A nominal controller designed via Linear Matrix Inequalities (LMIs), along with disturbance-observer compensation, is enhanced by a Recurrent Equilibrium Network (REN) trained offline using stored trajectories and nominal-model prediction errors, enabling sufficient small-gain conditions for local closed-loop stability and disturbance string stability.
Abstract
This paper proposes a residual learning-based control framework for heterogeneous vehicle platoons subject to parametric uncertainty and external disturbances. A nominal controller designed via Linear Matrix Inequalities (LMIs), along with disturbance-observer compensation, is enhanced by a Recurrent Equilibrium Network (REN) trained offline using stored trajectories and nominal-model prediction errors. The REN is constrained to satisfy a prescribed $\ell_2$-gain bound, enabling sufficient small-gain conditions for local closed-loop stability and disturbance string stability. Experiments demonstrate reduced spacing and velocity errors relative to the nominal controller.
This paper proposes a reinforcement learning (RL) based optimal platoon control framework for autonomous vehicles (AVs) subject to uncertainties and cyberattacks. Within the optimized backstepping design, an actor-critic-identifier architecture is employed to improve platoon control performance. A cyberattack compe...
Zi-Ming Wang, K. H. Johansson, Apostolos I. Rikos· Journal of Intelligent and C...· 1 citation
This article presents a nearly optimal control scheme in a hybrid data-event mechanism for constrained nonlinear overhead crane systems. Initially, an identifer neural network (NN) is designed to acquire the knowledge of original constrained systems through input-output data. Then, a modified non-quadratic value functi...
Xiong Yang, Ying-Jiang Zhou· IEEE Transactions on Automat...· 0 citations
This paper addresses the stabilization problem of uncertain vehicle systems in the presence of disturbances, on the basis of $H_{\infty}$ dynamic observer (HDO). Based on the parameterizations of algebraic constraints, the control design problem is reduced to the determination of two parameter matrices. By using Lyapun...
Jun Zhang· 2026 IEEE 9th Information Te...· 0 citations
A certified imitation-learning framework for approximating model predictive control (MPC) policies with feedforward neural controllers with feedforward neural controllers is developed and validates it on autonomous-vehicle lateral control.
Tien Dat Vu, Minh Q. Nguyen, Anh Tuan Vu et al.· 0 citations
Simulation results demonstrate that the proposed method outperforms robust control and adaptive model predictive control in tracking accuracy under varying attack intensities, validating the effectiveness of the unified RL-based framework for AVs under coupled physical-cyber threats.
An intelligent robust control framework that integrates predefined-time prescribed-performance control, actor–critic reinforcement learning, and an integral sliding-mode disturbance observer for USV trajectory and velocity tracking provides a promising solution for time-sensitive autonomous marine missions.
Mostafa Jalalnezha· Journal of the Brazilian Soc...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
