A Bio-Inspired Horned Lizard Optimisation-Driven Super-Twisting Sliding Mode Control Framework for High-Performance Global MPPT in PV Systems Under Multiple Partial Shading Patterns

This paper proposes a hybrid control scheme that integrates the horned lizard optimisation algorithm (HLOA) with a super-twisting sliding mode control (ST-SMC) for robust global maximum power point tracking (GMPPT) in photovoltaic (PV) systems under partial shading conditions (PSC). The method employs a dual-loop structure: The HLOA performs a global search of the power-voltage (Formula presented.) curve in the outer loop, while the inner-loop ST-SMC ensures finite-time convergence of the converter's duty cycle to the computed reference. This decouples global exploration from fast tracking, achieving both high accuracy and rapid response. The framework's superiority is validated through simulation and an experimental prototype. In a comparative analysis against advanced metaheuristics including the grey wolf optimiser (GWO), whale optimisation algorithm (WOA), flower pollination algorithm (FPA), and enhanced leader particle swarm optimisation (ELPSO), the proposed HLOA-ST-SMC technique converges within 0.5 s, exceeding ELPSO by 29% and achieving over 50% faster convergence than GWO, more than 67% faster convergence compared with PSO, and over 69% faster convergence relative to WOA and FPA, while consistently maintaining a high tracking accuracy of 99.87%. Experimental results confirmed a tracking efficiency of 99.6% with negligible steady-state oscillations. The proposed HLOA-ST-SMC framework thus sets a new benchmark for dynamic performance and robustness in GMPPT applications.