A Wiener Model Based Closed Loop FES for Positional Control During Wrist Flexion

Functional electrical stimulation is an assistive technique used to produce functional movements in patients suffering from neurological impairments. However, existing open-loop clinical FES systems are not adequately equipped to compensate for the nonlinear, time-varying behaviour of the muscles. On the other hand, closed-loop FES systems can compensate for the aforementioned effects by regulating the stimulation to induce desired contractions. Therefore, this work aims to present an approach to implement a closed-loop FES system to enable angular positional control during wrist flexion. First, a Wiener model describing the response of the wrist flexor to pulse width modulated stimulation was identified for two healthy volunteers. Second, a nonlinear PID controller (subject-specific) was designed based on the identified models to enable angular positional control during wrist flexion. Subsequently, the controller was implemented in real-time and was tested against two reference angles on healthy volunteers. This study shows promise that the presented closed-loop FES approach can be implemented to control the angular position during wrist flexion or a novelty of the work when compared with the existing work.

An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine

Lithium-ion batteries (LiBs) are the most important part of electric vehicle (EV) systems. Because there are two different degradation rates during LiB degradation, there are many two-phase models for LiBs. However, most of these methods do not consider the randomness of the changing point in the two-phase model and cannot update the change time in real time. Therefore, this paper proposes a method based on the combination of the two-phase Wiener model and an extreme learning machine (ELM). The two-phase Wiener model is used to derive the mathematical expression of the remaining useful life (RUL), and the ELM is implemented to adaptively detect the changing point. Based on the Poisson distribution, the distribution of the changing time is derived as a gamma distribution. To evaluate the theoretical results and practicality of the proposed method, we perform both numerical and practical simulations. The results of the simulations show that due to the precise and adaptive detection of changing points, the proposed method produces a more accurate RUL prediction than existing methods. The error of our method for detecting the changing point is about 4% and the mean prediction error of RUL in the second phase is improved from 4.39 cycles to 1.61 cycles.