What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Mechanical engineering
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 most cited work include:
- Extremum seeking from 1922 to 2010 (242 citations)
- Fuel economy improvements for urban driving : Hybrid vs. intelligent vehicles (128 citations)
- Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery (121 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Control theory (43.89%)
- Control theory (22.14%)
- Mathematical optimization (19.85%)
What were the highlights of his more recent work (between 2019-2021)?
- Mathematical optimization (19.85%)
- Control theory (43.89%)
- Bottleneck (3.82%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping (14 citations)
- Fast Calibration of a Robust Model Predictive Controller for Diesel Engine Airpath (8 citations)
- Adaptive Scan for Atomic Force Microscopy Based on Online Optimization: Theory and Experiment (5 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Mechanical engineering
- Artificial intelligence
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
- Flexibility which intersects with area such as Trajectory,
- Robot end effector which connect with Machining, Control engineering, Test bench and Advanced manufacturing.
His study in Control system is interdisciplinary in nature, drawing from both Mechanical system, Control theory, Actuator and Constraint satisfaction, Constraint.
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