17 citations · 30 across the 22 of their papers we have counts for
23 papers
Self-Optimizing Control of Continuous Processes Based on Reinforcement Learning
Ziqi Zhuo, Junghui Chen, Lei Xie +1
This paper addresses the Self-Optimizing Control (SOC) problem in industrial continuous processes and proposes a Reinforcement-Learning (RL)-based SOC approach to improve dynamic p…
Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control
Runze Lin, Ziqi Zhuo, Junghui Chen +2
A significant limitation of Deep Reinforcement Learning (DRL) is the stochastic uncertainty in actions generated during exploration-exploitation, which poses substantial safety ris…
Vision-Augmented On-Track System Identification for Autonomous Racing via Attention-Based Priors and Iterative Neural Correction
Zhiping Wu, Cheng Hu, Yiqin Wang +2
Operating autonomous vehicles at the absolute limits of handling requires precise, real-time identification of highly non-linear tire dynamics. However, traditional online optimiza…
Robust Spatiotemporal Motion Planning for Multi-Agent Autonomous Racing via Topological Gap Identification and Accelerated MPC
Mingyi Zhang, Cheng Hu, Yiqin Wang +3
High-speed multi-agent autonomous racing demands robust spatiotemporal planning and precise control under strict computational limits. Current methods often oversimplify interactio…
A Rapid Iterative Trajectory Planning Method for Automated Parking through Differential Flatness
Zhouheng Li, Lei Xie, Cheng Hu +1
As autonomous driving continues to advance, automated parking is becoming increasingly essential. However, significant challenges arise when implementing path velocity decompositio…
MCTR: Midpoint Corrected Triangulation for Autonomous Racing via Digital Twin Simulation in CARLA
Junhao Ye, Cheng Hu, Yiqin Wang +6
In autonomous racing, reactive controllers eliminate the computational burden of the full See-Think-Act autonomy stack by directly mapping sensor inputs to control actions. This by…
Residual Koopman Model Predictive Control for Enhanced Vehicle Dynamics with Small On-Track Data Input
Yonghao Fu, Cheng Hu, Haokun Xiong +6
In vehicle trajectory tracking tasks, the simplest approach is the Pure Pursuit (PP) Control. However, this single-point preview tracking strategy fails to consider vehicle model c…
A Learning-based Planning and Control Framework for Inertia Drift Vehicles
Bei Zhou, Zhouheng Li, Lei Xie +2
Inertia drift is a transitional maneuver between two sustained drift stages in opposite directions, which provides valuable insights for navigating consecutive sharp corners for au…
Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning
Runze Lin, Junghui Chen, Biao Huang +2
In the era of Industry 4.0 and smart manufacturing, process systems engineering must adapt to digital transformation. While reinforcement learning offers a model-free approach to p…
Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition
Zhouchang Li, Runze Lin, Hongye Su +1
In the era of smart manufacturing and Industry 4.0, the refining industry is evolving towards large-scale integration and flexible production systems. In response to these new dema…
Adaptive Learning-based Model Predictive Control Strategy for Drift Vehicles
Bei Zhou, Cheng Hu, Jun Zeng +4
Drift vehicle control offers valuable insights to support safe autonomous driving in extreme conditions, which hinges on tracking a particular path while maintaining the vehicle st…
Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method
Zhouheng Li, Lei Xie, Cheng Hu +1
The widespread application of autonomous driving technology has significantly advanced the field of autonomous racing. Model Predictive Contouring Control (MPCC) is a highly effect…
Three-Dimensional Sparse Random Mode Decomposition for Mode Disentangling with Crossover Instantaneous Frequencies
Chen Luo, Tao Chen, Lei Xie +1
Sparse random mode decomposition (SRMD) is a novel algorithm that constructs a random time-frequency feature space to sparsely approximate spectrograms, effectively separating mode…
An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency
Wule Mao, Zhouheng Li, Entao Sun +2
Generating overtaking trajectories in high-speed scenarios is typically addressed through hierarchical planning, which often suffers from local optima due to single initial solutio…
A Data-Driven Aggressive Autonomous Racing Framework Utilizing Local Trajectory Planning with Velocity Prediction
Zhouheng Li, Bei Zhou, Cheng Hu +2
The development of autonomous driving has boosted the research on autonomous racing. However, existing local trajectory planning methods have difficulty planning trajectories with…
Learning to Drift in Extreme Turning with Active Exploration and Gaussian Process Based MPC
Guoqiang Wu, Cheng Hu, Wangjia Weng +4
Extreme cornering in racing often leads to large sideslip angles, presenting a significant challenge for vehicle control. Conventional vehicle controllers struggle to manage this s…
Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis
Qi Zhang, Weihua Xu, Lei Xie +1
Electrolytic hydrogen production serves as not only a vital source of green hydrogen but also a key strategy for addressing renewable energy consumption challenges. For the safe pr…
Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning
Qi Zhang, Lei Xie, Weihua Xu +1
Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially co…
Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
Qi Zhang, Lei Wang, Weihua Xu +2
The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamic…
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives
Runze Lin, Junghui Chen, Lei Xie +1
In the context of Industry 4.0 and smart manufacturing, the field of process industry optimization and control is also undergoing a digital transformation. With the rise of Deep Re…
Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control
Runze Lin, Yangyang Luo, Xialai Wu +4
The Organic Rankine Cycle (ORC) is widely used in industrial waste heat recovery due to its simple structure and easy maintenance. However, in the context of smart manufacturing in…
Obstacle Avoidance for Unicycle-Modelled Mobile Robots with Time-varying Control Barrier Functions
Jihao Huang, Zhitao Liu, Jun Zeng +2
In this paper, we propose a safety-critical controller based on time-varying control barrier functions (CBFs) for a robot with an unicycle model in the continuous-time domain to ac…
Velocity Obstacle for Polytopic Collision Avoidance for Distributed Multi-robot Systems
Jihao Huang, Jun Zeng, Xuemin Chi +3
Obstacle avoidance for multi-robot navigation with polytopic shapes is challenging. Existing works simplify the system dynamics or consider it as a convex or non-convex optimizatio…