Concurrent estimation and control of robotic systems remains an ongoing challenge, where controllers rely on data extracted from states/parameters riddled with uncertainties and noises. Framework suitability hinges on task complexity and computational constraints, demanding a balance between computational efficiency and mission-critical accuracy. This study leverages recent advancements in neuromorphic computing, particularly spiking neural networks (SNNs), for estimation and control applications. Our presented framework employs a recurrent network of leaky integrate-and-fire (LIF) neurons, mimicking a linear quadratic regulator (LQR) through a robust filtering strategy, a modified sliding innovation filter (MSIF). Benefiting from both the robustness of MSIF and the computational efficiency of SNN, our framework customizes SNN weight matrices to match the desired system model without requiring training. Additionally, the network employs a biologically plausible firing rule similar to predictive coding. In the presence of uncertainties, we compare the SNN-LQR-MSIF with non-spiking LQR-MSIF and the optimal linear quadratic Gaussian (LQG) strategy. Evaluation across a workbench linear problem and a satellite rendezvous maneuver, implementing the Clohessy-Wiltshire (CW) model in space robotics, demonstrates that the SNN-LQR-MSIF achieves acceptable performance in computational efficiency, robustness, and accuracy. This positions it as a promising solution for addressing dynamic systems' concurrent estimation and control challenges in dynamic systems.

翻译：机器人系统的同步估计与控制仍面临持续挑战，控制器依赖于从充满不确定性和噪声的状态/参数中提取的数据。框架的适用性取决于任务复杂度与计算约束，需要在计算效率与任务关键精度之间寻求平衡。本研究利用神经形态计算的最新进展，特别是脉冲神经网络（SNNs），开展估计与控制应用。所提出的框架采用漏波整合-发放（LIF）神经元的循环网络，通过鲁棒滤波策略——改进型滑动创新滤波器（MSIF）来模拟线性二次型调节器（LQR）。得益于MSIF的鲁棒性与SNN的计算效率，本框架可定制SNN权重矩阵以匹配期望的系统模型，无需训练。此外，该网络采用类似预测编码的生物合理发放规则。在存在不确定性的情况下，我们将SNN-LQR-MSIF与非脉冲LQR-MSIF及最优线性二次型高斯（LQG）策略进行比较。通过工作台线性问题及空间机器人领域采用Clohessy-Wiltshire（CW）模型的卫星交会机动评估表明，SNN-LQR-MSIF在计算效率、鲁棒性和精度方面均达到可接受性能，使其成为解决动态系统同步估计与控制难题的有效方案。