The Adaptive Control For Intelligent Lower Limb Prosthesis Of Multi-motion Mode

To improve the living quality and welfare benefits of amputees, research has been made on the intellective artificial leg, which is also an attentive research project in the fields of robotics and biomedical engineering in these years. The high-level intelligentized bionic artificial legs have appeared in foreign countries, but research status in this field is not optimistic in China, there is no mature intellective artificial leg product. The exploration and development of intellective artificial leg is of great significance to provide high-performance, low-cost products, and it is also useful to shorten the gap with the developed countries and to promote rehabilitation engineering and prosthesis industry of China.The success of the development of intellective artificial leg must be based on the accurate identification of moving posture, Considering the problems that single angle of knee joint is not enough for complex posture identification and the noise brought by the result of muscle defect, muscle fatigue, changes in the electrode location and other factors disturbs the EMG.. It is necessary to develop a terrace of getting multi-source kinetic mechanical information, seek some good methods of multi-locomotion mode recognition of lower limb and the adaptive control of lower limb prosthesis.This article closely around the National Natural Science Foundation on acquisition of kinetic mechanical information and control method of multi-motion model of AK prosthesis(60705010). In this paper the major research work are as follows:(1) In order to obtain enough kinetic mechanical information, referring to the kinetic characteristics of the lower limb, the paper presents a set of multi-source kinetic mechanical information system by using the existing equipments which create essential conditions for intelligent control of lower limb prosthesis. This information system includes three parts: MyoTrace 400 (obtaining EMG of four different femoral muscles); PVDF force sensor attached to the shoe (obtaining heel and toe regional pressure); MTx sensor (obtaining the angle of knee joint).(2) Through the experimental method, this paper gets much information about SEMG of four different femoral muscles, heel and toe regional pressure and the angle of knee joint, in this paper three feature extraction methods are proposed, the feature extraction based on HHT is used in SEMG, the feature extraction based on the ratio of integral voltage is used in plantar pressure signal and the feature extraction based on the ratio of angle meen is used in the angle signal of knee joint.(3) In the analysis of SEMG, this paper contains two aspects'work through using HHT:Bringing a method of threshold denoising based on empirical mode decomposition (EMD). This method based on different characteristics of signal with noise in different intrinsic mode function (IMFs), fistly the signal can be divided through EMD, finally the high-frequency IMFs are processed by adaptive filter through different threshold;Bringing a feature extraction method based on HHT margin spectrum. This method contains three steps: fistly, the useful IMFs are selected and the self-adapting subsection for HHT margin spectrum is determined; secondly, the energy of each margin spectrum subsection is considered as the sharp feature of the margin spectrum for each SEMG; finally, each dimension of the feature vector is normalized by the method designed based on the experiment.(4) In the research of multi-locomotion mode recognition of lower limb, the two mature neural networks (BP improved algorithm based on L-M & LVQ) are used in identification of moving posture. The experimental results show that the two methods are both suitable to recognize multi-locomotion mode of lower limb, the correct rate of BP neural networks based on L-M is 75%, and the correct rate of LVQ is 84%. Through the results, it shows that LVQ is more recognize rate, more repeatability and more robust.(5) Considering the intellective artificial leg is a complicated system which is nonlinear, multilateral variable and parameters changed with time. In this paper, the neural network based model reference adaptive control technology to simulate research of the lower limb. Experimental result shows that the neural network based model reference adaptive control system is satisfied.