His primary areas of study are Control theory, Mathematical optimization, Adaptive control, Optimal control and Stability. His Control theory research is multidisciplinary, incorporating perspectives in Estimator and Model predictive control. His Mathematical optimization research integrates issues from Adaptation, Partial differential equation, Lipschitz continuity and Maxima and minima.
Adaptive control is a subfield of Control theory that Chris Manzie tackles. His work carried out in the field of Optimal control brings together such families of science as Gradient descent, Stochastic programming, Stochastic optimization and Vehicle dynamics. In his study, which falls under the umbrella issue of Stability, Pooling, Spark-ignition engine, Inlet manifold, State of health and State of charge is strongly linked to Observer.
His main research concerns Control theory, Control theory, Mathematical optimization, Model predictive control and Automotive engineering. In most of his Control theory studies, his work intersects topics such as Control engineering. As part of the same scientific family, Chris Manzie usually focuses on Control theory, concentrating on Diesel fuel and intersecting with Diesel engine.
His Mathematical optimization research focuses on subjects like Exponential stability, which are linked to Stability theory. His research in Model predictive control intersects with topics in Linear system and Motion control. In Automotive engineering, Chris Manzie works on issues like Powertrain, which are connected to Hybrid vehicle.
Chris Manzie mainly focuses on Mathematical optimization, Control theory, Bottleneck, Robustness and Assignment problem. His Mathematical optimization study combines topics in areas such as Inverted pendulum and Model predictive control. His studies examine the connections between Model predictive control and genetics, as well as such issues in Nonlinear system, with regards to Stability, Minimisation and Computational complexity theory.
Chris Manzie interconnects Control and Constraint satisfaction in the investigation of issues within Control theory. The various areas that Chris Manzie examines in his Robustness study include Robotics and Linear system. His studies in Control theory integrate themes in fields like Linear programming and Sequence learning.
Chris Manzie focuses on Control theory, Task, Mathematical optimization, Robust control and Motion control. His Control theory study incorporates themes from Control and Implicit function. His Task study integrates concerns from other disciplines, such as Tracking, Driving cycle, Diesel fuel and Calibration.
His work on Optimal control, Karush–Kuhn–Tucker conditions and Differential dynamic programming as part of general Mathematical optimization research is frequently linked to Bottleneck, thereby connecting diverse disciplines of science. His Motion control research also works with subjects such as
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Extremum seeking from 1922 to 2010
Y. Tan;W.H. Moase;C. Manzie;D. Nesic.
chinese control conference (2010)
Fuel economy improvements for urban driving : Hybrid vs. intelligent vehicles
Chris Manzie;Harry Watson;Saman Halgamuge.
Transportation Research Part C-emerging Technologies (2007)
Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery
Changfu Zou;Chris Manzie;Dragan Nešić;Abhijit G. Kallapur.
Journal of Power Sources (2016)
Newton-Like Extremum-Seeking for the Control of Thermoacoustic Instability
W H Moase;C Manzie;M J Brear.
IEEE Transactions on Automatic Control (2010)
Effects of moving the center's in an RBF network
C. Panchapakesan;M. Palaniswami;D. Ralph;C. Manzie.
IEEE Transactions on Neural Networks (2002)
A unifying approach to extremum seeking: Adaptive schemes based on estimation of derivatives
D. Nesic;Y. Tan;W. H. Moase;C. Manzie.
conference on decision and control (2010)
Extremum Seeking With Stochastic Perturbations
C. Manzie;M. Krstic.
IEEE Transactions on Automatic Control (2009)
A Framework for Simplification of PDE-Based Lithium-Ion Battery Models
Changfu Zou;Chris Manzie;Dragan Nesic.
IEEE Transactions on Control Systems and Technology (2016)
The forced response of choked nozzles and supersonic diffusers
William H. Moase;Michael J. Brear;Chris Manzie.
Journal of Fluid Mechanics (2007)
Model Predictive Control of a Fuel Injection System with a Radial Basis Function Network Observer
Chris Manzie;Marimuthu Palaniswami;Daniel Ralph;Harry Watson.
Journal of Dynamic Systems Measurement and Control-transactions of The Asme (2002)
Profile was last updated on December 6th, 2021.
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