Optimized PID neural network closed-loop control for basal ganglia network in Parkinson’s disease

Conventional open-loop deep brain stimulation (DBS) systems with fixed parameters fail to accommodate inter-individual pathological differences in Parkinson’s disease (PD) management while potentially inducing adverse effects and causing excessive energy consumption. In this paper, we present an adaptive closed-loop framework integrating a Yogi-optimized proportional-integral-derivative neural network (Yogi-PIDNN) controller. The Yogi-augmented gradient adaptation mechanism accelerates the convergence of general PIDNN controllers in high-dimensional nonlinear control systems while reducing control energy usage. In addition, a system identification method establishes input-output dynamics for pre-training stimulation waveforms, bypassing real-time parameter-tuning constraints and thereby enhancing closed-loop adaptability. Finally, a theoretical analysis based on Lyapunov stability criteria establishes a sufficient condition for closed-loop stability within the identified model. Computational validations demonstrate that our approach restores thalamic relay reliability while reducing energy consumption by (81.0±0.7)% across multi-frequency tests. This study advances adaptive neuromodulation by synergizing data-driven pre-training with stability-guaranteed real-time control, offering a novel framework for energy-efficient and personalized Parkinson’s therapy.