Mo Rastgaar

Mo_Main

Dr. Mohammad Rastgaar-Aagah is an assistant professor in the Mechanical Engineering-Engineering Mechanics Department at Michigan Technological University since 2011. Dr. Rastgaar received his Ph.D. degree in Mechanical Engineering from Virginia Tech in 2008. He was a post-doctoral associate in the Newman Laboratory for Biomechanics and Human Rehabilitation at MIT. Dr. Rastgaar is a recipient of 2014 NSF CAREER award.

Dr. Rastgaar is the founding director of the Human-Interactive Robotics Lab (HIRoLab) at Michigan Tech. The research at the HIRoLab is focused on the development of lower extremity assistive and rehabilitation devices for enhanced agility and improved mobility. The research goal at the HIRoLab is to further the critical understanding about the dynamics of gait, especially during different maneuvers, through experiments with human subjects and modeling.

Areas of Expertise

  • Dynamics and Controls
  • Robotics
  • System Identification

 

Advanced Control of Wave Energy Converters

Sandia National Laboratory

Background
A new multi-year effort has been launched by the Department of Energy to validate the extent to which control strategies can increase the power produced by resonant Wave Energy Converters (WEC) devices. A large number of theoretical studies have shown promising results in the additional energy that can be captured through control of the power conversion chains of resonant WEC devices.
However, most of the previous work has been completed on highly idealized systems and there is little to no validation work. This program will specifically target controls development for nonlinear, multi-degree of freedom WEC devices. Multiple control strategies will be developed and the efficacy of the strategies will be compared within the “metric matrix.”
Objective: The purpose of this contract is to provide the labor to develop and implement custom control strategies for a specified WEC device.

Scope of Work
Michigan Technological University (MTU) will provide optimization expertise (Dynamic Programing, pseudo-spectral, shape optimization, others) to support MTPA-FF (mid-targeting phase and amplitude-feedforward) designs and analysis specific to the performance model WEC. This will include numerical simulations specific to the metric matrix requirements. In addition, MTU will provide expertise and support for feedforward real-time implementation and investigations.

Investigator: Ossama Abdelkhalik

Advanced Control and Energy Storage Architectures for Microgrids

Sandia National Laboratory

Consult on advanced control and energy storage architectures for microgrids.
Tasks:
1) Multiple Spinning Machines on a Single AC Bus – Finish the development of the Hamiltonian Surface Shaping Power Flow Controller (HSSPFC), controller design for multiple spinning machines on a single AC Bus.
2) Unstable Pulse Power Controller – Perform simulation studies on the unstable pulse power controller relative to the optimal feedforward (stable) controller for a single DC bus in order to determine the effectiveness of the unstable controller design relative to performance and stability.

Help characterize path forward for nonlinear control design.
Tasks:
1) Review dynamic programming interior point method (DPIP) for feedforward/optimal reference trajectory,
2) HSSPFC (Hamiltonian Surface Shaping Power Flow Controller (nonlinear dynamic structure for feedback),
3) Preliminary assessment of nonlinear wave model and impact on power absorbed.

Investigators: Wayne Weaver, Ossama Abdelkhalik

Modeling and Control Technologies for Near-Term and Long-Term Networked Microgrids

Argonne National Laboratory

Introduction
Microgrids offer attractive options for enhancing energy surety and increasing renewable energy penetration. Within a single microgrid energy generation, storage and utilization is localized. Greater enhancements to energy surety can be accomplished by networking multiple microgrids into a collective which can lead to almost unlimited use of renewable sources, reduction of fossil fuels and self-healing and adaptive systems. However, one pitfall to avoid is losing the surety within the individual microgrids. This produces design and control challenges that are currently unsolved in networked microgrids. To help solve this dilemma, development of analysis methods for design and control of networked microgrids is the general focus of this activity.

Specific tasks include:
1. Collaborate and form a coalition with national labs and other microgrid stakeholders to identify key R&D topics in networked microgrids.
2. Look at near term solutions that can quickly and easily be integrated into existing microgrids,
3. Determine best practices and optimized control strategies for the ground-up design of future networked microgrids.
4. Work within the DOE and national lab partnerships to produce the FOA whitepaper on single microgrid systems.
Tasks 1 through 3 will include microgrid modeling, control and optimizations of single and networked microgrids with focus on achieving DOE 2020 microgrid targets. Specifically, targets include developing commercial scale microgrid systems that reduce outage time, improve reliability and reduce emissions.

TASK 1: Collaborate and form a coalition with national labs and other microgrid stakeholders to identify key R&D topics in networked microgrids.

TASK 2: Look at near term solutions that can quickly and easily be integrated into existing microgrids Model development is one of the first steps in the microgrid control design process and incurs trade-offs between fidelity and computational expense. Models used for modelbased control implementation must be real-time while having sufficient accuracy so that feed-forward information can be maximized to achieve specified requirements. The expected outcomes of this study are (1) determination of appropriate time scales for networked microgrid modeling (2) a MATLAB/ Simulink reduced order model library of networked microgrid components and (3) lab scale hardware validation of networked microgrid models. These model libraries will then be used to construct models and develop control and optimization algorithms of current microgrid systems and equipment.

Task 3: Determine best practices and optimized control strategies for the ground-up design of future networked microgrids. Demonstrating robust networked microgrids will require system-level optimization. This includes both its physical and control system designs. This task will build upon the models and optimizations achieved in task 2 applied to the design of future networked microgrids. The expected outcomes of this study are (1) energy-optimal design methods suitable for networked microgrid design and control of future long-term application architectures and (2) integration of these strategies with the microgrid model environment and bench scale hardware described in task 2.

Investigators: Wayne Weaver, Gordon Parker

Distributed Agent-Based Management of Agile Microgrids

US Department of Defense, Army Research Laboratory

This project plan (APP) describes the third year of the four year program for distributed agent-based management of agile microgrids. In year 1, the team has evaluated modeling and forecasting techniques for renewable energy sources as well as developed relevant case studies. In year 2 the further developed the models and forecasting techniques as well as begin implementation of simulations and hardware test cases.

The existing simulation models a user-definable, network ofmicrogrids and the Autonomous Agile Microgrid (AAM) control system. The AAM has three main components – (1) a low-level, asset control system (Decentralized Closed Loop Controller agent- DCLC), (2) a mid-level, optimal, grid state-change solver (Decentralized Model Based Control agent – DMBC) and the highest level reasoning layer, (Distributed Grid Management agent- DGM).
The entire system is “driven” by a user-configurable, time-history of prioritized loads and events based on field data.

The focus of the year three plan is to (1) increase the reasoning capability of the DGM, (2) develop an optimal power flow strategy at the DMBC level and (3) design a human-in-the-loop interface that permits real-time interaction with the simulation.

Deliverable 1. The AAM uses a command line approach to execute the simulation and observe the grid’s evolution based on a pre-defined time history scenario of events and loads. While the process for designing rich scenarios has a well-defined workflow, the system currently lacks the ability to respond to real-time inputs from a user. The deliverable is demonstration of a new human-in-the-loop capability for the AAM simulator. It will permit one user to “actuate” the power grid manually, or in an AAM-assist mode where the user can optionally decide to implement the AAM’s recommended actions. A second user will be able to trigger events and load changes in real-time, including policy and scenario changes in the DGM. The intent for year three is to create an environment for more complete testing of the AAM and demonstrate its capabilities. This feature would then be available for future studies to increase the reasoning functions of the DGM using human-in-the-loop training.

Deliverable 2. The DMBC currently computes an optimal solution to transition the power grid from its current state to a new state as requested by the DGM. These requests are based on load, generation and storage forecast agent calculations. The DMBC also triggers a new solution based on high-tempo changes to the bus voltage, independent of the DGM, due to unforecasted changes in loads or generation. All DMBC solutions are based on the assumption of a fully functioning, well-defined set of loads, generation, and storage assets. The DMBC does not compute optimal redirection of power flow based on catastrophic generation or load failures. The deliverable is the development and demonstration of a scheme to optimally redistribute power flow for contingency and catastrophic events including equipment faults and attack damage. This redistribution strategy control may be at the level of the DMBC, DCLC or both. The human-in-the-loop capability, described in Deliverable I, will be used to demonstrate this new feature by instantaneously removing generation assets and loads.

Deliverable 3. The DGM relies on data-driven load and generation forecasts to compute grid state change requests for the DMBC. The forecasts will be improved with the inclusion of additional knowledge of inventory, asset models, and situational information. While load prioritization is accommodated, there is not functionality for addressing situations where there is not enough power to accommodate all of the highest priority loads. Policies and negotiation protocols for the DGM multi-agent system that enable power sharing among microgrids will be explored. Additional policies for fine control of load shedding will be examined and simulated. The ERDC-CERL VFOB project can potentially provide a rich source of data and models to the DGM design that support more elaborate forecasts and reasoning under conflict. In addition, the data can drive methods for scenario classification (prediction of the current and future events of the base, e.g., patrols, heightened alerts, etc.). The deliverable is a report and demonstration of improved forecasting agents and conflict resolution handling, through the power scheduling agent, based on knowledge-based reasoning mechanisms and statistical risk analysis metrics. The report will document the agent models and reasoning strategies along with a description of opportunities and gaps for implementing a fully autonomous, resilient power grid.

Investigators: Gordon Parker, Laura Brown, Wayne Weaver, Steven Goldsmith

SGAS Drive Train Model Calibration

IMECO

Introduction
Calibration is an important step in creating a physical model that can be used for predictive control system design. IMECO has a MATLAB/Simulink model of their Steering Gear Actuation System (SGAS). It contains parameters that can be classified as known (e.g. control system gains), known with uncertainty (e.g. mass properties) and unknown (e.g. damping coefficients). IMECO has also obtained experimental data that can be used to run the model and compare model outputs to sensor measurements. An optimization-based method for identifying the model parameters is needed to help automate the calibration process.

Statement of Work
Using the model and experimental data supplied by IMECO, calibrate the model using advanced numerical optimization strategies. Separate calibration parameters for several data sets will be developed in addition to a single calibration across multiple data sets. While the calibration is of primary importance, development of a methodology for automating the process will also be developed.

Investigators: Gordon Parker, Ed Trinklein

Agent Based Control with Application to Microgrids with High Penetration Renewables

Sandia National Laboratory

Abstract
Prior Work is leveraged; MTU has developed and demonstrated through simulation a prototype multiagent system that coordinates the life cycle operations of a microgrid collective composed of independent electric power sources, loads, and storage. MTU has performed simulations of DC micro grids of varying compositions and characteristics. MTU has analyzed simulation results, and developed candidate architectures and protocols for agent-based microgrid controls.

Objective
Execution of this project will further technical innovations associated with multi-agent software controlling microgrid collectives. The microgrid control algorithms for microgrid collectives will be developed and refined using Michigan Tech microgrid models and simulations validated against the MTU test bench. The algorithms will then be applied to SNL hardware models in simulation and finally against the SNL hardware test bed.

Scope
Agent-based control systems will be further developed by MTU in Matlab/Simulink blocks, tested, and refined through simulations. Once control performance objectives have been achieved, the systems will be ported to the MTU situated multi-agent system (MAS) and supporting servo loop controllers on the MTU test bench for evaluation. New Matlab simulations will be tailored and tuned to control the SNL test bed models and verified in simulation. SNL will re-apply the MTU MAS to the physical SNL test bed. SNL will collaborate with MTU on implementation and validation. Collaborative efforts will ensure that SNL attains the technology necessary to achieve the final project objectives for the SNL test bed

Required Research Innovations:
1. Identify control system performance issues between agent informatics and DC nonlinear controls. Since global computations require input from various points, processor speed and network bandwidth may dominate the performance of collaborative protocols that rely on nonlinear control approaches. Research must identify the computational and communication limits for porting nonlinear controls to agent control layers.
2. Investigate scaling properties for controls applied to increasing the number of interconnected DC microgrids. Trading power between microgrids may not be feasible due to geographical distances or communication time latencies. There may also be thresholds identified for collaboration considerations, such as partnering with 10 microgrids or less, due to the global computation requirements. Control scaling results should describe the appropriate considerations at various time scales (seconds, minutes, hours, and days). Additional considerations for scalability may include increasing the number of components within a single microgrid and increasing the variety of components within the microgrid.

Investigators: Gordon Parker, Wayne Weaver, Steven Goldsmith

Microgrid Modeling and Optimization for High Penetration Renewables Integration

Sandia National Laboratory

Abstract
Future microgrids are envisioned having a large renewable energy penetration. While this feature is attractive it also produces design and control challenges that are currently unsolved. To help solve this dilemma, development of analysis methods for design and control of microgrids with high renewable penetration is the general focus of this activity. The specific foci are (1) reduced order microgrid modeling and (2) optimization strategies to facilitate improved design and control. This will be investigated over a multi-year process that will include simplified microgrid modeling and control, single microgrid modeling and control, collective microgrid modeling and control, and microgrid (single and collective) testing and validation.

Microgrid Reduced Order Modeling (ROM)
Model development is one of the first steps in the microgrid control design process and incurs trade-offs between fidelity and computational expense. Models used for model-based control implementation must be real-time while having sufficient accuracy so that feedforward information can be maximized to achieve specified requirements. The expected outcomes of this study are (1) quantification of model uncertainty as a function of the assumptions with particular interest given to reduced order models (2) determination of appropriate time scales for reduced order modeling and (3) a MATLAB / Simulink reduced order model library of microgrid components. Contrasting different microgrid reduced order modeling approaches and simulation results that demonstrate the reduced order microgrid simulation.

Microgrid Optimization
Demonstrating microgrids with robust and high renewable penetration requires system-level extremization. This includes both its physical and control system designs. The expected outcomes of this study are (1) energy-optimal design methods suitable for microgrid design and control and (2) integration of these strategies with the microgrid reduced order model environment described above. How energy-optimal design can be exploited for microgrid design and control.

Investigators: Gordon Parker, Wayne Weaver

Low-Cost Underwater Glider Fleet for Littoral Marine Research

Office of Naval Research

This research is focused on development of innovative practical solutions for control of individual and multiple unmanned underwater vehicles (UUVs) and address challenges such as underwater communication and localization that currently limit UUV use. More specifically, the Nonlinear and Autonomous Systems Laboratory (NAS Lab) team are developing a rigorous framework for analyzing and controlling underwater gliders (UGs) in harsh dynamic environments for the purpose of advancing efficient, collaborative behavior of UUVs.

Underwater gliders are now utilized for much more than long-term, basin-scale oceanographic sampling. In addition to environmental monitoring, UGs are increasingly depended on for littoral surveillance and other military applications. This research will facilitate the transition between academic modeling/simulation problem solving approach to real-world Navy applications. The importance of this research is evident in the Littoral BattleSpace Sensing (LBS) Program contract at the Naval Space and Naval Warfare Systems Command for 150 underwater gliders, designated the LBS-G. These gliders will be operated by the Navy in forward areas to rapidly assess and exploit environmental characteristics to improve the maneuvering of ships and submarines and advance the performance of fleet sensors.

Research results will provide the coordination tools necessary to enable the integration of these efficient and quiet vehicles as part of a heterogeneous network of autonomous vehicles capable of performing complex, tactical missions. The objective is to develop practical, energy-efficient motion control strategies for both individual and multiple UGs while performing in inhospitable, uncertain, and dynamic underwater environments.

The specific goals of this project are twofold. The first goal is to design and fabricate a fleet of low-cost highly maneuverable lightweight underwater gliders. The second goal is to evaluate the capability of the single and multiple developed UGs in littoral zones. The proposed work will develop UGs that would share the buoyancy-driven concept with the first generation of gliders called “legacy gliders.” However, the NAS Lab UGs will be smaller in size, lighter in weight, and lower in price than legacy gliders. This will result in more affordable and novel UG applications. Moreover, the NAS Lab design to development approach allows for technological innovation that overcomes known challenges and responds to unexpected needs that arise during testing. Therefore, the significance of this research is that it will enable implementation of recently developed efficient motion planning algorithms, multi-vehicle coordination algorithms, and extension of these algorithms in realistic conditions where absolute location and orientation of each vehicle is not known and the time-varying flow field is not locally determined.

 

Investigators: Nina Mahmoudian

Understanding the Cavity Mode of Tires

Ford Motor Company

Background:
The purpose of this research is to be able to predict the natural frequencies associated with the cavity modes of tires mounted oil wheels. Ford has experienced difficulties in the past when these natural frequencies have aligned themselves with the natural frequencies of other vehicle components and hence caused an objectionable noise in the vehicle. The goal of this project is to provide the tools to Ford to allow them to make decisions in advance of mounting tire/wheel combinations on vehicles by estimating what these tire cavity natural frequencies will be. It is anticipated that to fully understand the frequencies of the tire cavity modes will require a combination of modeling and experimental testing.

Approach:
To meet these objectives start with a finite element model of the cavity of a tire mounted on a wheel. The initial model includes effectively a rigid tire and wheel. This model is not a coupled vibro-acoustic model but instead just an estimate the natural frequencies of the tire cavity itself with zero velocity boundary conditions. This model will be modified to simulate the change in the tire cavity shape when the wheel is loaded in a static configuration. The results of the loaded and unloaded models are compared to help to understand the effects of changing the tire cavity’s shape. If the results of this model show promise, simpler modeling methods will be explored.

The next step in the modeling process includes a flexible tire and wheel and be a fully coupled vibro-acoustic model. In this model, the wheel will have actual material properties assigned while the tire will be modeled as an isotropic material with estimated material properties that will be iterated to achieve natural frequencies of the coupled system similar to those measured in the laboratory of a stationary tire. This model will then be modified to a statically loaded condition and the model re-run to observe the effects of loading the tire on the natural frequencies.

Models will be validated experimentally by testing a tire/wheel assembly in the laboratory at MTU. Testing will be done in the both the unloaded and the statically loaded case by exciting both the wheel and the tire patch in separate tests. Natural frequencies will be estimated from all tests and used to validate the models. Models and testing will be performed on several different tire/wheel combinations to assess the ability to estimate the natural frequencies of different configurations. Based on the results of the modeling and testing the final deliverable from this project will be the simplest approach that can be determined for estimating the natural frequencies of a tire cavity based on a minimum set of information or data.

Investigators: Jason Blough