Neuromorphic robust framework for integrated estimation and control in dynamical systems using spiking neural networks.

Integrated estimation and control present an ongoing challenge for robotic systems. Because controllers depend on data derived from measured states and parameters, which are often subject to uncertainties and noise. The suitability of frameworks depends on the complexity of the task and the constraints of computational resources. They must strike a balance between computational efficiency for rapid responses while maintaining accuracy and robustness for safe and reliable missions. This study capitalizes on recent advancements in neuromorphic computing tools, especially spiking neural networks (SNNs), and their applications in robotic and dynamical systems. We present a learning-free framework featuring a recurrent network of leaky integrate-and-fire (LIF) neurons, designed to mimic a linear quadratic regulator (LQR) provided by a robust filtering strategy called extended modified sliding innovation filter (EMSIF). Thus, our proposed framework benefits from the robustness of EMSIF and the computational efficiency of SNN. The weight matrices of SNN are tailored to match the desired system model, eliminating the need for training. Moreover, the network leverages a biologically plausible firing rule akin to predictive coding. Furthermore, in the presence of various uncertainties, the SNN-LQR-EMSIF compared with non-spiking LQR-EMSIF, and the optimal strategy called linear quadratic Gaussian (LQG) based on extended Kalman filter. We evaluate their performance in a workbench problem and, next in the satellite rendezvous maneuver implement the Clohessy-Wiltshire (CW) model. Results demonstrated that the SNN-LQR-EMSIF achieves acceptable performance in terms of computational efficiency, robustness, and accuracy, positioning it as a promising approach for addressing the challenges of Integrated estimation and control in dynamic systems.