This study proposes a layered hybrid control framework for automation and robotics systems that combines Tube-based Robust MPC (RMPC) as a safety envelope, adaptive parameter estimation to narrow online uncertainty, Sliding Mode Control (SMC) as a high-resilience fail-safe, and Reinforcement Learning (RL) as a performance booster through a warm-start/residual policy scheme that is always constrained by Control Barrier Functions (CBF). The study aims to achieve better tracking performance and computational efficiency without sacrificing safety and constraint compliance. The methodology includes constrained nonlinear plant modeling, design of tube invariance-based RMPC with constraint tightening, RLS/concurrent learning-based adaptive estimator, SMC for infeasibility/model drift conditions, and a CBF-based safety filter formulated as an online small QP. Evaluation is done through ablation studies on multi-domain benchmarks and HIL-style tests, with finite-horizon cost, ITAE, constraint violation rate, and computational latency (average and WCET) metrics. The results show that the RL+CBF configuration reduces the finite-horizon cost by about 15.7% compared to pure RMPC, maintaining a violation rate of ~0.35% (better than RMPC: 0.80%) and significantly below that of unfiltered RL (4.20%). The warm-start RL scheme accelerates the completion time of the MPC solver by ≈15–18% over various prediction horizons and reduces the WCET (26.4 → 23.3 ms), supporting real-time implementation. These findings confirm that the integration of RMPC–Adaptive–SMC–RL(+CBF) effectively bridges model-based optimality, online adaptivity, robust resilience, and learning from data in a single architecture that is safety-certified and feasible for real-world applications demanding high uptime.
Ari Wanto (Fri,) studied this question.