发表机构
Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于脉冲神经元通信的框架，用于镇定受扰动的被控对象，经单连杆机械臂数值仿真验证，该框架适用于非线性系统及可镇定可检测的线性时不变系统。
AI 中文摘要
神经形态工程开发受生物神经元启发的硬件与软件系统，旨在实现高能效、低延迟、鲁棒且自适应的计算、通信与控制，其对系统与控制领域的潜在影响重大，或可借助类脑计算与通信原理为控制和估计提供新方法。在该背景下，本文提出一种框架，用于镇定受扰动影响的被控对象，其中噪声传感器与控制器间的通信依赖类神经元方案生成的脉冲信号。该通信方案包含传感器侧的脉冲编码器，其基于积分放电神经元，将模拟被控对象输出测量值转换为脉冲信号；以及控制器侧受突触处理启发的脉冲解码器，将接收的脉冲信号转换为模拟信号。本文给出脉冲解码器、脉冲编码器及控制器的设计条件，在此条件下闭环系统具备实用输入-状态稳定性，其中可调参数为脉冲幅度。结果表明，该框架适用于一类非线性系统，以及任何可镇定且可检测的线性时不变系统。对单连杆机械臂的数值仿真验证了该方法的潜力。
英文摘要
Neuromorphic engineering develops hardware and software systems inspired by biological neurons, with the goal of achieving energy-efficient, low-latency, robust, and adaptive computation, communication and control. Its potential impact on systems and control is significant, as it may enable novel approaches to control and estimation by leveraging brain-inspired computation and communication principles. In this context, we present a framework for the robust stabilization of a plant subject to disturbances when the communication between noisy sensors and the controller relies on spiking signals generated by neuron-inspired schemes. The communication scheme consists of a spike encoder on the sensors side, which is based on integrate-and-fire neurons that convert the analog plant output measurement into a spiking signal, and a spike decoder on the controller side inspired by synaptic processing to convert the received spiking signal into an analog signal. We provide design conditions on the spike decoder, the spike encoder as well as on the controller under which the closed-loop system exhibits a practical input-to-state stability property, where the adjustable parameters are the amplitudes of the spikes. The results are shown to be applicable to a class of nonlinear systems as well as to any stabilizable and detectable linear time-invariant system. Numerical simulations on a single-link manipulator illustrate the potential of the approach.
