What is he best known for?
The fields of study he is best known for:
- Control theory
- Statistics
- Electrical engineering
The scientist’s investigation covers issues in Control theory, Adaptive control, Linear system, Mathematical optimization and Convergence.
His research on Control theory frequently connects to adjacent areas such as Model predictive control.
Graham C. Goodwin has included themes like Estimation theory, Discrete time and continuous time, Matrix, Stochastic control and Key in his Adaptive control study.
His studies deal with areas such as Parametrization, Transfer function, Feature and Multivariable calculus as well as Linear system.
His Mathematical optimization study also includes fields such as
- Control theory which is related to area like Nonlinear system,
- Stochastic process that connect with fields like Computation.
Graham C. Goodwin focuses mostly in the field of Convergence, narrowing it down to matters related to Bounded function and, in some cases, Tracking error and Minimum phase.
His most cited work include:
- Adaptive filtering prediction and control (3393 citations)
- Control System Design (1390 citations)
- Dynamic System Identification: Experiment Design and Data Analysis (954 citations)
What are the main themes of his work throughout his whole career to date?
Graham C. Goodwin mainly focuses on Control theory, Mathematical optimization, Linear system, Control engineering and Adaptive control.
His Control theory study frequently draws connections between adjacent fields such as Model predictive control.
His work is dedicated to discovering how Mathematical optimization, Algorithm are connected with System identification and Frequency domain and other disciplines.
His Linear system research includes elements of Transfer function and Multivariable calculus.
His work in Control engineering is not limited to one particular discipline; it also encompasses Control.
His Adaptive control study combines topics in areas such as Convergence, Discrete time and continuous time, Stochastic control and Robust control, Robustness.
He most often published in these fields:
- Control theory (48.56%)
- Mathematical optimization (20.63%)
- Linear system (14.44%)
What were the highlights of his more recent work (between 2013-2021)?
- Control theory (48.56%)
- Model predictive control (12.93%)
- Applied mathematics (8.25%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Control theory, Model predictive control, Applied mathematics, Mathematical optimization and Nonlinear system.
The Control theory study which covers Voltage that intersects with Electronic engineering.
His Model predictive control study integrates concerns from other disciplines, such as Common-mode signal, Control engineering, Power electronics, Harmonic and Inverter.
The Applied mathematics study combines topics in areas such as Zero, Sampling, Sampling and Spectral density.
His work on Mathematical optimization is being expanded to include thematically relevant topics such as Bayesian probability.
Control theory is closely attributed to Control system in his research.
Between 2013 and 2021, his most popular works were:
- Robust model predictive control: reflections and opportunities (36 citations)
- A fundamental control limitation for linear positive systems with application to Type 1 diabetes treatment (36 citations)
- A Generalized MPC Framework for the Design and Comparison of VSI Current Controllers (34 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Control theory
- Electrical engineering
His primary scientific interests are in Control theory, Model predictive control, Inverter, Voltage and Mathematical optimization.
His study in the fields of Control theory, Automatic frequency control and Optimal control under the domain of Control theory overlaps with other disciplines such as Context.
His research integrates issues of Compensation, Common-mode signal, Control engineering, Power electronics and Harmonic in his study of Model predictive control.
His Control engineering study combines topics from a wide range of disciplines, such as Scope, Interpretation and Electronics.
His Mathematical optimization research is multidisciplinary, relying on both Stochastic process, Theoretical computer science, Bayesian probability and Nonlinear system.
Graham C. Goodwin has researched Nonlinear system in several fields, including Zero, Variables and Euler's formula.
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