AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A dynamic event-triggered mechanism (DETM) is constructed to reduce redundant controller-to-actuator signal transmissions and weight update laws are derived from the negative gradients of positive definite functions associated with the Hamilton-Jacobi-Bellman (HJB) equation.
This work proposes a reinforcement learning (RL) based optimal distributed control algorithm for the multi-agent systems (MASs) with stochastic uncertainties that uses the actor-critic-identifier structure and provides a Lyapunov-based stability proof that guarantees all errors are bounded, ensuring precise tracking be...
An off-policy integral reinforcement learning (IRL) algorithm is devised to ascertain the Nash equilibrium solution utilizing quantifiable system data, thereby obviating the necessity for precise knowledge of the internal system matrices.
This article analyzes the optimal containment control problem of discrete-time multiagent systems (MASs) and proposes an online off-policy multistep PG algorithm that achieves optimal control performance under completely unknown system dynamics and accommodates asynchronous policy updates among agents.
A low-complexity prescribed-time control method integrated with a switching dynamic event-triggered control (DETC) scheme that effectively eliminates the “explosion of complexity” associated with traditional backstepping, avoids the use of dynamic surfaces and command filters, and ensures user-prescribed-time stability...
Simulation results demonstrate that the proposed approach achieves substantial improvements in both unknown disturbance estimation accuracy and tracking control performance, while significantly reducing communication overhead, thereby validating the effectiveness and superiority of the proposed theoretical framework.
This article addresses the optimal stabilization of unknown nonlinear networked industrial systems (NISs) with limited communication resources, proposing an innovative self-triggered approximate optimal control framework fused with generalized fuzzy hyperbolic model (GFHM) and reinforcement learning (RL) gradient desce...
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