Method Of Identification Of Lower Extremity Exoskeleton Based On The Characteristic Analysis Of Man-machine Coupling System

Lower extremity exoskeleton is a device which assists human in moving and enhances body function.Model-based control is the most popular control method of lower extremity exoskeleton.The method has high requirements for the accuracy of the dynamic parameters whether in the controller design or in the practical application.However,the existing parameter identification algorithm for obtaining high-precision parameter identification results,increasing the complexity of the experiment,that is doubled to increase the amount of experimental data.Therefore,the high precise and low complicated parameter identification algorithm is of great significance to improve the accuracy of lower extremity exoskeleton dynamic model.According to the demand of highly accurate kinetic model and the mutual exclusion of precision and complexity in the current dynamic model parameter identification algorithms.Highly precise and low complicated identification algorithms for lower extremity exoskeleton were studied.The correctness and validity of the algorithm was verified by simulation and prototype experiments.The main researches are as follows:Firstly,in order to improve the accuracy of parameter identification,this paper proposed a particle swarm optimization algorithm based on the robustness analysis of man-machine coupled control system,although the precision of the current particle swarm algorithm is high,but the delineation of search space depends on the drawbacks of engineering experience.Space division method,combined with the recursive least squares method to be identified parameters fluctuation range,to delineate the particle swarm algorithm search space.The particle swarm optimization algorithm is used to define the parameters accurately.Based on the analysis of the characteristics of man-machine coupling system,the method of particle space algorithm search space is given.Based on the robustness analysis of the maneuver coupled active power control system in horizontal walking,the scaling coefficients of the search space of the particle swarm algorithm are obtained.Secondly,because the accuracy of the current identification algorithm depends on the complexity of the identification experiment,that is,the higher the identification accuracy,the greater the amount of identification data r equired.In order to improve the accuracy of identification and reduce the complexity of identification experiment,this paper proposes a parameter identification algorithm based on recursive least squares and particle swarm optimization.Through the simulation,the difference of the least squares method,the genetic algorithm and the particle swarm algorithm under the same experimental conditions is compared and analyzed,and the rationality of the combination of the particle swarm optimization algorithm a nd the recursive least squares method is verified.Lastly,highly accurate parameters identification of lower extremity exoskeleton were achieved through the experiments which were carried out on the prototype platforms.The evaluation of parameter identif ication were proposed based on the torque deviation when the parameters were unknown.The correctness and validity of the proposed parameter identification algorithm were verified.