Education:

University of Michigan
PhD Aerospace Engineering ’95. Advisor: Harris McClamroch
MA Applied Mathematics ’95
MS Aerospace Engineering ’93

Teaching: ^top

AERO 347/343 – Space Flight Mechanics
AERO 470 – Control of Aerospace Vehicles
AERO 573 – Spacecraft Dynamics and Control
AERO 575 – Flight and Trajectory Optimization
AERO 551 – Nonlinear Systems and Control
AERO 548 – Astrodynamics
AERO 740 – Model Predictive Control

Research Interests: ^top

Professor Kolmanovsky has broad interests in control systems and their applications as enabling technology across different domains.  His recent research focuses on control theory for systems with constraints and on control of advanced aerospace and automotive systems. His recent publications address challenges in spacecraft orbital and attitude control, in control of very flexible aircraft, and in control of engines, propulsion systems and autonomous driving.

Professor Kolmanovsky’s special area of interests in control theory is control of systems with constraints. With the increasing trend towards system downsizing (smaller satellites, air vehicles, drones, engines, etc.) and the growing stringency of requirements, constraint handling and limit protection are becoming increasingly important and critical for engineered systems. Constraints can represent actuator range/rate limits, safety requirements, operational limits, and stationary and moving obstacle avoidance requirements. While in some cases constraint violation has only benign consequences (e.g. loss of efficiency), it can be catastrophic in others. Effective controllers for systems with constraints must be nonlinear and often predictive, and they have to sufficiently but non-conservatively account for the uncertainty in the operating environment. Professor Kolmanovsky’s and his students research addresses the development of variety of schemes for constraint enforcement, including reference governors,, chained invariant/contractive set controllers, and drift counteraction optimal controllers.  Professor Kolmanovsky also conducts research in the general area of model predictive control (MPC) and numerical algorithms for real-time optimization that are used in control of constrained systems. He is interested in applications and demonstration of these algorithms on a variety of space, air, ground and marine vehicle platforms. He is also interested in integrating learning and adaptation into constrained control systems.

Biography: ^top

Professor Kolmanovsky is a former graduate of the Department of Aerospace Engineering and has spent close to 15 years of his subsequent professional career at Ford Research and Advanced Engineering in Dearborn, Michigan addressing challenges and opportunities in control systems for advanced powertrain and propulsion systems. Professor Kolmanovsky re-joined the department of aerospace engineering as a professor in January 2010.

Awards: ^top

• Fellow of IEEE
• AIAA Associate Fellow
• 102 United States patents
• Plenary Speaker at 2018 IFAC Conference on Nonlinear Model Predictive Control
• Plenary Speaker at 2018 IFAC Engine and Powertrain Control, Simulation and Modeling Conference
• Technology Award of Society of Instrument and Control Engineers (SICE), 2015
• Outstanding Paper Award of IEEE Transactions on Control Systems Technology, 2016
• Best Paper Award of the IEEE International Conference on Cybernetics, 2013
• ASEE/Air Force Research Laboratory Summer Faculty Fellowship awarded for Spring/Summer 2012
• ASEE/Air Force Research Laboratory Summer Faculty Fellowship awarded for Spring/Summer 2011
• Technical Achievement Award of Ford Research and Advanced Engineering, 2009
• Technical Achievement Award of Ford Research and Advanced Engineering, 2004
• Plenary speaker at 2003 American Control Conference
• Eckman Award of American Automatic Control Council, 2002
• Outstanding Paper Award of IEEE Transactions on Control Systems Technology, 2002
•  Ted Kennedy Family Faculty Team Excellence Award of the University of Michigan’s College of Engineering, 2020
• François-Xavier Bagnoud doctoral fellow (1994-1995)

Publications: ^top

Full List of Publications

Selected Recent Publications

Control theory for systems with constraints

1. Liao-McPherson, D., Nicotra, M., Dontchev, A.L., Kolmanovsky, I., and Veliov, V., “Sensitivity-based warmstarting for nonlinear model predictive control with polyhedral state and control constraints,” IEEE Transactions on Automatic Control, 2020.
2. Liao-McPherson, D., Nicotra, M., and Kolmanovsky, I., “Time-distributed optimization for real-time model predictive control: Stability, robustness and constraint satisfaction,” Automatica, vol. 117, 108973, July, 2020.
3. Dontchev, A.L., Kolmanovsky, I., Krastanov, M.I., Veliov, V., Vuong, P.T., “Approximating optimal finite horizon feedback by model predictive control,” Systems and Control Letters, vol. 139, 104666, May 2020.
4. Liao-McPherson, D., and Kolmanovsky, I., “FBstab: A proximally stabilized semismooth algorithm for convex quadratic programming,” Automatica, vol. 113, 108801, March, 2020.
5. Kalabic, U., Li, N., Vermillion, C., and Kolmanovsky, I., “Reference governors for chance-constrained systems,” Automatica, vol. 109, 108500, November, 2019.
6. Dontchev, A.L., Huang, M., Kolmanovsky, I., and Nicotra, M., “Inexact Newton-Kantorovich methods for constrained nonlinear Model Predictive Control,” IEEE Transactions on Automatic Control, vol. 64, no. 9, September, 2019.
7. Li, N., Girard, A., and Kolmanovsky, I., “Stochastic predictive control for partially observable Markov Decision Processes with time-joint chance constraints and application to autonomous vehicle control,” ASME Journal of Dynamic Systems, Measurement and Control, vol. 141, pp.  071007-071007-12, July 2019.
8. Liao-McPherson, D., Huang, M., and Kolmanovsky, I., “A regularized and smoothed Fischer-Burmeister method for quadratic programming with applications to Model Predictive Control,” IEEE Transactions on Automatic Control, vol. 64, no. 7, pp. 2937-2944, July 2019.
9. Nicotra, M., Nguyen, T.W., Garone, E., and Kolmanovsky, I., “Explicit reference governor for the constrained control of linear time-delay systems,” IEEE Transactions on Automatic Control, vol. 64, no. 7, pp. 2883-2889, July, 2019.
10. Nicotra, M., Liao-McPherson, D., and Kolmanovsky, I., “Embedding constrained Model Predictive Control in a continuous-time dynamic feedback,” IEEE Transactions on Automatic Control, vol. 64, no. 5, pp. 1932-1946, May 2019.
11. Dontchev, A.L., Kolmanovsky, I., Krastanov, M.I., Nicotra, M.M., Veliov, V.M., “Lipschitz stability in discretized optimal control,” SIAM Journal on Control and Optimizatio, vol. 57, no. 1, pp. 468-489, 2019.
12. Wang, H., Kolmanovsky, I., and Sun, J., “Zonotope based recursive estimation of the feasible set for linear static systems with additive and multiplicative uncertainties,” Automatica, vol. 95C, pp. 236-245, September, 2018.
13. Zidek, R., and Kolmanovsky, I., “Drift counteraction optimal control for deterministic systems and enhancing convergence of value iteration,” Automatica, vol. 83, pp. 108-115, 2017.
14. Kalabic, U., Gupta, R., Di Cairano, S., Bloch, A.M, and Kolmanovsky, I., “MPC on manifolds with application to the control of spacecraft attitude on SO(3),” Automatica, vol. 76, pp. 293-300, 2017.
15. Garone, E., Di Cairano, S., and Kolmanovsky, I., “Reference and command governors for systems with constraints: A survey of their theory and application,” Automatica, vol. 75, pp. 306-328, 2017.
16. Di Cairano, S., Kalabic, U. and Kolmanovsky, I., “Reference governor for network control systems subject to variable time-delay,” Automatica, vol. 62, pp. 77-86, 2015.
17. Vermillion, C., Menezes, A., and Kolmanovsky, I.V., “Stable hierarchical Model Predictive Control using an inner loop reference model and lambda-contractive terminal constraint sets, “Automatica, vol. 50, pp. 92-99, 2014.
18. Kalabic, U., Kolmanovsky, I., and Gilbert, E.G., “Reduced order extended command governor,” Automatica, vol. 50, pp. 1466-1472, 2014.
19. Ghaemi, R., Sun, J., and Kolmanovsky, I., “Robust control of constrained linear systems with bounded disturbances,” IEEE Transactions on Automatic Control, vol. 57, pp. 2683-2688, 2012.
20. Gielen, R.H., Lazar, M., and Kolmanovsky, I., “Lyapunov methods for time-invariant delay difference inclusions,” SIAM Journal on Control and Optimization, vol. 50, pp. 110-132, 2012.
21. Ghaemi, R., Kolmanovsky, I., and Sun, J., “Robust control of linear systems with disturbances bounded in a state dependent set,” IEEE Transactions on Automatic Control, no. 7, pp. 1740-1745, 2011.

Spacecraft control applications

1. Taheri, E., Junkins, J.L., Kolmanovsky, I., and Girard, A., “A novel approach for optimal trajectory design with multiple operation modes of propulsion system, Part 1, Acta Astronautica, 2020.
2. Taheri, E., Junkins, J.L., Kolmanovsky, I., and Girard, A., “A novel approach for optimal trajectory design with multiple operation modes of propulsion system, Part 2, Acta Astronautica,, 2020.
3. Nicotra, M., Liao-McPherson, D., Burlion, L., and Kolmanovsky, I., “Spacecraft attitude control with nonconvex constraints: An explicit reference governor approach,” EEE Transactions on Automatic Control, 2020.
4. Zidek, R., Kolmanovsky, I., and Bemporad, A., “Spacecraft drift counteraction optimal control: Open-loop and receding horizon solutions,” AIAA Journal of Guidance, Control and Dynamics, vol. 41, no. 9, pp. 1859-1872, September 2018.
5. Zagaris, C., Park, H., Virgili-Llop, J., Zappulla, R., Romano, M., and Kolmanovsky, I., “Model predictive control of spacecraft relative motion with convexified keep-out-zone constraints,” AIAA Journal of Guidance, Control, and Dynamics, vol. 41, no. 9, pp. 2054-2062, September, 2018.
6. Frey, G., Petersen, C., Leve, F., Kolmanovsky, I., and Girard, A., “Constrained spacecraft relative motion planning exploiting periodic natural motion trajectories and invariance,” AIAA Journal of Guidance, Control and Dynamics,, vol. 40, no. 12, pp. 3100-3115, December 2017.
7. Frey, G., Petersen, C., Leve, F., Garone, E., Kolmanovsky, I.. and Girard, A., “Parameter governors for coordinated control of n-spacecraft formations,” AAIAA Journal of Guidance, Control, and Dynamics, vol. 40, no. 11, pp. 3020-3025, November, 2017.
8. Petersen, C., Leve, F., and Kolmanovsky, I., “Model Predictive Control of an underactuated spacecraft with two reaction wheels,” AIAA Journal of Guidance, Dynamics and Control, vol. 40, pp. 320-332, 2017.
9. Taheri, E., Kolmanovsky, I., and Atkins, E., “Enhanced smoothing technique for indirect optimization of minimum-fuel low-thrust trajectories,” AIAA Journal of Guidance, Dynamics and Control, vol. 39, pp. 2500-2511, 2016.
10. Petersen, C., Leve, F., and Kolmanovsky, I., “Recovering linear controllability of an underactuated spacecraft by exploiting solar radiation pressure,” AIAA Journal of Guidance, Control, and Dynamics, vol. 39, pp. 826-837, 2016.
11. Weiss, A., Baldwin, M., Erwin, R.S., Kolmanovsky, I., “Model Predictive Control for spacecraft rendezvous and docking: Strategies for handling constraints and case studies,” EEE Transactions on Control Systems Technology, vol. 24, pp. 1638-1647, 2015.
12. Weiss, A., Petersen, C., Baldwin, M., Erwin R.S., and Kolmanovsky, I.., “Safe positively invariant sets for spacecraft obstacle avoidance,” AIAA Journal of Guidance, Control, and Dynamics, vol. 38, pp. 720-732, 2015.
13. Starek, J., and Kolmanovsky, I. “Nonlinear Model Predictive Control strategy for low thrust spacecraft missions,” Optimal Control, Applications and Methods, vol. 35, pp. 1–20, 2014.
14. Weiss A., Kolmanovsky, I.., Bernstein, D.S., and Sanyal, A., “Inertia-free spacecraft attitude control using reaction wheels,” AIAA Journal of Guidance, Control, and Dynamics, vol. 36, pp. 1425-1439, 2013.
15. Di Cairano, S., Park, H., and Kolmanovsky, I., “Model predictive control approach to guidance of spacecraft rendezvous and proximity maneuvering,” International Journal of Robust and Nonlinear Control, vol. 22, pp. 1398-1427, 2012.

Aircraft control applications

1. Davoudi, B., Taheri, E., Duraisamy, K., Jayaraman, B., and Kolmanovsky, I., “Quad-rotor flight simulation in realistic atmospheric conditions,” AIAA Journal, vol. 58, no. 5, May 2020.
2. Dunham, W., Hencey, B., Girard, A., and Kolmanovsky, I. “Distributed Model Predictive Control for more electric aircraft subsystems operating at multiple time scales”, IEEE Transactions on Control Systems Technology, 2020.
3. Pereira, M. de F.V, Kolmanovsky, I, and Cesnik, C., “Model Predictive Control with constraint aggregation applied to conventional and very flexible aircraft,” Proceedings of the 58th IEEE Conference on Decision and Control, Nice, France, pp. 431-437, 2019.
4. Seok, J., Kolmanovsky, I., and Girard, A., “Coordinated model predictive control of aircraft gas turbine engine and power system,” AIAA Journal of Guidance, Dynamics and Control, vol. 40, no. 10, pp. 2538-2555, October, 2017.
5. McDonough, K., and Kolmanovsky, I., “Fast computable recoverable sets and their use for aircraft loss-of-control handling,” AIAA Journal of Guidance, Control and Dynamics, vol. 40, pp. 934-947, 2017.
6. Di Donato, P.F.A., Balachandran, S., McDonough, K., Atkins, E., and Kolmanovsky, I., “Envelope-aware flight management for loss of control prevention given rudder jam,” AIAA Journal of Guidance, Control, and Dynamics, vol. 40, pp. 1027-1041, 2017.
7. Vermillion, C., Grunnagle, T., Lim, R., and Kolmanovsky, I., “Model-based plant design and hierarchical control of a prototype lighter-than-air wind energy system, with experimental flight test results,” IEEE Transactions on Control Systems Technology, vol. 22, pp. 531-542, 2014.

Automotive control applications

1. Maldonado, B.P., Li, N., Kolmanovsky, I., and Stefanopoulou, A., “Learning reference governor for cycle-to-cycle combustion control with misfire avoidance in SI engines and high EGR-diluted conditions,” International Journal of Engine Research (IJER), 2020.
2. Amini, M.R., Kolmanovsky, I., and Sun. J., “Hierarchical MPC for robust eco-cooling of connected and automated vehicles and its application to electric vehicle battery thermal management,” IEEE Transactions on Control Systems Technology, 2020
3. Amini, M.R., Wang, H., Gong, X., Liao-McPherson, D., Kolmanovsky, I., and Sun, J., “Cabin and battery thermal management of connected and automated HEVs for improved energy efficiency using hierarchical Model Predictive Control,” IEEE Transactions on Control Systems Technology, 2020.
4. Dunham, W., Seok, J., Girard, A., Kolmanovsky, I., Chen, W. and Dai, E., “A model-based approach to the estimation and control of a continuously variable transmission,” IEEE Transactions on Control Systems Technology, 2020.
5. Tulga, E., Kolmanovsky, I., Massoud, N., Ozay, N., Scruggs, J., Vasudevan, R., Orosz, G., “Connnected and automated road vehicles: State of the art and future challenges,” Vehicle Systems Dynamics,, vol. 58, no. 5, May, 2020.
6. Li, N., Oyler, D., Zhang, M., Yildiz, Y., Kolmanovsky, I., and Girard, A., “Game-theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems,” EEE Transactions on Control Systems Technology, IEEE Transactions on Control Systems Technology, vol. 26, no. 5, pp. 1782-1797, September, 2018.
7. Li, Z., Kolmanovsky, I., Kalabic, U., Atkins, E., Lu, J., and Filev, D., “Optimal state estimation for systems driven by jump-diffusion process with application to road anomaly detection,” IEEE Transactions on Control Systems Technology, vol. 25, no. 5, pp. 1634-1643, 2017.
8. Zaseck, K., Brusstar, M., and Kolmanovsky, I., “Stability, control, and constraint enforcement of piston motion in a hydraulic free-piston engine,” IEEE Transactions on Control Systems Technology, vol. 25, pp. 1284-1296, 2017.
9. Huang, M., Zaseck, K., Butts, K., and Kolmanovsky, I., “Rate-based Model Predictive Controller for diesel engine air path: Design and experimental evaluation,” IEEE Transactions on Control Systems Technology, vol. 24, pp. 1922-1955, 2016.
10. Li, Z., Filev, D., Kolmanovsky, I., Atkins, E., Lu, J., “A new clustering algorithm for processing GPS-based road anomaly reports with a Mahalanobis distance,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, pp. 1980-1988, 2016.
11. Li, Z., Kolmanovsky, I., Atkins, E., Lu, J., Filev, D., and Michelini, J., “Road risk modeling and cloud aided safety-based route planning,” IEEE Transactions on Cybernetics, vol. 46, pp. 2473-2483, 2016.
12. Filev, D.P., and Kolmanovsky, I., “Generalized Markov models for real time modeling of continuous systems,” IEEE Transactions of Fuzzy Systems, vol. 22, pp. 983-998, 2014.
13. Di Cairano, S., Bernardini, D., Bemporad, A., Kolmanovsky, I., “A stochastic MPC with learning framework for driver-aware vehicle control, and its application to HEV energy management,” IEEE Transactions on Control Systems Technology, vol. 22, pp. 1018 – 1031, 2014.
14. Dextreit, C., and Kolmanovsky, I., “Game theory controller for hybrid electric vehicles,” IEEE Transactions on Control Systems Technology, vol. 22, pp. 652-663, 2014.