What is he best known for?
The fields of study he is best known for:
- Control theory
- Mechanical engineering
- Artificial intelligence
Sergio M. Savaresi mainly focuses on Control theory, Control engineering, Control system, Nonlinear system and Control theory.
His Control theory research integrates issues from Slip and System identification.
His research in Control engineering intersects with topics in Control, Benchmark, Vehicle dynamics and Transfer function.
The Control system study combines topics in areas such as Electrical network, Transmission, Sliding mode control, Automotive engineering and Adaptive control.
The various areas that Sergio M. Savaresi examines in his Nonlinear system study include Upper and lower bounds, Discrete system and Actuator.
His research in the fields of Open-loop controller overlaps with other disciplines such as Set.
His most cited work include:
- Brief Virtual reference feedback tuning: a direct method for the design of feedback controllers (668 citations)
- Unsupervised learning techniques for an intrusion detection system (242 citations)
- Direct nonlinear control design: the virtual reference feedback tuning (VRFT) approach (194 citations)
What are the main themes of his work throughout his whole career to date?
Sergio M. Savaresi focuses on Control theory, Control engineering, Automotive engineering, Control system and Control theory.
His Control theory study frequently draws parallels with other fields, such as Damper.
His studies deal with areas such as Vibration control and Suspension as well as Damper.
The study incorporates disciplines such as Identification, Data-driven, Task, Control and Robustness in addition to Control engineering.
His Control theory study focuses on PID controller in particular.
His work deals with themes such as Slip, Simulation and Acceleration, which intersect with Vehicle dynamics.
He most often published in these fields:
- Control theory (43.89%)
- Control engineering (29.15%)
- Automotive engineering (21.27%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (43.89%)
- Automotive engineering (21.27%)
- Control theory (17.42%)
In recent papers he was focusing on the following fields of study:
Sergio M. Savaresi spends much of his time researching Control theory, Automotive engineering, Control theory, Vehicle dynamics and Real-time computing.
His research in Control theory is mostly focused on Torque.
His research integrates issues of Garbage and Energy management system in his study of Automotive engineering.
His Control theory research is within the category of Control engineering.
His Control engineering research incorporates elements of Calibration and Bayesian optimization.
His Vehicle dynamics research is multidisciplinary, incorporating perspectives in Electrical network, Smart fluid, Acceleration and Damper.
Between 2019 and 2021, his most popular works were:
- Torque Vectoring for High-Performance Electric Vehicles: An Efficient MPC Calibration (7 citations)
- Least costly energy management for extended-range electric vehicles: An economic optimization framework (5 citations)
- Mining the electrification potential of fuel-based vehicles mobility patterns: a data-based approach (2 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mechanical engineering
- Electrical engineering
His primary areas of investigation include Control theory, Control theory, Vehicle dynamics, Automotive engineering and Suspension.
His biological study deals with issues like Damper, which deal with fields such as Automatic control, Control and Work.
His Control theory research is multidisciplinary, incorporating elements of Weighting, Tracking, Actuator and Center of gravity.
His Automotive engineering research includes themes of Retrofitting, Acceleration, Aerodynamics and Sensitivity.
His Suspension research includes elements of Control engineering and Task.
His study in the fields of Semi active under the domain of Control engineering overlaps with other disciplines such as Small number.
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