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

Ossama Abdelkhalik

Abdelkhalik_0219Dr. Abdelkhalik conducts research in the area of dynamics, control, and  global optimization with applications to spacecraft trajectory planning, data assimilation in oil reservoirs, systems design, and traffic engineering. In some applications, the design space has numerous local minima, with mixed variables (integer and real), and the number of optimization variables can be varied among different solutions to explore new regions in the design space. Global optimization methods can handle problems with mixed variables and numerous local minima, but variable-size design space optimization is yet to be explored. The research focus is on the study of global optimization methods that can handle variable-size design space problems. Other research efforts include the recursive implementation of evolutionary optimization algorithms for the sake of improving the computational efficiency in data assimilation problems.

Areas of Expertise

  • Estimation of Dynamic Systems
  • Global Optimization
  • Data Assimilation
  • Controls and Control Systems

Gordon Parker

parkerDr. Parker specializes in control system design and correlation of nonlinear dynamic models to experimental data. A key area of his research is the optimal control of microgrids with particular attention given to networked topologies. Closed loop control and real-time optimization for harmonizing use of available energy generation and storage assets, while satisfying loads, is the main theme. Applications requiring temporary or remote power motivate much of his funded research along with disaster relief scenarios. Development of a scalable, optimal control solution is critical for allowing the interconnection, in both power and communication, of separately deployed microgrids. The main challenge stems from a microgrid’s ever-changing energy asset and load portfolio and their effect on the system models used for optimal planning and control system design. Rational segregation of distributed versus centralized optimization and control is another research area. In the past year Dr. Parker and his colleagues formed the Agile and Interconnected Micrgorid (AIM) Center to bring together faculty from Computer Science, Mathematics, Cognitive Sciences and Learning, Electrical and Computer Engineering and Mechanical Engineering to focus an interdisciplinary team on this technical area. More generally, nonlinear control, system simulation, nonlinear system parameter identification and optimization, are present in most of Dr. Parker’s ongoing projects. Examples include active control of diesel engine aftertreatment systems and at-sea control of naval equipment.

Prepositioned Power Research

Overview

Prepositioned Power RobotsResearch is focused on developing technology to create systems that can autonomously create a microgrid, for situations that require the ability to preposition a basic level of energy infrastructure such as areas damaged by natural or man-made disasters, and autonomously deploying forward operating bases. Modeling and control of robotics and power conversion systems provides the ability to create such prepositioned electric power networks.

Active Projects

Applications

Autonomous Robots can carry a variety of power equipment:

  • Intelligent power electronics for energy conversion
  • Power connection hardware
  • Generation sources, both traditional and renewable
  • Energy storage

 

Prepositioned Power

Prepositioned Power

Four autonomous microgrid robots, each with different power network functionality. Two have renewable energy generation and storage capability, another has a conventional diesel genset, and the third contains intelligent power electronics for conversion and hard-line interconnection, and switchgear. After assessing the power requirements and available resources they would physically organize and electrically interconnect to form a micro-grid.

Distributed Agent-Based Management of Agile Microgrids Research

Overview

A remote microgrid is a class of stand-alone power grids that services diverse loads, employs distributed generation with renewable resources, and requires on-line control and optimization to maintain stability and power flow. The grid control system is both agile and autonomous, accommodating rapid changes in generation and load resources with minimal training or intervention on the part of human operators.

Active Research Projects

Applications

  • Control based on a hybrid approach that marries novel model-predictive control strategies with multi-agent systems.
  • Utilizes artificial intelligence and machine learning techniques.
  • By imbuing software agents with component models and knowledge about grid operations the collective can cooperatively plan and execute coordinated operations that essentially re-organize grid structure in real-time while maintaining uninterrupted service.

Distributed Agent Based Management Layout

High Order Nonlinear Droop

High Order Nonlinear Droop

Distributed Flowchart

Control and Optimization of Microgrids Research

Overview

Optimal Control Surface

Optimal Control Surface

Researchers are focused on the control of individual energy load, source, and storage energy points as building blocks in a microgrid. This technology enables operation of a stable and optimized system through an agent based approach of the power electronics energy conversion points, enabling a robust and re-configurable system that does not rely on central control or communication.

Active Research Projects

Applications

Research is ongoing to develop new modeling, simulation, control and optimization tools for rational decisions for the best use of microgids with high penetrations renewable and dispatchable loads:

  • Rapid deployment of survivable, flexible, reconfigurable, stable, smart microgrids for military forward operating bases and humanitarian missions.
  • Transformation of U.S. military installations to be net neutral with safe, reliable power generation.
  • Training engineers who can adapt to new interdisciplinary challenges associated with delivering secure energy for both civilian and military applications.
AIM Microgrid Strategy

AIM Microgrid Strategy

Control and Optimization

Control and Optimization