Abstract: An event-triggered output feedback control approach is proposed via a disturbance observer and adaptive dynamic programming (ADP). The solution starts by constructing a nonlinear disturbance ...
Abstract: Platooning connected and autonomous vehicles (CAVs) provide significant benefits in terms of traffic efficiency and fuel economy. However, most existing platooning systems assume the ...
Abstract: In this study, the adaptive dynamic programming (ADP)-based fixed-time optimal trajectory tracking control is investigated for wheeled mobile robots. An ADP-based fixed-time optimal tracking ...
TIOBE Index for December 2025: Top 10 Most Popular Programming Languages Your email has been sent December’s TIOBE Index lands with a quieter top tier but a livelier shuffle just beneath it. The main ...
Abstract: Safe operation of systems such as robots requires them to plan and execute trajectories subject to safety constraints. When those systems are subject to uncertainties in their dynamics, it ...
Abstract: This article proposes two decentralized multiagent optimal control methods that combine the computational efficiency and scalability of differential dynamic programming (DDP) and the ...
Abstract: The policy gradient adaptive dynamic programming (PGADP) technique has gained recognition as an effective approach for optimizing the performance of nonlinear systems. Nonetheless, existing ...
Abstract: Robust economic dispatch (ED) is of paramount importance for obtaining robust unit commitment when considering the uncertainty in the system, which is a typical multistage robust ...
Abstract: This article presents a novel efficient experience-replay-based adaptive dynamic programming (ADP) for the optimal control problem of a class of nonlinear dynamical systems within the ...
Abstract: Recent work (Rantzer, 2022) formulated a class of optimal control problems involving positive linear systems, linear stage costs, and elementwise constraints on control. It was shown that ...
Abstract: A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and ...
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