What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Mathematical analysis
His scientific interests lie mostly in Control theory, Trajectory, Trajectory optimization, Mathematical optimization and Control engineering.
As part of one scientific family, he deals mainly with the area of Control theory, narrowing it down to issues related to the Angle of attack, and often Predictor–corrector method.
His studies deal with areas such as Space Shuttle, Piecewise linear function, Guidance system, Heading and Drag as well as Trajectory.
His Trajectory optimization research focuses on subjects like Sequential quadratic programming, which are linked to Lagrange multiplier, Stability, Gain scheduling and State variable.
His Mathematical optimization research integrates issues from Constraint and Convex optimization.
His work carried out in the field of Optimal control brings together such families of science as Closed loop, Launch vehicle, Path, Feedback control and Realization.
His most cited work include:
- Onboard Generation of Three-Dimensional Constrained Entry Trajectories (242 citations)
- Entry Guidance: A Unified Method (194 citations)
- Nonlinear predictive controllers for continuous systems (167 citations)
What are the main themes of his work throughout his whole career to date?
Ping Lu spends much of his time researching Control theory, Trajectory, Trajectory optimization, Optimal control and Mathematical optimization.
Ping Lu has included themes like Control engineering and Angle of attack in his Control theory study.
As a member of one scientific family, he mostly works in the field of Trajectory, focusing on Aerospace engineering and, on occasion, Mars Exploration Program.
His Trajectory optimization research includes elements of Nonlinear programming, Minimax, Inverse dynamics, Sequential quadratic programming and Hypersonic flight.
His Mathematical optimization study frequently intersects with other fields, such as Convex optimization.
His Nonlinear system research incorporates themes from Control theory and Model predictive control.
He most often published in these fields:
- Control theory (39.27%)
- Trajectory (23.56%)
- Trajectory optimization (18.32%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (5.76%)
- Deep learning (4.19%)
- Control theory (39.27%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Deep learning, Control theory, Descent and Optimal control are his primary areas of study.
In the subject of general Artificial intelligence, his work in Generative grammar and Compressed sensing is often linked to Interpretation and Lithology, thereby combining diverse domains of study.
His Deep learning research is multidisciplinary, incorporating perspectives in Artificial neural network, Acoustics, Process and Geophysical imaging.
His study of Trajectory is a part of Control theory.
His Descent research incorporates elements of Learning based, Aeronautics and Planetary missions, Mars Exploration Program.
His studies in Optimal control integrate themes in fields like Quadratic programming and Nonlinear programming.
Between 2017 and 2021, his most popular works were:
- Using generative adversarial networks to improve deep-learning fault interpretation networks (36 citations)
- Trajectory Design Employing Convex Optimization for Landing on Irregularly Shaped Asteroids (25 citations)
- Augmented Apollo Powered Descent Guidance (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mathematical analysis
- Control theory
His main research concerns Control theory, Descent, Artificial intelligence, Deep learning and Thrust.
His study involves Trajectory optimization, Trajectory and Nonlinear programming, a branch of Control theory.
His Trajectory research integrates issues from Spacecraft, Euler angles, Quadratic programming and Convex optimization.
The concepts of his Descent study are interwoven with issues in Aeronautics and Planetary missions, Mars Exploration Program.
His biological study spans a wide range of topics, including Acoustics, Process and Identification.
Ping Lu has researched Deep learning in several fields, including Artificial neural network, Preprocessor, Deterministic algorithm and Generative grammar.
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