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Concurrent estimation and control of robotic systems remains an ongoingchallenge, where controllers rely on data extracted from states/parametersriddled with uncertainties and noises. Framework suitability hinges on taskcomplexity and computational constraints, demanding a balance betweencomputational efficiency and mission-critical accuracy. This study leveragesrecent advancements in neuromorphic computing, particularly spiking neuralnetworks (SNNs), for estimation and control applications. Our presentedframework employs a recurrent network of leaky integrate-and-fire (LIF)neurons, mimicking a linear quadratic regulator (LQR) through a robustfiltering strategy, a modified sliding innovation filter (MSIF). Benefitingfrom both the robustness of MSIF and the computational efficiency of SNN, ourframework customizes SNN weight matrices to match the desired system modelwithout requiring training. Additionally, the network employs a biologicallyplausible firing rule similar to predictive coding. In the presence ofuncertainties, we compare the SNN-LQR-MSIF with non-spiking LQR-MSIF and theoptimal linear quadratic Gaussian (LQG) strategy. Evaluation across a workbenchlinear problem and a satellite rendezvous maneuver, implementing theClohessy-Wiltshire (CW) model in space robotics, demonstrates that theSNN-LQR-MSIF achieves acceptable performance in computational efficiency,robustness, and accuracy. This positions it as a promising solution foraddressing dynamic systems' concurrent estimation and control challenges indynamic systems.

Neuromorphic Robust Framework for Concurrent Estimation and Control in Dynamical Systems using Spiking Neural Networks