On Optimal Event-Triggered Distributed Control for Stochastic Multi-Agent Systems via Reinforcement Learning

Reinforcement learning-based dynamic event-triggered control for nonlinear stochastic multiagent systems under mismatched disturbances

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.

Event-triggered Neural Network Adaptive Distributed Optimal Control for Nonlinear Multi-agent Systems

Robust formation tracking of multi-agent systems via reinforcement learning-based hybrid impulsive control.

This paper explores robust H∞ formation tracking control of multi-agent systems in terms of hybrid impulsive approach combined with reinforcement learning. The study establishes the robust stabilization of the formation under the proposed control protocol by employing the Razumikhin technique and implementing feasible constraints to ensure sustained robust H∞ performance. Compared to recent continuous and impulsive control methods, the developed hybrid impulsive control framework offers enhanced adaptability, faster corrective actions, and improved system robustness in uncertain and evolving environments. Furthermore, in contrast to the majority of current formation stabilization techniques, we introduce the Hierarchical Multi-agent Cooperative Reinforcement Learning (HMAC-RL) framework to optimally and adaptively refine both control parameters and impulsive moments. This framework features a central agent module, a continuous control agents module, and a pulsed control agents module, each serving crucial roles in addressing specific aspects of the hybrid impulsive control protocol design. Finally, numerical simulations are presented to support the theoretical analysis and demonstrate the optimization efficiency achieved through the reinforcement learning framework HMAC-RL.

Event-Triggered Data-Based Reinforcement Learning Control for Optimal Consensus of Heterogeneous Multi-Agent Systems in Graphical Games

This study discusses the optimal consensus issue for heterogeneous multi-agent systems (MASs) defined by partially unknown dynamics within a graphical game framework. Data-driven reinforcement learning has shown efficacy in such systems; however, conventional implementations frequently depend on continuous time data transmission, which puts too much strain on computers and communication systems. This paper proposes a new event-triggered, data-based reinforcement learning control scheme to fix these problems. By adding an event-triggered mechanism (ETM) to the heterogeneous graphical game formulation, the control protocol is only updated when certain error thresholds are crossed. This uses much less resources than time-triggered methods. 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. A theoretical analysis using Lyapunov stability theory shows that the suggested event-triggered strategy ensures asymptotic consensus and convergence to the best control weights. Finally, numerical simulation examples show that the proposed approach works well and is better than other methods. These examples show that the controller update frequency and communication load can be greatly reduced while keeping the system stable and optimal.

A dual-event-triggered adaptive neural network control for resource-constrained UAV

This paper proposes a dual-event-triggered adaptive neural-network control strategy for pose regulation of resource-constrained quadrotor unmanned aerial vehicles (UAVs). The proposed method addresses the difficulty of compensating for model uncertainties and external disturbances while optimizing communication and computational resources under limited bandwidth and onboard processing capability. In the developed framework, state information is transmitted from the UAV to the controller only when the prescribed triggering conditions are violated, and control commands are updated only when their deviation from the previously transmitted commands exceeds a prescribed threshold. This dual-event-triggered framework reduces communication and computation from both the state-transmission and control-update channels. In addition, a trigger-interval-weighted neural-network updating law is introduced to improve the applicability of the adaptive mechanism under different operating conditions. Based on Lyapunov stability theory, it is shown that all signals in the closed-loop system are uniformly ultimately bounded and that Zeno behavior is excluded. Simulations under both constant and time-varying reference signals verify the effectiveness of the proposed method in terms of control performance and communication–computation trade-off.

Reinforcement Learning for Real-Time Auto-Tuning of Robot’s Controllers

This research introduces an innovative control technique for Series Elastic Actuators (SEAs) that utilizes Reinforcement Learning (RL) to address the shortcomings of previously fixed-gain adaptive controllers, which are a hybrid of State Feedback Control (SFC) and Model Reference Adaptive Control (MRAC) by using Lyapunov Stability Analysis. This controller is optimized by adjusting the adaptation factor. b. This study presents an intelligent agent based on reinforcement learning to find the value of b with a dynamic auto-tuner. It trains via the Soft Actor-Critic (SAC) algorithm for 100,000 time steps. A comparison between the two methods was presented according to simulation results under different conditions; the RL-based controller shows much better tracking accuracy, how quickly it reaches the target output, and how little control torque it uses, where the agent's policy could automatically adjust in real-time based on system conditions, such as uncertainties and disturbances, where it has a settling time of 1.7 seconds, while the fixed parameter controller has a 1.95-second settling time, resulting in a reduction of 15.3%. It also lowers the control torque caused by disturbances by 19.5% compared to the fixed parameter controller, which has a control torque of 3.99 Nm, while the maximum control torque for the RL-optimized controller is 3.21 Nm.