indices. We show that the d-LQR and MPC compared to the P control, manage to reduce the energy index by 41.2% and 46% respectively, and the discomfort index from 3.8 to 0. While d-LQR and MPC have similar performance concerning energy and discomfort index, simulation time in the case of d-LQR is significantly less than that of MPC.

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Mehdi Maasoumy, Barzin Moridian, Meysam Razmara, Mahdi Shahbakhti and Alberto Sangiovanni-Vincentelli, "Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control", Dynamic System and Control Conference (DSCC 2013), Stanford, CA, USA. (BEST PAPER AWARD Finalist) [ URL ] [ pdf ] [ bib ] [ Slides ]

Abstract: Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are usually difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, modelbased controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose an adaptive modeling framework for on-line estimation of states and unknown parameters. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design an adaptive building model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed adaptive framework is tested with the experimental data collected from a university building. Our results indicate that the new framework can accurately predict state and parameters of the building thermal model. The new modeling framework is expected to simplify design of a building predictive control by replacing nonlinear terms in a control model with linear adaptive parameters.

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Mehdi Maasoumy, Qi Zhu, Cheng Li, Forrest Meggers and Alberto Sangiovanni-Vincentelli, "Co-design of Control Algorithm and Embedded Platform for HVAC Systems", The 4th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2013), Philadelphia, USA. (BEST PAPER AWARD)[ URL ] [ pdf ] [ bib ] [ Slides ]

Abstract: The design of heating, ventilation and air conditioning (HVAC) systems is crucial for reducing the energy consumption in buildings. As complex cyber-physical systems, HVAC systems involve three closely-related subsystems - the control algorithm, the physical environment and the embedded implementation platform. In the traditional top-down approach, the control algorithm and the embedded platform are mostly designed separately leading to sub-optimal systems. In this paper, we propose a co-design approach that analyzes the interaction between the control algorithm and the embedded platform through a set of interface variables in particular the sensing accuracy. We design six control algorithms that take into account the sensing error, and model the relation of control performance and cost versus sensing error. We also capture the relation of embedded platform cost versus sensing error by analysis of the collected data from a testbed. Based on these models, we explore the co-design of the control algorithm and the embedded platform to optimize a system with respect to energy cost and monetary cost while satisfying the constraints for user comfort level.

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Mehdi Maasoumy, Alberto Sangiovanni-Vincentelli, "Total and Peak Energy Consumption Minimization of Building HVAC Systems Using Model Predictive Control", IEEE Design & Test of Computers, Special Issus on Green Buildings, July/Aug 2012.
[ URL ] [ pdf ] [ bib ]

Abstract: A non-linear model representing the thermal dynamics of buildings is developed which incorporates the thermal heat exchange between neighboring nodes (i.e. walls and rooms) in the building and the input conditioned air from the HVAC system, the internal heat loads from occupants and external heat gains. The proposed model is calibrated against historical data and the parameters. Un-modeled dynamics are identified using non-linear regression technique. Two different controllers are proposed to reduce the total energy consumption of the system and the maximum airflow rate of an HVAC system. The implemented model predictive controller achieves a reduction of 67.2% in total airflow input, 33.3% in the maximum airflow rate and 73.2% in total energy consumption with respect to the controller used presently in the building HVAC system.

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Yang Yang, Qi Zhu, Mehdi Maasoumy, and Alberto Sangiovanni-Vincentelli, "Development of Building Automation and Control Systems", IEEE Design & Test of Computers, Special Issue on Green Buildings, July/Aug 2012. [ URL ] [ pdf ] [ bib ]

Abstract: A Building Automation and Control (BAC) system is the brain of a modern building. It controls various aspects of the building operations including heating, ventilation, air conditioning (HVAC), lighting, fire and security. The design of BAC systems is crucial to building performance and energy efficiency. In this paper, we propose an automated system-level design flow for BAC systems that addresses heterogeneous input specification and implementation platform while performing automatic design space exploration and code generation. An intermediate format (IF) is defined as the central hub of the design flow to facilitate the integration of heterogeneous inputs, the leverage of back-end tools and the design space exploration. Case studies of a temperature control system are presented to demonstrate the flow.

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Mehdi Maasoumy, Alberto Sangiovanni-Vincentelli, "Optimal Control of Building HVAC Systems in the Presence of Imperfect Predictions, Dynamic System Control Conference, Fort Lauderdale, FL, Oct 2012 [ URL ] [ pdf ] [ Slides ]

Abstract: This paper deals with the problem of robust model predictive control of an uncertain linearized model of a building envelope and HVAC system. Uncertainty of the model is due to the imperfect predictions of internal and external heat gains of the building. The Open-Loop prediction formulation of the Robust Model Predictive Control (OL-RMPC) is known to be unnecessarily over-conservative in practice. Therefore, we adopt a Closed-Loop prediction formulation of Robust Model Predictive Control (CL-RMPC) which exploits an uncertainty feedback parameterization of the control sequence and results in a tractable formulation of the problem. To improve on the efficiency of CLRMPC we propose a new uncertainty feedback parameterization of the control input, which leads to a number of decision variables linear in time horizon as opposed to quadratic as in previous approaches. To assess our approach we compare three different robust optimal control strategies: nominal MPC which does not have a priori information of the uncertainty, OL-RMPC and CL-RMPC. We show results from a quantitative analysis of performance of these controllers at different prediction error values of the disturbance. Simulations show that CL-RMPC provides a higher level of comfort with respect to OL-RMPC while consuming 36% less energy. Moreover, CL-RMPC maintains perfect comfort level for up to 75% error in the disturbance prediction. Finally, the newly proposed parameterization maintains the performance of CL-RMPC while reducing the simulation time by an average of 30%.

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Mehdi Maasoumy, Alessandro Pinto, Alberto Sangiovanni-Vincentelli, "Model-based Hierarchical Optimal Control Design for HVAC Systems" Dynamic System Control Conference, Arlington, VA 2011.[ URL ] [ pdf ] [ bib ] [ Slides ]

Abstract: A hierarchical control architecture for balancing comfort and energy consumption in buildings is presented. The control design is based on a simplified, yet accurate model of the temperature within each room of the building. The model is validated against real measurements. The control architecture comprises a first level that regulates low level quantities such as air flow, and a second level that balances comfort (i.e. distance between the desired and actual temperature) and energy consumption (i.e. total energy consumed for the required level of comfort). We show the effectiveness of our approach by simulation using validated models.

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Mehdi Maasoumy, "Building Operating Platform Design for High Performance Zero-Energy Buildings, Master's Thesis, University of California, Berkeley, May 2010 [ pdf ]

Abstract: We have Modeled the thermal behavior of a building using Nodal Circuit Analysis. A hierarchical optimal control algorithm was proposed which was shown to save energy while providing the desired comfort level by optimizing a cost function. We have Solved the optimization problem, using Dynamic Programming method to calculate the optimal input to the control system being the optimal mass air flow (MAF) through each duct. We have simulated the behavior of an 8-room building model using Simscape from Simulink. We have validated the Nodal Circuit Analysis approach using the temperature data of UC Berkeley, DOE library. At the end the results of the controller design are presented, which show how much energy is saved by using this control algorithm.

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Mehdi Maasoumy "Construction of a flow model for assessing human motion in buildings", Class project [ pdf ]

Abstract: Sensor Utility Network (SUN) method for occupancy estimation in buildings through the solution of a receding horizon convex optimization problem was investigated. Belief Network method application of a class of graphical probability models called belief network for the purpose of prediction, diagnosis and etc was studied.

Mehdi Maasoumy "Potentials for energy savings in buildings by improving fans in HVAC systems", Class Project [ pdf ]

Abstract: It was shown that a 20% increase in fan efficiency will lead to almost 6% decrease in the energy that goes into HVAC. This is quite substantial, because the HVAC system accounts for one third of the total energy in a commercial building.

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Mehdi Maasoumy "Nonlinear Controller Design for the HVAC system of Energy Efficient Buildings", Class Project [ pdf ]

Abstract: We have implemented input-output and input-state linearization techniques to transform the original system model into equivalent models of simpler forms. Theorem of Frobenius is used to determine the feasibility of input-state linearization. Sliding mode control was designed as a robust controller for the system.

Mehdi Maasoumy, "Optimal Control for the Operation of Building Cooling Systems with VAV Boxes", Class Project [ pdf ]

Abstract:s We have addressed two issues related to the cooling and heating system in Sutardja Dai Hall. First, the performance of the chilling system and heating system in Sutardja Dai Hall are analyzed in terms of Coefficient of Performance (COP). Second, we study the problem of temperature regulation in a network of building thermal zones. The control sequences for Air Handling Units (AHU) and Variable Air Volume (VAV) boxes are optimized by incorporating the predictive information of weather and occupancy and adopting Model Predictive Control (MPC).

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Aircraft Load Management Systems:

Publications:

Mehdi Maasoumy, Pierluigi Nuzzo, Forrest Iandola, Maryam Kamgarpour, Alberto Sangiovanni-Vincentelli, and Claire Tomlin "Optimal Load Management System for Aircraft Electric Power Distribution" Conference on Decision and Control, CDC 2013. Accepted. [ pdf ] [bib]

Abstract: Aircraft Electric Power Systems (EPS) route power from generators to vital avionic loads by configuring a set of electronic control switches denoted as contactors. In this paper, we address the problem of designing a hierarchical optimal control strategy for the EPS contactors in the presence of system faults. We first formalize the system connectivity, safety and performance requirements in terms of mathematical constraints. We then show that the EPS control problem can be formulated as a Mixed-Integer Linear Program (MILP) and efficiently solved to yield load shedding, source allocation, contactor switching and battery charging policies, while optimizing a number of performance metrics, such as the number of used generators and shed loads. This solution is then integrated into a hierarchical control scheme consisting of two layers of controllers. The high-level controller provides control optimality by solving the MILP within a receding horizon approach. The low-level controller handles system faults, by directly actuating the EPS contactors, and implements the solution from the high-level controller only if it is safe. Simulation results confirm the effectiveness of the proposed approach.

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Smart Grid:

Publications:

Tianshu Wei, Taeyoung Kim, Sangyoung Parky, Qi Zhu, Sheldon X.-D. Tan, Naehyuck Changy, Sadrul Ula and Mehdi Maasoumy, "Battery Management and Application for Energy-Efficient Buildings" to appear in the 51st IEEE/ACM Design Automation Conference (DAC), San Francisco, CA, June 2014

Abstract: As the building stock consumes 40% of the U.S. primary energy consumption, it is critically important to improve building energy efficiency. This involves reducing the total energy consumption of buildings, reducing the peak energy demand, and leveraging renewable energy sources, etc. To achieve such goals, hybrid energy supply has becoming popular, where multiple energy sources such as grid electricity, on-site fuel cell generators, solar, wind, and battery storage are scheduled together to improve energy efficiency. In this work, we focus on the application and management of battery storage for energy-efficient buildings. We will first introduce a system-level approach to co-schedule the usage of battery storage (in addition to grid electricity) with the control of building HVAC (heating, ventilation, and air conditioning) system, to reduce the total building energy cost, including the electricity consumption charge, the peak demand charge, and the battery cost. Then, in a separate formulation, we will introduce another system-level study to reduce the energy cost of EV charging and other fixed building energy load through the usage of battery storage and solar PV. Finally, we will present an ARM processor based programmable embedded battery management system (BMS), which monitors battery status, controls charging and discharging at the circuit level, and provides battery protection. The system also works with off-the-shelf battery management IC (Texas Instrument BMS sensor IC) from industry. Comparing to conventional BMS, this software module based BMS is a more suitable solution for energy-efficient buildings due to its high exibility, scalability, and reusability. We will introduce an industrial building testbed with battery storage and solar PV at the University of California, Riverside, and present initial field tests and simulation results for above approaches.

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Vasumathi Raman, Mehdi Maasoumy, and Alexandre Donze, "Model predictive control from signal temporal logic specifications: a case study" Proceedings of the 4th ACM SIGBED International Workshop on Design, Modeling, and Evaluation of Cyber-Physical Systems. April 2014, Berlin, Germany [URL] [pdf] [bib]

Abstract: This paper describes current work on framing the model predictive control (MPC) of cyber-physical systems as synthesis from signal temporal logic (STL) specifications. We provide a case study using a simplified power grid model with uncertain demand and generation; the model-predictive control problem here is that of the ancillary service power flow from the buildings. We show how various relevant constraints can be captured using STL formulas, and incorporated into an MPC framework. We also provide preliminary simulation results to illustrate the promise of the proposed approach.

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Mehdi Maasoumy, Borhan M Sanandaji, Alberto Sangiovanni-Vincentelli and Kameshwar Poolla, "Model Predictive Control of Regulation Services from Commercial Buildings to the Smart Grid" IEEE American Control Conference, ACC 2014. Accepted. [ URL ] [ pdf ] [ bib ]

Abstract: We first demonstrate that the demand-side flexibility of the Heating Ventilation and Air Conditioning (HVAC) system of a typical commercial building can be exploited for providing frequency regulation service to the power grid using at-scale experiments. We then show how this flexibility in power consumption of building HVAC system can be leveraged for providing regulation service. To this end, we consider a simplified model of the power grid with uncertain demand and generation. We present a Model Predictive Control (MPC) scheme to direct the ancillary service power flow from buildings to improve upon the classical Automatic Generation Control (AGC) practice. We show how constraints such as slow and fast ramping rates for various ancillary service providers, and short-term load forecast information can be integrated into the proposed MPC framework. Finally, we provide extensive simulation results to illustrate the effectiveness of the proposed methodology for enhancing grid frequency regulation.

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Mehdi Maasoumy, Jorge Ortiz, David Culler, Alberto Sangiovanni-Vincentelli "Flexibility of Commercial Building HVAC Fan as Ancillary Service for Smart Grid" IEEE Green Energy and Systems Conference (IGESC 2013). [ URL ] [ pdf ] [ bib ] [Slides]

Abstract: In this paper, we model energy use in commercial buildings using empirical data captured through sMAP, a campus building data portal at UC Berkeley. We conduct atscale experiments in a newly constructed building on campus. By modulating the supply duct static pressure (SDSP) for the main supply air duct, we induce a response on the main supply fan and determine how much ancillary power flexibility can be provided by a typical commercial building. We show that the consequent intermittent fluctuations in the air mass flow into the building does not influence the building climate in a human-noticeable way. We estimate that at least 4 GW of regulation reserve is readily available only through commercial buildings in the US. Based on predictions this value will reach to 5.6 GW in 2035. We also show how thermal slack can be leveraged to provide an ancillary service to deal with transient frequency fluctuations in the grid. We consider a simplified model of the grid power system with time varying demand and generation and present a simple control scheme to direct the ancillary service power flow from buildings to improve on the classical automatic generation control (AGC)-based approach. Simulation results are provided to show the effectiveness of the proposed methodology for enhancing grid frequency regulation.

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Chaos in Dynamical Systems:

Publications:

Zohreh Mohammadi, Azadeh Marouf Mashat, Hassan Salarieh, Mehdi Maasoumy, Mohammad Abediny, Aria Alasty "Experimental Investigating of non-linear and chaotic behavior of a doubly-clamped beam under electromagnetic excitation" [ pdf ]

Abstract: This paper studies the chaotic behavior of a doubly clamped Euler-Bernoulli beam under magnetic excitation via an experimental approach. The Responses of vibrations of the beam under different magnetic excitations are being investigated. The setup includes a doubly-clamped beam which is installed on two static bases and it is excited in the middle by an electromagnetic exciter. The excitation frequency varies from 1 to 50 Hz with different bias and amplitude voltages. Using various numerical methods such as Fast Fourier Transformation, Phase diagrams and Maximum Lyapunov Exponents, the experimental results are examined to find regular and irregular responses. The experimental results show that there exists some harmonic and super harmonic and in some cases chaotic responses in this system.


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