This paper studies event-triggered parameter estimation in sensor fusion systems where sensors transmit measurements to a gradient based estimator. We introduce a regressor-driven local triggering rule that requires no knowledge of the current parameter estimate and depends solely on the regressor signals. Under a persistent excitation condition on the aggregate regressor, we derive explicit design inequalities on the estimator gain and event thresholds that guarantee global exponential convergence. The analysis is based on a time-varying Lyapunov function. We further provide a sufficient condition on the regressor dynamics that enforces a uniform lower bound on inter-event times, excluding Zeno behavior. Simulations show substantial communication savings while preserving exponential convergence.
This paper introduces a new framework for event-triggered gain scheduling applied to linear hyperbolic Partial Differential Equations (PDEs) with time- and space-varying coefficients. The approach leverages neural operators to address the challenges of real-time control in such s…
We propose a reinforcement learning (RL) based optimal distributed control algorithm for the multi-agent systems (MASs) with stochastic uncertainties. Unlike existing methods, during the optimized backstepping design process, we use the actor-critic-identifier structure. The acto…
Accurate and efficient parameter estimation is essential for applying electrochemical battery models in simulation, state estimation, control, and repeated model updating. However, conventional optimization methods, such as particle swarm optimization (PSO) and genetic algorithms…
We propose a notion of robust adaptive backup control barrier functions for nonlinear control affine systems with parametric uncertainty in both the drift dynamics and actuation matrix. Backup control barrier functions guarantee safety by predicting the system's trajectory under…
This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentiable MPCC with online parameter estimation to handle varying road-surface conditions. For online parameter estimation, we leverag…
Reliable roadside perception of vulnerable road users (VRUs) remains challenging under occlusions, variable lighting, and diverse weather conditions, particularly under strict edge-computing and latency constraints. Existing multi-sensor fusion systems rely on cloud or server-gra…