Adaptive control Lyapunov function based model predictive control for continuous nonlinear systems

Abstract

A stochastic model predictive control (MPC) framework is presented in this paper for nonlinear affine system with inputs constraints. The proposed MPC can achieve asymptotic stability in probability while ensuring safe guarantee. We first introduce the concept of stochastic control Lyapunov-barrier function and provide a method to construct control Lyapunov-barrier function (CLBF) by combining an unconstrained control Lyapunov function (CLF) and control barrier functions. The unconstrained CLF is obtained from its corresponding semi-linear system through dynamic feedback linearization. Based on the constructed CLBF, we utilize MPC framework to deal with constraints and ensure feasibility. The proposed CLBF based MPC is validated via a wheeled mobile robot example.

LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion

“…The proposed LXL provides a framework capable of inspiring subsequent researches in camera and 4D imaging radar fusion-based 3D object detection. Future works includes improving the robustness of LXL via attention-based transformer to achieve adaptive interaction between modals, and its applications in subsequent planning and control tasks [62] [63].…”

Resilient consensus tracking of a nonlinear multi‐agent system with distributed event‐triggered mechanism

“…A1 [2]. Matrix G has a directed spanning tree, that is, there exists a node r (a root) such that all other nodes can be linked to node r via a directed path.…”

“…1 Among them, the network-based consensus is the essential task for navigation control of a MAS, which means that all agents' states converge to the optimal trajectories, subject to the limitations of network bandwidths and ranges. 2 In the network environment, it is of great importance to investigate the event-triggered consensus control protocol (ETCCP), because it reduces the efforts of distributed controllers applied in the whole MAS. 3 In ETCCP, the sampling instants of agents are determined by their triggered conditions, and the controllers are updated, respectively, in aperiodic manners.…”