Conventional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&O), suffer from steady-state oscillations and slow convergence under rapidly varying environmental conditions, leading to suboptimal energy extraction and unnecessary switching activity. To address these limitations, we propose a predictive control strategy in which the DC–DC converter control signal is adaptively updated only when significant deviations are detected between measured and model-predicted voltage and current values. The approach leverages power-of-two quantized Artificial Neural Networks (2QANNs), enabling highly accurate inference with extreme weight quantization (2–3 bits) while remaining suitable for MPPT. A dataset-driven evaluation using year-long climatic records from geographically distinct locations indicates annual energy yields of up to 99.90% of the ideal maximum under the adopted modeling assumptions. Under the adopted fixed-condition evaluation protocol, compared with conventional P&O implementations, the proposed method requires 20–40× fewer internal control updates to approach the same efficiency region. Additionally, a robustness experiment with perturbed voltage and current measurements further shows that the recurrent 2QANN controllers remain above 98% aggregated efficiency even under the strongest tested sensing-noise condition, without retraining. Finally, post-place-and-route FPGA implementation estimates on a highly resource-constrained device indicate that the resulting architecture supports low-resource edge-oriented implementation.
Fernández et al. (Wed,) studied this question.