Hybrid soft computing and adaptive learning strategies for intelligent autonomous systems

The intelligent autonomous systems need to be reliable in the situations when there is uncertainty as well as nonlinear dynamics and time-varying disturbances. Traditional model-driven controllers are not flexible and purely learning-based models can be unstable and not easily interpretable. The current hybrid techniques strive to unite these paradigms, but they are generally based on offline optimization or loosely coupled structures of learning and control. This paper offers a hybrid soft computing and adaptive learning model based on combining fuzzy inference with an online learning process to make decisions in real-time. The fuzzy aspect provides the ability to deal with uncertainty and nonlinear mappings whereas the adaptive learning aspect optimizes control parameters through performance feedback with limited updates. Experimental analysis shows the presented framework can reach control accuracy of 95.2 which is 3-5 points better than the representative hybrid and learning-based baselines, with adaptation time lowered to 2.6 s. Stability analysis indicates a much lower level of control signal variance than with the unconstrained learning strategies. The primary value of the research is the single hybrid architecture that maintains the interpretability and allows further adaptation, which is a feasible and reliable solution to intelligent autonomous control in continuously evolving environments.