Zhenbo Wang

Assistant Professor

University of Tennessee

United States

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Dr. Zhenbo Wang is an Assistant Professor at the University of Tennessee, USA. His research focuses on advanced air mobility, including autonomous landing and trajectory optimization for eVTOL vehicles, as well as real-time control of vehicular systems using convex optimization techniques. He also explores machine learning applications in engineering design, specifically for microwaveable food products and drone-assisted pollination systems.

Zhenbo Wang

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Articles (22)

Deep reinforcement learning approach to autonomous landing of eVTOL vehicles for advanced air mobility

Trajectory optimization for vertical take-off and landing vehicles in urban air mobility via data-driven sequential convex programming

Convex Approach to Real-Time Multiphase Trajectory Optimization for Urban Air Mobility

A Reinforcement Learning Approach to Vehicle Coordination for Structured Advanced Air Mobility

A survey on convex optimization for guidance and control of vehicular systems

3-D geometric design of microwaveable food products for optimal heating uniformity based on machine learning-supervised multiphysics models

Three-dimensional path planning and collision-free flight control for drone-assisted autonomous pollination systems

A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles

A Comprehensive Evaluation of Microwave Reheating Performance Using Dynamic Complementary-Frequency Shifting Strategy in a Solid-State System

Pseudospectral convex optimization for on-ramp merging control of connected vehicles

Mars Entry Tracking Guidance via Quadratically Constrained Quadratic Programming and Pseudospectral Method

Real‐time control of connected vehicles in signalized corridors using pseudospectral convex optimization

Relative Position Keeping for Precise Formation Flying Using Linear Momentum Exchange

Perception, Path Planning, and Flight Control for a Drone-Enabled Autonomous Pollination System

Geometric Design of Hypersonic Vehicles for Optimal Mission Performance with High-Fidelity Aerodynamic Models

Convex Approach to Covariance Control with Application to Stochastic Low-Thrust Trajectory Optimization

Real-time trajectory optimization for powered planetary landings based on analytical shooting equations

Adaptive control of hypersonic vehicles with unknown dynamics based on dual network architecture

Efficient high-accuracy north-south station-keeping strategy for geostationary satellites

An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Onboard Generation of Optimal Trajectories for Hypersonic Vehicles Using Deep Learning

Adaptive neural network control of nonlinear systems with unknown dynamics

Collaborators (5)

Mark Morgan

Jiajia Chen

Hao Gan

Won Suk Lee

bin yang

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