TD3-Based Adaptive Acceleration-Level Integral Sliding Mode Control for Heterogeneous Dual-Robot Collaboration

Abstract

Heterogeneous dual-robot collaborative systems offer significant operational flexibility but present coordination challenges due to kinematic mismatches. While Integral Sliding Mode Control (ISMC) effectively handles nonlinear dynamics, traditional fixed-gain ISMC suffers from a fundamental tradeoff between robustness and chattering suppression. To address this, this paper proposes a TD3-based Adaptive Acceleration-Level ISMC strategy. Unlike conventional velocity-level approaches, the proposed method operates at the acceleration level to generate smoother commands and employs a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent as a dynamic gain scheduler. By integrating a lookahead mechanism, the agent anticipates curvature changes and optimizes sliding mode parameters—switching gains (K1, K2), and integral gain (K3)—in real-time. This allows for aggressive disturbance suppression during maneuvers while minimizing control effort on straight paths. Simulation results in a high-fidelity ROS 2/Gazebo environment demonstrate that the proposed approach significantly reduces chattering and achieves superior tracking precision (lower RMSE and maximum error) compared to fixed-gain baselines.