What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Control theory
Bo Wahlberg mainly investigates System identification, Control theory, Applied mathematics, Mathematical optimization and Algorithm.
His System identification study combines topics in areas such as Dynamical systems theory, Linear system and Sampling, Statistics, Covariance matrix.
In his work, Trajectory, Trailer, Truck and Reversing is strongly intertwined with Control engineering, which is a subfield of Control theory.
His Applied mathematics study incorporates themes from Transfer function, Laguerre polynomials and Bounded function.
His work on Augmented Lagrangian method as part of general Mathematical optimization study is frequently connected to Convex optimization, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Algorithm research is multidisciplinary, incorporating elements of Subspace topology, Estimator and Maximum a posteriori estimation.
His most cited work include:
- System identification using Laguerre models (672 citations)
- System identification using Kautz models (301 citations)
- An adaptive array for mobile communication systems (271 citations)
What are the main themes of his work throughout his whole career to date?
Bo Wahlberg mainly focuses on Control theory, System identification, Mathematical optimization, Algorithm and Applied mathematics.
His Control theory research incorporates elements of Estimation theory and Model predictive control.
He interconnects Control engineering, Function, Covariance matrix and Linear system in the investigation of issues within System identification.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Spectral density estimation and Markov model.
In his study, which falls under the umbrella issue of Algorithm, Observability is strongly linked to Subspace topology.
His Applied mathematics research is multidisciplinary, relying on both Transfer function, Linear regression, Laguerre polynomials, Frequency domain and Autoregressive model.
He most often published in these fields:
- Control theory (32.49%)
- System identification (29.96%)
- Mathematical optimization (28.16%)
What were the highlights of his more recent work (between 2017-2021)?
- Motion planning (5.42%)
- Control theory (32.49%)
- Mathematical optimization (28.16%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Motion planning, Control theory, Mathematical optimization, Algorithm and Model predictive control.
His research brings together the fields of Distributed model predictive control and Control theory.
While working in this field, Bo Wahlberg studies both Mathematical optimization and Action.
Bo Wahlberg has included themes like Linear regression and Graph in his Algorithm study.
His Stochastic process research focuses on Noise measurement and how it connects with System identification.
To a larger extent, Bo Wahlberg studies Identification with the aim of understanding System identification.
Between 2017 and 2021, his most popular works were:
- Combining Lattice-Based Planning and Path Optimization in Autonomous Heavy Duty Vehicle Applications (13 citations)
- Experimental validation of model predictive control stability for autonomous driving (11 citations)
- Estimating Private Beliefs of Bayesian Agents Based on Observed Decisions (9 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Control theory
His primary areas of study are Motion planning, Mathematical optimization, Frame, Algorithm and Stochastic process.
In his study, Vehicle dynamics, Torque, Actuator and Smoothness is strongly linked to Trajectory, which falls under the umbrella field of Motion planning.
Specifically, his work in Mathematical optimization is concerned with the study of Optimal control.
His studies in Frame integrate themes in fields like Control engineering, Optimization problem and Trailer.
His Algorithm study incorporates themes from Covariance, State space, Robustness and Signal processing.
His Stochastic process research incorporates elements of Dynamical systems theory, Noise, Covariance matrix, Noise measurement and Hidden Markov model.
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