Accurate acceleration acquisition is a critical issue in the robotic exoskeleton system, but it is difficult to directly obtain the acceleration via the existing sensing systems. The existing algorithm-based acceleration acquisition methods put more attention on finite-time convergence and disturbance suppression but ignore the error constraint and initial state irrelevant techniques. To this end, a novel radical bias function neural network (RBFNN) based fixed-time reconstruction scheme with error constraints is designed to realize high-performance acceleration estimation. In this scheme, a novel exponential-type barrier Lyapunov function is proposed to handle the error constraints. It also provides a unified and concise Lyapunov stability-proof template for constrained and non-constrained systems. Moreover, a fractional power sliding mode control law is designed to realize fixed-time convergence, where the convergence time is irrelevant to initial states or external disturbance, and depends only on the chosen parameters. To further enhance observer robustness, an RBFNN with the adaptive weight matrix is proposed to approximate and attenuate the completely unknown disturbances. Numerical simulation and human subject experimental results validate the unique properties and practical robustness.

准确的加速度采集是机器人外骨骼系统中的关键问题，但是很难通过现有的传感系统直接获得加速度。现有的基于算法的加速度获取方法更加关注有限时间收敛和干扰抑制，而忽略了误差约束和初始状态无关技术。为此，设计了一种基于新颖的偏激函数神经网络（RBFNN）的具有误差约束的固定时间重构方案，以实现高性能的加速度估计。在该方案中，提出了一种新颖的指数型势垒李雅普诺夫函数来处理误差约束。它还为受约束和不受约束的系统提供了一个统一而简洁的Lyapunov稳定性证明模板。此外，设计分数功率滑模控制律以实现固定时间收敛，其中收敛时间与初始状态或外部干扰无关，并且仅取决于所选参数。为了进一步增强观察者的鲁棒性，提出了带有自适应权重矩阵的RBFNN来近似和衰减完全未知的干扰。数值模拟和人体实验结果验证了其独特的性能和实用的鲁棒性。