SGAS Drive Train Model Calibration


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

CPS: Breakthrough: Toward Revolutionary Algorithms for Cyber-Physical Systems Architecture Optimization

National Science Foundation

Design optimization of cyber-physical systems (CPS) includes optimizing the system architecture (topology) in addition to the system variables. Optimizing the system architecture renders the dimension of the design space variable (the number of design variables to be optimized is a variable.) This class of Variable-Size Design Space (VSDS) optimization problems arises in many CPS applications including (1) microgrid design, (2) automated construction, (2) optimal grouping, and (3) space mission design optimization.

Evolutionary Algorithms (EAs) present a paradigm for statistical inference that implements a simplified computational model of the mechanisms embedded in natural evolution, with potential to solve this problem. However, existing EAs cannot optimize among solutions of different architectures because of the inherent strategy for coding the variables in EAs. Existing EAs resembles natural evolution in which a given architecture can evolve by improving the state of its variables but cannot be revolutionized. Inspired by the concept of hidden genes in biology, this project investigates revolutionary optimization algorithms that can optimize among different solution architectures and autonomously develop new architectures that might not be known a priori, yet are more fit solution architectures. Efficacy of the new algorithms for CPS is evaluated in the context of space mission design optimization.

Intellectual Merit:
There is an increasing demand in the scientific community for autonomous design optimization tools that can revolutionize systems designs and capabilities. Most existing optimization algorithms can only search for optimal solutions in a fixed-size design space; and hence they cannot be used for solution architecture optimization. Few existing algorithms can search for optimal solutions in VSDS problems; however these are problem-specific algorithms and cannot be used as a general framework for VSDS optimization. This project investigates the novel concept of hidden genes in coding the variables in evolutionary algorithms so that the resulting algorithms can be used for optimizing VSDS problems. The key innovation in these new algorithms is the new coding strategies. In addition, in this project, the standard operations in EAs will be replaced by new operations that are defined to enable revolutionizing a current population of solution architectures using the new coding strategy. The Pl’s recent research results, in the context of space mission design optimization, demonstrate that the hidden genes optimization algorithms can search for optimal solutions among different solution architectures, revolutionize an initial population of solutions, and construct new solution architectures that are more fit than the initial population solutions.

Investigator: Ossama Abdelkhalik

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

Rush Robinett


Dr. Robinett specializes in nonlinear control and optimal system design of energy, robotics, and aerospace systems.
Of particular interest in the energy arena is the distributed, decentralized nonlinear control and optimization of networked microgrids with up to 100% penetration of transient renewable energy sources (i.e., photovoltaics and wind turbines).  At 100% penetration, the optimal design of energy storage systems is critical to the stability and performance of networked microgrids because all of the spinning inertia and fossil fuel of the generators have been removed from the system.  In the robotics area, collective control of teams of simple, dumb robots that solve complicated problems is of continuing research interest.  The application areas span the space from chemical plume tracing of buried land mines to underwater detection of targets of interest to airborne surveillance systems to spacecraft formations.  In the aerospace area, system identification, trajectory optimization, guidance algorithm development, and autopilot design form the fundamentals of all of these research topics.  These fundamentals are presently being applied to stall flutter suppression and meta-stable controller design research.

Areas of Expertise

  • Renewable Energy Grid Integration
  • Collective Systems Control
  • Nonlinear Controls
  • Optimization
  • Dynamics
  • Aeroelasticity

Research Interests

  • Energy storage system design for renewable energy grid integration
  • High penetration renewable energy microgrids
  • Collective control of networked microgrids and teams of robots
  • Exergy control for buildings
  • Flutter suppression for wind turbines
  • Nonlinear control system design

Control and Optimization of Microgrids Research


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


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