1st Edition
Learning-Enabled Autonomous Systems Control, Verification, and Monitoring
1 Introduction 2 Data-Driven Reachability-Based Model Predictive Control 3 Data-Driven Dual-Loop Model Predictive Control 4 Data-Driven Constrained Control Policy Learning 5 Data-Driven Nonlinear Control Using Nonlinearity Cancellation 6 Data-Driven Sliding Mode Control 7 Learning Observer-Based Control 8 Verification of NN-Controlled Linear Systems 9 Learning Lyapunov Barrier Certificate for Nonlinear Systems 10 Runtime Monitoring for NN-Controlled Linear Systems 11 Runtime Monitoring for NN-Controlled Nonlinear Systems References
Biography
Dr. Jianglin Lan is a Lecturer in Autonomous Systems and Leverhulme Early Career Fellow at the James Watt School of Engineering, University of Glasgow, and an Honorary Research Fellow at Imperial College London. He received his PhD in Control and Intelligent Systems Engineering from the University of Hull, following master’s and bachelor’s degrees in control and automation from leading Chinese universities. His research focuses on safe and robust autonomous systems, data-driven control, and neural network verification. Dr. Lan has held visiting positions at Carnegie Mellon University, Wageningen University, and LAMIH CNRS, France,. He also serves as an Editor for the International Journal of Adaptive Control and Signal Processing.
