What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Electrical engineering
Reza Langari mainly investigates Fuzzy logic, Control theory, Control engineering, Fuzzy control system and Energy management.
His Fuzzy logic study is focused on Artificial intelligence in general.
His study in Control theory is interdisciplinary in nature, drawing from both Simulation and Task.
His Fuzzy control system study incorporates themes from Fuzzy set and Robustness.
His work carried out in the field of Energy management brings together such families of science as Energy consumption and Hybrid vehicle.
He interconnects Fuzzy number, Fuzzy set operations and Fuzzy classification in the investigation of issues within Neuro-fuzzy.
His most cited work include:
- Fuzzy Logic: Intelligence, Control, and Information (795 citations)
- Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process (238 citations)
- Industrial Applications of Fuzzy Logic and Intelligent Systems (204 citations)
What are the main themes of his work throughout his whole career to date?
Control theory, Fuzzy logic, Control engineering, Fuzzy control system and Artificial intelligence are his primary areas of study.
His Control theory research incorporates elements of Control and Damper.
His research on Fuzzy logic frequently connects to adjacent areas such as Mathematical optimization.
His Control engineering study also includes
- Energy management and related Torque, Energy consumption, Electric vehicle and Simulation,
- Hybrid vehicle which connect with Intelligent agent.
His Fuzzy control system study integrates concerns from other disciplines, such as Control system, Linear matrix inequality and Robustness.
His work carried out in the field of Neuro-fuzzy brings together such families of science as Fuzzy number, Fuzzy set operations, Fuzzy classification and Artificial neural network.
He most often published in these fields:
- Control theory (39.39%)
- Fuzzy logic (25.97%)
- Control engineering (20.78%)
What were the highlights of his more recent work (between 2017-2021)?
- Control theory (39.39%)
- Control theory (14.29%)
- Artificial intelligence (17.32%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Control theory, Control theory, Artificial intelligence, Fuzzy logic and Model predictive control.
His work in the fields of Manipulator, Nonlinear system and Torque overlaps with other areas such as Stackelberg competition.
His work in Control theory addresses subjects such as Vehicle dynamics, which are connected to disciplines such as Lyapunov stability, Fuzzy control system, Compensation and Optimal control.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
His Fuzzy logic research focuses on Fuzzy set in particular.
Reza Langari interconnects Control engineering and Electric power in the investigation of issues within Model predictive control.
Between 2017 and 2021, his most popular works were:
- Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook (99 citations)
- A human-like game theory-based controller for automatic lane changing (70 citations)
- Energy Management of Hybrid Electric Vehicle Using Vehicle Lateral Dynamic in Velocity Prediction (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Electrical engineering
- Control theory
Reza Langari focuses on Control theory, Control theory, Gesture, Gesture recognition and Artificial intelligence.
His Control theory research incorporates themes from Regenerative brake, Efficient energy use and Adaptive algorithm.
His research integrates issues of Estimation and Position in his study of Control theory.
The Gesture study combines topics in areas such as Algorithm, Inertial measurement unit and Robotic arm.
His Algorithm research is multidisciplinary, relying on both Normalization, Electromyography and Machine learning.
The various areas that he examines in his Gesture recognition study include Majority rule and Pattern recognition.
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