Event-triggered Control and Online Learning for Networked Systems under Computational Delays
Authors: Xiaobing Dai, Armin Lederer, Zewen Yang, Sihua Zhang, Lu Wan, Yang Tang, Sandra Hirche
Organizations: Chair of Information-oriented Control, School of Computation, Information and Technology, Technical University of Munich, Germany · Learning and Adaptive Systems Group, Department of Computer Science, ETH Zurich, Switzerland · Chair of Robotics and Systems Intelligence, Munich Institute of Robotics and Machine Intelligence, Technical University of Munich · Information Systems in the Built Environment, Eindhoven University of Technology, Eindhoven, Netherlands · Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
Online learning-based control is a promising approach to control uncertain systems, where unknown components are identified during operation to improve control performance. However, resource-intensive online learning algorithms introduce non-negligible computational delays, especially when executed on systems with limited local computational resources. To mitigate this, an in-network online learning-based control structure is employed by deploying the learning-based controller on a remote computation node and connecting it via a communication channel. In this paper, control performance guarantee is first established by deriving tracking error bound for the in-network control architecture, while accounting for computational delays. The derived tracking error bound allows for diverse communication and computation strategies under a specific condition, including time-/event-triggered mechanisms. Additionally, the trade-off between communication and computation performances is shown for a given desired control performance. Furthermore, to enhance the efficiency in both communication and computation, an efficient control framework with an asynchronous event-triggered mechanism in both control and online learning is devised under the existence of computational delay. The proposed event-triggered strategy is proven to achieve the same control performance as time-triggered scenario while excluding Zeno behavior. Finally, we derive an explicit expression of the proposed event-trigger condition for exponentially stabilizable systems, and demonstrate its effectiveness through simulations.
Figures & tables
Fig. 1 : Block diagram for in-network online learning-based control with event-triggered control and online data collection mechanism. The black blocks and lines imply fast computation and communication, while the computation and communication in red blocks and lines are slow.
Fig. 2 : Illustration of effects from computational delay.
Symbol
Description
Δp(⋅),Δu(⋅)
Computation time for prediction and update
Δˉp,Δˉu
Upper bounds of Δp(⋅),Δu(⋅)
Δ(⋅),Δˉ
Total computation time and its upper bound
{tpf}p∈N,{tku}k∈N
Time sequence for online learning and control
V(⋅),Vˉ(⋅)
Lyapunov function and its upper bound
ϕp,1(⋅),ϕp,2(⋅,⋅)
Value of ϕ(⋅,⋅,⋅) in [tpf,tp+1f) and [tp+1f,tp+2f)
TABLE I : Summary of Main Notations in Section IV
Fig. 3 : a) Franka Emika Panda in its initial position. The red line represents the reference trajectory of the end effector, and the blue line is generated by online learning-based control with the proposed asynchronous event-triggered mechanism. b) The desired joint reference trajectory qr=[qr,1,qr,2,qr,3,qr,4,qr,5,qr,6,qr,7]T over time t , which is generalized by inverse kinematics.
Fig. 4 : The value of Lyapunov function V(t) and tracking error e(t) w.r.t time t from 4 learning-based control methods.
Fig. 5 : Prediction performance by using Double Trigger.
Fig. 6 : Value of trigger function ρu(t) and ρf(t) over time t and trigger instances for control update and online learning, respectively. It is obvious to see ρu(t)≤0 for any t∈R0,+ , indicating V(t)≤V^(t) . This also means the control triggers occur at t when ρu(t)=0 , i.e., V(t)=V^(t) . Due to the existence of computational delay, the value of ρf(⋅) may greater than 0 . However, the online learning triggered when ρf(⋅)≥0 ensures ρf(⋅)<0 after GP model update.
Event-triggered control updates controllers or communicates only when system conditions warrant, reducing resource use while maintaining stability and performance.
Event-triggered control provides a mechanism for avoiding excessive use of constrained communication bandwidth in networked multi-agent systems. However, most existing methods rely on accurate system models, which may be unavailable in practice. In this work, we propose a model-free, priority-driven reinforcement learning algorithm that learns communication priorities and control policies jointly from data in decentralized multi-agent systems. By learning communication priorities, we circumvent the hybrid action space typical in event-triggered control with binary communication decisions. We evaluate our algorithm on benchmark tasks and demonstrate that it outperforms the baseline method.
Qingyun Guo, Junyi Shi, Tomasz Piotr Kucner +1
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland · Center for Artificial Intelligence, Helsinki, Finland
Ensuring the safe operation of multi-agent systems (MASs) under uncertain environments is crucial for cooperative robotic, where external disturbances and inaccurate dynamic models can significantly compromise performance and reliability. To address this challenge, calibrated machine learning models, particularly Gaussian process (GP) regression, are extensively employed due to their interpretable performance quantification. As the interconnected communication of MASs facilitates cooperative learning, agents are able to enhance learning performance by exchanging local GP inferences with their neighbors and aggregating the received information via distributed GP strategies. However, variations in computational power and prediction tasks among agents inevitably lead to heterogeneous computational delays and differences in query points, which are often overlooked in existing aggregation methods. To overcome these limitations, this work proposes an asynchronous cooperative learning strategy that explicitly accounts for prediction accuracy, query point variations and delay effects. Additionally, a distributed control law based on an adjoint MAS is developed to ensure the desired control performance. Simulations on unmanned surface vehicles validate the effectiveness of the proposed approach, demonstrating substantial improvements in both learning and control performance compared to the state-of-the-art approaches.
Xiaobing Dai, Zewen Yang, Wei Ren +1
Chair of Information-oriented Control, Technical University of Munich, 80333 Munich, Germany · Chair of Robotics and Systems Intelligence, Technical University of Munich, 80992 Munich, Germany · Department of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA 92521 USA
This paper investigates the control problem of dual-arm unmanned aerial manipulator systems (DAUAMs). Strong coupling between the dual-arm and the multirotor platform, together with unmodeled dynamics and external disturbances, poses significant challenges to stable and accurate operation. An adaptive event-triggered control scheme with neural network-based approximation is proposed to address these issues while explicitly considering communication constraints. First, a dynamic model of the DAUAM system is derived, and a command-filter-based backstepping framework with error compensation is constructed. Then, a neural network is employed to approximate external frictions, and an event-triggered mechanism is designed to reduce the transmission frequency of control updates, thereby alleviating communication and energy burdens. Lyapunov-based analysis shows that all closed-loop signals remain bounded and that the tracking error converges to a neighborhood of the desired trajectory within a fixed time. Finally, experiments on a self-built DAUAM platform demonstrate that the proposed approach achieves accurate trajectory tracking.
Yang Wang, Hai Yu, Wei He +3
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, and Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China. · Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.