This paper develops an event-triggered fixed-time integral reinforcement learning framework for optimal control of unknown nonlinear systems. An integral data-driven identifier is first used to reconstruct the unknown dynamics, after which an inverse-optimal formulation is employed to construct a fixed-time running cos...
This paper develops a resilient learning controller for unknown nonlinear systems operating under actuator limits, false-data-injection attacks, and external disturbances. The key idea is to learn a saturated secure policy directly from finite trajectory data while guaranteeing that both the learning error and the clos...
A learning-based control method that allows the designer to prescribe the desired convergence time in advance and an integral reinforcement-learning framework is proposed to avoid requiring exact knowledge of the system dynamics while ensuring that the control input always satisfies the actuator constraints.
This paper addresses secure leader-follower formation of unknown nonlinear multi-agent systems under actuator constraints, external disturbances, and false-data-injection attacks by proposing a two-power state-cost structure and a deadline-parameterized critic update that connect optimal learning with predefined-time s...
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
A cost function construction method and a critic learning law are proposed, which together guarantee the practical fixed-time stability of the system while overcoming the limitations of existing fixed-time reinforcement learning formulations.
Tien Dat Vu, M. Doan· 2 citations
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